Mechanisms behind the estimation of photosynthesis traits from leaf reflectance observations

NASA Astrophysics Data System (ADS)

Dechant, Benjamin; Cuntz, Matthias; Doktor, Daniel; Vohland, Michael

2016-04-01

Many studies have investigated the reflectance-based estimation of leaf chlorophyll, water and dry matter contents of plants. Only few studies focused on photosynthesis traits, however. The maximum potential uptake of carbon dioxide under given environmental conditions is determined mainly by RuBisCO activity, limiting carboxylation, or the speed of photosynthetic electron transport. These two main limitations are represented by the maximum carboxylation capacity, V cmax,25, and the maximum electron transport rate, Jmax,25. These traits were estimated from leaf reflectance before but the mechanisms underlying the estimation remain rather speculative. The aim of this study was therefore to reveal the mechanisms behind reflectance-based estimation of V cmax,25 and Jmax,25. Leaf reflectance, photosynthetic response curves as well as nitrogen content per area, Narea, and leaf mass per area, LMA, were measured on 37 deciduous tree species. V cmax,25 and Jmax,25 were determined from the response curves. Partial Least Squares (PLS) regression models for the two photosynthesis traits V cmax,25 and Jmax,25 as well as Narea and LMA were studied using a cross-validation approach. Analyses of linear regression models based on Narea and other leaf traits estimated via PROSPECT inversion, PLS regression coefficients and model residuals were conducted in order to reveal the mechanisms behind the reflectance-based estimation. We found that V cmax,25 and Jmax,25 can be estimated from leaf reflectance with good to moderate accuracy for a large number of species and different light conditions. The dominant mechanism behind the estimations was the strong relationship between photosynthesis traits and leaf nitrogen content. This was concluded from very strong relationships between PLS regression coefficients, the model residuals as well as the prediction performance of Narea- based linear regression models compared to PLS regression models. While the PLS regression model for V cmax,25 was fully based on the correlation to Narea, the PLS regression model for Jmax,25 was not entirely based on it. Analyses of the contributions of different parts of the reflectance spectrum revealed that the information contributing to the Jmax,25 PLS regression model in addition to the main source of information, Narea, was mainly located in the visible part of the spectrum (500-900 nm). Estimated chlorophyll content could be excluded as potential source of this extra information. The PLS regression coefficients of the Jmax,25 model indicated possible contributions from chlorophyll fluorescence and cytochrome f content. In summary, we found that the main mechanism behind the estimation of V cmax,25 and Jmax,25 from leaf reflectance observations is the correlation to Narea but that there is additional information related to Jmax,25 mainly in the visible part of the spectrum.

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

PubMed

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Additive Partial Least Squares for efficient modelling of independent variance sources demonstrated on practical case studies.

PubMed

Luoma, Pekka; Natschläger, Thomas; Malli, Birgit; Pawliczek, Marcin; Brandstetter, Markus

2018-05-12

A model recalibration method based on additive Partial Least Squares (PLS) regression is generalized for multi-adjustment scenarios of independent variance sources (referred to as additive PLS - aPLS). aPLS allows for effortless model readjustment under changing measurement conditions and the combination of independent variance sources with the initial model by means of additive modelling. We demonstrate these distinguishing features on two NIR spectroscopic case-studies. In case study 1 aPLS was used as a readjustment method for an emerging offset. The achieved RMS error of prediction (1.91 a.u.) was of similar level as before the offset occurred (2.11 a.u.). In case-study 2 a calibration combining different variance sources was conducted. The achieved performance was of sufficient level with an absolute error being better than 0.8% of the mean concentration, therefore being able to compensate negative effects of two independent variance sources. The presented results show the applicability of the aPLS approach. The main advantages of the method are that the original model stays unadjusted and that the modelling is conducted on concrete changes in the spectra thus supporting efficient (in most cases straightforward) modelling. Additionally, the method is put into context of existing machine learning algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study.

PubMed

Divya, O; Mishra, Ashok K

2007-05-29

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.

Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares

NASA Astrophysics Data System (ADS)

Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

2018-01-01

Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.

[Research on fast detecting tomato seedlings nitrogen content based on NIR characteristic spectrum selection].

PubMed

Wu, Jing-zhu; Wang, Feng-zhu; Wang, Li-li; Zhang, Xiao-chao; Mao, Wen-hua

2015-01-01

In order to improve the accuracy and robustness of detecting tomato seedlings nitrogen content based on near-infrared spectroscopy (NIR), 4 kinds of characteristic spectrum selecting methods were studied in the present paper, i. e. competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variables elimination (MCUVE), backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS). There were totally 60 tomato seedlings cultivated at 10 different nitrogen-treatment levels (urea concentration from 0 to 120 mg . L-1), with 6 samples at each nitrogen-treatment level. They are in different degrees of over nitrogen, moderate nitrogen, lack of nitrogen and no nitrogen status. Each sample leaves were collected to scan near-infrared spectroscopy from 12 500 to 3 600 cm-1. The quantitative models based on the above 4 methods were established. According to the experimental result, the calibration model based on CARS and MCUVE selecting methods show better performance than those based on BiPLS and SiPLS selecting methods, but their prediction ability is much lower than that of the latter. Among them, the model built by BiPLS has the best prediction performance. The correlation coefficient (r), root mean square error of prediction (RMSEP) and ratio of performance to standard derivate (RPD) is 0. 952 7, 0. 118 3 and 3. 291, respectively. Therefore, NIR technology combined with characteristic spectrum selecting methods can improve the model performance. But the characteristic spectrum selecting methods are not universal. For the built model based or single wavelength variables selection is more sensitive, it is more suitable for the uniform object. While the anti-interference ability of the model built based on wavelength interval selection is much stronger, it is more suitable for the uneven and poor reproducibility object. Therefore, the characteristic spectrum selection will only play a better role in building model, combined with the consideration of sample state and the model indexes.

Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data

NASA Astrophysics Data System (ADS)

Yin, Shen; Wang, Guang; Yang, Xu

2014-07-01

In practical industrial applications, the key performance indicator (KPI)-related prediction and diagnosis are quite important for the product quality and economic benefits. To meet these requirements, many advanced prediction and monitoring approaches have been developed which can be classified into model-based or data-driven techniques. Among these approaches, partial least squares (PLS) is one of the most popular data-driven methods due to its simplicity and easy implementation in large-scale industrial process. As PLS is totally based on the measured process data, the characteristics of the process data are critical for the success of PLS. Outliers and missing values are two common characteristics of the measured data which can severely affect the effectiveness of PLS. To ensure the applicability of PLS in practical industrial applications, this paper introduces a robust version of PLS to deal with outliers and missing values, simultaneously. The effectiveness of the proposed method is finally demonstrated by the application results of the KPI-related prediction and diagnosis on an industrial benchmark of Tennessee Eastman process.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods

NASA Astrophysics Data System (ADS)

Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; Mello, Paola de Azevedo; Ferrão, Marco Flores; dos Santos, Maria de Fátima Pereira; Guimarães, Regina Célia Lourenço; Müller, Edson Irineu; Flores, Erico Marlon Moraes

2012-04-01

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm-1). This model produced a RMSECV of 400 mg kg-1 S and RMSEP of 420 mg kg-1 S, showing a correlation coefficient of 0.990.

Quantitative analysis of glycated albumin in serum based on ATR-FTIR spectrum combined with SiPLS and SVM.

PubMed

Li, Yuanpeng; Li, Fucui; Yang, Xinhao; Guo, Liu; Huang, Furong; Chen, Zhenqiang; Chen, Xingdan; Zheng, Shifu

2018-08-05

A rapid quantitative analysis model for determining the glycated albumin (GA) content based on Attenuated total reflectance (ATR)-Fourier transform infrared spectroscopy (FTIR) combining with linear SiPLS and nonlinear SVM has been developed. Firstly, the real GA content in human serum was determined by GA enzymatic method, meanwhile, the ATR-FTIR spectra of serum samples from the population of health examination were obtained. The spectral data of the whole spectra mid-infrared region (4000-600 cm -1 ) and GA's characteristic region (1800-800 cm -1 ) were used as the research object of quantitative analysis. Secondly, several preprocessing steps including first derivative, second derivative, variable standardization and spectral normalization, were performed. Lastly, quantitative analysis regression models were established by using SiPLS and SVM respectively. The SiPLS modeling results are as follows: root mean square error of cross validation (RMSECV T ) = 0.523 g/L, calibration coefficient (R C ) = 0.937, Root Mean Square Error of Prediction (RMSEP T ) = 0.787 g/L, and prediction coefficient (R P ) = 0.938. The SVM modeling results are as follows: RMSECV T  = 0.0048 g/L, R C  = 0.998, RMSEP T  = 0.442 g/L, and R p  = 0.916. The results indicated that the model performance was improved significantly after preprocessing and optimization of characteristic regions. While modeling performance of nonlinear SVM was considerably better than that of linear SiPLS. Hence, the quantitative analysis model for GA in human serum based on ATR-FTIR combined with SiPLS and SVM is effective. And it does not need sample preprocessing while being characterized by simple operations and high time efficiency, providing a rapid and accurate method for GA content determination. Copyright © 2018 Elsevier B.V. All rights reserved.

An in vitro approach for lipolysis measurement using high-resolution mass spectrometry and partial least squares based analysis.

PubMed

Chang, Wen-Qi; Zhou, Jian-Liang; Li, Yi; Shi, Zi-Qi; Wang, Li; Yang, Jie; Li, Ping; Liu, Li-Fang; Xin, Gui-Zhong

2017-01-15

The elevation of free fatty acids (FFAs) has been regarded as a universal metabolic signature of excessive adipocyte lipolysis. Nowadays, in vitro lipolysis assay is generally essential for drug screening prior to the animal study. Here, we present a novel in vitro approach for lipolysis measurement combining UHPLC-Orbitrap and partial least squares (PLS) based analysis. Firstly, the calibration matrix was constructed by serial proportions of mixed samples (blended with control and model samples). Then, lipidome profiling was performed by UHPLC-Orbitrap, and 403 variables were extracted and aligned as dataset. Owing to the high resolution of Orbitrap analyzer and open source lipid identification software, 28 FFAs were further screened and identified. Based on the relative intensity of the screened FFAs, PLS regression model was constructed for lipolysis measurement. After leave-one-out cross-validation, ten principal components have been designated to build the final PLS model with excellent performances (RMSECV, 0.0268; RMSEC, 0.0173; R 2 , 0.9977). In addition, the high predictive accuracy (R 2  = 0.9907 and RMSEP = 0.0345) of the trained PLS model was also demonstrated using test samples. Finally, taking curcumin as a model compound, its antilipolytic effect on palmitic acid-induced lipolysis was successfully predicted as 31.78% by the proposed approach. Besides, supplementary evidences of curcumin induced modification in FFAs compositions as well as lipidome were given by PLS extended methods. Different from general biological assays, high resolution MS-based method provide more sophisticated information included in biological events. Thus, the novel biological evaluation model proposed here showed promising perspectives for drug evaluation or disease diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

Kernel analysis of partial least squares (PLS) regression models.

PubMed

Shinzawa, Hideyuki; Ritthiruangdej, Pitiporn; Ozaki, Yukihiro

2011-05-01

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods.

PubMed

Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; de Azevedo Mello, Paola; Ferrão, Marco Flores; de Fátima Pereira dos Santos, Maria; Guimarães, Regina Célia Lourenço; Müller, Edson Irineu; Flores, Erico Marlon Moraes

2012-04-01

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm(-1)). This model produced a RMSECV of 400 mg kg(-1) S and RMSEP of 420 mg kg(-1) S, showing a correlation coefficient of 0.990. Copyright © 2011 Elsevier B.V. All rights reserved.

Modelling mercury accumulation in minerogenic peat combining FTIR-ATR spectroscopy and partial least squares (PLS)

NASA Astrophysics Data System (ADS)

Pérez-Rodríguez, Marta; Horák-Terra, Ingrid; Rodríguez-Lado, Luis; Martínez Cortizas, Antonio

2016-11-01

Despite its potential, infrared spectroscopy combined with multivariate statistics has been seldom used to model peat properties with environmental value, such us the concentration of potentially toxic metals. In this research, we applied attenuated total reflectance (ATR) Fourier-Transform Infrared (FTIR) spectroscopy to evaluate the ability of the technique to predict mercury concentrations in late-Pleistocene/Holocene peat from a minerogenic peatland from Minas Gerais (Brazil). Mercury concentrations were analysed using a Milestone DMA-80 analyzer and attenuated total reflectance FTIR-ATR was performed using a Gladi-ATR (Pike Technologies) in the mid IR spectrum (4000-400 cm- 1). Concentrations were modelled using principal components (PCR) and partial least squares regression (PLS). The performance of the models varied between moderate and very good (R2 0.67-0.90), with low RMSD values (0.35-1.06). A PLS model based on three latent vectors (LV1 to LV3) provided the best (R2 0.90, RMSD 0.35) results. LV1 reflected total organic matter content versus mineral matter (mainly quartz from local fluxes), LV2 was related to dust deposition from regional sources, and LV3 reflected peat organic matter decomposition. Compared to a previous investigation based on geochemical data, the spectroscopy-based PLS model performed better, but it has to be complemented with additional data (as δ13 C ratios) to reliably reproduce the changes of the factors controlling mercury accumulation over time. This, time- and cost-effective, methodology may help to develop multi-core approaches to study the within and between mire (of a similar type and area) variability in mercury accumulation, and probably also other peat properties. Fig. S2 Loadings weights of the three and two significant components from the direct (dPCR) and transposed (trPCR) PCR models. Fig. S3 Depth records of the cumulative effects of the factors involved in the variation of mercury concentrations. Left, MIR-PLS model; centre, MIR-PLS + δ13 C data model; right, geochemical model from Pérez-Rodríguez et al. [44].

UV–Vis and ATR–FTIR spectroscopic investigations of postmortem interval based on the changes in rabbit plasma

PubMed Central

Wang, Qi; He, Haijun; Li, Bing; Lin, Hancheng; Zhang, Yinming; Zhang, Ji

2017-01-01

Estimating PMI is of great importance in forensic investigations. Although many methods are used to estimate the PMI, a few investigations focus on the postmortem redistribution. In this study, ultraviolet–visible (UV–Vis) measurement combined with visual inspection indicated a regular diffusion of hemoglobin into plasma after death showing the redistribution of postmortem components in blood. Thereafter, attenuated total reflection–Fourier transform infrared (ATR–FTIR) spectroscopy was used to confirm the variations caused by this phenomenon. First, full-spectrum partial least-squares (PLS) and genetic algorithm combined with PLS (GA-PLS) models were constructed to predict the PMI. The performance of GA-PLS model was better than that of full-spectrum PLS model based on its root mean square error (RMSE) of cross-validation of 3.46 h (R2 = 0.95) and the RMSE of prediction of 3.46 h (R2 = 0.94). The investigation on the similarity of spectra between blood plasma and formed elements also supported the role of redistribution of components in spectral changes in postmortem plasma. These results demonstrated that ATR-FTIR spectroscopy coupled with the advanced mathematical methods could serve as a convenient and reliable tool to study the redistribution of postmortem components and estimate the PMI. PMID:28753641

Development and validation of a Partial Least Squares-Discriminant Analysis (PLS-DA) model based on the determination of ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs) in hair for the diagnosis of chronic alcohol abuse.

PubMed

Alladio, E; Giacomelli, L; Biosa, G; Corcia, D Di; Gerace, E; Salomone, A; Vincenti, M

2018-01-01

The chronic intake of an excessive amount of alcohol is currently ascertained by determining the concentration of direct alcohol metabolites in the hair samples of the alleged abusers, including ethyl glucuronide (EtG) and, less frequently, fatty acid ethyl esters (FAEEs). Indirect blood biomarkers of alcohol abuse are still determined to support hair EtG results and diagnose a consequent liver impairment. In the present study, the supporting role of hair FAEEs is compared with indirect blood biomarkers with respect to the contexts in which hair EtG interpretation is uncertain. Receiver Operating Characteristics (ROC) curves and multivariate Principal Component Analysis (PCA) demonstrated much stronger correlation of EtG results with FAEEs than with any single indirect biomarker or their combinations. Partial Least Squares Discriminant Analysis (PLS-DA) models based on hair EtG and FAEEs were developed to maximize the biomarkers information content on a multivariate background. The final PLS-DA model yielded 100% correct classification on a training/evaluation dataset of 155 subjects, including both chronic alcohol abusers and social drinkers. Then, the PLS-DA model was validated on an external dataset of 81 individual providing optimal discrimination ability between chronic alcohol abusers and social drinkers, in terms of specificity and sensitivity. The PLS-DA scores obtained for each subject, with respect to the PLS-DA model threshold that separates the probabilistic distributions for the two classes, furnished a likelihood ratio value, which in turn conveys the strength of the experimental data support to the classification decision, within a Bayesian logic. Typical boundary real cases from daily work are discussed, too. Copyright © 2017 Elsevier B.V. All rights reserved.

Fast and nondestructive determination of protein content in rapeseeds (Brassica napus L.) using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS).

PubMed

Lu, Yuzhen; Du, Changwen; Yu, Changbing; Zhou, Jianmin

2014-08-01

Fast and non-destructive determination of rapeseed protein content carries significant implications in rapeseed production. This study presented the first attempt of using Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) to quantify protein content of rapeseed. The full-spectrum model was first built using partial least squares (PLS). Interval selection methods including interval partial least squares (iPLS), synergy interval partial least squares (siPLS), backward elimination interval partial least squares (biPLS) and dynamic backward elimination interval partial least squares (dyn-biPLS) were then employed to select the relevant band or band combination for PLS modeling. The full-spectrum PLS model achieved an ratio of prediction to deviation (RPD) of 2.047. In comparison, all interval selection methods produced better results than full-spectrum modeling. siPLS achieved the best predictive accuracy with an RPD of 3.215 when the spectrum was sectioned into 25 intervals, and two intervals (1198-1335 and 1614-1753 cm(-1) ) were selected. iPLS excelled biPLS and dyn-biPLS, and dyn-biPLS performed slightly better than biPLS. FTIR-PAS was verified as a promising analytical tool to quantify rapeseed protein content. Interval selection could extract the relevant individual band or synergy band associated with the sample constituent of interest, and then improve the prediction accuracy of the full-spectrum model. © 2013 Society of Chemical Industry.

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

PubMed

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

Thermal-to-visible face recognition using partial least squares.

PubMed

Hu, Shuowen; Choi, Jonghyun; Chan, Alex L; Schwartz, William Robson

2015-03-01

Although visible face recognition has been an active area of research for several decades, cross-modal face recognition has only been explored by the biometrics community relatively recently. Thermal-to-visible face recognition is one of the most difficult cross-modal face recognition challenges, because of the difference in phenomenology between the thermal and visible imaging modalities. We address the cross-modal recognition problem using a partial least squares (PLS) regression-based approach consisting of preprocessing, feature extraction, and PLS model building. The preprocessing and feature extraction stages are designed to reduce the modality gap between the thermal and visible facial signatures, and facilitate the subsequent one-vs-all PLS-based model building. We incorporate multi-modal information into the PLS model building stage to enhance cross-modal recognition. The performance of the proposed recognition algorithm is evaluated on three challenging datasets containing visible and thermal imagery acquired under different experimental scenarios: time-lapse, physical tasks, mental tasks, and subject-to-camera range. These scenarios represent difficult challenges relevant to real-world applications. We demonstrate that the proposed method performs robustly for the examined scenarios.

Modeling of temperature-induced near-infrared and low-field time-domain nuclear magnetic resonance spectral variation: chemometric prediction of limonene and water content in spray-dried delivery systems.

PubMed

Andrade, Letícia; Farhat, Imad A; Aeberhardt, Kasia; Bro, Rasmus; Engelsen, Søren Balling

2009-02-01

The influence of temperature on near-infrared (NIR) and nuclear magnetic resonance (NMR) spectroscopy complicates the industrial applications of both spectroscopic methods. The focus of this study is to analyze and model the effect of temperature variation on NIR spectra and NMR relaxation data. Different multivariate methods were tested for constructing robust prediction models based on NIR and NMR data acquired at various temperatures. Data were acquired on model spray-dried limonene systems at five temperatures in the range from 20 degrees C to 60 degrees C and partial least squares (PLS) regression models were computed for limonene and water predictions. The predictive ability of the models computed on the NIR spectra (acquired at various temperatures) improved significantly when data were preprocessed using extended inverted signal correction (EISC). The average PLS regression prediction error was reduced to 0.2%, corresponding to 1.9% and 3.4% of the full range of limonene and water reference values, respectively. The removal of variation induced by temperature prior to calibration, by direct orthogonalization (DO), slightly enhanced the predictive ability of the models based on NMR data. Bilinear PLS models, with implicit inclusion of the temperature, enabled limonene and water predictions by NMR with an error of 0.3% (corresponding to 2.8% and 7.0% of the full range of limonene and water). For NMR, and in contrast to the NIR results, modeling the data using multi-way N-PLS improved the models' performance. N-PLS models, in which temperature was included as an extra variable, enabled more accurate prediction, especially for limonene (prediction error was reduced to 0.2%). Overall, this study proved that it is possible to develop models for limonene and water content prediction based on NIR and NMR data, independent of the measurement temperature.

Fast Measurement of Soluble Solid Content in Mango Based on Visible and Infrared Spectroscopy Technique

NASA Astrophysics Data System (ADS)

Yu, Jiajia; He, Yong

Mango is a kind of popular tropical fruit, and the soluble solid content is an important in this study visible and short-wave near-infrared spectroscopy (VIS/SWNIR) technique was applied. For sake of investigating the feasibility of using VIS/SWNIR spectroscopy to measure the soluble solid content in mango, and validating the performance of selected sensitive bands, for the calibration set was formed by 135 mango samples, while the remaining 45 mango samples for the prediction set. The combination of partial least squares and backpropagation artificial neural networks (PLS-BP) was used to calculate the prediction model based on raw spectrum data. Based on PLS-BP, the determination coefficient for prediction (Rp) was 0.757 and root mean square and the process is simple and easy to operate. Compared with the Partial least squares (PLS) result, the performance of PLS-BP is better.

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

PubMed

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

2018-01-01

In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by dosing solutions of 0.1% green tea extract with differing concentrations of eight flavour keys deemed to be important for green tea aroma and taste, based on a D-optimal experimental design, before undergoing commercial sterilisation. Sensory evaluation of the green tea model system was carried out using an untrained consumer panel to obtain hedonic liking scores of the samples. Regression models were subsequently trained to objectively predict the consumer liking scores of the green tea model systems. A linear partial least squares (PLS) regression model was developed to describe the effects of the eight flavour keys on consumer liking, with a coefficient of determination (R 2 ) of 0.733, and a root-mean-square error (RMSE) of 3.53%. The PLS model was further augmented with an artificial neural network (ANN) to establish a PLS-ANN hybrid model. The established hybrid model was found to give a better prediction of consumer liking scores, based on its R 2 (0.875) and RMSE (2.41%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Towards molecular design using 2D-molecular contour maps obtained from PLS regression coefficients

NASA Astrophysics Data System (ADS)

Borges, Cleber N.; Barigye, Stephen J.; Freitas, Matheus P.

2017-12-01

The multivariate image analysis descriptors used in quantitative structure-activity relationships are direct representations of chemical structures as they are simply numerical decodifications of pixels forming the 2D chemical images. These MDs have found great utility in the modeling of diverse properties of organic molecules. Given the multicollinearity and high dimensionality of the data matrices generated with the MIA-QSAR approach, modeling techniques that involve the projection of the data space onto orthogonal components e.g. Partial Least Squares (PLS) have been generally used. However, the chemical interpretation of the PLS-based MIA-QSAR models, in terms of the structural moieties affecting the modeled bioactivity has not been straightforward. This work describes the 2D-contour maps based on the PLS regression coefficients, as a means of assessing the relevance of single MIA predictors to the response variable, and thus allowing for the structural, electronic and physicochemical interpretation of the MIA-QSAR models. A sample study to demonstrate the utility of the 2D-contour maps to design novel drug-like molecules is performed using a dataset of some anti-HIV-1 2-amino-6-arylsulfonylbenzonitriles and derivatives, and the inferences obtained are consistent with other reports in the literature. In addition, the different schemes for encoding atomic properties in molecules are discussed and evaluated.

Multivariate estimation of the limit of detection by orthogonal partial least squares in temperature-modulated MOX sensors.

PubMed

Burgués, Javier; Marco, Santiago

2018-08-17

Metal oxide semiconductor (MOX) sensors are usually temperature-modulated and calibrated with multivariate models such as partial least squares (PLS) to increase the inherent low selectivity of this technology. The multivariate sensor response patterns exhibit heteroscedastic and correlated noise, which suggests that maximum likelihood methods should outperform PLS. One contribution of this paper is the comparison between PLS and maximum likelihood principal components regression (MLPCR) in MOX sensors. PLS is often criticized by the lack of interpretability when the model complexity increases beyond the chemical rank of the problem. This happens in MOX sensors due to cross-sensitivities to interferences, such as temperature or humidity and non-linearity. Additionally, the estimation of fundamental figures of merit, such as the limit of detection (LOD), is still not standardized in multivariate models. Orthogonalization methods, such as orthogonal projection to latent structures (O-PLS), have been successfully applied in other fields to reduce the complexity of PLS models. In this work, we propose a LOD estimation method based on applying the well-accepted univariate LOD formulas to the scores of the first component of an orthogonal PLS model. The resulting LOD is compared to the multivariate LOD range derived from error-propagation. The methodology is applied to data extracted from temperature-modulated MOX sensors (FIS SB-500-12 and Figaro TGS 3870-A04), aiming at the detection of low concentrations of carbon monoxide in the presence of uncontrolled humidity (chemical noise). We found that PLS models were simpler and more accurate than MLPCR models. Average LOD values of 0.79 ppm (FIS) and 1.06 ppm (Figaro) were found using the approach described in this paper. These values were contained within the LOD ranges obtained with the error-propagation approach. The mean LOD increased to 1.13 ppm (FIS) and 1.59 ppm (Figaro) when considering validation samples collected two weeks after calibration, which represents a 43% and 46% degradation, respectively. The orthogonal score-plot was a very convenient tool to visualize MOX sensor data and to validate the LOD estimates. Copyright © 2018 Elsevier B.V. All rights reserved.

On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

NASA Astrophysics Data System (ADS)

Yang, Yue; Wang, Lei; Wu, Yongjiang; Liu, Xuesong; Bi, Yuan; Xiao, Wei; Chen, Yong

2017-07-01

There is a growing need for the effective on-line process monitoring during the manufacture of traditional Chinese medicine to ensure quality consistency. In this study, the potential of near infrared (NIR) spectroscopy technique to monitor the extraction process of Flos Lonicerae Japonicae was investigated. A new algorithm of synergy interval PLS with genetic algorithm (Si-GA-PLS) was proposed for modeling. Four different PLS models, namely Full-PLS, Si-PLS, GA-PLS, and Si-GA-PLS, were established, and their performances in predicting two quality parameters (viz. total acid and soluble solid contents) were compared. In conclusion, Si-GA-PLS model got the best results due to the combination of superiority of Si-PLS and GA. For Si-GA-PLS, the determination coefficient (Rp2) and root-mean-square error for the prediction set (RMSEP) were 0.9561 and 147.6544 μg/ml for total acid, 0.9062 and 0.1078% for soluble solid contents, correspondingly. The overall results demonstrated that the NIR spectroscopy technique combined with Si-GA-PLS calibration is a reliable and non-destructive alternative method for on-line monitoring of the extraction process of TCM on the production scale.

Random sample consensus combined with partial least squares regression (RANSAC-PLS) for microbial metabolomics data mining and phenotype improvement.

PubMed

Teoh, Shao Thing; Kitamura, Miki; Nakayama, Yasumune; Putri, Sastia; Mukai, Yukio; Fukusaki, Eiichiro

2016-08-01

In recent years, the advent of high-throughput omics technology has made possible a new class of strain engineering approaches, based on identification of possible gene targets for phenotype improvement from omic-level comparison of different strains or growth conditions. Metabolomics, with its focus on the omic level closest to the phenotype, lends itself naturally to this semi-rational methodology. When a quantitative phenotype such as growth rate under stress is considered, regression modeling using multivariate techniques such as partial least squares (PLS) is often used to identify metabolites correlated with the target phenotype. However, linear modeling techniques such as PLS require a consistent metabolite-phenotype trend across the samples, which may not be the case when outliers or multiple conflicting trends are present in the data. To address this, we proposed a data-mining strategy that utilizes random sample consensus (RANSAC) to select subsets of samples with consistent trends for construction of better regression models. By applying a combination of RANSAC and PLS (RANSAC-PLS) to a dataset from a previous study (gas chromatography/mass spectrometry metabolomics data and 1-butanol tolerance of 19 yeast mutant strains), new metabolites were indicated to be correlated with tolerance within certain subsets of the samples. The relevance of these metabolites to 1-butanol tolerance were then validated from single-deletion strains of corresponding metabolic genes. The results showed that RANSAC-PLS is a promising strategy to identify unique metabolites that provide additional hints for phenotype improvement, which could not be detected by traditional PLS modeling using the entire dataset. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates.

PubMed

Gottfried, Jennifer L

2011-07-01

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.

Navy Fuel Composition and Screening Tool (FCAST) v2.8

DTIC Science & Technology

2016-05-10

allowed us to develop partial least squares (PLS) models based on gas chromatography–mass spectrometry (GC-MS) data that predict fuel properties. The...Chemometric property modeling Partial least squares PLS Compositional profiler Naval Air Systems Command Air-4.4.5 Patuxent River Naval Air Station Patuxent...Cumulative predicted residual error sum of squares DiEGME Diethylene glycol monomethyl ether FCAST Fuel Composition and Screening Tool FFP Fit for

Error propagation of partial least squares for parameters optimization in NIR modeling.

PubMed

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

Error propagation of partial least squares for parameters optimization in NIR modeling

NASA Astrophysics Data System (ADS)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

Sub-Model Partial Least Squares for Improved Accuracy in Quantitative Laser Induced Breakdown Spectroscopy

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Clegg, S. M.; Frydenvang, J.

2015-12-01

One of the primary challenges faced by the ChemCam instrument on the Curiosity Mars rover is developing a regression model that can accurately predict the composition of the wide range of target types encountered (basalts, calcium sulfate, feldspar, oxides, etc.). The original calibration used 69 rock standards to train a partial least squares (PLS) model for each major element. By expanding the suite of calibration samples to >400 targets spanning a wider range of compositions, the accuracy of the model was improved, but some targets with "extreme" compositions (e.g. pure minerals) were still poorly predicted. We have therefore developed a simple method, referred to as "submodel PLS", to improve the performance of PLS across a wide range of target compositions. In addition to generating a "full" (0-100 wt.%) PLS model for the element of interest, we also generate several overlapping submodels (e.g. for SiO2, we generate "low" (0-50 wt.%), "mid" (30-70 wt.%), and "high" (60-100 wt.%) models). The submodels are generally more accurate than the "full" model for samples within their range because they are able to adjust for matrix effects that are specific to that range. To predict the composition of an unknown target, we first predict the composition with the submodels and the "full" model. Then, based on the predicted composition from the "full" model, the appropriate submodel prediction can be used (e.g. if the full model predicts a low composition, use the "low" model result, which is likely to be more accurate). For samples with "full" predictions that occur in a region of overlap between submodels, the submodel predictions are "blended" using a simple linear weighted sum. The submodel PLS method shows improvements in most of the major elements predicted by ChemCam and reduces the occurrence of negative predictions for low wt.% targets. Submodel PLS is currently being used in conjunction with ICA regression for the major element compositions of ChemCam data.

Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging

PubMed Central

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S.; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-01-01

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting. PMID:24763251

Non-destructive quality evaluation of pepper (Capsicum annuum L.) seeds using LED-induced hyperspectral reflectance imaging.

PubMed

Mo, Changyeun; Kim, Giyoung; Lee, Kangjin; Kim, Moon S; Cho, Byoung-Kwan; Lim, Jongguk; Kang, Sukwon

2014-04-24

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400-700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares-discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400-700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600-700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Correlation of sensory bitterness in dairy protein hydrolysates: Comparison of prediction models built using sensory, chromatographic and electronic tongue data.

PubMed

Newman, J; Egan, T; Harbourne, N; O'Riordan, D; Jacquier, J C; O'Sullivan, M

2014-08-01

Sensory evaluation can be problematic for ingredients with a bitter taste during research and development phase of new food products. In this study, 19 dairy protein hydrolysates (DPH) were analysed by an electronic tongue and their physicochemical characteristics, the data obtained from these methods were correlated with their bitterness intensity as scored by a trained sensory panel and each model was also assessed by its predictive capabilities. The physiochemical characteristics of the DPHs investigated were degree of hydrolysis (DH%), and data relating to peptide size and relative hydrophobicity from size exclusion chromatography (SEC) and reverse phase (RP) HPLC. Partial least square regression (PLS) was used to construct the prediction models. All PLS regressions had good correlations (0.78 to 0.93) with the strongest being the combination of data obtained from SEC and RP HPLC. However, the PLS with the strongest predictive power was based on the e-tongue which had the PLS regression with the lowest root mean predicted residual error sum of squares (PRESS) in the study. The results show that the PLS models constructed with the e-tongue and the combination of SEC and RP-HPLC has potential to be used for prediction of bitterness and thus reducing the reliance on sensory analysis in DPHs for future food research. Copyright © 2014 Elsevier B.V. All rights reserved.

Assessment of Various Organic Matter Properties by Infrared Reflectance Spectroscopy of Sediments and Filters

NASA Astrophysics Data System (ADS)

Alaoui, G.; Leger, M.; Gagne, J.; Tremblay, L.

2009-05-01

The goal of this work was to evaluate the capability of infrared reflectance spectroscopy for a fast quantification of the elemental and molecular compositions of sedimentary and particulate organic matter (OM). A partial least-squares (PLS) regression model was used for analysis and values were compared to those obtained by traditional methods (i.e., elemental, humic and HPLC analyses). PLS tools are readily accessible from software such as GRAMS (Thermo-Fisher) used in spectroscopy. This spectroscopic-chemometric approach has several advantages including its rapidity and use of whole unaltered samples. To predict properties, a set of infrared spectra from representative samples must first be fitted to form a PLS calibration model. In this study, a large set (180) of sediments and particles on GFF filters from the St. Lawrence estuarine system were used. These samples are very heterogenous (e.g., various tributaries, terrigenous vs. marine, events such as landslides and floods) and thus represent a challenging test for PLS prediction. For sediments, the infrared spectra were obtained with a diffuse reflectance, or DRIFT, accessory. Sedimentary carbon, nitrogen, humic substance contents as well as humic substance proportions in OM and N:C ratios were predicted by PLS. The relative root mean square error of prediction (%RMSEP) for these properties were between 5.7% (humin content) and 14.1% (total humic substance yield) using the cross-validation, or leave-one out, approach. The %RMSEP calculated by PLS for carbon content was lower with the PLS model (7.6%) than with an external calibration method (11.7%) (Tremblay and Gagné, 2002, Anal. Chem., 74, 2985). Moreover, the PLS approach does not require the extraction of POM needed in external calibration. Results highlighted the importance of using a PLS calibration set representative of the unknown samples (e.g., same area). For filtered particles, the infrared spectra were obtained using a novel approach based on attenuated total reflectance, or ATR, allowing the direct analysis of the filters. In addition to carbon and nitrogen contents, amino acid and muramic acid (a bacterial biomarker) yields were predicted using PLS. Calculated %RMSEP varied from 6.4% (total amino acid content) to 18.6% (muramic acid content) with cross-validation. PLS regression modeling does not require a priori knowledge of the spectral bands associated with the properties to be predicted. In turn, the spectral regions that give good PLS predictions provided valuable information on band assignment and geochemical processes. For instance, nitrogen and humin contents were greatly determined by an absorption band caused by aluminosilicate OH group. This supports the idea that OM-clay interactions, important in humin formation and OM preservation, are mediated by nitrogen-containing groups.

Discrimination of tomatoes bred by spaceflight mutagenesis using visible/near infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Shao, Yongni; Xie, Chuanqi; Jiang, Linjun; Shi, Jiahui; Zhu, Jiajin; He, Yong

2015-04-01

Visible/near infrared spectroscopy (Vis/NIR) based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate different tomatoes bred by spaceflight mutagenesis from their leafs or fruits (green or mature). The tomato breeds were mutant M1, M2 and their parent. Partial least squares (PLS) analysis and least squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavebands including the visible region (400-700 nm) and the near infrared region (700-1000 nm). The best PLS models were achieved in the visible region for the leaf and green fruit samples and in the near infrared region for the mature fruit samples. Furthermore, different latent variables (4-8 LVs for leafs, 5-9 LVs for green fruits, and 4-9 LVs for mature fruits) were used as inputs of LS-SVM to develop the LV-LS-SVM models with the grid search technique and radial basis function (RBF) kernel. The optimal LV-LS-SVM models were achieved with six LVs for the leaf samples, seven LVs for green fruits, and six LVs for mature fruits, respectively, and they outperformed the PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs of 550-560 nm, 562-574 nm, 670-680 nm and 705-715 nm for the leaf samples; 548-556 nm, 559-564 nm, 678-685 nm and 962-974 nm for the green fruit samples; and 712-718 nm, 720-729 nm, 968-978 nm and 820-830 nm for the mature fruit samples. All of them had better performance than PLS and LV-LS-SVM, with the parameters of correlation coefficient (rp), root mean square error of prediction (RMSEP) and bias of 0.9792, 0.2632 and 0.0901 based on leaf discrimination, 0.9837, 0.2783 and 0.1758 based on green fruit discrimination, 0.9804, 0.2215 and -0.0035 based on mature fruit discrimination, respectively. The overall results indicated that ICA was an effective way for the selection of SWs, and the Vis/NIR combined with LS-SVM models had the capability to predict the different breeds (mutant M1, mutant M2 and their parent) of tomatoes from leafs and fruits.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

NASA Astrophysics Data System (ADS)

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-01

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

Determination of propranolol hydrochloride in pharmaceutical preparations using near infrared spectrometry with fiber optic probe and multivariate calibration methods.

PubMed

Marques Junior, Jucelino Medeiros; Muller, Aline Lima Hermes; Foletto, Edson Luiz; da Costa, Adilson Ben; Bizzi, Cezar Augusto; Irineu Muller, Edson

2015-01-01

A method for determination of propranolol hydrochloride in pharmaceutical preparation using near infrared spectrometry with fiber optic probe (FTNIR/PROBE) and combined with chemometric methods was developed. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). The treatments based on the mean centered data and multiplicative scatter correction (MSC) were selected for models construction. A root mean square error of prediction (RMSEP) of 8.2 mg g(-1) was achieved using siPLS (s2i20PLS) algorithm with spectra divided into 20 intervals and combination of 2 intervals (8501 to 8801 and 5201 to 5501 cm(-1)). Results obtained by the proposed method were compared with those using the pharmacopoeia reference method and significant difference was not observed. Therefore, proposed method allowed a fast, precise, and accurate determination of propranolol hydrochloride in pharmaceutical preparations. Furthermore, it is possible to carry out on-line analysis of this active principle in pharmaceutical formulations with use of fiber optic probe.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm

NASA Astrophysics Data System (ADS)

Jiang, Hui; Liu, Guohai; Mei, Congli; Yu, Shuang; Xiao, Xiahong; Ding, Yuhan

2012-11-01

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV = 0.0776, Rc = 0.9777, RMSEP = 0.0963, and Rp = 0.9686 for pH model; RMSECV = 1.3544% w/w, Rc = 0.8871, RMSEP = 1.4946% w/w, and Rp = 0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.

Harvest-time prediction of apple physiological indices using fiber optic Fourier transform near-infrared spectrometer

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Lu, Huishan; Fu, Xiaping

2004-12-01

This work evaluates the feasibility of Fourier transform near infrared (FT-NIR) spectrometry for rapid determining the total soluble solids content and acidity of apple fruit. Intact apple fruit were measured by reflectance FT-NIR in 800-2500 nm range. FT-NIR models were developed based on partial least square (PLS) regression and principal component regress (PCR) with respect to the reflectance and its first derivative, the logarithms of the reflectance reciprocal and its second derivative. The above regression models, related the FT-NIR spectra to soluble solids content (SSC), titratable acidity (TA) and available acidity (pH). The best combination, based on the prediction results, was PLS models with respect to the logarithms of the reflectance reciprocal. Predictions with PLS models resulted standard errors of prediction (SEP) of 0.455, 0.044 and 0.068, and correlation coefficients of 0.968, 0.728 and 0.831 for SSC, TA and pH, respectively. It was concluded that by using the FT-NIR spectrometry measurement system, in the appropriate spectral range, it is possible to nondestructively assess the maturity factors of apple fruit.

A preliminary MTD-PLS study for androgen receptor binding of steroid compounds

NASA Astrophysics Data System (ADS)

Bora, Alina; Seclaman, E.; Kurunczi, L.; Funar-Timofei, Simona

The relative binding affinities (RBA) of a series of 30 steroids for Human Androgen Receptor (AR) were used to initiate a MTD-PLS study. The 3D structures of all the compounds were obtained through geometry optimization in the framework of AM1 semiempirical quantum chemical method. The MTD hypermolecule (HM) was constructed, superposing these structures on the AR-bonded dihydrotestosterone (DHT) skeleton obtained from PDB (AR complex, ID 1I37). The parameters characterizing the HM vertices were collected using: AM1 charges, XlogP fragmental values, calculated fragmental polarizabilities (from refractivities), volumes, and H-bond parameters (Raevsky's thermodynamic originated scale). The resulted QSAR data matrix was submitted to PCA (Principal Component Analysis) and PLS (Projections in Latent Structures) procedure (SIMCA P 9.0); five compounds were selected as test set, and the remaining 25 molecules were used as training set. In the PLS procedure supplementary chemical information was introduced, i.e. the steric effect was always considered detrimental, and the hydrophobic and van der Waals interactions were imposed to be beneficial. The initial PLS model using the entire training set has the following characteristics: R2Y = 0.584, Q2 = 0.344. Based on distances to the model criterions (DMODX and DMODY), five compounds were eliminated and the obtained final model had the following characteristics: R2Y D 0.891, Q2 D 0.591. For this the external predictivity on the test set was unsatisfactory. A tentative explanation for these behaviors is the weak information content of the input QSAR matrix for the present series comparatively with other successful MTD-PLS modeling published elsewhere.

Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

PubMed

Bevilacqua, Marta; Marini, Federico

2014-08-01

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks). Copyright © 2014 Elsevier B.V. All rights reserved.

Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification

NASA Astrophysics Data System (ADS)

De Lucia, Frank C., Jr.; Gottfried, Jennifer L.

2011-02-01

Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.

Determination of total phenolic compounds in compost by infrared spectroscopy.

PubMed

Cascant, M M; Sisouane, M; Tahiri, S; Krati, M El; Cervera, M L; Garrigues, S; de la Guardia, M

2016-06-01

Middle and near infrared (MIR and NIR) were applied to determine the total phenolic compounds (TPC) content in compost samples based on models built by using partial least squares (PLS) regression. The multiplicative scatter correction, standard normal variate and first derivative were employed as spectra pretreatment, and the number of latent variable were optimized by leave-one-out cross-validation. The performance of PLS-ATR-MIR and PLS-DR-NIR models was evaluated according to root mean square error of cross validation and prediction (RMSECV and RMSEP), the coefficient of determination for prediction (Rpred(2)) and residual predictive deviation (RPD) being obtained for this latter values of 5.83 and 8.26 for MIR and NIR, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm.

PubMed

Jiang, Hui; Liu, Guohai; Mei, Congli; Yu, Shuang; Xiao, Xiahong; Ding, Yuhan

2012-11-01

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV=0.0776, R(c)=0.9777, RMSEP=0.0963, and R(p)=0.9686 for pH model; RMSECV=1.3544% w/w, R(c)=0.8871, RMSEP=1.4946% w/w, and R(p)=0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry. Copyright © 2012 Elsevier B.V. All rights reserved.

[Rapid determination of COD in aquaculture water based on LS-SVM with ultraviolet/visible spectroscopy].

PubMed

Liu, Xue-Mei; Zhang, Hai-Liang

2014-10-01

Ultraviolet/visible (UV/Vis) spectroscopy was studied for the rapid determination of chemical oxygen demand (COD), which was an indicator to measure the concentration of organic matter in aquaculture water. In order to reduce the influence of the absolute noises of the spectra, the extracted 135 absorbance spectra were preprocessed by Savitzky-Golay smoothing (SG), EMD, and wavelet transform (WT) methods. The preprocessed spectra were then used to select latent variables (LVs) by partial least squares (PLS) methods. Partial least squares (PLS) was used to build models with the full spectra, and back- propagation neural network (BPNN) and least square support vector machine (LS-SVM) were applied to build models with the selected LVs. The overall results showed that BPNN and LS-SVM models performed better than PLS models, and the LS-SVM models with LVs based on WT preprocessed spectra obtained the best results with the determination coefficient (r2) and RMSE being 0. 83 and 14. 78 mg · L(-1) for calibration set, and 0.82 and 14.82 mg · L(-1) for the prediction set respectively. The method showed the best performance in LS-SVM model. The results indicated that it was feasible to use UV/Vis with LVs which were obtained by PLS method, combined with LS-SVM calibration could be applied to the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Remote quantification of phycocyanin in potable water sources through an adaptive model

NASA Astrophysics Data System (ADS)

Song, Kaishan; Li, Lin; Tedesco, Lenore P.; Li, Shuai; Hall, Bob E.; Du, Jia

2014-09-01

Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (Rrs) were measured. A partial least squares-artificial neural network (PLS-ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the Rrs with PC concentration. Our results indicate that the PLS-ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS-ANN model resulted in a high coefficient of determination (R2) for CIN dataset (R2 = 0.92, R: 0.3-220.7 μg/L) and SA (R2 = 0.98, R: 0.2-13.2 μg/L). In comparison, the TBM model yielded an R2 = 0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R2 = 0.86; SA: R2 = 0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS-ANN model still achieved good performance (OLCI: R2 = 0.84; Hyperion: R2 = 0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R2 = 0.65, Hyperion: R2 = 0.70). Based on the results, the PLS-ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R2 = 0.81; SA: R2 = 0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry.

PubMed

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-15

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications. Copyright © 2012 Elsevier B.V. All rights reserved.

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.

PubMed

Oliveira, Aline A; Lipinski, Célio F; Pereira, Estevão B; Honorio, Kathia M; Oliveira, Patrícia R; Weber, Karen C; Romero, Roseli A F; de Sousa, Alexsandro G; da Silva, Albérico B F

2017-10-02

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r 2 training = 0.761, q 2 = 0.656, r 2 test = 0.746, MSE test = 0.132 and MAE test = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSE test values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r 2 test = 0.824, MSE test = 0.088 and MAE test = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r 2 test = 0.811, MSE test = 0.100 and MAE test = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Discrimination of tomatoes bred by spaceflight mutagenesis using visible/near infrared spectroscopy and chemometrics.

PubMed

Shao, Yongni; Xie, Chuanqi; Jiang, Linjun; Shi, Jiahui; Zhu, Jiajin; He, Yong

2015-04-05

Visible/near infrared spectroscopy (Vis/NIR) based on sensitive wavelengths (SWs) and chemometrics was proposed to discriminate different tomatoes bred by spaceflight mutagenesis from their leafs or fruits (green or mature). The tomato breeds were mutant M1, M2 and their parent. Partial least squares (PLS) analysis and least squares-support vector machine (LS-SVM) were implemented for calibration models. PLS analysis was implemented for calibration models with different wavebands including the visible region (400-700 nm) and the near infrared region (700-1000 nm). The best PLS models were achieved in the visible region for the leaf and green fruit samples and in the near infrared region for the mature fruit samples. Furthermore, different latent variables (4-8 LVs for leafs, 5-9 LVs for green fruits, and 4-9 LVs for mature fruits) were used as inputs of LS-SVM to develop the LV-LS-SVM models with the grid search technique and radial basis function (RBF) kernel. The optimal LV-LS-SVM models were achieved with six LVs for the leaf samples, seven LVs for green fruits, and six LVs for mature fruits, respectively, and they outperformed the PLS models. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights. The optimal LS-SVM model was achieved with SWs of 550-560 nm, 562-574 nm, 670-680 nm and 705-71 5 nm for the leaf samples; 548-556 nm, 559-564 nm, 678-685 nm and 962-974 nm for the green fruit samples; and 712-718 nm, 720-729 nm, 968-978 nm and 820-830 nm for the mature fruit samples. All of them had better performance than PLS and LV-LS-SVM, with the parameters of correlation coefficient (rp), root mean square error of prediction (RMSEP) and bias of 0.9792, 0.2632 and 0.0901 based on leaf discrimination, 0.9837, 0.2783 and 0.1758 based on green fruit discrimination, 0.9804, 0.2215 and -0.0035 based on mature fruit discrimination, respectively. The overall results indicated that ICA was an effective way for the selection of SWs, and the Vis/NIR combined with LS-SVM models had the capability to predict the different breeds (mutant M1, mutant M2 and their parent) of tomatoes from leafs and fruits. Copyright © 2015 Elsevier B.V. All rights reserved.

Partial least squares methods for spectrally estimating lunar soil FeO abundance: A stratified approach to revealing nonlinear effect and qualitative interpretation

NASA Astrophysics Data System (ADS)

Li, Lin

2008-12-01

Partial least squares (PLS) regressions were applied to lunar highland and mare soil data characterized by the Lunar Soil Characterization Consortium (LSCC) for spectral estimation of the abundance of lunar soil chemical constituents FeO and Al2O3. The LSCC data set was split into a number of subsets including the total highland, Apollo 16, Apollo 14, and total mare soils, and then PLS was applied to each to investigate the effect of nonlinearity on the performance of the PLS method. The weight-loading vectors resulting from PLS were analyzed to identify mineral species responsible for spectral estimation of the soil chemicals. The results from PLS modeling indicate that the PLS performance depends on the correlation of constituents of interest to their major mineral carriers, and the Apollo 16 soils are responsible for the large errors of FeO and Al2O3 estimates when the soils were modeled along with other types of soils. These large errors are primarily attributed to the degraded correlation FeO to pyroxene for the relatively mature Apollo 16 soils as a result of space weathering and secondary to the interference of olivine. PLS consistently yields very accurate fits to the two soil chemicals when applied to mare soils. Although Al2O3 has no spectrally diagnostic characteristics, this chemical can be predicted for all subset data by PLS modeling at high accuracies because of its correlation to FeO. This correlation is reflected in the symmetry of the PLS weight-loading vectors for FeO and Al2O3, which prove to be very useful for qualitative interpretation of the PLS results. However, this qualitative interpretation of PLS modeling cannot be achieved using principal component regression loading vectors.

Simultaneous quantitative determination of paracetamol and tramadol in tablet formulation using UV spectrophotometry and chemometric methods

NASA Astrophysics Data System (ADS)

Glavanović, Siniša; Glavanović, Marija; Tomišić, Vladislav

2016-03-01

The UV spectrophotometric methods for simultaneous quantitative determination of paracetamol and tramadol in paracetamol-tramadol tablets were developed. The spectrophotometric data obtained were processed by means of partial least squares (PLS) and genetic algorithm coupled with PLS (GA-PLS) methods in order to determine the content of active substances in the tablets. The results gained by chemometric processing of the spectroscopic data were statistically compared with those obtained by means of validated ultra-high performance liquid chromatographic (UHPLC) method. The accuracy and precision of data obtained by the developed chemometric models were verified by analysing the synthetic mixture of drugs, and by calculating recovery as well as relative standard error (RSE). A statistically good agreement was found between the amounts of paracetamol determined using PLS and GA-PLS algorithms, and that obtained by UHPLC analysis, whereas for tramadol GA-PLS results were proven to be more reliable compared to those of PLS. The simplest and the most accurate and precise models were constructed by using the PLS method for paracetamol (mean recovery 99.5%, RSE 0.89%) and the GA-PLS method for tramadol (mean recovery 99.4%, RSE 1.69%).

Fingerprinting of egg and oil binders in painted artworks by matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis of lipid oxidation by-products.

PubMed

Calvano, C D; van der Werf, I D; Palmisano, F; Sabbatini, L

2011-06-01

A matrix-assisted laser desorption ionization time-of-flight mass spectrometry-based approach was applied for the detection of various lipid classes, such as triacylglycerols (TAGs) and phospholipids (PLs), and their oxidation by-products in extracts of small (50-100 μg) samples obtained from painted artworks. Ageing of test specimens under various conditions, including the presence of different pigments, was preliminarily investigated. During ageing, the TAGs and PLs content decreased, whereas the amount of diglycerides, short-chain oxidative products arising from TAGs and PLs, and oxidized TAGs and PLs components increased. The examination of a series of model paint samples gave a clear indication that specific ions produced by oxidative cleavage of PLs and/or TAGs may be used as markers for egg and drying oil-based binders. Their elemental composition and hypothetical structure are also tentatively proposed. Moreover, the simultaneous presence of egg and oil binders can be easily and unambiguously ascertained through the simultaneous occurrence of the relevant specific markers. The potential of the proposed approach was demonstrated for the first time by the analysis of real samples from a polyptych of Bartolomeo Vivarini (fifteenth century) and a "French school" canvas painting (seventeenth century).

Semi-quantitative prediction of a multiple API solid dosage form with a combination of vibrational spectroscopy methods.

PubMed

Hertrampf, A; Sousa, R M; Menezes, J C; Herdling, T

2016-05-30

Quality control (QC) in the pharmaceutical industry is a key activity in ensuring medicines have the required quality, safety and efficacy for their intended use. QC departments at pharmaceutical companies are responsible for all release testing of final products but also all incoming raw materials. Near-infrared spectroscopy (NIRS) and Raman spectroscopy are important techniques for fast and accurate identification and qualification of pharmaceutical samples. Tablets containing two different active pharmaceutical ingredients (API) [bisoprolol, hydrochlorothiazide] in different commercially available dosages were analysed using Raman- and NIR Spectroscopy. The goal was to define multivariate models based on each vibrational spectroscopy to discriminate between different dosages (identity) and predict their dosage (semi-quantitative). Furthermore the combination of spectroscopic techniques was investigated. Therefore, two different multiblock techniques based on PLS have been applied: multiblock PLS (MB-PLS) and sequential-orthogonalised PLS (SO-PLS). NIRS showed better results compared to Raman spectroscopy for both identification and quantitation. The multiblock techniques investigated showed that each spectroscopy contains information not present or captured with the other spectroscopic technique, thus demonstrating that there is a potential benefit in their combined use for both identification and quantitation purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

NASA Astrophysics Data System (ADS)

Lee, Loong Chuen; Liong, Choong-Yeun; Jemain, Abdul Aziz

2018-04-01

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance - Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The `best' performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample prediction via partial least squares - discriminant analysis (PLS2-DA). Error rate of each model was estimated repeatedly via: (a) AP on full data (APF, error); (b) AP on training set (APS, error); and (c) ET on the respective test set (ETS, error). A good PLS2-DA model is expected to produce APS, error and EVS, error that is similar to the APF, error. Bearing that in mind, the similarities between (a) APS, error vs. APF, error; (b) ETS, error vs. APF, error and; (c) APS, error vs. ETS, error were evaluated using correlation tests (i.e. Pearson and Spearman's rank test), using series of PLS2-DA models computed from KS-set and IRS-set, respectively. Overall, models constructed from IRS-set exhibits more similarities between the internal and external error rates than the respective KS-set, i.e. less risk of overfitting. In conclusion, IRS is more reliable than KS in sampling representative training set.

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

DOE PAGES

Li, Muyang; Williams, Daniel L.; Heckwolf, Marlies; ...

2016-10-04

In this paper, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant's response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These weremore » compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.« less

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Muyang; Williams, Daniel L.; Heckwolf, Marlies

In this paper, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant's response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These weremore » compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.« less

[Application of characteristic NIR variables selection in portable detection of soluble solids content of apple by near infrared spectroscopy].

PubMed

Fan, Shu-Xiang; Huang, Wen-Qian; Li, Jiang-Bo; Guo, Zhi-Ming; Zhaq, Chun-Jiang

2014-10-01

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including unin- formative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were pro- posed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS- SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.

Simultaneous determination of the impurity and radial tensile strength of reduced glutathione tablets by a high selective NIR-PLS method.

PubMed

Li, Juan; Jiang, Yue; Fan, Qi; Chen, Yang; Wu, Ruanqi

2014-05-05

This paper establishes a high-throughput and high selective method to determine the impurity named oxidized glutathione (GSSG) and radial tensile strength (RTS) of reduced glutathione (GSH) tablets based on near infrared (NIR) spectroscopy and partial least squares (PLS). In order to build and evaluate the calibration models, the NIR diffuse reflectance spectra (DRS) and transmittance spectra (TS) for 330 GSH tablets were accurately measured by using the optimized parameter values. For analyzing GSSG or RTS of GSH tablets, the NIR-DRS or NIR-TS were selected, subdivided reasonably into calibration and prediction sets, and processed appropriately with chemometric techniques. After selecting spectral sub-ranges and neglecting spectrum outliers, the PLS calibration models were built and the factor numbers were optimized. Then, the PLS models were evaluated by the root mean square errors of calibration (RMSEC), cross-validation (RMSECV) and prediction (RMSEP), and by the correlation coefficients of calibration (R(c)) and prediction (R(p)). The results indicate that the proposed models have good performances. It is thus clear that the NIR-PLS can simultaneously, selectively, nondestructively and rapidly analyze the GSSG and RTS of GSH tablets, although the contents of GSSG impurity were quite low while those of GSH active pharmaceutical ingredient (API) quite high. This strategy can be an important complement to the common NIR methods used in the on-line analysis of API in pharmaceutical preparations. And this work expands the NIR applications in the high-throughput and extraordinarily selective analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms

NASA Astrophysics Data System (ADS)

Kang, Qian; Ru, Qingguo; Liu, Yan; Xu, Lingyan; Liu, Jia; Wang, Yifei; Zhang, Yewen; Li, Hui; Zhang, Qing; Wu, Qing

2016-01-01

An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2 mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R2) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines.

Design of experiments-based monitoring of critical quality attributes for the spray-drying process of insulin by NIR spectroscopy.

PubMed

Maltesen, Morten Jonas; van de Weert, Marco; Grohganz, Holger

2012-09-01

Moisture content and aerodynamic particle size are critical quality attributes for spray-dried protein formulations. In this study, spray-dried insulin powders intended for pulmonary delivery were produced applying design of experiments methodology. Near infrared spectroscopy (NIR) in combination with preprocessing and multivariate analysis in the form of partial least squares projections to latent structures (PLS) were used to correlate the spectral data with moisture content and aerodynamic particle size measured by a time of flight principle. PLS models predicting the moisture content were based on the chemical information of the water molecules in the NIR spectrum. Models yielded prediction errors (RMSEP) between 0.39% and 0.48% with thermal gravimetric analysis used as reference method. The PLS models predicting the aerodynamic particle size were based on baseline offset in the NIR spectra and yielded prediction errors between 0.27 and 0.48 μm. The morphology of the spray-dried particles had a significant impact on the predictive ability of the models. Good predictive models could be obtained for spherical particles with a calibration error (RMSECV) of 0.22 μm, whereas wrinkled particles resulted in much less robust models with a Q (2) of 0.69. Based on the results in this study, NIR is a suitable tool for process analysis of the spray-drying process and for control of moisture content and particle size, in particular for smooth and spherical particles.

PLS modelling of structure—activity relationships of catechol O-methyltransferase inhibitors

NASA Astrophysics Data System (ADS)

Lotta, Timo; Taskinen, Jyrki; Bäckström, Reijo; Nissinen, Erkki

1992-06-01

Quantitative structure-activity analysis was carried out for in vitro inhibition of rat brain soluble catechol O-methyltransferase by a series (N=99) of 1,5-substituted-3,4-dihydroxybenzenes using computational chemistry and multivariate PLS modelling of data sets. The molecular structural descriptors (N=19) associated with the electronics of the catecholic ring and sizes of substituents were derived theoretically. For the whole set of molecules two separate PLS models have to be used. A PLS model with two significant (crossvalidated) model dimensions describing 82.2% of the variance in inhibition activity data was capable of predicting all molecules except those having the largest R1 substituent or having a large R5 substituent compared to the NO2 group. The other PLS model with three significant (crossvalidated) model dimensions described 83.3% of the variance in inhibition activity data. This model could not handle compounds having a small R5 substituent, compared to the NO2 group, or the largest R1 substituent. The predictive capability of these PLS models was good. The models reveal that inhibition activity is nonlinearly related to the size of the R5 substituent. The analysis of the PLS models also shows that the binding affinity is greatly dependent on the electronic nature of both R1 and R5 substituents. The electron-withdrawing nature of the substituents enhances inhibition activity. In addition, the size of the R1 substituent and its lipophilicity are important in the binding of inhibitors. The size of the R1 substituent has an upper limit. On the other hand, ionized R1 substituents decrease inhibition activity.

A Cultural Diffusion Model for the Rise and Fall of Programming Languages.

PubMed

Valverde, Sergi; Solé, Ricard V

2015-07-01

Our interaction with complex computing machines is mediated by programming languages (PLs), which constitute one of the major innovations in the evolution of technology. PLs allow flexible, scalable, and fast use of hardware and are largely responsible for shaping the history of information technology since the rise of computers in the 1950s. The rapid growth and impact of computers were followed closely by the development of PLs. As occurs with natural, human languages, PLs have emerged and gone extinct. There has been always a diversity of coexisting PLs that compete somewhat while occupying special niches. Here we show that the statistical patterns of language adoption, rise, and fall can be accounted for by a simple model in which a set of programmers can use several PLs, decide to use existing PLs used by other programmers, or decide not to use them. Our results highlight the influence of strong communities of practice in the diffusion of PL innovations.

Real time flaw detection and characterization in tube through partial least squares and SVR: Application to eddy current testing

NASA Astrophysics Data System (ADS)

Ahmed, Shamim; Miorelli, Roberto; Calmon, Pierre; Anselmi, Nicola; Salucci, Marco

2018-04-01

This paper describes Learning-By-Examples (LBE) technique for performing quasi real time flaw localization and characterization within a conductive tube based on Eddy Current Testing (ECT) signals. Within the framework of LBE, the combination of full-factorial (i.e., GRID) sampling and Partial Least Squares (PLS) feature extraction (i.e., GRID-PLS) techniques are applied for generating a suitable training set in offine phase. Support Vector Regression (SVR) is utilized for model development and inversion during offine and online phases, respectively. The performance and robustness of the proposed GIRD-PLS/SVR strategy on noisy test set is evaluated and compared with standard GRID/SVR approach.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.

PubMed

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-01-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS

NASA Astrophysics Data System (ADS)

Jiang, Hui; Zhang, Hang; Chen, Quansheng; Mei, Congli; Liu, Guohai

2015-10-01

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

PubMed

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Quantitative determination of wool in textile by near-infrared spectroscopy and multivariate models.

PubMed

Chen, Hui; Tan, Chao; Lin, Zan

2018-08-05

The wool content in textiles is a key quality index and the corresponding quantitative analysis takes an important position due to common adulterations in both raw and finished textiles. Conventional methods are maybe complicated, destructive, time-consuming, environment-unfriendly. Developing a quick, easy-to-use and green alternative method is interesting. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and several partial least squares (PLS)-based algorithms and elastic component regression (ECR) algorithms for measuring wool content in textile. A total of 108 cloth samples with wool content ranging from 0% to 100% (w/w) were collected and all the compositions are really existent in the market. The dataset was divided equally into the training and test sets for developing and validating calibration models. When using local PLS, the original spectrum axis was split into 20 sub-intervals. No obvious difference of performance can be seen for the local PLS models. The ECR model is comparable or superior to the other models due its flexibility, i.e., being transition state from PCR to PLS. It seems that ECR combined with NIR technique may be a potential method for determining wool content in textile products. In addition, it might have regulatory advantages to avoid time-consuming and environmental-unfriendly chemical analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

At-line determination of pharmaceuticals small molecule's blending end point using chemometric modeling combined with Fourier transform near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Tewari, Jagdish; Strong, Richard; Boulas, Pierre

2017-02-01

This article summarizes the development and validation of a Fourier transform near infrared spectroscopy (FT-NIR) method for the rapid at-line prediction of active pharmaceutical ingredient (API) in a powder blend to optimize small molecule formulations. The method was used to determine the blend uniformity end-point for a pharmaceutical solid dosage formulation containing a range of API concentrations. A set of calibration spectra from samples with concentrations ranging from 1% to 15% of API (w/w) were collected at-line from 4000 to 12,500 cm- 1. The ability of the FT-NIR method to predict API concentration in the blend samples was validated against a reference high performance liquid chromatography (HPLC) method. The prediction efficiency of four different types of multivariate data modeling methods such as partial least-squares 1 (PLS1), partial least-squares 2 (PLS2), principal component regression (PCR) and artificial neural network (ANN), were compared using relevant multivariate figures of merit. The prediction ability of the regression models were cross validated against results generated with the reference HPLC method. PLS1 and ANN showed excellent and superior prediction abilities when compared to PLS2 and PCR. Based upon these results and because of its decreased complexity compared to ANN, PLS1 was selected as the best chemometric method to predict blend uniformity at-line. The FT-NIR measurement and the associated chemometric analysis were implemented in the production environment for rapid at-line determination of the end-point of the small molecule blending operation. FIGURE 1: Correlation coefficient vs Rank plot FIGURE 2: FT-NIR spectra of different steps of Blend and final blend FIGURE 3: Predictions ability of PCR FIGURE 4: Blend uniformity predication ability of PLS2 FIGURE 5: Prediction efficiency of blend uniformity using ANN FIGURE 6: Comparison of prediction efficiency of chemometric models TABLE 1: Order of Addition for Blending Steps

[Effect of near infrared spectrum on the precision of PLS model for oil yield from oil shale].

PubMed

Wang, Zhi-Hong; Liu, Jie; Chen, Xiao-Chao; Sun, Yu-Yang; Yu, Yang; Lin, Jun

2012-10-01

It is impossible to use present measurement methods for the oil yield of oil shale to realize in-situ detection and these methods unable to meet the requirements of the oil shale resources exploration and exploitation. But in-situ oil yield analysis of oil shale can be achieved by the portable near infrared spectroscopy technique. There are different correlativities of NIR spectrum data formats and contents of sample components, and the different absorption specialities of sample components shows in different NIR spectral regions. So with the proportioning samples, the PLS modeling experiments were done by 3 formats (reflectance, absorbance and K-M function) and 4 regions of modeling spectrum, and the effect of NIR spectral format and region to the precision of PLS model for oil yield from oil shale was studied. The results show that the best data format is reflectance and the best modeling region is combination spectral range by PLS model method and proportioning samples. Therefore, the appropriate data format and the proper characteristic spectral region can increase the precision of PLS model for oil yield form oil shale.

The Picmonic(®) Learning System: enhancing memory retention of medical sciences, using an audiovisual mnemonic Web-based learning platform.

PubMed

Yang, Adeel; Goel, Hersh; Bryan, Matthew; Robertson, Ron; Lim, Jane; Islam, Shehran; Speicher, Mark R

2014-01-01

Medical students are required to retain vast amounts of medical knowledge on the path to becoming physicians. To address this challenge, multimedia Web-based learning resources have been developed to supplement traditional text-based materials. The Picmonic(®) Learning System (PLS; Picmonic, Phoenix, AZ, USA) is a novel multimedia Web-based learning platform that delivers audiovisual mnemonics designed to improve memory retention of medical sciences. A single-center, randomized, subject-blinded, controlled study was conducted to compare the PLS with traditional text-based material for retention of medical science topics. Subjects were randomly assigned to use two different types of study materials covering several diseases. Subjects randomly assigned to the PLS group were given audiovisual mnemonics along with text-based materials, whereas subjects in the control group were given the same text-based materials with key terms highlighted. The primary endpoints were the differences in performance on immediate, 1 week, and 1 month delayed free-recall and paired-matching tests. The secondary endpoints were the difference in performance on a 1 week delayed multiple-choice test and self-reported satisfaction with the study materials. Differences were calculated using unpaired two-tailed t-tests. PLS group subjects demonstrated improvements of 65%, 161%, and 208% compared with control group subjects on free-recall tests conducted immediately, 1 week, and 1 month after study of materials, respectively. The results of performance on paired-matching tests showed an improvement of up to 331% for PLS group subjects. PLS group subjects also performed 55% greater than control group subjects on a 1 week delayed multiple choice test requiring higher-order thinking. The differences in test performance between the PLS group subjects and the control group subjects were statistically significant (P<0.001), and the PLS group subjects reported higher overall satisfaction with the material. The data of this pilot site demonstrate marked improvements in the retention of disease topics when using the PLS compared with traditional text-based materials. The use of the PLS in medical education is supported.

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements.

PubMed

Burggraeve, A; Van den Kerkhof, T; Hellings, M; Remon, J P; Vervaet, C; De Beer, T

2011-04-18

Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively, flowabililty as Y-matrix, were developed. The scores of the 4 test batches were used to examine the predictive ability of the model. Copyright © 2011 Elsevier B.V. All rights reserved.

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

PubMed

Ramírez, J; Górriz, J M; Segovia, F; Chaves, R; Salas-Gonzalez, D; López, M; Alvarez, I; Padilla, P

2010-03-19

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Data Mining of Chemogenomics Data Using Bi-Modal PLS Methods and Chemical Interpretation for Molecular Design.

PubMed

Hasegawa, Kiyoshi; Funatsu, Kimito

2014-12-01

Chemogenomics is a new strategy in drug discovery for interrogating all molecules capable of interacting with all biological targets. Because of the almost infinite number of drug-like organic molecules, bench-based experimental chemogenomics methods are not generally feasible. Several in silico chemogenomics models have therefore been developed for high-throughput screening of large numbers of drug candidate compounds and target proteins. In previous studies, we described two novel bi-modal PLS approaches. These methods provide a significant advantage in that they enable direct connections to be made between biological activities and ligand and protein descriptors. In this special issue, we review these two PLS-based approaches using two different chemogenomics datasets for illustration. We then compare the predictive and interpretive performance of the two methods using the same congeneric data set. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis.

PubMed

Nespeca, Maurilio Gustavo; Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm -1 . The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

PubMed Central

Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time. PMID:29629209

Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

PubMed

Catana, Cornel; Gao, Hua; Orrenius, Christian; Stouten, Pieter F W

2005-01-01

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

PubMed

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

Calibration sets selection strategy for the construction of robust PLS models for prediction of biodiesel/diesel blends physico-chemical properties using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Palou, Anna; Miró, Aira; Blanco, Marcelo; Larraz, Rafael; Gómez, José Francisco; Martínez, Teresa; González, Josep Maria; Alcalà, Manel

2017-06-01

Even when the feasibility of using near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for prediction of physico-chemical properties of biodiesel/diesel blends has been widely demonstrated, inclusion in the calibration sets of the whole variability of diesel samples from diverse production origins still remains as an important challenge when constructing the models. This work presents a useful strategy for the systematic selection of calibration sets of samples of biodiesel/diesel blends from diverse origins, based on a binary code, principal components analysis (PCA) and the Kennard-Stones algorithm. Results show that using this methodology the models can keep their robustness over time. PLS calculations have been done using a specialized chemometric software as well as the software of the NIR instrument installed in plant, and both produced RMSEP under reproducibility values of the reference methods. The models have been proved for on-line simultaneous determination of seven properties: density, cetane index, fatty acid methyl esters (FAME) content, cloud point, boiling point at 95% of recovery, flash point and sulphur.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

Near-infrared Raman spectroscopy to detect anti-Toxoplasma gondii antibody in blood sera of domestic cats: quantitative analysis based on partial least-squares multivariate statistics

NASA Astrophysics Data System (ADS)

Duarte, Janaína; Pacheco, Marcos T. T.; Villaverde, Antonio Balbin; Machado, Rosangela Z.; Zângaro, Renato A.; Silveira, Landulfo

2010-07-01

Toxoplasmosis is an important zoonosis in public health because domestic cats are the main agents responsible for the transmission of this disease in Brazil. We investigate a method for diagnosing toxoplasmosis based on Raman spectroscopy. Dispersive near-infrared Raman spectra are used to quantify anti-Toxoplasma gondii (IgG) antibodies in blood sera from domestic cats. An 830-nm laser is used for sample excitation, and a dispersive spectrometer is used to detect the Raman scattering. A serological test is performed in all serum samples by the enzyme-linked immunosorbent assay (ELISA) for validation. Raman spectra are taken from 59 blood serum samples and a quantification model is implemented based on partial least squares (PLS) to quantify the sample's serology by Raman spectra compared to the results provided by the ELISA test. Based on the serological values provided by the Raman/PLS model, diagnostic parameters such as sensitivity, specificity, accuracy, positive prediction values, and negative prediction values are calculated to discriminate negative from positive samples, obtaining 100, 80, 90, 83.3, and 100%, respectively. Raman spectroscopy, associated with the PLS, is promising as a serological assay for toxoplasmosis, enabling fast and sensitive diagnosis.

Locally-Based Kernal PLS Smoothing to Non-Parametric Regression Curve Fitting

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Wheeler, Kevin; Korsmeyer, David (Technical Monitor)

2002-01-01

We present a novel smoothing approach to non-parametric regression curve fitting. This is based on kernel partial least squares (PLS) regression in reproducing kernel Hilbert space. It is our concern to apply the methodology for smoothing experimental data where some level of knowledge about the approximate shape, local inhomogeneities or points where the desired function changes its curvature is known a priori or can be derived based on the observed noisy data. We propose locally-based kernel PLS regression that extends the previous kernel PLS methodology by incorporating this knowledge. We compare our approach with existing smoothing splines, hybrid adaptive splines and wavelet shrinkage techniques on two generated data sets.

QSAR Study of p56lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using MLR and GA-PLS

PubMed Central

Fassihi, Afshin; Sabet, Razieh

2008-01-01

Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously. PMID:19325836

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Determination of total flavonoids content in fresh Ginkgo biloba leaf with different colors using near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Shi, Ji-yong; Zou, Xiao-bo; Zhao, Jie-wen; Mel, Holmes; Wang, Kai-liang; Wang, Xue; Chen, Hong

Total flavonoids content is often considered an important quality index of Ginkgo biloba leaf. The feasibility of using near infrared (NIR) spectra at the wavelength range of 10,000-4000 cm-1 for rapid and nondestructive determination of total flavonoids content in G. biloba leaf was investigated. 120 fresh G. biloba leaves in different colors (green, green-yellowish and yellow) were used to spectra acquisition and total flavonoids determination. Partial least squares (PLS), interval partial least squares (iPLS) and synergy interval partial least squares (SiPLS) were used to develop calibration models for total flavonoids content in two colors leaves (green-yellowish and yellow) and three colors leaves (green, green-yellowish and yellow), respectively. The level of total flavonoids content for green, green-yellowish and yellow leaves was in an increasing order. Two characteristic wavelength regions (5840-6090 cm-1 and 6620-6880 cm-1), which corresponded to the absorptions of two aromatic rings in basic flavonoid structure, were selected by SiPLS. The optimal SiPLS model for total flavonoids content in the two colors leaves (r2 = 0.82, RMSEP = 2.62 mg g-1) had better performance than PLS and iPLS models. It could be concluded that NIR spectroscopy has significant potential in the nondestructive determination of total flavonoids content in fresh G. biloba leaf.

[Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS].

PubMed

Li, Wen-bing; Yao, Lin-tao; Liu, Mu-hua; Huang, Lin; Yao, Ming-yin; Chen, Tian-bing; He, Xiu-wen; Yang, Ping; Hu, Hui-qin; Nie, Jiang-hui

2015-05-01

Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.

Consistent Partial Least Squares Path Modeling via Regularization.

PubMed

Jung, Sunho; Park, JaeHong

2018-01-01

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.

A comparison of different strategies in multivariate regression models for the direct determination of Mn, Cr, and Ni in steel samples using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; Gonzaga, Fabiano B.; da Rocha, Werickson F. C.; Lima, Igor C. A.

2018-01-01

Laser-induced breakdown spectroscopy (LIBS) analysis was carried out on eleven steel samples to quantify the concentrations of chromium, nickel, and manganese. LIBS spectral data were correlated to known concentrations of the samples using different strategies in partial least squares (PLS) regression models. For the PLS analysis, one predictive model was separately generated for each element, while different approaches were used for the selection of variables (VIP: variable importance in projection and iPLS: interval partial least squares) in the PLS model to quantify the contents of the elements. The comparison of the performance of the models showed that there was no significant statistical difference using the Wilcoxon signed rank test. The elliptical joint confidence region (EJCR) did not detect systematic errors in these proposed methodologies for each metal.

[Research on partial least squares for determination of impurities in the presence of high concentration of matrix by ICP-AES].

PubMed

Wang, Yan-peng; Gong, Qi; Yu, Sheng-rong; Liu, You-yan

2012-04-01

A method for detecting trace impurities in high concentration matrix by ICP-AES based on partial least squares (PLS) was established. The research showed that PLS could effectively correct the interference caused by high level of matrix concentration error and could withstand higher concentrations of matrix than multicomponent spectral fitting (MSF). When the mass ratios of matrix to impurities were from 1 000 : 1 to 20 000 : 1, the recoveries of standard addition were between 95% and 105% by PLS. For the system in which interference effect has nonlinear correlation with the matrix concentrations, the prediction accuracy of normal PLS method was poor, but it can be improved greatly by using LIN-PPLS, which was based on matrix transformation of sample concentration. The contents of Co, Pb and Ga in stream sediment (GBW07312) were detected by MSF, PLS and LIN-PPLS respectively. The results showed that the prediction accuracy of LIN-PPLS was better than PLS, and the prediction accuracy of PLS was better than MSF.

[Study on the early detection of Sclerotinia of Brassica napus based on combinational-stimulated bands].

PubMed

Liu, Fei; Feng, Lei; Lou, Bing-gan; Sun, Guang-ming; Wang, Lian-ping; He, Yong

2010-07-01

The combinational-stimulated bands were used to develop linear and nonlinear calibrations for the early detection of sclerotinia of oilseed rape (Brassica napus L.). Eighty healthy and 100 Sclerotinia leaf samples were scanned, and different preprocessing methods combined with successive projections algorithm (SPA) were applied to develop partial least squares (PLS) discriminant models, multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) models. The results indicated that the optimal full-spectrum PLS model was achieved by direct orthogonal signal correction (DOSC), then De-trending and Raw spectra with correct recognition ratio of 100%, 95.7% and 95.7%, respectively. When using combinational-stimulated bands, the optimal linear models were SPA-MLR (DOSC) and SPA-PLS (DOSC) with correct recognition ratio of 100%. All SPA-LSSVM models using DOSC, De-trending and Raw spectra achieved perfect results with recognition of 100%. The overall results demonstrated that it was feasible to use combinational-stimulated bands for the early detection of Sclerotinia of oilseed rape, and DOSC-SPA was a powerful way for informative wavelength selection. This method supplied a new approach to the early detection and portable monitoring instrument of sclerotinia.

Three-dimensional displacement measurement of image point by point-diffraction interferometry

NASA Astrophysics Data System (ADS)

He, Xiao; Chen, Lingfeng; Meng, Xiaojie; Yu, Lei

2018-01-01

This paper presents a method for measuring the three-dimensional (3-D) displacement of an image point based on point-diffraction interferometry. An object Point-light-source (PLS) interferes with a fixed PLS and its interferograms are captured by an exit pupil. When the image point of the object PLS is slightly shifted to a new position, the wavefront of the image PLS changes. And its interferograms also change. Processing these figures (captured before and after the movement), the wavefront difference of the image PLS can be obtained and it contains the information of three-dimensional (3-D) displacement of the image PLS. However, the information of its three-dimensional (3-D) displacement cannot be calculated until the distance between the image PLS and the exit pupil is calibrated. Therefore, we use a plane-parallel-plate with a known refractive index and thickness to determine this distance, which is based on the Snell's law for small angle of incidence. Thus, since the distance between the exit pupil and the image PLS is a known quantity, the 3-D displacement of the image PLS can be simultaneously calculated through two interference measurements. Preliminary experimental results indicate that its relative error is below 0.3%. With the ability to accurately locate an image point (whatever it is real or virtual), a fiber point-light-source can act as the reticle by itself in optical measurement.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis.

PubMed

Lee, Byeong-Ju; Kim, Hye-Youn; Lim, Sa Rang; Huang, Linfang; Choi, Hyung-Kyoon

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis

PubMed Central

Lim, Sa Rang; Huang, Linfang

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values. PMID:29049369

A Piecewise Local Partial Least Squares (PLS) Method for the Quantitative Analysis of Plutonium Nitrate Solutions

DOE PAGES

Lascola, Robert; O'Rourke, Patrick E.; Kyser, Edward A.

2017-10-05

Here, we have developed a piecewise local (PL) partial least squares (PLS) analysis method for total plutonium measurements by absorption spectroscopy in nitric acid-based nuclear material processing streams. Instead of using a single PLS model that covers all expected solution conditions, the method selects one of several local models based on an assessment of solution absorbance, acidity, and Pu oxidation state distribution. The local models match the global model for accuracy against the calibration set, but were observed in several instances to be more robust to variations associated with measurements in the process. The improvements are attributed to the relativemore » parsimony of the local models. Not all of the sources of spectral variation are uniformly present at each part of the calibration range. Thus, the global model is locally overfitting and susceptible to increased variance when presented with new samples. A second set of models quantifies the relative concentrations of Pu(III), (IV), and (VI). Standards containing a mixture of these species were not at equilibrium due to a disproportionation reaction. Therefore, a separate principal component analysis is used to estimate of the concentrations of the individual oxidation states in these standards in the absence of independent confirmatory analysis. The PL analysis approach is generalizable to other systems where the analysis of chemically complicated systems can be aided by rational division of the overall range of solution conditions into simpler sub-regions.« less

A Piecewise Local Partial Least Squares (PLS) Method for the Quantitative Analysis of Plutonium Nitrate Solutions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lascola, Robert; O'Rourke, Patrick E.; Kyser, Edward A.

Here, we have developed a piecewise local (PL) partial least squares (PLS) analysis method for total plutonium measurements by absorption spectroscopy in nitric acid-based nuclear material processing streams. Instead of using a single PLS model that covers all expected solution conditions, the method selects one of several local models based on an assessment of solution absorbance, acidity, and Pu oxidation state distribution. The local models match the global model for accuracy against the calibration set, but were observed in several instances to be more robust to variations associated with measurements in the process. The improvements are attributed to the relativemore » parsimony of the local models. Not all of the sources of spectral variation are uniformly present at each part of the calibration range. Thus, the global model is locally overfitting and susceptible to increased variance when presented with new samples. A second set of models quantifies the relative concentrations of Pu(III), (IV), and (VI). Standards containing a mixture of these species were not at equilibrium due to a disproportionation reaction. Therefore, a separate principal component analysis is used to estimate of the concentrations of the individual oxidation states in these standards in the absence of independent confirmatory analysis. The PL analysis approach is generalizable to other systems where the analysis of chemically complicated systems can be aided by rational division of the overall range of solution conditions into simpler sub-regions.« less

Improving Global Models of Remotely Sensed Ocean Chlorophyll Content Using Partial Least Squares and Geographically Weighted Regression

NASA Astrophysics Data System (ADS)

Gholizadeh, H.; Robeson, S. M.

2015-12-01

Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.

Application of partial least squares near-infrared spectral classification in diabetic identification

NASA Astrophysics Data System (ADS)

Yan, Wen-juan; Yang, Ming; He, Guo-quan; Qin, Lin; Li, Gang

2014-11-01

In order to identify the diabetic patients by using tongue near-infrared (NIR) spectrum - a spectral classification model of the NIR reflectivity of the tongue tip is proposed, based on the partial least square (PLS) method. 39sample data of tongue tip's NIR spectra are harvested from healthy people and diabetic patients , respectively. After pretreatment of the reflectivity, the spectral data are set as the independent variable matrix, and information of classification as the dependent variables matrix, Samples were divided into two groups - i.e. 53 samples as calibration set and 25 as prediction set - then the PLS is used to build the classification model The constructed modelfrom the 53 samples has the correlation of 0.9614 and the root mean square error of cross-validation (RMSECV) of 0.1387.The predictions for the 25 samples have the correlation of 0.9146 and the RMSECV of 0.2122.The experimental result shows that the PLS method can achieve good classification on features of healthy people and diabetic patients.

[Study on brand traceability of vinegar based on near infrared spectroscopy technology].

PubMed

Guan, Xiao; Liu, Jing; Gu, Fang-Qing; Yang, Yong-Jian

2014-09-01

In the present paper, 152 vinegar samples with four different brands were chosen as research targets, and their near infrared spectra were collected by diffusion reflection mode and transmission mode, respectively. Furthermore, the brand traceability models for edible vinegar were constructed. The effects of the collection mode and pretreatment methods of spectrum on the precision of traceability models were investigated intensively. The models constructed by PLS1-DA modeling method using spectrum data of 114 training samples were applied to predict 38 test samples, and R2, RMSEC and RMSEP of the model based on transmission mode data were 0.92, 0.113 and 0.127, respectively, with recognition rate of 76.32%, and those based on diffusion reflection mode data were 0.97, 0.102 and 0.119, with recognition rate of 86.84%. The results demonstrated that the near infrared spectrum combined with PLS1-DA can be used to establish the brand traceability models for edible vinegar, and diffuse reflection mode is more beneficial for predictive ability of the model.

Firmness prediction in Prunus persica 'Calrico' peaches by visible/short-wave near infrared spectroscopy and acoustic measurements using optimised linear and non-linear chemometric models.

PubMed

Lafuente, Victoria; Herrera, Luis J; Pérez, María del Mar; Val, Jesús; Negueruela, Ignacio

2015-08-15

In this work, near infrared spectroscopy (NIR) and an acoustic measure (AWETA) (two non-destructive methods) were applied in Prunus persica fruit 'Calrico' (n = 260) to predict Magness-Taylor (MT) firmness. Separate and combined use of these measures was evaluated and compared using partial least squares (PLS) and least squares support vector machine (LS-SVM) regression methods. Also, a mutual-information-based variable selection method, seeking to find the most significant variables to produce optimal accuracy of the regression models, was applied to a joint set of variables (NIR wavelengths and AWETA measure). The newly proposed combined NIR-AWETA model gave good values of the determination coefficient (R(2)) for PLS and LS-SVM methods (0.77 and 0.78, respectively), improving the reliability of MT firmness prediction in comparison with separate NIR and AWETA predictions. The three variables selected by the variable selection method (AWETA measure plus NIR wavelengths 675 and 697 nm) achieved R(2) values 0.76 and 0.77, PLS and LS-SVM. These results indicated that the proposed mutual-information-based variable selection algorithm was a powerful tool for the selection of the most relevant variables. © 2014 Society of Chemical Industry.

Rapid detection of talcum powder in tea using FT-IR spectroscopy coupled with chemometrics

PubMed Central

Li, Xiaoli; Zhang, Yuying; He, Yong

2016-01-01

This paper investigated the feasibility of Fourier transform infrared transmission (FT-IR) spectroscopy to detect talcum powder illegally added in tea based on chemometric methods. Firstly, 210 samples of tea powder with 13 dose levels of talcum powder were prepared for FT-IR spectra acquirement. In order to highlight the slight variations in FT-IR spectra, smoothing, normalize and standard normal variate (SNV) were employed to preprocess the raw spectra. Among them, SNV preprocessing had the best performance with high correlation of prediction (RP = 0.948) and low root mean square error of prediction (RMSEP = 0.108) of partial least squares (PLS) model. Then 18 characteristic wavenumbers were selected based on a hybrid of backward interval partial least squares (biPLS) regression, competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA). These characteristic wavenumbers only accounted for 0.64% of the full wavenumbers. Following that, 18 characteristic wavenumbers were used to build linear and nonlinear determination models by PLS regression and extreme learning machine (ELM), respectively. The optimal model with RP = 0.963 and RMSEP = 0.137 was achieved by ELM algorithm. These results demonstrated that FT-IR spectroscopy with chemometrics could be used successfully to detect talcum powder in tea. PMID:27468701

Coexistence of antiphospholipid antibodies and cephalalgia.

PubMed

Islam, Md Asiful; Alam, Fahmida; Gan, Siew Hua; Cavestro, Cinzia; Wong, Kah Keng

2018-03-01

Background The occurrence of antiphospholipid antibodies (aPLs) and headache comorbidity in the presence or absence of underlying autoimmune diseases remains unclear. Aim The aim of this review was to summarize the relationship between headache and aPLs based on evidences from cohort studies and case reports, in addition to examining the treatment strategies that resolved headache in aPLs-positive individuals. Methods A comprehensive literature search was conducted through PubMed, ISI Web of Science and Google Scholar. A total of 559 articles were screened and the appropriate articles were selected based on quality and level of evidence. Results Cohort studies (n = 27) from Europe, North America and Asia demonstrated comorbidity of aPLs and headache in antiphospholipid syndrome, systemic lupus erythematosus (SLE) and neuropsychiatric SLE patients. Significantly higher association between migraine and aPLs was observed (n = 170/779; p < 0.0001) in individuals without any underlying diseases. Our analysis of shortlisted case reports (n = 17) showed that a higher frequency of anticardiolipin antibodies were present in subjects with different autoimmune disorders (70.6%). Corticosteroids were highly effective in resolving headache in aPLs-positive individuals. Conclusion Higher frequency of comorbidity between aPLs and headache was observed in healthy individuals and patient cases. Therefore, experimental studies are warranted to evaluate the aPLs-induced pathogenic mechanism of headache.

Nearest clusters based partial least squares discriminant analysis for the classification of spectral data.

PubMed

Song, Weiran; Wang, Hui; Maguire, Paul; Nibouche, Omar

2018-06-07

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time. Copyright © 2018 Elsevier B.V. All rights reserved.

Green method by diffuse reflectance infrared spectroscopy and spectral region selection for the quantification of sulphamethoxazole and trimethoprim in pharmaceutical formulations.

PubMed

da Silva, Fabiana E B; Flores, Érico M M; Parisotto, Graciele; Müller, Edson I; Ferrão, Marco F

2016-03-01

An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

PubMed Central

Ferragina, A.; de los Campos, G.; Vazquez, A. I.; Cecchinato, A.; Bittante, G.

2017-01-01

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict “difficult-to-predict” dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm−1 were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R2 value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R2 (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R2 of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations. PMID:26387015

A Partial Least Squares Based Procedure for Upstream Sequence Classification in Prokaryotes.

PubMed

Mehmood, Tahir; Bohlin, Jon; Snipen, Lars

2015-01-01

The upstream region of coding genes is important for several reasons, for instance locating transcription factor, binding sites, and start site initiation in genomic DNA. Motivated by a recently conducted study, where multivariate approach was successfully applied to coding sequence modeling, we have introduced a partial least squares (PLS) based procedure for the classification of true upstream prokaryotic sequence from background upstream sequence. The upstream sequences of conserved coding genes over genomes were considered in analysis, where conserved coding genes were found by using pan-genomics concept for each considered prokaryotic species. PLS uses position specific scoring matrix (PSSM) to study the characteristics of upstream region. Results obtained by PLS based method were compared with Gini importance of random forest (RF) and support vector machine (SVM), which is much used method for sequence classification. The upstream sequence classification performance was evaluated by using cross validation, and suggested approach identifies prokaryotic upstream region significantly better to RF (p-value < 0.01) and SVM (p-value < 0.01). Further, the proposed method also produced results that concurred with known biological characteristics of the upstream region.

Quantification of adulterations in extra virgin flaxseed oil using MIR and PLS.

PubMed

de Souza, Letícia Maria; de Santana, Felipe Bachion; Gontijo, Lucas Caixeta; Mazivila, Sarmento Júnior; Borges Neto, Waldomiro

2015-09-01

This paper proposes a new method for the quantitative analysis of soybean oil (SO) and sunflower oil (SFO) as adulterants in extra virgin flaxseed oil (EFO) by applying Mid Infrared Spectroscopy (MIR) associated with chemometric technique of Partial Least Squares (PLS). The PLS models were built in accordance with standard method ASTM E1655-05 and these showed good correlation between the reference values and those calculated using the PLS models with low error values, with R = 0.998 for SFO and R = 0.999 for SO in EFO. These models were validated analytically in accordance with Brazilian and international guidelines through the estimate of figures of merit parameters, thus showing an effective and feasible method to control the quality of extra virgin flaxseed oil. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improved Quantitative Analysis of Ion Mobility Spectrometry by Chemometric Multivariate Calibration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fraga, Carlos G.; Kerr, Dayle; Atkinson, David A.

2009-09-01

Traditional peak-area calibration and the multivariate calibration methods of principle component regression (PCR) and partial least squares (PLS), including unfolded PLS (U-PLS) and multi-way PLS (N-PLS), were evaluated for the quantification of 2,4,6-trinitrotoluene (TNT) and cyclo-1,3,5-trimethylene-2,4,6-trinitramine (RDX) in Composition B samples analyzed by temperature step desorption ion mobility spectrometry (TSD-IMS). The true TNT and RDX concentrations of eight Composition B samples were determined by high performance liquid chromatography with UV absorbance detection. Most of the Composition B samples were found to have distinct TNT and RDX concentrations. Applying PCR and PLS on the exact same IMS spectra used for themore » peak-area study improved quantitative accuracy and precision approximately 3 to 5 fold and 2 to 4 fold, respectively. This in turn improved the probability of correctly identifying Composition B samples based upon the estimated RDX and TNT concentrations from 11% with peak area to 44% and 89% with PLS. This improvement increases the potential of obtaining forensic information from IMS analyzers by providing some ability to differentiate or match Composition B samples based on their TNT and RDX concentrations.« less

Using FTIR spectroscopy to model alkaline pretreatment and enzymatic saccharification of six lignocellulosic biomasses.

PubMed

Sills, Deborah L; Gossett, James M

2012-04-01

Fourier transform infrared, attenuated total reflectance (FTIR-ATR) spectroscopy, combined with partial least squares (PLS) regression, accurately predicted solubilization of plant cell wall constituents and NaOH consumption through pretreatment, and overall sugar productions from combined pretreatment and enzymatic hydrolysis. PLS regression models were constructed by correlating FTIR spectra of six raw biomasses (two switchgrass cultivars, big bluestem grass, a low-impact, high-diversity mixture of prairie biomasses, mixed hardwood, and corn stover), plus alkali loading in pretreatment, to nine dependent variables: glucose, xylose, lignin, and total solids solubilized in pretreatment; NaOH consumed in pretreatment; and overall glucose and xylose conversions and yields from combined pretreatment and enzymatic hydrolysis. PLS models predicted the dependent variables with the following values of coefficient of determination for cross-validation (Q²): 0.86 for glucose, 0.90 for xylose, 0.79 for lignin, and 0.85 for total solids solubilized in pretreatment; 0.83 for alkali consumption; 0.93 for glucose conversion, 0.94 for xylose conversion, and 0.88 for glucose and xylose yields. The sugar yield models are noteworthy for their ability to predict overall saccharification through combined pretreatment and enzymatic hydrolysis per mass dry untreated solids without a priori knowledge of the composition of solids. All wavenumbers with significant variable-important-for-projection (VIP) scores have been attributed to chemical features of lignocellulose, demonstrating the models were based on real chemical information. These models suggest that PLS regression can be applied to FTIR-ATR spectra of raw biomasses to rapidly predict effects of pretreatment on solids and on subsequent enzymatic hydrolysis. Copyright © 2011 Wiley Periodicals, Inc.

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy.

PubMed

Payne, Courtney E; Wolfrum, Edward J

2015-01-01

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. It is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

Microwave-assisted of dispersive liquid-liquid microextraction and spectrophotometric determination of uranium after optimization based on Box-Behnken design and chemometrics methods.

PubMed

Niazi, Ali; Khorshidi, Neda; Ghaemmaghami, Pegah

2015-01-25

In this study an analytical procedure based on microwave-assisted dispersive liquid-liquid microextraction (MA-DLLME) and spectrophotometric coupled with chemometrics methods is proposed to determine uranium. In the proposed method, 4-(2-pyridylazo) resorcinol (PAR) is used as a chelating agent, and chloroform and ethanol are selected as extraction and dispersive solvent. The optimization strategy is carried out by using two level full factorial designs. Results of the two level full factorial design (2(4)) based on an analysis of variance demonstrated that the pH, concentration of PAR, amount of dispersive and extraction solvents are statistically significant. Optimal condition for three variables: pH, concentration of PAR, amount of dispersive and extraction solvents are obtained by using Box-Behnken design. Under the optimum conditions, the calibration graphs are linear in the range of 20.0-350.0 ng mL(-1) with detection limit of 6.7 ng mL(-1) (3δB/slope) and the enrichment factor of this method for uranium reached at 135. The relative standard deviation (R.S.D.) is 1.64% (n=7, c=50 ng mL(-1)). The partial least squares (PLS) modeling was used for multivariate calibration of the spectrophotometric data. The orthogonal signal correction (OSC) was used for preprocessing of data matrices and the prediction results of model, with and without using OSC, were statistically compared. MA-DLLME-OSC-PLS method was presented for the first time in this study. The root mean squares error of prediction (RMSEP) for uranium determination using PLS and OSC-PLS models were 4.63 and 0.98, respectively. This procedure allows the determination of uranium synthesis and real samples such as waste water with good reliability of the determination. Copyright © 2014. Published by Elsevier B.V.

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization.

PubMed

Gomes, Adriano de Araújo; Alcaraz, Mirta Raquel; Goicoechea, Hector C; Araújo, Mario Cesar U

2014-02-06

In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV-vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL. Copyright © 2013 Elsevier B.V. All rights reserved.

Inferential modeling and predictive feedback control in real-time motion compensation using the treatment couch during radiotherapy

NASA Astrophysics Data System (ADS)

Qiu, Peng; D'Souza, Warren D.; McAvoy, Thomas J.; Liu, K. J. Ray

2007-09-01

Tumor motion induced by respiration presents a challenge to the reliable delivery of conformal radiation treatments. Real-time motion compensation represents the technologically most challenging clinical solution but has the potential to overcome the limitations of existing methods. The performance of a real-time couch-based motion compensation system is mainly dependent on two aspects: the ability to infer the internal anatomical position and the performance of the feedback control system. In this paper, we propose two novel methods for the two aspects respectively, and then combine the proposed methods into one system. To accurately estimate the internal tumor position, we present partial-least squares (PLS) regression to predict the position of the diaphragm using skin-based motion surrogates. Four radio-opaque markers were placed on the abdomen of patients who underwent fluoroscopic imaging of the diaphragm. The coordinates of the markers served as input variables and the position of the diaphragm served as the output variable. PLS resulted in lower prediction errors compared with standard multiple linear regression (MLR). The performance of the feedback control system depends on the system dynamics and dead time (delay between the initiation and execution of the control action). While the dynamics of the system can be inverted in a feedback control system, the dead time cannot be inverted. To overcome the dead time of the system, we propose a predictive feedback control system by incorporating forward prediction using least-mean-square (LMS) and recursive least square (RLS) filtering into the couch-based control system. Motion data were obtained using a skin-based marker. The proposed predictive feedback control system was benchmarked against pure feedback control (no forward prediction) and resulted in a significant performance gain. Finally, we combined the PLS inference model and the predictive feedback control to evaluate the overall performance of the feedback control system. Our results show that, with the tumor motion unknown but inferred by skin-based markers through the PLS model, the predictive feedback control system was able to effectively compensate intra-fraction motion.

Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.

PubMed

Yoo, Kwangsun; Rosenberg, Monica D; Hsu, Wei-Ting; Zhang, Sheng; Li, Chiang-Shan R; Scheinost, Dustin; Constable, R Todd; Chun, Marvin M

2018-02-15

Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM. Copyright © 2017 Elsevier Inc. All rights reserved.

Feasibility of the simultaneous determination of polycyclic aromatic hydrocarbons based on two-dimensional fluorescence correlation spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Renjie; Dong, Guimei; Sun, Xueshan; Yang, Yanrong; Yu, Yaping; Liu, Haixue; Zhang, Weiyu

2018-02-01

A new approach for quantitative determination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on two-dimensional (2D) fluorescence correlation spectroscopy in conjunction with multivariate method. 40 mixture solutions of anthracene and pyrene were prepared in the laboratory. Excitation-emission matrix (EEM) fluorescence spectra of all samples were collected. And 2D fluorescence correlation spectra were calculated under the excitation perturbation. The N-way partial least squares (N-PLS) models were developed based on 2D fluorescence correlation spectra, showing a root mean square error of calibration (RMSEC) of 3.50 μg L- 1 and root mean square error of prediction (RMSEP) of 4.42 μg L- 1 for anthracene and of 3.61 μg L- 1 and 4.29 μg L- 1 for pyrene, respectively. Also, the N-PLS models were developed for quantitative analysis of anthracene and pyrene using EEM fluorescence spectra. The RMSEC and RMSEP were 3.97 μg L- 1 and 4.63 μg L- 1 for anthracene, 4.46 μg L- 1 and 4.52 μg L- 1 for pyrene, respectively. It was found that the N-PLS model using 2D fluorescence correlation spectra could provide better results comparing with EEM fluorescence spectra because of its low RMSEC and RMSEP. The methodology proposed has the potential to be an alternative method for detection of PAHs in environment.

Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional gas chromatography.

PubMed

D'Archivio, Angelo Antonio; Incani, Angela; Ruggieri, Fabrizio

2011-01-01

In this paper, we use a quantitative structure-retention relationship (QSRR) method to predict the retention times of polychlorinated biphenyls (PCBs) in comprehensive two-dimensional gas chromatography (GC×GC). We analyse the GC×GC retention data taken from the literature by comparing predictive capability of different regression methods. The various models are generated using 70 out of 209 PCB congeners in the calibration stage, while their predictive performance is evaluated on the remaining 139 compounds. The two-dimensional chromatogram is initially estimated by separately modelling retention times of PCBs in the first and in the second column ((1) t (R) and (2) t (R), respectively). In particular, multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is performed to extract two small subsets of predictors for (1) t (R) and (2) t (R) from a large set of theoretical molecular descriptors provided by the popular software Dragon, which after removal of highly correlated or almost constant variables consists of 237 structure-related quantities. Based on GA-MLR analysis, a four-dimensional and a five-dimensional relationship modelling (1) t (R) and (2) t (R), respectively, are identified. Single-response partial least square (PLS-1) regression is alternatively applied to independently model (1) t (R) and (2) t (R) without the need for preliminary GA variable selection. Further, we explore the possibility of predicting the two-dimensional chromatogram of PCBs in a single calibration procedure by using a two-response PLS (PLS-2) model or a feed-forward artificial neural network (ANN) with two output neurons. In the first case, regression is carried out on the full set of 237 descriptors, while the variables previously selected by GA-MLR are initially considered as ANN inputs and subjected to a sensitivity analysis to remove the redundant ones. Results show PLS-1 regression exhibits a noticeably better descriptive and predictive performance than the other investigated approaches. The observed values of determination coefficients for (1) t (R) and (2) t (R) in calibration (0.9999 and 0.9993, respectively) and prediction (0.9987 and 0.9793, respectively) provided by PLS-1 demonstrate that GC×GC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GC×GC chromatogram of 139 PCBs not involved in the calibration stage closely resembles the experimental one. Based on the above lines of evidence, the proposed approach ensures accurate simulation of the whole GC×GC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.

Consistent Partial Least Squares Path Modeling via Regularization

PubMed Central

Jung, Sunho; Park, JaeHong

2018-01-01

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present. PMID:29515491

Prediction of the distillation temperatures of crude oils using ¹H NMR and support vector regression with estimated confidence intervals.

PubMed

Filgueiras, Paulo R; Terra, Luciana A; Castro, Eustáquio V R; Oliveira, Lize M S L; Dias, Júlio C M; Poppi, Ronei J

2015-09-01

This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using (1)H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6°C was obtained in comparison with 15.6°C for PLS, 15.1°C for ePLS and 28.4°C for SVR. The RMSEPs for T50% were 24.2°C, 23.4°C, 22.8°C and 14.4°C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0°C, 39.9°C and 39.9°C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS. Copyright © 2015 Elsevier B.V. All rights reserved.

Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

NASA Astrophysics Data System (ADS)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2014-03-01

Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

Real‐time monitoring and control of the load phase of a protein A capture step

PubMed Central

Rüdt, Matthias; Brestrich, Nina; Rolinger, Laura

2016-01-01

ABSTRACT The load phase in preparative Protein A capture steps is commonly not controlled in real‐time. The load volume is generally based on an offline quantification of the monoclonal antibody (mAb) prior to loading and on a conservative column capacity determined by resin‐life time studies. While this results in a reduced productivity in batch mode, the bottleneck of suitable real‐time analytics has to be overcome in order to enable continuous mAb purification. In this study, Partial Least Squares Regression (PLS) modeling on UV/Vis absorption spectra was applied to quantify mAb in the effluent of a Protein A capture step during the load phase. A PLS model based on several breakthrough curves with variable mAb titers in the HCCF was successfully calibrated. The PLS model predicted the mAb concentrations in the effluent of a validation experiment with a root mean square error (RMSE) of 0.06 mg/mL. The information was applied to automatically terminate the load phase, when a product breakthrough of 1.5 mg/mL was reached. In a second part of the study, the sensitivity of the method was further increased by only considering small mAb concentrations in the calibration and by subtracting an impurity background signal. The resulting PLS model exhibited a RMSE of prediction of 0.01 mg/mL and was successfully applied to terminate the load phase, when a product breakthrough of 0.15 mg/mL was achieved. The proposed method has hence potential for the real‐time monitoring and control of capture steps at large scale production. This might enhance the resin capacity utilization, eliminate time‐consuming offline analytics, and contribute to the realization of continuous processing. Biotechnol. Bioeng. 2017;114: 368–373. © 2016 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc. PMID:27543789

Assessing statistical differences between parameters estimates in Partial Least Squares path modeling.

PubMed

Rodríguez-Entrena, Macario; Schuberth, Florian; Gelhard, Carsten

2018-01-01

Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.

Monitoring of chicken meat freshness by means of a colorimetric sensor array.

PubMed

Salinas, Yolanda; Ros-Lis, José V; Vivancos, José-L; Martínez-Máñez, Ramón; Marcos, M Dolores; Aucejo, Susana; Herranz, Nuria; Lorente, Inmaculada

2012-08-21

A new optoelectronic nose to monitor chicken meat ageing has been developed. It is based on 16 pigments prepared by the incorporation of different dyes (pH indicators, Lewis acids, hydrogen-bonding derivatives, selective probes and natural dyes) into inorganic materials (UVM-7, silica and alumina). The colour changes of the sensor array were characteristic of chicken ageing in a modified packaging atmosphere (30% CO(2)-70% N(2)). The chromogenic array data were processed with qualitative (PCA) and quantitative (PLS) tools. The PCA statistical analysis showed a high degree of dispersion, with nine dimensions required to explain 95% of variance. Despite this high dimensionality, a tridimensional representation of the three principal components was able to differentiate ageing with 2-day intervals. Moreover, the PLS statistical analysis allows the creation of a model to correlate the chromogenic data with chicken meat ageing. The model offers a PLS prediction model for ageing with values of 0.9937, 0.0389 and 0.994 for the slope, the intercept and the regression coefficient, respectively, and is in agreement with the perfect fit between the predicted and measured values observed. The results suggest the feasibility of this system to help develop optoelectronic noses that monitor food freshness.

3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and PLS-LS-SVM.

PubMed

Ghafouri, Hamidreza; Ranjbar, Mohsen; Sakhteman, Amirhossein

2017-08-01

A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q 2 LOO-CV =1, R 2 ext =0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

DOE PAGES

Payne, Courtney E.; Wolfrum, Edward J.

2015-03-12

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics tomore » demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.« less

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Payne, Courtney E.; Wolfrum, Edward J.

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics tomore » demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.« less

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.

PubMed

Ferragina, A; de los Campos, G; Vazquez, A I; Cecchinato, A; Bittante, G

2015-11-01

The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Detection of Lipitor counterfeits: a comparison of NIR and Raman spectroscopy in combination with chemometrics.

PubMed

de Peinder, P; Vredenbregt, M J; Visser, T; de Kaste, D

2008-08-05

Research has been carried on the feasibility of near infrared (NIR) and Raman spectroscopy as rapid screening methods to discriminate between genuine and counterfeits of the cholesterol-lowering medicine Lipitor. Classification, based on partial least squares discriminant analysis (PLS-DA) models, appears to be successful for both spectroscopic techniques, irrespective of whether atorvastatine or lovastatine has been used as the active pharmaceutical ingredient (API). The discriminative power of the NIR model, in particular, largely relies on the spectral differences of the tablet matrix. This is due to the relative large sample volume that is probed with NIR and the strong spectroscopic activity of the excipients. PLS-DA models based on NIR or Raman spectra can also be applied to distinguish between atorvastatine and lovastatine as the API used in the counterfeits tested in this study. A disadvantage of Raman microscopy for this type of analysis is that it is primarily a surface technique. As a consequence spectra of the coating and the tablet core might differ. Besides, spectra may change with the position of the laser in case the sample is inhomogeneous. However, the robustness of the PLS-DA models turned out to be sufficiently large to allow a reliable discrimination. Principal component analysis (PCA) of the spectra revealed that the conditions, at which tablets have been stored, affect the NIR data. This effect is attributed to the adsorption of water from the atmosphere after unpacking from the blister. It implies that storage conditions should be taken into account when the NIR technique is used for discriminating purposes. However, in this study both models based on NIR spectra and Raman data enabled reliable discrimination between genuine and counterfeited Lipitor tablets, regardless of their storage conditions.

Metabolic profiling and predicting the free radical scavenging activity of guava (Psidium guajava L.) leaves according to harvest time by 1H-nuclear magnetic resonance spectroscopy.

PubMed

Kim, So-Hyun; Cho, Somi K; Hyun, Sun-Hee; Park, Hae-Eun; Kim, Young-Suk; Choi, Hyung-Kyoon

2011-01-01

Guava leaves were classified and the free radical scavenging activity (FRSA) evaluated according to different harvest times by using the (1)H-NMR-based metabolomic technique. A principal component analysis (PCA) of (1)H-NMR data from the guava leaves provided clear clusters according to the harvesting time. A partial least squares (PLS) analysis indicated a correlation between the metabolic profile and FRSA. FRSA levels of the guava leaves harvested during May and August were high, and those leaves contained higher amounts of 3-hydroxybutyric acid, acetic acid, glutamic acid, asparagine, citric acid, malonic acid, trans-aconitic acid, ascorbic acid, maleic acid, cis-aconitic acid, epicatechin, protocatechuic acid, and xanthine than the leaves harvested during October and December. Epicatechin and protocatechuic acid among those compounds seem to have enhanced FRSA of the guava leaf samples harvested in May and August. A PLS regression model was established to predict guava leaf FRSA at different harvesting times by using a (1)H-NMR data set. The predictability of the PLS model was then tested by internal and external validation. The results of this study indicate that (1)H-NMR-based metabolomic data could usefully characterize guava leaves according to their time of harvesting.

Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Wang, Xiu; Peng, Yankun

This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.

Traceability of Boletaceae mushrooms using data fusion of UV-visible and FTIR combined with chemometrics methods.

PubMed

Yao, Sen; Li, Tao; Liu, HongGao; Li, JieQing; Wang, YuanZhong

2018-04-01

Boletaceae mushrooms are wild-grown edible mushrooms that have high nutrition, delicious flavor and large economic value distributing in Yunnan Province, China. Traceability is important for the authentication and quality assessment of Boletaceae mushrooms. In this study, UV-visible and Fourier transform infrared (FTIR) spectroscopies were applied for traceability of 247 Boletaceae mushroom samples in combination with chemometrics. Compared with a single spectroscopy technique, data fusion strategy can obviously improve the classification performance in partial least square discriminant analysis (PLS-DA) and grid-search support vector machine (GS-SVM) models, for both species and geographical origin traceability. In addition, PLS-DA and GS-SVM models can provide 100.00% accuracy for species traceability and have reliable evaluation parameters. For geographical origin traceability, the accuracy of prediction in the PLS-DA model by data fusion was just 64.63%, but the GS-SVM model based on data fusion was 100.00%. The results demonstrated that the data fusion strategy of UV-visible and FTIR combined with GS-SVM could provide a higher synergic effect for traceability of Boletaceae mushrooms and have a good generalization ability for the comprehensive quality control and evaluation of similar foods. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration.

PubMed

Tsopelas, Fotios; Konstantopoulos, Dimitris; Kakoulidou, Anna Tsantili

2018-07-26

In the present work, two approaches for the voltammetric fingerprinting of oils and their combination with chemometrics were investigated in order to detect the adulteration of extra virgin olive oil with olive pomace oil as well as the most common seed oils, namely sunflower, soybean and corn oil. In particular, cyclic voltammograms of diluted extra virgin olive oils, regular (pure) olive oils (blends of refined olive oils with virgin olive oils), olive pomace oils and seed oils in presence of dichloromethane and 0.1 M of LiClO 4 in EtOH as electrolyte were recorded at a glassy carbon working electrode. Cyclic voltammetry was also employed in methanolic extracts of olive and seed oils. Datapoints of cyclic voltammograms were exported and submitted to Principal Component Analysis (PCA), Partial Least Square- Discriminant Analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). In diluted oils, PLS-DA provided a clear discrimination between olive oils (extra virgin and regular) and olive pomace/seed oils, while SIMCA showed a clear discrimination of extra virgin olive oil in regard to all other samples. Using methanolic extracts and considering datapoints recorded between 0.6 and 1.3 V, PLS-DA provided more information, resulting in three clusters-extra virgin olive oils, regular olive oils and seed/olive pomace oils-while SIMCA showed inferior performance. For the quantification of extra virgin olive oil adulteration with olive pomace oil or seed oils, a model based on Partial Least Square (PLS) analysis was developed. Detection limit of adulteration in olive oil was found to be 2% (v/v) and the linearity range up to 33% (v/v). Validation and applicability of all models was proved using a suitable test set. In the case of PLS, synthetic oil mixtures with 4 known adulteration levels in the range of 4-26% were also employed as a blind test set. Copyright © 2018 Elsevier B.V. All rights reserved.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Imaging Findings Associated with Cognitive Performance in Primary Lateral Sclerosis and Amyotrophic Lateral Sclerosis

PubMed Central

Meoded, Avner; Kwan, Justin Y.; Peters, Tracy L.; Huey, Edward D.; Danielian, Laura E.; Wiggs, Edythe; Morrissette, Arthur; Wu, Tianxia; Russell, James W.; Bayat, Elham; Grafman, Jordan; Floeter, Mary Kay

2013-01-01

Introduction Executive dysfunction occurs in many patients with amyotrophic lateral sclerosis (ALS), but it has not been well studied in primary lateral sclerosis (PLS). The aims of this study were to (1) compare cognitive function in PLS to that in ALS patients, (2) explore the relationship between performance on specific cognitive tests and diffusion tensor imaging (DTI) metrics of white matter tracts and gray matter volumes, and (3) compare DTI metrics in patients with and without cognitive and behavioral changes. Methods The Delis-Kaplan Executive Function System (D-KEFS), the Mattis Dementia Rating Scale (DRS-2), and other behavior and mood scales were administered to 25 ALS patients and 25 PLS patients. Seventeen of the PLS patients, 13 of the ALS patients, and 17 healthy controls underwent structural magnetic resonance imaging (MRI) and DTI. Atlas-based analysis using MRI Studio software was used to measure fractional anisotropy, and axial and radial diffusivity of selected white matter tracts. Voxel-based morphometry was used to assess gray matter volumes. The relationship between diffusion properties of selected association and commissural white matter and performance on executive function and memory tests was explored using a linear regression model. Results More ALS than PLS patients had abnormal scores on the DRS-2. DRS-2 and D-KEFS scores were related to DTI metrics in several long association tracts and the callosum. Reduced gray matter volumes in motor and perirolandic areas were not associated with cognitive scores. Conclusion The changes in diffusion metrics of white matter long association tracts suggest that the loss of integrity of the networks connecting fronto-temporal areas to parietal and occipital areas contributes to cognitive impairment. PMID:24052798

Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

NASA Astrophysics Data System (ADS)

Fu, Y.; Yang, W.; Xu, O.; Zhou, L.; Wang, J.

2017-04-01

To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately.

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

[Analyzing and modeling methods of near infrared spectroscopy for in-situ prediction of oil yield from oil shale].

PubMed

Liu, Jie; Zhang, Fu-Dong; Teng, Fei; Li, Jun; Wang, Zhi-Hong

2014-10-01

In order to in-situ detect the oil yield of oil shale, based on portable near infrared spectroscopy analytical technology, with 66 rock core samples from No. 2 well drilling of Fuyu oil shale base in Jilin, the modeling and analyzing methods for in-situ detection were researched. By the developed portable spectrometer, 3 data formats (reflectance, absorbance and K-M function) spectra were acquired. With 4 different modeling data optimization methods: principal component-mahalanobis distance (PCA-MD) for eliminating abnormal samples, uninformative variables elimination (UVE) for wavelength selection and their combina- tions: PCA-MD + UVE and UVE + PCA-MD, 2 modeling methods: partial least square (PLS) and back propagation artificial neural network (BPANN), and the same data pre-processing, the modeling and analyzing experiment were performed to determine the optimum analysis model and method. The results show that the data format, modeling data optimization method and modeling method all affect the analysis precision of model. Results show that whether or not using the optimization method, reflectance or K-M function is the proper spectrum format of the modeling database for two modeling methods. Using two different modeling methods and four different data optimization methods, the model precisions of the same modeling database are different. For PLS modeling method, the PCA-MD and UVE + PCA-MD data optimization methods can improve the modeling precision of database using K-M function spectrum data format. For BPANN modeling method, UVE, UVE + PCA-MD and PCA- MD + UVE data optimization methods can improve the modeling precision of database using any of the 3 spectrum data formats. In addition to using the reflectance spectra and PCA-MD data optimization method, modeling precision by BPANN method is better than that by PLS method. And modeling with reflectance spectra, UVE optimization method and BPANN modeling method, the model gets the highest analysis precision, its correlation coefficient (Rp) is 0.92, and its standard error of prediction (SEP) is 0.69%.

Novel near-infrared spectrum analysis tool: Synergy adaptive moving window model based on immune clone algorithm.

PubMed

Wang, Shenghao; Zhang, Yuyan; Cao, Fuyi; Pei, Zhenying; Gao, Xuewei; Zhang, Xu; Zhao, Yong

2018-02-13

This paper presents a novel spectrum analysis tool named synergy adaptive moving window modeling based on immune clone algorithm (SA-MWM-ICA) considering the tedious and inconvenient labor involved in the selection of pre-processing methods and spectral variables by prior experience. In this work, immune clone algorithm is first introduced into the spectrum analysis field as a new optimization strategy, covering the shortage of the relative traditional methods. Based on the working principle of the human immune system, the performance of the quantitative model is regarded as antigen, and a special vector corresponding to the above mentioned antigen is regarded as antibody. The antibody contains a pre-processing method optimization region which is created by 11 decimal digits, and a spectrum variable optimization region which is formed by some moving windows with changeable width and position. A set of original antibodies are created by modeling with this algorithm. After calculating the affinity of these antibodies, those with high affinity will be selected to clone. The regulation for cloning is that the higher the affinity, the more copies will be. In the next step, another import operation named hyper-mutation is applied to the antibodies after cloning. Moreover, the regulation for hyper-mutation is that the lower the affinity, the more possibility will be. Several antibodies with high affinity will be created on the basis of these steps. Groups of simulated dataset, gasoline near-infrared spectra dataset, and soil near-infrared spectra dataset are employed to verify and illustrate the performance of SA-MWM-ICA. Analysis results show that the performance of the quantitative models adopted by SA-MWM-ICA are better especially for structures with relatively complex spectra than traditional models such as partial least squares (PLS), moving window PLS (MWPLS), genetic algorithm PLS (GAPLS), and pretreatment method classification and adjustable parameter changeable size moving window PLS (CA-CSMWPLS). The selected pre-processing methods and spectrum variables are easily explained. The proposed method will converge in few generations and can be used not only for near-infrared spectroscopy analysis but also for other similar spectral analysis, such as infrared spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Free variable selection QSPR study to predict 19F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods

NASA Astrophysics Data System (ADS)

Goudarzi, Nasser

2016-04-01

In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the 19F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the 19F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.

Mid-infrared and near-infrared spectroscopy for rapid detection of Gardeniae Fructus by a liquid-liquid extraction process.

PubMed

Tao, Lingyan; Lin, Zhonglin; Chen, Jiashan; Wu, Yongjiang; Liu, Xuesong

2017-10-25

Gardeniae Fructus is widely used in the pharmaceutical industry, and many studies have confirmed its medical and economic value. In this study, samples collected from different liquid-liquid extraction batches of Gardeniae Fructus were detected by mid-infrared (MIR) and near-infrared (NIR) spectroscopy. Seven analytes, neochlorogenic acid (5-CQA), cryptochlorogenic acid (4-CQA), chlorogenic acid (3-CQA), geniposidic acid (GEA), deacetyl-asperulosidic acid methyl ester (DAAME), genipin-gentiobioside (GGB), and gardenoside (GA), were chosen as quality property indexes of Gardeniae Fructus. The two kinds of spectra were each used to build models by single partial least squares (PLS). Additionally, both spectral data were combined and modeled by multiblock PLS. For single spectroscopy modeling results, NIR had a better prediction for high-concentration analytes (3-CQA, DAAME, GGB, and GA) whereas MIR performed better for low-concentration analytes (5-CQA, 4-CQA, and GEA). The multiblock methodology was found to be better compared to single spectroscopy models for all seven analytes. Specifically, the coefficients of determination (R 2 ) of the NIR, MIR, and multiblock PLS calibration models of all seven components were higher than 0.95. Relative standard errors of prediction (RSEP) were all less than 7%, except for models of GGB, which were 10.36%, 13.24%, and 8.15% for the NIR-PLS, MIR-PLS, and multiblock models, respectively. These results indicate that MIR and NIR spectrographic techniques could provide a new choice for quality control in industrial production of Gardeniae Fructus. Copyright © 2017 Elsevier B.V. All rights reserved.

Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

PubMed

Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

2018-07-12

The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample-associated changes occurring over time. In order to detect the time point when drifts start causing prediction bias, we here explore a universal drift detector based on a faded version of the Page-Hinkley (PH) statistic, which we test in three data streams from an industrial MF resin production process. We employ committee disagreement (CD), computed as the variance of model predictions from an ensemble of partial least squares (PLS) models, as a measure for sample-wise prediction uncertainty and use the PH statistic to detect changes in this quantity. We further explore supervised and unsupervised strategies for (semi-)automatic model adaptation upon detection of a drift. For the former, manual reference measurements are requested whenever statistical thresholds on Hotelling's T 2 and/or Q-Residuals are violated. Models are subsequently re-calibrated using weighted partial least squares in order to increase the influence of newer samples, which increases the flexibility when adapting to new (drifted) states. Unsupervised model adaptation is carried out exploiting the dual antecedent-consequent structure of a recently developed fuzzy systems variant of PLS termed FLEXFIS-PLS. In particular, antecedent parts are updated while maintaining the internal structure of the local linear predictors (i.e. the consequents). We found improved drift detection capability of the CD compared to Hotelling's T 2 and Q-Residuals when used in combination with the proposed PH test. Furthermore, we found that active selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.

Solid-phase cadmium speciation in soil using L3-edge XANES spectroscopy with partial least-squares regression.

PubMed

Siebers, Nina; Kruse, Jens; Eckhardt, Kai-Uwe; Hu, Yongfeng; Leinweber, Peter

2012-07-01

Cadmium (Cd) has a high toxicity and resolving its speciation in soil is challenging but essential for estimating the environmental risk. In this study partial least-square (PLS) regression was tested for its capability to deconvolute Cd L(3)-edge X-ray absorption near-edge structure (XANES) spectra of multi-compound mixtures. For this, a library of Cd reference compound spectra and a spectrum of a soil sample were acquired. A good coefficient of determination (R(2)) of Cd compounds in mixtures was obtained for the PLS model using binary and ternary mixtures of various Cd reference compounds proving the validity of this approach. In order to describe complex systems like soil, multi-compound mixtures of a variety of Cd compounds must be included in the PLS model. The obtained PLS regression model was then applied to a highly Cd-contaminated soil revealing Cd(3)(PO(4))(2) (36.1%), Cd(NO(3))(2)·4H(2)O (24.5%), Cd(OH)(2) (21.7%), CdCO(3) (17.1%) and CdCl(2) (0.4%). These preliminary results proved that PLS regression is a promising approach for a direct determination of Cd speciation in the solid phase of a soil sample.

Peculiarities of energy trapping of the UHF elastic waves in diamond-based piezoelectric layered structure. I. Waveguide criterion.

PubMed

Kvashnin, G M; Sorokin, B P; Novoselov, A S

2018-03-01

Finite Element Modeling of the peculiarities of the trapping energy phenomenon in application to the piezoelectric layered structure (PLS) "Al/(0 0 1) AlN/Mo/(1 0 0) diamond" has been fulfilled. The resonant properties of longitudinal bulk acoustic waves (BAW) as well as frequency dependence of impedance within the 1 - 6 GHz band have been studied. The investigation of distribution of elastic energy flow and elastic displacements in a PLS cross-section allowed us to obtain an important information on energy trapping (ET) in PLS. Experimentally and as a result of modeling, it has been found that Q minimums are observed in PLS at quarter-wave resonance in the thin-film piezoelectric transducer (TFPT). Maximal Q value was observed at half-wave resonance in TFPT. It has been established that the ET-effect depends considerably on the mutual location of the n-th overtone's antiresonant frequency f a , n and cut-off frequencies of substrate f s , n-k- 1 and f s , n-k where f s , n-k- 1 f s , n-k , when the BAW energy excites the symmetrical or antisymmetrical Lamb waves. Copyright © 2017. Published by Elsevier B.V.

Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis

PubMed Central

Zhang, Xue-Xi; Yin, Jian-Hua; Mao, Zhi-Hua; Xia, Yang

2015-01-01

Abstract. Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens. PMID:26057029

Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis.

PubMed

Zhang, Xue-Xi; Yin, Jian-Hua; Mao, Zhi-Hua; Xia, Yang

2015-06-01

Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens.

Advanced spectrophotometric chemometric methods for resolving the binary mixture of doxylamine succinate and pyridoxine hydrochloride.

PubMed

Katsarov, Plamen; Gergov, Georgi; Alin, Aylin; Pilicheva, Bissera; Al-Degs, Yahya; Simeonov, Vasil; Kassarova, Margarita

2018-03-01

The prediction power of partial least squares (PLS) and multivariate curve resolution-alternating least squares (MCR-ALS) methods have been studied for simultaneous quantitative analysis of the binary drug combination - doxylamine succinate and pyridoxine hydrochloride. Analysis of first-order UV overlapped spectra was performed using different PLS models - classical PLS1 and PLS2 as well as partial robust M-regression (PRM). These linear models were compared to MCR-ALS with equality and correlation constraints (MCR-ALS-CC). All techniques operated within the full spectral region and extracted maximum information for the drugs analysed. The developed chemometric methods were validated on external sample sets and were applied to the analyses of pharmaceutical formulations. The obtained statistical parameters were satisfactory for calibration and validation sets. All developed methods can be successfully applied for simultaneous spectrophotometric determination of doxylamine and pyridoxine both in laboratory-prepared mixtures and commercial dosage forms.

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

Polar lows in the Labrador Sea based on the Moravian historical collection of meteorological data in Labrador and Greenland since the mid-18th century

NASA Astrophysics Data System (ADS)

Matiu, Michael; Lüdecke, Cornelia; Newell, Dianne; Menzel, Annette

2017-04-01

Systematically recorded daily instrumental meteorological data from the Moravian Brethern mission stations located on the east coast of Labrador and southwest coast of Greenland during the 18th, 19th and 20th centuries provide a most valuable source of historical climatological data in the Subarctic region. Although the collections of original data themselves are both scattered in physical location and fragmented in their coverage of time and place, and large amounts still need to be digitized, this data provides large potential for studying climate extreme events in this remote region. In this paper, we study polar lows (PLs). They are high-latitude intense maritime cyclones with only 200 to 1000 km in diameter, a short life-time of only two days, mostly occurring in wintertime, e.g. in the Norwegian, Barents, but also Labrador and Greenland seas. Due to high wind speeds exceeding 30 m s-1, high ocean waves and heavy snow showers, they constitute a major hazard risk difficult to forecast. Published papers indicate that with future climate warming, the frequency of PLs is predicted to decrease; however, climatologies of PLs for the last 7 decades (1948-2009) based on reanalysis data and satellite remote sensing products did not indicate any change in their mean annual frequency. In our digitized long-term dataset (1846-2015) for one Moravian station at Nain, Labrador, we identified PLs as follows: If there was a drop in air pressure of at least 30hPa during 48 hours, we marked it as a preliminary event. Then, each preliminary event was checked manually to see whether additional changes in air pressure, air temperature, wind direction and wind speed matched the known textbook example. If more than two variables showed the required pattern, the preliminary event was identified as PL. Our analysis revealed an average frequency of 5.6 PLs yr-1 for 1846-1853, 5.2 PLs yr-1(1882-1913), and 4.4 PLs yr-1 (1926-1939), largely confirming long-term averages for the more recent periods 1948-2005 (4.9 PLs yr-1) as well as 1977-1994 (4.4 PLs yr-1) reported in the literature. Once more data from the historical Moravian collection is digitized, it may be checked whether there is a stable tendency of more annual PLs in the mid-19th century compared to recent numbers of this extreme event. With respect of the boundary conditions in which PLs are developing, our data from the mid-19th century cannot confirm recent findings that the occurrence of PLs is mainly associated with NAO+ phases. Due to additional concurrently operating Moravian climate stations at the eastern Labrador and southwestern Greenland coasts, the moving of PLs and PL clusters over the Labrador Sea and southern Davis Strait can be confirmed based on this unique historical subarctic climate data.

The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS2regression method

NASA Astrophysics Data System (ADS)

Yulia, M.; Suhandy, D.

2018-03-01

NIR spectra obtained from spectral data acquisition system contains both chemical information of samples as well as physical information of the samples, such as particle size and bulk density. Several methods have been established for developing calibration models that can compensate for sample physical information variations. One common approach is to include physical information variation in the calibration model both explicitly and implicitly. The objective of this study was to evaluate the feasibility of using explicit method to compensate the influence of different particle size of coffee powder in NIR calibration model performance. A number of 220 coffee powder samples with two different types of coffee (civet and non-civet) and two different particle sizes (212 and 500 µm) were prepared. Spectral data was acquired using NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement. A discrimination method based on PLS-DA was conducted and the influence of different particle size on the performance of PLS-DA was investigated. In explicit method, we add directly the particle size as predicted variable results in an X block containing only the NIR spectra and a Y block containing the particle size and type of coffee. The explicit inclusion of the particle size into the calibration model is expected to improve the accuracy of type of coffee determination. The result shows that using explicit method the quality of the developed calibration model for type of coffee determination is a little bit superior with coefficient of determination (R2) = 0.99 and root mean square error of cross-validation (RMSECV) = 0.041. The performance of the PLS2 calibration model for type of coffee determination with particle size compensation was quite good and able to predict the type of coffee in two different particle sizes with relatively high R2 pred values. The prediction also resulted in low bias and RMSEP values.

Dealing with gene expression missing data.

PubMed

Brás, L P; Menezes, J C

2006-05-01

Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA). The influence in prediction accuracy of some factors, such as methods' parameters, type of data relationships used in the estimation process (i.e. row-wise, column-wise or both), missing rate and pattern and type of experiment [time series (TS), non-time series (NTS) or mixed (MIX) experiments] is elucidated. Improvements based on the iterative use of data (iterative LLS and PLS imputation--ILLSimpute and IPLSimpute), the need to perform initial imputations (modified PLS and Helland PLS imputation--MPLSimpute and HPLSimpute) and the type of relationships employed (KNNarray, LLSarray, HPLSarray and alternating PLS--APLSimpute) are proposed. Overall, it is shown that data set properties (type of experiment, missing rate and pattern) affect the data similarity structure, therefore influencing the methods' performance. LLSimpute and ILLSimpute are preferable in the presence of data with a stronger similarity structure (TS and MIX experiments), whereas PLS-based methods (MPLSimpute, IPLSimpute and APLSimpute) are preferable when estimating NTS missing data.

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes.

PubMed

Fischer, Katharina E

2012-08-02

Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare. Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM. After modification by dropping two indicators that showed poor measures in the measurement models' quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of 'transparency', 'participation', 'scientific rigour' and 'reasonableness'. The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there are links between several non-observable constructs. PLS-PM was compatible in accounting for the complexity of coverage decisions to obtain a more realistic perspective for empirical analysis. The model specification can be used for hypothesis testing by using larger sample sizes and for data in the full domain of health technologies.

Real-time monitoring of process parameters in rice wine fermentation by a portable spectral analytical system combined with multivariate analysis.

PubMed

Ouyang, Qin; Zhao, Jiewen; Pan, Wenxiu; Chen, Quansheng

2016-01-01

A portable and low-cost spectral analytical system was developed and used to monitor real-time process parameters, i.e. total sugar content (TSC), alcohol content (AC) and pH during rice wine fermentation. Various partial least square (PLS) algorithms were implemented to construct models. The performance of a model was evaluated by the correlation coefficient (Rp) and the root mean square error (RMSEP) in the prediction set. Among the models used, the synergy interval PLS (Si-PLS) was found to be superior. The optimal performance by the Si-PLS model for the TSC was Rp = 0.8694, RMSEP = 0.438; the AC was Rp = 0.8097, RMSEP = 0.617; and the pH was Rp = 0.9039, RMSEP = 0.0805. The stability and reliability of the system, as well as the optimal models, were verified using coefficients of variation, most of which were found to be less than 5%. The results suggest this portable system is a promising tool that could be used as an alternative method for rapid monitoring of process parameters during rice wine fermentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recognition of beer brand based on multivariate analysis of volatile fingerprint.

PubMed

Cajka, Tomas; Riddellova, Katerina; Tomaniova, Monika; Hajslova, Jana

2010-06-18

Automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-time-of-flight mass spectrometry (GC-TOFMS), was developed and employed for obtaining of fingerprints (GC profiles) of beer volatiles. In total, 265 speciality beer samples were collected over a 1-year period with the aim to distinguish, based on analytical (profiling) data, (i) the beers labelled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. For the chemometric evaluation of the data, partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. The best prediction ability was obtained for the model that distinguished a group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided 100% correct classification. Slightly worse prediction abilities were achieved for the models "Trappist vs. non-Trappist beers" with the values of 93.9% (PLS-DA), 91.9% (LDA) and 97.0% (ANN-MLP) and "Rochefort 8 vs. the rest" with the values of 87.9% (PLS-DA) and 84.8% (LDA) and 93.9% (ANN-MLP). In addition to chromatographic profiling, also the potential of direct coupling of SPME (extraction/pre-concentration device) with high-resolution TOFMS employing a direct analysis in real time (DART) ion source has been demonstrated as a challenging profiling approach. Copyright (c) 2010 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coble, Jamie; Orton, Christopher; Schwantes, Jon

Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test threemore » fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.« less

Ultrasound-triggered effects of the microbubbles coupled to GDNF- and Nurr1-loaded PEGylated liposomes in a rat model of Parkinson's disease.

PubMed

Yue, Peijian; Gao, Lin; Wang, Xuejing; Ding, Xuebing; Teng, Junfang

2018-06-01

The purpose of this study was to investigate ultrasound-triggered effects of the glial cell line-derived neurotrophic factor (GDNF) + nuclear receptor-related factor 1 (Nurr1)-polyethylene glycol (PEG)ylated liposomes-coupled microbubbles (PLs-GDNF + Nurr1-MBs) on behavioral impairment and neuron loss in a rat model of Parkinson's disease (PD). The unloaded PEGylated liposomes-coupled microbubbles (PLs-MBs) were characterized for zeta potential, particle size, and concentration. 6-hydroxydopamine (6-OHDA) was used to establish the PD rat model. Rotational, climbing pole, and suspension tests were used to detect behavioral impairment. The immunohistochemical staining of tyrosine hydroxylase (TH) and dopamine transporter (DAT) was used to assess the neuron loss. Western blot and quantitative real-time PCR (qRT-PCR) analysis were used to measure the expression levels of GDNF and Nurr1. The particle size of PLs-MBs was gradually increased, while the concentration and absolute zeta potential were gradually decreased as the time prolongs. 6-OHDA increased amphetamine-induced rotations and loss of dopaminergic neurons as compared to sham group. Interestingly, PLs-GDNF-MBs or PLs-Nurr1-MBs decreased rotations and increased the TH and DAT immunoreactivity. Combined of both genes resulted in a robust reduction in the rotations and a greater increase of the dopaminergic neurons. The delivery of PLs-GDNF + Nurr1-MBs into the brains using magnetic resonance imaging (MRI)-guided focused ultrasound may be more efficacious for the treatment of PD than the single treatment. © 2017 Wiley Periodicals, Inc.

Determination of cellulose I crystallinity by FT-Raman spectroscopy

Treesearch

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2009-01-01

Two new methods based on FT-Raman spectroscopy, one simple, based on band intensity ratio, and the other, using a partial least-squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in semicrystalline cellulose I samples was determined based on univariate regression that was first developed using the...

Imaging of glia activation in people with primary lateral sclerosis.

PubMed

Paganoni, Sabrina; Alshikho, Mohamad J; Zürcher, Nicole R; Cernasov, Paul; Babu, Suma; Loggia, Marco L; Chan, James; Chonde, Daniel B; Garcia, David Izquierdo; Catana, Ciprian; Mainero, Caterina; Rosen, Bruce R; Cudkowicz, Merit E; Hooker, Jacob M; Atassi, Nazem

2018-01-01

Glia activation is thought to contribute to neuronal damage in several neurodegenerative diseases based on preclinical and human post - mortem studies, but its role in primary lateral sclerosis (PLS) is unknown. To localize and measure glia activation in people with PLS compared to healthy controls (HC). Ten participants with PLS and ten age-matched HCs underwent simultaneous magnetic resonance (MR) and proton emission tomography (PET). The radiotracer [ 11 C]-PBR28 was used to obtain PET-based measures of 18 kDa translocator protein (TSPO) expression, a marker of activated glial cells. MR techniques included a structural sequence to measure cortical thickness and diffusion tensor imaging (DTI) to assess white matter integrity. PET data showed increased [ 11 C]-PBR28 uptake in anatomically-relevant motor regions which co-localized with areas of regional gray matter atrophy and decreased subcortical fractional anisotropy. This study supports a link between glia activation and neuronal degeneration in PLS, and suggests that these disease mechanisms can be measured in vivo in PLS. Future studies are needed to determine the longitudinal changes of these imaging measures and to clarify if MR-PET with [ 11 C]-PBR28 can be used as a biomarker for drug development in the context of clinical trials for PLS.

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes

PubMed Central

2012-01-01

Background Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare. Methods Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM. Results After modification by dropping two indicators that showed poor measures in the measurement models’ quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of ‘transparency’, ‘participation’, ‘scientific rigour’ and ‘reasonableness’. Conclusions The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there are links between several non-observable constructs. PLS-PM was compatible in accounting for the complexity of coverage decisions to obtain a more realistic perspective for empirical analysis. The model specification can be used for hypothesis testing by using larger sample sizes and for data in the full domain of health technologies. PMID:22856325

Association Between Dietary-related Risk Factors and Ischemic Stroke Using Reduced Rank Regression: The Multi-Ethnic Study of Atherosclerosis (MESA), USA.

PubMed

Nazari, Seyed Saeed Hashemi; Mokhayeri, Yaser; Mansournia, Mohammad Ali; Khodakarim, Soheila; Soori, Hamid

2018-05-21

Some studies shed light on the association between dietary patterns and stroke, though, none of them applied reduced rank regression (RRR). Therefore, we sought to extract dietary patterns using RRR, and showed how well the extracted scores by RRR predict stroke in comparison to those scores produced by partial least squares (PLS) and principal components regression (PCR). Diet data at baseline with four response variables including body mass index (BMI), fibrinogen, IL-6, low-density lipoprotein (LDL) cholesterol were used to extract dietary patterns. Analyses were based on 5468 men and women aged 45-84 y who had no clinical cardiovascular diseases (CVD) from Multi-Ethnic Study of Atherosclerosis (MESA). Dietary patterns were created by three methods RRR, PLS, and PCR. The RRR1 was positively associated with stroke incidence in both models (for model 1 hazard ratio (HR): 7.49; 95% CI: 1.66, 33.69 P for trend = 0.01 and for model 2 HR: 6.83; 95% CI: 1.51, 30.87 for quintile 5 compared with the reference category P for trend = 0.02). The RRR1, PLS1, and PCR1 were high in fats and oils, poultry, tomatoes, fried potato and processed meat. Additionally, RRR1 and PLS1 were high in dark-yellow and cruciferous vegetables which negatively were correlated with the first dietary pattern. Mainly according to the RRR, we identified that a dietary pattern high in fats and oil, poultry, non-diet soda, processed meat, tomatoes, legumes, chicken, tuna and egg salad, fried potato and low in dark-yellow and cruciferous vegetables may increase the incidence of stroke.

Partial Least Squares for Discrimination in fMRI Data

PubMed Central

Andersen, Anders H.; Rayens, William S.; Liu, Yushu; Smith, Charles D.

2011-01-01

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using either principal component analysis (PCA), partial least squares (PLS), or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contains more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using fMRI as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk. PMID:22227352

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures.

PubMed

Kumar, Keshav

2018-03-01

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2 fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2 fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

Analyses of direct and indirect impacts of a positive list system on pharmaceutical R&D investments.

PubMed

Han, Euna; Kim, Tae Hyun; Jeung, Myung Jin; Lee, Eui-Kyung

2013-07-01

The South Korean government recently enacted a Positive List System (PLS) as a major change of the national formulary listing system and reimbursed prices for pharmaceutical products. Regardless of the primary goal of the PLS, its implementation might have spillover effects by influencing the pharmaceutical industry's research and development (R&D), potentially leading to a variety of responses by firms in relation to their R&D activities. We investigated the spillover effect of the PLS on R&D investments of the pharmaceutical industry in Korea through both direct and indirect channels, examining the influence of the PLS on sales profit and cash flow. Data from 9 years (5 before and 4 after PLS implementation) were drawn from the financial statements of firms whose stocks were exchanged in 2 official stock markets in Korea (526 firms) and additional pharmaceutical firms whose financial performance was officially audited by external reviewers (263 firms). Longitudinal analyses were conducted, using the panel nature of the data to control for permanent unobserved firm heterogeneity. Our results showed that the PLS was directly associated with R&D investments. In contrast, its indirect impacts stemming from the influence on sales profit and cash flow were minimal and statistically nonsignificant. The gross impact of the PLS on R&D investments increased moving further from the enactment year; R&D investments were reduced by 18.3% to 25.8% in 2009-2010 (compared with before PLS implementation) in the firm fixed-effects model. We also found that such negative direct and gross impacts of the PLS on R&D investments were significant only in firms without newly developed chemical entities. Considering the gross negative impact of the PLS on R&D investments of pharmaceutical firms and the heterogeneous response of these firms by the R&D activities, governmental efforts of cost-containment may need to consider the spillover impact of the PLS on pharmaceutical innovation. Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling.

PubMed

Povey, Jane F; O'Malley, Christopher J; Root, Tracy; Martin, Elaine B; Montague, Gary A; Feary, Marc; Trim, Carol; Lang, Dietmar A; Alldread, Richard; Racher, Andrew J; Smales, C Mark

2014-08-20

Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data are used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10L bioreactor model of Lonza's large-scale (up to 20,000L) fed-batch cell culture processes. Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements. Copyright © 2014 Elsevier B.V. All rights reserved.

Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression.

PubMed

Borràs, Eva; Ferré, Joan; Boqué, Ricard; Mestres, Montserrat; Aceña, Laura; Calvo, Angels; Busto, Olga

2016-08-01

Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV-vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010-2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors. Copyright © 2016 Elsevier B.V. All rights reserved.

Potential of near-infrared spectroscopy for quality evaluation of cattle leather.

PubMed

Braz, Carlos Eduardo M; Jacinto, Manuel Antonio C; Pereira-Filho, Edenir R; Souza, Gilberto B; Nogueira, Ana Rita A

2018-05-09

Models using near-infrared spectroscopy (NIRS) were constructed based on physical-mechanical tests to determine the quality of cattle leather. The following official parameters were used, considering the industry requirements: tensile strength (TS), percentage elongation (%E), tear strength (TT), and double hole tear strength (DHS). Classification models were constructed with the use of k-nearest neighbor (kNN), soft independent modeling of class analogy (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The evaluated figures of merit, accuracy, sensitivity, and specificity presented results between 85% and 93%, and the false alarm rates from 9% to 14%. The model with lowest validation percentage (92%) was kNN, and the highest was PLS-DA (100%). For TS, lower values were obtained, from 52% for kNN and 74% for SIMCA. The other parameters %E, TT, and DHS presented hit rates between 87 and 100%. The abilities of the models were similar, showing they can be used to predict the quality of cattle leather. Copyright © 2018 Elsevier B.V. All rights reserved.

"If There Is a Job Description I Don't Think I've Read One": A Case Study of Programme Leadership in a UK Pre-1992 University

ERIC Educational Resources Information Center

Mitchell, Rafael

2015-01-01

This paper reports on an exploratory study of the role of programme leaders (PLs) in a pre-1992 university, based on interviews with PLs (7) and a survey of taught Masters students (54) in a single school. The study elicits PLs' activities, most of which might be categorised as managerial and administrative, with leadership required…

Selecting the optimum number of partial least squares components for the calibration of attenuated total reflectance-mid-infrared spectra of undesigned kerosene samples.

PubMed

Gómez-Carracedo, M P; Andrade, J M; Rutledge, D N; Faber, N M

2007-03-07

Selecting the correct dimensionality is critical for obtaining partial least squares (PLS) regression models with good predictive ability. Although calibration and validation sets are best established using experimental designs, industrial laboratories cannot afford such an approach. Typically, samples are collected in an (formally) undesigned way, spread over time and their measurements are included in routine measurement processes. This makes it hard to evaluate PLS model dimensionality. In this paper, classical criteria (leave-one-out cross-validation and adjusted Wold's criterion) are compared to recently proposed alternatives (smoothed PLS-PoLiSh and a randomization test) to seek out the optimum dimensionality of PLS models. Kerosene (jet fuel) samples were measured by attenuated total reflectance-mid-IR spectrometry and their spectra where used to predict eight important properties determined using reference methods that are time-consuming and prone to analytical errors. The alternative methods were shown to give reliable dimensionality predictions when compared to external validation. By contrast, the simpler methods seemed to be largely affected by the largest changes in the modeling capabilities of the first components.

The Plasmin-Sensitive Protein Pls in Methicillin-Resistant Staphylococcus aureus (MRSA) Is a Glycoprotein.

PubMed

Bleiziffer, Isabelle; Eikmeier, Julian; Pohlentz, Gottfried; McAulay, Kathryn; Xia, Guoqing; Hussain, Muzaffar; Peschel, Andreas; Foster, Simon; Peters, Georg; Heilmann, Christine

2017-01-01

Most bacterial glycoproteins identified to date are virulence factors of pathogenic bacteria, i.e. adhesins and invasins. However, the impact of protein glycosylation on the major human pathogen Staphylococcus aureus remains incompletely understood. To study protein glycosylation in staphylococci, we analyzed lysostaphin lysates of methicillin-resistant Staphylococcus aureus (MRSA) strains by SDS-PAGE and subsequent periodic acid-Schiff's staining. We detected four (>300, ∼250, ∼165, and ∼120 kDa) and two (>300 and ∼175 kDa) glycosylated surface proteins with strain COL and strain 1061, respectively. The ∼250, ∼165, and ∼175 kDa proteins were identified as plasmin-sensitive protein (Pls) by mass spectrometry. Previously, Pls has been demonstrated to be a virulence factor in a mouse septic arthritis model. The pls gene is encoded by the staphylococcal cassette chromosome (SCC)mec type I in MRSA that also encodes the methicillin resistance-conferring mecA and further genes. In a search for glycosyltransferases, we identified two open reading frames encoded downstream of pls on the SCCmec element, which we termed gtfC and gtfD. Expression and deletion analysis revealed that both gtfC and gtfD mediate glycosylation of Pls. Additionally, the recently reported glycosyltransferases SdgA and SdgB are involved in Pls glycosylation. Glycosylation occurs at serine residues in the Pls SD-repeat region and modifying carbohydrates are N-acetylhexosaminyl residues. Functional characterization revealed that Pls can confer increased biofilm formation, which seems to involve two distinct mechanisms. The first mechanism depends on glycosylation of the SD-repeat region by GtfC/GtfD and probably also involves eDNA, while the second seems to be independent of glycosylation as well as eDNA and may involve the centrally located G5 domains. Other previously known Pls properties are not related to the sugar modifications. In conclusion, Pls is a glycoprotein and Pls glycosyl residues can stimulate biofilm formation. Thus, sugar modifications may represent promising new targets for novel therapeutic or prophylactic measures against life-threatening S. aureus infections.

Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

Treesearch

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2010-01-01

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

A holistic high-throughput screening framework for biofuel feedstock assessment that characterises variations in soluble sugars and cell wall composition in Sorghum bicolor

PubMed Central

2013-01-01

Background A major hindrance to the development of high yielding biofuel feedstocks is the ability to rapidly assess large populations for fermentable sugar yields. Whilst recent advances have outlined methods for the rapid assessment of biomass saccharification efficiency, none take into account the total biomass, or the soluble sugar fraction of the plant. Here we present a holistic high-throughput methodology for assessing sweet Sorghum bicolor feedstocks at 10 days post-anthesis for total fermentable sugar yields including stalk biomass, soluble sugar concentrations, and cell wall saccharification efficiency. Results A mathematical method for assessing whole S. bicolor stalks using the fourth internode from the base of the plant proved to be an effective high-throughput strategy for assessing stalk biomass, soluble sugar concentrations, and cell wall composition and allowed calculation of total stalk fermentable sugars. A high-throughput method for measuring soluble sucrose, glucose, and fructose using partial least squares (PLS) modelling of juice Fourier transform infrared (FTIR) spectra was developed. The PLS prediction was shown to be highly accurate with each sugar attaining a coefficient of determination (R 2 ) of 0.99 with a root mean squared error of prediction (RMSEP) of 11.93, 5.52, and 3.23 mM for sucrose, glucose, and fructose, respectively, which constitutes an error of <4% in each case. The sugar PLS model correlated well with gas chromatography–mass spectrometry (GC-MS) and brix measures. Similarly, a high-throughput method for predicting enzymatic cell wall digestibility using PLS modelling of FTIR spectra obtained from S. bicolor bagasse was developed. The PLS prediction was shown to be accurate with an R 2 of 0.94 and RMSEP of 0.64 μg.mgDW-1.h-1. Conclusions This methodology has been demonstrated as an efficient and effective way to screen large biofuel feedstock populations for biomass, soluble sugar concentrations, and cell wall digestibility simultaneously allowing a total fermentable yield calculation. It unifies and simplifies previous screening methodologies to produce a holistic assessment of biofuel feedstock potential. PMID:24365407

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

PubMed

Zhang, Hong-guang; Lu, Jian-gang

2016-02-01

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Liming, Ye; Yufei, Liu; Chunyan, Li; Han, Chen

2017-05-01

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to human CBG complexes

NASA Astrophysics Data System (ADS)

Norinder, Ulf

1990-12-01

An experimental design based 3-D QSAR analysis using a combination of principal component and PLS analysis is presented and applied to human corticosteroid-binding globulin complexes. The predictive capability of the created model is good. The technique can also be used as guidance when selecting new compounds to be investigated.

Rapid activity prediction of HIV-1 integrase inhibitors: harnessing docking energetic components for empirical scoring by chemometric and artificial neural network approaches

NASA Astrophysics Data System (ADS)

Thangsunan, Patcharapong; Kittiwachana, Sila; Meepowpan, Puttinan; Kungwan, Nawee; Prangkio, Panchika; Hannongbua, Supa; Suree, Nuttee

2016-06-01

Improving performance of scoring functions for drug docking simulations is a challenging task in the modern discovery pipeline. Among various ways to enhance the efficiency of scoring function, tuning of energetic component approach is an attractive option that provides better predictions. Herein we present the first development of rapid and simple tuning models for predicting and scoring inhibitory activity of investigated ligands docked into catalytic core domain structures of HIV-1 integrase (IN) enzyme. We developed the models using all energetic terms obtained from flexible ligand-rigid receptor dockings by AutoDock4, followed by a data analysis using either partial least squares (PLS) or self-organizing maps (SOMs). The models were established using 66 and 64 ligands of mercaptobenzenesulfonamides for the PLS-based and the SOMs-based inhibitory activity predictions, respectively. The models were then evaluated for their predictability quality using closely related test compounds, as well as five different unrelated inhibitor test sets. Weighting constants for each energy term were also optimized, thus customizing the scoring function for this specific target protein. Root-mean-square error (RMSE) values between the predicted and the experimental inhibitory activities were determined to be <1 (i.e. within a magnitude of a single log scale of actual IC50 values). Hence, we propose that, as a pre-functional assay screening step, AutoDock4 docking in combination with these subsequent rapid weighted energy tuning methods via PLS and SOMs analyses is a viable approach to predict the potential inhibitory activity and to discriminate among small drug-like molecules to target a specific protein of interest.

A PLS-based extractive spectrophotometric method for simultaneous determination of carbamazepine and carbamazepine-10,11-epoxide in plasma and comparison with HPLC

NASA Astrophysics Data System (ADS)

Hemmateenejad, Bahram; Rezaei, Zahra; Khabnadideh, Soghra; Saffari, Maryam

2007-11-01

Carbamazepine (CBZ) undergoes enzyme biotransformation through epoxidation with the formation of its metabolite, carbamazepine-10,11-epoxide (CBZE). A simple chemometrics-assisted spectrophotometric method has been proposed for simultaneous determination of CBZ and CBZE in plasma. A liquid extraction procedure was operated to separate the analytes from plasma, and the UV absorbance spectra of the resultant solutions were subjected to partial least squares (PLS) regression. The optimum number of PLS latent variables was selected according to the PRESS values of leave-one-out cross-validation. A HPLC method was also employed for comparison. The respective mean recoveries for analysis of CBZ and CBZE in synthetic mixtures were 102.57 (±0.25)% and 103.00 (±0.09)% for PLS and 99.40 (±0.15)% and 102.20 (±0.02)%. The concentrations of CBZ and CBZE were also determined in five patients using the PLS and HPLC methods. The results showed that the data obtained by PLS were comparable with those obtained by HPLC method.

Novel pure component contribution, mean centering of ratio spectra and factor based algorithms for simultaneous resolution and quantification of overlapped spectral signals: An application to recently co-formulated tablets of chlorzoxazone, aceclofenac and paracetamol

NASA Astrophysics Data System (ADS)

Toubar, Safaa S.; Hegazy, Maha A.; Elshahed, Mona S.; Helmy, Marwa I.

2016-06-01

In this work, resolution and quantitation of spectral signals are achieved by several univariate and multivariate techniques. The novel pure component contribution algorithm (PCCA) along with mean centering of ratio spectra (MCR) and the factor based partial least squares (PLS) algorithms were developed for simultaneous determination of chlorzoxazone (CXZ), aceclofenac (ACF) and paracetamol (PAR) in their pure form and recently co-formulated tablets. The PCCA method allows the determination of each drug at its λmax. While, the mean centered values at 230, 302 and 253 nm, were used for quantification of CXZ, ACF and PAR, respectively, by MCR method. Partial least-squares (PLS) algorithm was applied as a multivariate calibration method. The three methods were successfully applied for determination of CXZ, ACF and PAR in pure form and tablets. Good linear relationships were obtained in the ranges of 2-50, 2-40 and 2-30 μg mL- 1 for CXZ, ACF and PAR, in order, by both PCCA and MCR, while the PLS model was built for the three compounds each in the range of 2-10 μg mL- 1. The results obtained from the proposed methods were statistically compared with a reported one. PCCA and MCR methods were validated according to ICH guidelines, while PLS method was validated by both cross validation and an independent data set. They are found suitable for the determination of the studied drugs in bulk powder and tablets.

Detection of drug active ingredients by chemometric processing of solid-state NMR spectrometry data -- the case of acetaminophen.

PubMed

Paradowska, Katarzyna; Jamróz, Marta Katarzyna; Kobyłka, Mariola; Gowin, Ewelina; Maczka, Paulina; Skibiński, Robert; Komsta, Łukasz

2012-01-01

This paper presents a preliminary study in building discriminant models from solid-state NMR spectrometry data to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The dataset, containing 11 spectra of pure substances and 21 spectra of various formulations, was processed by partial least squares discriminant analysis (PLS-DA). The model found coped with the discrimination, and its quality parameters were acceptable. It was found that standard normal variate preprocessing had almost no influence on unsupervised investigation of the dataset. The influence of variable selection with the uninformative variable elimination by PLS method was studied, reducing the dataset from 7601 variables to around 300 informative variables, but not improving the model performance. The results showed the possibility to construct well-working PLS-DA models from such small datasets without a full experimental design.

Random forest models to predict aqueous solubility.

PubMed

Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O

2007-01-01

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.

Nontargeted metabolomics approach for the differentiation of cultivation ages of mountain cultivated ginseng leaves using UHPLC/QTOF-MS.

PubMed

Chang, Xiangwei; Zhang, Juanjuan; Li, Dekun; Zhou, Dazheng; Zhang, Yuling; Wang, Jincheng; Hu, Bing; Ju, Aichun; Ye, Zhengliang

2017-07-15

The adulteration or falsification of the cultivation age of mountain cultivated ginseng (MCG) has been a serious problem in the commercial MCG market. To develop an efficient discrimination tool for the cultivation age and to explore potential age-dependent markers, an optimized ultra high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS)-based metabolomics approach was applied in the global metabolite profiling of 156 MCG leaf (MGL) samples aged from 6 to 18 years. Multivariate statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to compare the derived patterns between MGL samples of different cultivation ages. The present study demonstrated that 6-18-year-old MGL samples can be successfully discriminated using two simple successive steps, together with four PLS-DA discrimination models. Furthermore, 39 robust age-dependent markers enabling differentiation among the 6-18-year-old MGL samples were discovered. The results were validated by a permutation test and an external test set to verify the predictability and reliability of the established discrimination models. More importantly, without destroying the MCG roots, the proposed approach could also be applied to discriminate MCG root ages indirectly, using a minimum amount of homophyletic MGL samples combined with the established four PLS-DA models and identified markers. Additionally, to the best of our knowledge, this is the first study in which 6-18-year-old MCG root ages have been nondestructively differentiated by analyzing homophyletic MGL samples using UHPLC/QTOF-MS analysis and two simple successive steps together with four PLS-DA models. The method developed in this study can be used as a standard protocol for discriminating and predicting MGL ages directly and homophyletic MCG root ages indirectly. Copyright © 2017 Elsevier B.V. All rights reserved.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

PubMed Central

Afendi, Farit M.; Ono, Naoaki; Nakamura, Yukiko; Nakamura, Kensuke; Darusman, Latifah K.; Kibinge, Nelson; Morita, Aki Hirai; Tanaka, Ken; Horai, Hisayuki; Altaf-Ul-Amin, Md.; Kanaya, Shigehiko

2013-01-01

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology. PMID:24688691

A method to relate chemical accident properties and expert judgements in order to derive useful information for the development of Environment-Accident Index.

PubMed

Scott Andersson, Asa; Tysklind, Mats; Fängmark, Ingrid

2007-08-17

The environment consists of a variety of different compartments and processes that act together in a complex system that complicate the environmental risk assessment after a chemical accident. The Environment-Accident Index (EAI) is an example of a tool based on a strategy to join the properties of a chemical with site-specific properties to facilitate this assessment and to be used in the planning process. In the development of the EAI it is necessary to make an unbiased judgement of relevant variables to include in the formula and to estimate their relative importance. The development of EAI has so far included the assimilation of chemical accidents, selection of a representative set of chemical accidents, and response values (representing effects in the environment after a chemical accident) have been developed by means of an expert panel. The developed responses were then related to the chemical and site-specific properties, through a mathematical model based on multivariate modelling (PLS), to create an improved EAI model. This resulted in EAI(new), a PLS based EAI model connected to a new classification scale. The advantages of EAI(new) compared to the old EAI (EAI(old)) is that it can be calculated without the use of tables, it can estimate the effects for all included responses and make a rough classification of chemical accidents according to the new classification scale. Finally EAI(new) is a more stable model than EAI(old), built on a valid base of accident scenarios which makes it more reliable to use for a variety of chemicals and situations as it covers a broader spectra of accident scenarios. EAI(new) can be expressed as a regression model to facilitate the calculation of the index for persons that do not have access to PLS. Future work can be; an external validation of EAI(new); to complete the formula structure; to adjust the classification scale; and to make a real life evaluation of EAI(new).

Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools.

PubMed

Martelo-Vidal, M J; Vázquez, M

2014-09-01

Spectral analysis is a quick and non-destructive method to analyse wine. In this work, trans-resveratrol, oenin, malvin, catechin, epicatechin, quercetin and syringic acid were determined in commercial red wines from DO Rías Baixas and DO Ribeira Sacra (Spain) by UV-VIS-NIR spectroscopy. Calibration models were developed using principal component regression (PCR) or partial least squares (PLS) regression. HPLC was used as reference method. The results showed that reliable PLS models were obtained to quantify all polyphenols for Rías Baixas wines. For Ribeira Sacra, feasible models were obtained to determine quercetin, epicatechin, oenin and syringic acid. PCR calibration models showed worst reliable of prediction than PLS models. For red wines from mencía grapes, feasible models were obtained for catechin and oenin, regardless the geographical origin. The results obtained demonstrate that UV-VIS-NIR spectroscopy can be used to determine individual polyphenolic compounds in red wines. Copyright © 2014 Elsevier Ltd. All rights reserved.

Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches

USGS Publications Warehouse

Brooks, Wesley R.; Fienen, Michael N.; Corsi, Steven R.

2013-01-01

At public beaches, it is now common to mitigate the impact of water-borne pathogens by posting a swimmer's advisory when the concentration of fecal indicator bacteria (FIB) exceeds an action threshold. Since culturing the bacteria delays public notification when dangerous conditions exist, regression models are sometimes used to predict the FIB concentration based on readily-available environmental measurements. It is hard to know which environmental parameters are relevant to predicting FIB concentration, and the parameters are usually correlated, which can hurt the predictive power of a regression model. Here the method of partial least squares (PLS) is introduced to automate the regression modeling process. Model selection is reduced to the process of setting a tuning parameter to control the decision threshold that separates predicted exceedances of the standard from predicted non-exceedances. The method is validated by application to four Great Lakes beaches during the summer of 2010. Performance of the PLS models compares favorably to that of the existing state-of-the-art regression models at these four sites.

PREDICTION OF MOLECULAR PROPERTIES WITH MID-INFRARED SPECTRA AND INTERFEROGRAMS

EPA Science Inventory

We have built infrared spectroscopy-based partial least squares (PLS) models for molecular polarizabilities using a 97 member training set and a 59 member independent prediction set. These 156 compounds span a very wide range of chemical structure. Our goal was to use this well...

Microstructural Modeling of Brittle Materials for Enhanced Performance and Reliability.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teague, Melissa Christine; Teague, Melissa Christine; Rodgers, Theron

Brittle failure is often influenced by difficult to measure and variable microstructure-scale stresses. Recent advances in photoluminescence spectroscopy (PLS), including improved confocal laser measurement and rapid spectroscopic data collection have established the potential to map stresses with microscale spatial resolution (%3C2 microns). Advanced PLS was successfully used to investigate both residual and externally applied stresses in polycrystalline alumina at the microstructure scale. The measured average stresses matched those estimated from beam theory to within one standard deviation, validating the technique. Modeling the residual stresses within the microstructure produced general agreement in comparison with the experimentally measured results. Microstructure scale modelingmore » is primed to take advantage of advanced PLS to enable its refinement and validation, eventually enabling microstructure modeling to become a predictive tool for brittle materials.« less

Modeling RP-1 fuel advanced distillation data using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and partial least squares analysis.

PubMed

Kehimkar, Benjamin; Parsons, Brendon A; Hoggard, Jamin C; Billingsley, Matthew C; Bruno, Thomas J; Synovec, Robert E

2015-01-01

Recent efforts in predicting rocket propulsion (RP-1) fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as elucidating the relationships between fuel compositions and their properties. Herein, we study multidimensional chromatographic data obtained by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC-TOFMS) to analyze RP-1 fuels. For GC × GC separations, RTX-Wax (polar stationary phase) and RTX-1 (non-polar stationary phase) columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes (alkanes, cycloalkanes, aromatics, etc.), providing a significant level of chemical compositional information. The GC × GC-TOFMS data were analyzed using partial least squares regression (PLS) chemometric analysis to model and predict advanced distillation curve (ADC) data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the ADC data. The PLS modeling correlates compositional information found in the GC × GC-TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The root-mean-square error of calibration (RMSEC) ranged from 0.1 to 0.5 °C, and was typically below ∼0.2 °C, for the PLS calibration of the ADC modeling with GC × GC-TOFMS data, indicating a good fit of the model to the calibration data. Likewise, the predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation (LOOCV) yielding root-mean-square error of cross-validation (RMSECV) ranging from 1.4 to 2.6 °C, and was typically below ∼2.0 °C, at each % distilled measurement point during the ADC analysis.

Convolutional neural networks for vibrational spectroscopic data analysis.

PubMed

Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena

2017-02-15

In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

Terahertz time-domain attenuated total reflection spectroscopy applied to the rapid discrimination of the botanical origin of honeys

NASA Astrophysics Data System (ADS)

Liu, Wen; Zhang, Yuying; Yang, Si; Han, Donghai

2018-05-01

A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5 THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.

Use of Vis/NIRS for the determination of sugar content of cola soft drinks based on chemometric methods

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-03-01

Three different chemometric methods were performed for the determination of sugar content of cola soft drinks using visible and near infrared spectroscopy (Vis/NIRS). Four varieties of colas were prepared and 180 samples (45 samples for each variety) were selected for the calibration set, while 60 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay, standard normal variate (SNV) and Savitzky-Golay first derivative transformation were applied for the pre-processing of spectral data. The first eleven principal components (PCs) extracted by partial least squares (PLS) analysis were employed as the inputs of BP neural network (BPNN) and least squares-support vector machine (LS-SVM) model. Then the BPNN model with the optimal structural parameters and LS-SVM model with radial basis function (RBF) kernel were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.971, 1.259 and -0.335 for PLS, 0.986, 0.763, and -0.042 for BPNN, while 0.978, 0.995 and -0.227 for LS-SVM, respectively. All the three methods supplied a high and satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be utilized as a high precision way for the determination of sugar content of cola soft drinks.

A metabolic fingerprinting approach based on selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics: A reliable tool for Mediterranean origin-labeled olive oils authentication.

PubMed

Bajoub, Aadil; Medina-Rodríguez, Santiago; Ajal, El Amine; Cuadros-Rodríguez, Luis; Monasterio, Romina Paula; Vercammen, Joeri; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2018-04-01

Selected Ion flow tube mass spectrometry (SIFT-MS) in combination with chemometrics was used to authenticate the geographical origin of Mediterranean virgin olive oils (VOOs) produced under geographical origin labels. In particular, 130 oil samples from six different Mediterranean regions (Kalamata (Greece); Toscana (Italy); Meknès and Tyout (Morocco); and Priego de Córdoba and Baena (Spain)) were considered. The headspace volatile fingerprints were measured by SIFT-MS in full scan with H 3 O + , NO + and O 2 + as precursor ions and the results were subjected to chemometric treatments. Principal Component Analysis (PCA) was used for preliminary multivariate data analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to build different models (considering the three reagent ions) to classify samples according to the country of origin and regions (within the same country). The multi-class PLS-DA models showed very good performance in terms of fitting accuracy (98.90-100%) and prediction accuracy (96.70-100% accuracy for cross validation and 97.30-100% accuracy for external validation (test set)). Considering the two-class PLS-DA models, the one for the Spanish samples showed 100% sensitivity, specificity and accuracy in calibration, cross validation and external validation; the model for Moroccan oils also showed very satisfactory results (with perfect scores for almost every parameter in all the cases). Copyright © 2017 Elsevier Ltd. All rights reserved.

Standoff detection of chemical and biological threats using laser-induced breakdown spectroscopy.

PubMed

Gottfried, Jennifer L; De Lucia, Frank C; Munson, Chase A; Miziolek, Andrzej W

2008-04-01

Laser-induced breakdown spectroscopy (LIBS) is a promising technique for real-time chemical and biological warfare agent detection in the field. We have demonstrated the detection and discrimination of the biological warfare agent surrogates Bacillus subtilis (BG) (2% false negatives, 0% false positives) and ovalbumin (0% false negatives, 1% false positives) at 20 meters using standoff laser-induced breakdown spectroscopy (ST-LIBS) and linear correlation. Unknown interferent samples (not included in the model), samples on different substrates, and mixtures of BG and Arizona road dust have been classified with reasonable success using partial least squares discriminant analysis (PLS-DA). A few of the samples tested such as the soot (not included in the model) and the 25% BG:75% dust mixture resulted in a significant number of false positives or false negatives, respectively. Our preliminary results indicate that while LIBS is able to discriminate biomaterials with similar elemental compositions at standoff distances based on differences in key intensity ratios, further work is needed to reduce the number of false positives/negatives by refining the PLS-DA model to include a sufficient range of material classes and carefully selecting a detection threshold. In addition, we have demonstrated that LIBS can distinguish five different organophosphate nerve agent simulants at 20 meters, despite their similar stoichiometric formulas. Finally, a combined PLS-DA model for chemical, biological, and explosives detection using a single ST-LIBS sensor has been developed in order to demonstrate the potential of standoff LIBS for universal hazardous materials detection.

Mentalizing in schizophrenia: A multivariate functional MRI study.

PubMed

Martin, Andrew K; Dzafic, Ilvana; Robinson, Gail A; Reutens, David; Mowry, Bryan

2016-12-01

Schizophrenia is associated with mentalizing deficits that impact on social functioning and quality of life. Recently, schizophrenia has been conceptualized as a disorder of neural dysconnectivity and network level analyses offers a means of understanding the underlying deficits leading to mentalizing difficulty. Using an established mentalizing task (The Triangles Task), functional magnetic resonance images (fMRI) were acquired from 19 patients with schizophrenia and 17 age- and sex-matched healthy controls (HCs). Participants were required to watch short animations of two triangles interacting with each other with the interactions either random (no interaction), physical (patterned movement), or mental (intentional movement). Task-based Partial Least Squares (PLS) was used to analyze activation differences and commonalities between the three conditions and the two groups. Seed-based PLS was used to assess functional connectivity with peaks identified in the task-based PLS. Behavioural PLS was then performed using the accuracy from the mental conditions. Patients with schizophrenia performed worse on the mentalizing condition compared to HCs. Task-based PLS revealed one significant latent variable (LV) that explained 42.9% of the variance in the task, with theLV separating the mental condition from the physical and random conditions in patients with schizophrenia, but only the mental from physical in healthy controls. The mental animations were associated with increased modulation of the inferior frontal gyri bilaterally, left superior temporal gyrus, right postcentral gyrus, and left caudate nucleus. The physical/random animations were associated with increased modulation of the right medial frontal gyrus and left superior frontal gyrus. Seed-based PLS identified increased functional connectivity with the left inferior frontal gyrus (liFG) and caudate nucleus in patients with schizophrenia, during the mental and physical interactions, with functional connectivity with the liFG associated with increased performance on the mental animations. The results suggest that mentalizing deficits in schizophrenia may arise due to inefficient social brain networks. Copyright © 2016 Elsevier Ltd. All rights reserved.

ATR-FTIR spectroscopy for the determination of Na4EDTA in detergent aqueous solutions.

PubMed

Suárez, Leticia; García, Roberto; Riera, Francisco A; Diez, María A

2013-10-15

Fourier transform infrared spectroscopy in the attenuated total reflectance mode (ATR-FTIR) combined with partial last square (PLS) algorithms was used to design calibration and prediction models for a wide range of tetrasodium ethylenediaminetetraacetate (Na4EDTA) concentrations (0.1 to 28% w/w) in aqueous solutions. The spectra obtained using air and water as a background medium were tested for the best fit. The PLS models designed afforded a sufficient level of precision and accuracy to allow even very small amounts of Na4EDTA to be determined. A root mean square error of nearly 0.37 for the validation set was obtained. Over a concentration range below 5% w/w, the values estimated from a combination of ATR-FTIR spectroscopy and a PLS algorithm model were similar to those obtained from an HPLC analysis of NaFeEDTA complexes and subsequent detection by UV absorbance. However, the lowest detection limit for Na4EDTA concentrations afforded by this spectroscopic/chemometric method was 0.3% w/w. The PLS model was successfully used as a rapid and simple method to quantify Na4EDTA in aqueous solutions of industrial detergents as an alternative to HPLC-UV analysis which involves time-consuming dilution and complexation processes. © 2013 Elsevier B.V. All rights reserved.

Coulomb and CH-π interactions in (6-4) photolyase-DNA complex dominate DNA binding and repair abilities.

PubMed

Terai, Yuma; Sato, Ryuma; Yumiba, Takahiro; Harada, Ryuhei; Shimizu, Kohei; Toga, Tatsuya; Ishikawa-Fujiwara, Tomoko; Todo, Takeshi; Iwai, Shigenori; Shigeta, Yasuteru; Yamamoto, Junpei

2018-05-14

(6-4) Photolyases ((6-4)PLs) are flavoenzymes that repair the carcinogenic UV-induced DNA damage, pyrimidine(6-4)pyrimidone photoproducts ((6-4)PPs), in a light-dependent manner. Although the reaction mechanism of DNA photorepair by (6-4)PLs has been intensively investigated, the molecular mechanism of the lesion recognition remains obscure. We show that a well-conserved arginine residue in Xenopus laevis (6-4)PL (Xl64) participates in DNA binding, through Coulomb and CH-π interactions. Fragment molecular orbital calculations estimated attractive interaction energies of -80-100 kcal mol-1 for the Coulomb interaction and -6 kcal mol-1 for the CH-π interaction, and the loss of either of them significantly reduced the affinity for (6-4)PP-containing oligonucleotides, as well as the quantum yield of DNA photorepair. From experimental and theoretical observations, we formulated a DNA binding model of (6-4)PLs. Based on the binding model, we mutated this Arg in Xl64 to His, which is well conserved among the animal cryptochromes (CRYs), and found that the CRY-type mutant exhibited reduced affinity for the (6-4)PP-containing oligonucleotides, implying the possible molecular origin of the functional diversity of the photolyase/cryptochrome superfamily.

Application of Fourier transform infrared spectroscopy with chemometrics on postmortem interval estimation based on pericardial fluids.

PubMed

Zhang, Ji; Li, Bing; Wang, Qi; Wei, Xin; Feng, Weibo; Chen, Yijiu; Huang, Ping; Wang, Zhenyuan

2017-12-21

Postmortem interval (PMI) evaluation remains a challenge in the forensic community due to the lack of efficient methods. Studies have focused on chemical analysis of biofluids for PMI estimation; however, no reports using spectroscopic methods in pericardial fluid (PF) are available. In this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was applied to collect comprehensive biochemical information from rabbit PF at different PMIs. The PMI-dependent spectral signature was determined by two-dimensional (2D) correlation analysis. The partial least square (PLS) and nu-support vector machine (nu-SVM) models were then established based on the acquired spectral dataset. Spectral variables associated with amide I, amide II, COO - , C-H bending, and C-O or C-OH vibrations arising from proteins, polypeptides, amino acids and carbohydrates, respectively, were susceptible to PMI in 2D correlation analysis. Moreover, the nu-SVM model appeared to achieve a more satisfactory prediction than the PLS model in calibration; the reliability of both models was determined in an external validation set. The study shows the possibility of application of ATR-FTIR methods in postmortem interval estimation using PF samples.

Improving near-infrared prediction model robustness with support vector machine regression: a pharmaceutical tablet assay example.

PubMed

Igne, Benoît; Drennen, James K; Anderson, Carl A

2014-01-01

Changes in raw materials and process wear and tear can have significant effects on the prediction error of near-infrared calibration models. When the variability that is present during routine manufacturing is not included in the calibration, test, and validation sets, the long-term performance and robustness of the model will be limited. Nonlinearity is a major source of interference. In near-infrared spectroscopy, nonlinearity can arise from light path-length differences that can come from differences in particle size or density. The usefulness of support vector machine (SVM) regression to handle nonlinearity and improve the robustness of calibration models in scenarios where the calibration set did not include all the variability present in test was evaluated. Compared to partial least squares (PLS) regression, SVM regression was less affected by physical (particle size) and chemical (moisture) differences. The linearity of the SVM predicted values was also improved. Nevertheless, although visualization and interpretation tools have been developed to enhance the usability of SVM-based methods, work is yet to be done to provide chemometricians in the pharmaceutical industry with a regression method that can supplement PLS-based methods.

MALDI-TOF-MS with PLS Modeling Enables Strain Typing of the Bacterial Plant Pathogen Xanthomonas axonopodis

NASA Astrophysics Data System (ADS)

Sindt, Nathan M.; Robison, Faith; Brick, Mark A.; Schwartz, Howard F.; Heuberger, Adam L.; Prenni, Jessica E.

2018-02-01

Matrix-assisted desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) is a fast and effective tool for microbial species identification. However, current approaches are limited to species-level identification even when genetic differences are known. Here, we present a novel workflow that applies the statistical method of partial least squares discriminant analysis (PLS-DA) to MALDI-TOF-MS protein fingerprint data of Xanthomonas axonopodis, an important bacterial plant pathogen of fruit and vegetable crops. Mass spectra of 32 X. axonopodis strains were used to create a mass spectral library and PLS-DA was employed to model the closely related strains. A robust workflow was designed to optimize the PLS-DA model by assessing the model performance over a range of signal-to-noise ratios (s/n) and mass filter (MF) thresholds. The optimized parameters were observed to be s/n = 3 and MF = 0.7. The model correctly classified 83% of spectra withheld from the model as a test set. A new decision rule was developed, termed the rolled-up Maximum Decision Rule (ruMDR), and this method improved identification rates to 92%. These results demonstrate that MALDI-TOF-MS protein fingerprints of bacterial isolates can be utilized to enable identification at the strain level. Furthermore, the open-source framework of this workflow allows for broad implementation across various instrument platforms as well as integration with alternative modeling and classification algorithms.

Application of dielectric spectroscopy for monitoring high cell density in monoclonal antibody producing CHO cell cultivations.

PubMed

Párta, László; Zalai, Dénes; Borbély, Sándor; Putics, Akos

2014-02-01

The application of dielectric spectroscopy was frequently investigated as an on-line cell culture monitoring tool; however, it still requires supportive data and experience in order to become a robust technique. In this study, dielectric spectroscopy was used to predict viable cell density (VCD) at industrially relevant high levels in concentrated fed-batch culture of Chinese hamster ovary cells producing a monoclonal antibody for pharmaceutical purposes. For on-line dielectric spectroscopy measurements, capacitance was scanned within a wide range of frequency values (100-19,490 kHz) in six parallel cell cultivation batches. Prior to detailed mathematical analysis of the collected data, principal component analysis (PCA) was applied to compare dielectric behavior of the cultivations. PCA analysis resulted in detecting measurement disturbances. By using the measured spectroscopic data, partial least squares regression (PLS), Cole-Cole, and linear modeling were applied and compared in order to predict VCD. The Cole-Cole and the PLS model provided reliable prediction over the entire cultivation including both the early and decline phases of cell growth, while the linear model failed to estimate VCD in the later, declining cultivation phase. In regards to the measurement error sensitivity, remarkable differences were shown among PLS, Cole-Cole, and linear modeling. VCD prediction accuracy could be improved in the runs with measurement disturbances by first derivative pre-treatment in PLS and by parameter optimization of the Cole-Cole modeling.

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression

NASA Astrophysics Data System (ADS)

Dreissig, Isabell; Machill, Susanne; Salzer, Reiner; Krafft, Christoph

2009-01-01

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.

Fourier transform infrared spectroscopy to quantify collagen and elastin in an in vitro model of extracellular matrix degradation in aorta.

PubMed

Cheheltani, Rabee; McGoverin, Cushla M; Rao, Jayashree; Vorp, David A; Kiani, Mohammad F; Pleshko, Nancy

2014-06-21

Extracellular matrix (ECM) is a key component and regulator of many biological tissues including aorta. Several aortic pathologies are associated with significant changes in the composition of the matrix, especially in the content, quality and type of aortic structural proteins, collagen and elastin. The purpose of this study was to develop an infrared spectroscopic methodology that is comparable to biochemical assays to quantify collagen and elastin in aorta. Enzymatically degraded porcine aorta samples were used as a model of ECM degradation in abdominal aortic aneurysm (AAA). After enzymatic treatment, Fourier transform infrared (FTIR) spectra of the aortic tissue were acquired by an infrared fiber optic probe (IFOP) and FTIR imaging spectroscopy (FT-IRIS). Collagen and elastin content were quantified biochemically and partial least squares (PLS) models were developed to predict collagen and elastin content in aorta based on FTIR spectra. PLS models developed from FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (RMSE of validation = 8.4% and 11.1% of the range respectively), and IFOP spectra were successfully used to predict elastin content (RMSE = 11.3% of the range). The PLS regression coefficients from the FT-IRIS models were used to map collagen and elastin in tissue sections of degraded porcine aortic tissue as well as a human AAA biopsy tissue, creating a similar map of each component compared to histology. These results support further application of FTIR spectroscopic techniques for evaluation of AAA tissues.

Fourier Transform Infrared Spectroscopy to Quantify Collagen and Elastin in an In Vitro Model of Extracellular Matrix Degradation in Aorta

PubMed Central

Cheheltani, Rabee; McGoverin, Cushla M.; Rao, Jayashree; Vorp, David A.; Kiani, Mohammad F.; Pleshko, N.

2014-01-01

Extracellular matrix (ECM) is a key component and regulator of many biological tissues including aorta. Several aortic pathologies are associated with significant changes in the composition of the matrix, especially in the content, quality and type of aortic structural proteins, collagen and elastin. The purpose of this study was to develop an infrared spectroscopic methodology that is comparable to biochemical assays to quantify collagen and elastin in aorta. Enzymatically degraded porcine aorta samples were used as a model of ECM degradation in abdominal aortic aneurysm (AAA). After enzymatic treatment, Fourier transform infrared (FTIR) spectra of the aortic tissue were acquired by an infrared fiber optic probe (IFOP) and FTIR imaging spectroscopy (FT-IRIS). Collagen and elastin content were quantified biochemically and partial least squares (PLS) models were developed to predict collagen and elastin content in aorta based on FTIR spectra. PLS models developed from FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (RMSE of validation = 8.4% and 11.1% of the range respectively), and IFOP spectra were successfully used to predict elastin content (RMSE = 11.3% of the range). The PLS regression coefficients from the FT-IRIS models were used to map collagen and elastin in tissue sections of degraded porcine aortic tissue as well as a human AAA biopsy tissue, creating a similar map of each component compared to histology. These results support further application of FTIR spectroscopic techniques for evaluation of AAA tissues. PMID:24761431

Hyphopodium-Specific VdNoxB/VdPls1-Dependent ROS-Ca2+ Signaling Is Required for Plant Infection by Verticillium dahliae.

PubMed

Zhao, Yun-Long; Zhou, Ting-Ting; Guo, Hui-Shan

2016-07-01

Verticillium dahliae is a phytopathogenic fungus obligate in root infection. A few hyphopodia differentiate from large numbers of hyphae after conidia germination on the root surface for further infection. However, the molecular features and role of hyphopodia in the pathogenicity of V. dahliae remain elusive. In this study, we found that the VdPls1, a tetraspanin, and the VdNoxB, a catalytic subunit of membrane-bound NADPH oxidases for reactive oxygen species (ROS) production, were specifically expressed in hyphopodia. VdPls1 and VdNoxB highly co-localize with the plasma membrane at the base of hyphopodia, where ROS and penetration pegs are generated. Mutant strains, VdΔnoxb and VdΔpls1, in which VdPls1 and VdNoxB were deleted, respectively, developed defective hyphpodia incapable of producing ROS and penetration pegs. Defective plasma membrane localization of VdNoxB in VdΔpls1 demonstrates that VdPls1 functions as an adaptor protein for the recruitment and activation of the VdNoxB. Furthermore, in VdΔnoxb and VdΔpls1, tip-high Ca2+ accumulation was impaired in hyphopodia, but not in vegetative hyphal tips. Moreover, nuclear targeting of VdCrz1 and activation of calcineurin-Crz1 signaling upon hyphopodium induction in wild-type V. dahliae was impaired in both knockout mutants, indicating that VdPls1/VdNoxB-dependent ROS was specifically required for tip-high Ca2+ elevation in hyphopodia to activate the transcription factor VdCrz1 in the regulation of penetration peg formation. Together with the loss of virulence of VdΔnoxb and VdΔpls1, which are unable to initiate colonization in cotton plants, our data demonstrate that VdNoxB/VdPls1-mediated ROS production activates VdCrz1 signaling through Ca2+ elevation in hyphopodia, infectious structures of V. dahliae, to regulate penetration peg formation during the initial colonization of cotton roots.

The use of chemometrics to study multifunctional indole alkaloids from Psychotria nemorosa (Palicourea comb. nov.). Part II: Indication of peaks related to the inhibition of butyrylcholinesterase and monoamine oxidase-A.

PubMed

Klein-Júnior, Luiz C; Viaene, Johan; Tuenter, Emmy; Salton, Juliana; Gasper, André L; Apers, Sandra; Andries, Jan P M; Pieters, Luc; Henriques, Amélia T; Vander Heyden, Yvan

2016-09-09

Psychotria nemorosa is chemically characterized by indole alkaloids and displays significant inhibitory activity on butyrylcholinesterase (BChE) and monoamine oxidase-A (MAO-A), both enzymes related to neurodegenerative disorders. In the present study, 43 samples of P. nemorosa leaves were extracted and fractionated in accordance to previously optimized methods (see Part I). These fractions were analyzed by means of UPLC-DAD and assayed for their BChE and MAO-A inhibitory potencies. The chromatographic fingerprint data was first aligned using correlation optimized warping and Principal Component Analysis to explore the data structure was performed. Multivariate calibration techniques, namely Partial Least Squares (PLS1), PLS2 and Orthogonal Projections to Latent Structure (O-PLS1), were evaluated for modelling the activities as a function of the fingerprints. Since the best results were obtained with O-PLS1 model (RMSECV=9.3 and 3.3 for BChE and MAO-A, respectively), the regression coefficients of the model were analyzed and plotted relative to the original fingerprints. Four peaks were indicated as multifunctional compounds, with the capacity to impair both BChE and MAO-A activities. In order to confirm these results, a semi-prep HPLC technique was used and a fraction containing the four peaks was purified and evaluated in vitro. It was observed that the fraction exhibited an IC50 of 2.12μgmL(-1) for BChE and 1.07μgmL(-1) for MAO-A. These results reinforce the prediction obtained by O-PLS1 modelling. Copyright © 2016 Elsevier B.V. All rights reserved.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Multivariate analysis of standoff laser-induced breakdown spectroscopy spectra for classification of explosive-containing residues

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Lucia, Frank C. Jr.; Gottfried, Jennifer L.; Munson, Chase A.

2008-11-01

A technique being evaluated for standoff explosives detection is laser-induced breakdown spectroscopy (LIBS). LIBS is a real-time sensor technology that uses components that can be configured into a ruggedized standoff instrument. The U.S. Army Research Laboratory has been coupling standoff LIBS spectra with chemometrics for several years now in order to discriminate between explosives and nonexplosives. We have investigated the use of partial least squares discriminant analysis (PLS-DA) for explosives detection. We have extended our study of PLS-DA to more complex sample types, including binary mixtures, different types of explosives, and samples not included in the model. We demonstrate themore » importance of building the PLS-DA model by iteratively testing it against sample test sets. Independent test sets are used to test the robustness of the final model.« less

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cen Haiyan; Bao Yidan; He Yong

2006-10-10

Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set,100% accuracy is obtained by the BPNN. Thus it ismore » concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy.« less

The impact of culture and employee-focused criteria on productivity: A structural equation modelling approach

NASA Astrophysics Data System (ADS)

Ab Hamid, Mohd Rashid; Mustafa, Zainol; Mohd Suradi, Nur Riza; Idris, Fazli; Abdullah, Mokhtar

2013-04-01

Culture and employee-focused criteria are important factors for the success of any organization. These factors have to be aligned with the productivity initiatives in the organization in order to gear ahead for excellence. Therefore, this article investigated the impact of culture and employee-focused criteria on productivity in Higher Education Institutions (HEIs) in Malaysia using intangible indicators through core values. The hypothesized relationship was tested using Structural Equation Modeling (SEM) with the PLS estimation technique. 429 questionnaires were returned from the target population. The results of the modelling revealed that the PLS estimation confirmed all the hypotheses tested as in the hypothesized model. The results generally support significant relationships between culture values, employee-focused values and productivity-focused values. The study also confirmed the mediating role of employee-focused values for the relationship between culture values and productivity-focused values. In conclusion, the empirically validated results supported the adequacy of the hypothezised model of the impact of culture and employee-focused criteria on productivity in HEI through value-based indicators.

In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors.

PubMed

Whelan, Jessica; Craven, Stephen; Glennon, Brian

2012-01-01

In this study, the application of Raman spectroscopy to the simultaneous quantitative determination of glucose, glutamine, lactate, ammonia, glutamate, total cell density (TCD), and viable cell density (VCD) in a CHO fed-batch process was demonstrated in situ in 3 L and 15 L bioreactors. Spectral preprocessing and partial least squares (PLS) regression were used to correlate spectral data with off-line reference data. Separate PLS calibration models were developed for each analyte at the 3 L laboratory bioreactor scale before assessing its transferability to the same bioprocess conducted at the 15 L pilot scale. PLS calibration models were successfully developed for all analytes bar VCD and transferred to the 15 L scale. Copyright © 2012 American Institute of Chemical Engineers (AIChE).

Involvement of PlsX and the acyl-phosphate dependent sn-glycerol-3-phosphate acyltransferase PlsY in the initial stage of glycerolipid synthesis in Bacillus subtilis.

PubMed

Hara, Yoshinori; Seki, Masahide; Matsuoka, Satoshi; Hara, Hiroshi; Yamashita, Atsushi; Matsumoto, Kouji

2008-12-01

The gene responsible for the first acylation of sn-glycerol-3-phosphate (G3P) in Bacillus subtilis has not yet been determined with certainty. The product of this first acylation, lysophosphatidic acid (LPA), is subsequently acylated again to form phosphatidic acid (PA), the primary precursor to membrane glycerolipids. A novel G3P acyltransferase (GPAT), the gene product of plsY, which uses acyl-phosphate formed by the plsX gene product, has recently been found to synthesize LPA in Streptococcus pneumoniae. We found that in B. subtilis growth arrests after repression of either a plsY homologue or a plsX homologue were overcome by expression of E. coli plsB, which encodes an acyl-acylcarrier protein (acyl-ACP)-dependent GPAT, although in the case of plsX repression a high level of plsB expression was required. B. subtilis has, therefore, a capability to use the acyl-ACP dependent GPAT of PlsB. Simultaneous expression of plsY and plsX suppressed the glycerol requirement of a strict glycerol auxotrophic derivative of the E. coli plsB26 mutant, although either one alone did not. Membrane fractions from B. subtilis cells catalyzed palmitoylphosphate-dependent acylation of [14C]-labeled G3P to synthesize [14C]-labeled LPA, whereas those from DeltaplsY cells did not. The results indicate unequivocally that PlsY is an acyl-phosphate dependent GPAT. Expression of plsX corrected the glycerol auxotrophy of a DeltaygiH (the deleted allele of an E. coli homologue of plsY) derivative of BB26-36 (plsB26 plsX50), suggesting an essential role of plsX other than substrate supply for acyl-phosphate dependent LPA synthesis. Two-hybrid examinations suggested that PlsY is associated with PlsX and that each may exist in multimeric form.

Irreversible dual inhibitory mode: the novel Btk inhibitor PLS-123 demonstrates promising anti-tumor activity in human B-cell lymphoma.

PubMed

Ding, Ning; Li, Xitao; Shi, Yunfei; Ping, Lingyan; Wu, Lina; Fu, Kai; Feng, Lixia; Zheng, Xiaohui; Song, Yuqin; Pan, Zhengying; Zhu, Jun

2015-06-20

The B-cell receptor (BCR) signaling pathway has gained significant attention as a therapeutic target in B-cell malignancies. Recently, several drugs that target the BCR signaling pathway, especially the Btk inhibitor ibrutinib, have demonstrated notable therapeutic effects in relapsed/refractory patients, which indicates that pharmacological inhibition of BCR pathway holds promise in B-cell lymphoma treatment. Here we present a novel covalent irreversible Btk inhibitor PLS-123 with more potent anti-proliferative activity compared with ibrutinib in multiple cellular and in vivo models through effective apoptosis induction and dual-action inhibitory mode of Btk activation. The phosphorylation of BCR downstream activating AKT/mTOR and MAPK signal pathways was also more significantly reduced after treatment with PLS-123 than ibrutinib. Gene expression profile analysis further suggested that the different selectivity profile of PLS-123 led to significant downregulation of oncogenic gene PTPN11 expression, which might also offer new opportunities beyond what ibrutinib has achieved. In addition, PLS-123 dose-dependently attenuated BCR- and chemokine-mediated lymphoma cell adhesion and migration. Taken together, Btk inhibitor PLS-123 suggested a new direction to pharmacologically modulate Btk function and develop novel therapeutic drug for B-cell lymphoma treatment.

Irreversible dual inhibitory mode: the novel Btk inhibitor PLS-123 demonstrates promising anti-tumor activity in human B-cell lymphoma

PubMed Central

Ding, Ning; Li, Xitao; Shi, Yunfei; Ping, Lingyan; Wu, Lina; Fu, Kai; Feng, Lixia; Zheng, Xiaohui; Song, Yuqin; Pan, Zhengying; Zhu, Jun

2015-01-01

The B-cell receptor (BCR) signaling pathway has gained significant attention as a therapeutic target in B-cell malignancies. Recently, several drugs that target the BCR signaling pathway, especially the Btk inhibitor ibrutinib, have demonstrated notable therapeutic effects in relapsed/refractory patients, which indicates that pharmacological inhibition of BCR pathway holds promise in B-cell lymphoma treatment. Here we present a novel covalent irreversible Btk inhibitor PLS-123 with more potent anti-proliferative activity compared with ibrutinib in multiple cellular and in vivo models through effective apoptosis induction and dual-action inhibitory mode of Btk activation. The phosphorylation of BCR downstream activating AKT/mTOR and MAPK signal pathways was also more significantly reduced after treatment with PLS-123 than ibrutinib. Gene expression profile analysis further suggested that the different selectivity profile of PLS-123 led to significant downregulation of oncogenic gene PTPN11 expression, which might also offer new opportunities beyond what ibrutinib has achieved. In addition, PLS-123 dose-dependently attenuated BCR- and chemokine-mediated lymphoma cell adhesion and migration. Taken together, Btk inhibitor PLS-123 suggested a new direction to pharmacologically modulate Btk function and develop novel therapeutic drug for B-cell lymphoma treatment. PMID:25944695

Urban pavement surface temperature. Comparison of numerical and statistical approach

NASA Astrophysics Data System (ADS)

Marchetti, Mario; Khalifa, Abderrahmen; Bues, Michel; Bouilloud, Ludovic; Martin, Eric; Chancibaut, Katia

2015-04-01

The forecast of pavement surface temperature is very specific in the context of urban winter maintenance. to manage snow plowing and salting of roads. Such forecast mainly relies on numerical models based on a description of the energy balance between the atmosphere, the buildings and the pavement, with a canyon configuration. Nevertheless, there is a specific need in the physical description and the numerical implementation of the traffic in the energy flux balance. This traffic was originally considered as a constant. Many changes were performed in a numerical model to describe as accurately as possible the traffic effects on this urban energy balance, such as tires friction, pavement-air exchange coefficient, and infrared flux neat balance. Some experiments based on infrared thermography and radiometry were then conducted to quantify the effect fo traffic on urban pavement surface. Based on meteorological data, corresponding pavement temperature forecast were calculated and were compared with fiels measurements. Results indicated a good agreement between the forecast from the numerical model based on this energy balance approach. A complementary forecast approach based on principal component analysis (PCA) and partial least-square regression (PLS) was also developed, with data from thermal mapping usng infrared radiometry. The forecast of pavement surface temperature with air temperature was obtained in the specific case of urban configurtation, and considering traffic into measurements used for the statistical analysis. A comparison between results from the numerical model based on energy balance, and PCA/PLS was then conducted, indicating the advantages and limits of each approach.

Recombinant plasmids for encoding restriction enzymes DpnI and DpnII of streptococcus pneumontae

DOEpatents

Lacks, Sanford A.

1990-01-01

Chromosomal DNA cassettes containing genes encoding either the DpnI or DpnII restriction endonucleases from Streptococcus pneumoniae are cloned into a streptococcal vector, pLS101. Large amounts of the restriction enzymes are produced by cells containing the multicopy plasmids, pLS202 and pLS207, and their derivatives pLS201, pLS211, pLS217, pLS251 and pLS252.

Recombinant plasmids for encoding restriction enzymes DpnI and DpnII of Streptococcus pneumontae

DOEpatents

Lacks, S.A.

1990-10-02

Chromosomal DNA cassettes containing genes encoding either the DpnI or DpnII restriction endonucleases from Streptococcus pneumoniae are cloned into a streptococcal vector, pLS101. Large amounts of the restriction enzymes are produced by cells containing the multicopy plasmids, pLS202 and pLS207, and their derivatives pLS201, pLS211, pLS217, pLS251 and pLS252. 9 figs.

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea.

PubMed

Lee, Soo Yee; Mediani, Ahmed; Maulidiani, Maulidiani; Khatib, Alfi; Ismail, Intan Safinar; Zawawi, Norhasnida; Abas, Faridah

2018-01-01

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

A Comparison of Multivariate and Pre-Processing Methods for Quantitative Laser-Induced Breakdown Spectroscopy of Geologic Samples

NASA Technical Reports Server (NTRS)

Anderson, R. B.; Morris, R. V.; Clegg, S. M.; Bell, J. F., III; Humphries, S. D.; Wiens, R. C.

2011-01-01

The ChemCam instrument selected for the Curiosity rover is capable of remote laser-induced breakdown spectroscopy (LIBS).[1] We used a remote LIBS instrument similar to ChemCam to analyze 197 geologic slab samples and 32 pressed-powder geostandards. The slab samples are well-characterized and have been used to validate the calibration of previous instruments on Mars missions, including CRISM [2], OMEGA [3], the MER Pancam [4], Mini-TES [5], and Moessbauer [6] instruments and the Phoenix SSI [7]. The resulting dataset was used to compare multivariate methods for quantitative LIBS and to determine the effect of grain size on calculations. Three multivariate methods - partial least squares (PLS), multilayer perceptron artificial neural networks (MLP ANNs) and cascade correlation (CC) ANNs - were used to generate models and extract the quantitative composition of unknown samples. PLS can be used to predict one element (PLS1) or multiple elements (PLS2) at a time, as can the neural network methods. Although MLP and CC ANNs were successful in some cases, PLS generally produced the most accurate and precise results.

Concentration determination of collagen and proteoglycan in bovine nasal cartilage by Fourier transform infrared imaging and PLS

NASA Astrophysics Data System (ADS)

Zhang, Xuexi; Xiao, Zhi-Yan; Yin, Jianhua; Xia, Yang

2014-09-01

Fourier transform infrared imaging (FTIRI) combined with chemometrics can be used to detect the structure of bio-macromolecule, measure the concentrations of some components, and so on. In this study, FTIRI with Partial Least-Squares (PLS) regression was applied to study the concentration of two main components in bovine nasal cartilage (BNC), collagen and proteoglycan. An infrared spectrum library was built by mixing the collagen and chondroitin 6-sulfate (main of proteoglycan) at different ratios. Some pretreatments are needed for building PLS model. FTIR images were collected from BNC sections at 6.25μm and 25μm pixel size. The spectra extracted from BNC-FTIR images were imported into the PLS regression program to predict the concentrations of collagen and proteoglycan. These PLS-determined concentrations are agreed with the result in our previous work and biochemical analytical results. The prediction shows that the concentrations of collagen and proteoglycan in BNC are comparative on the whole. However, the concentration of proteoglycan is a litter higher than that of collagen, to some extent.

Detection and identification of multiple adulterants in plant food supplements using attenuated total reflectance-Infrared spectroscopy.

PubMed

Deconinck, E; Aouadi, C; Bothy, J L; Courselle, P

2018-04-15

Due to the rising popularity of dietary supplements, especially plant food supplements, and alternative herbal medicines, a whole market developed and these products became freely available through internet. Though several searches revealed that at least a part of these products, especially the ones obtained from websites disclosing their physical identity, are aldulterated with pharmaceutical compounds. This causes a threat for public health, since these compounds are not declared and therefore adverse effects will not immediately be related to the product. The more the adulterants can interfere with other medicinal treatments. Since the present active pharmaceutical ingredients are not declared on the package and the products are sold as 100% natural or herbal in nature, it is very difficult for custom personnel to discriminate between products to be confiscated or not. Therefore easy to apply analytical approaches to discriminate between adulterated and non-adulterated products are necessary. This paper presents an approach based on infrared spectroscopy combined with attenuated total reflectance (ATR) and partial least squares- discriminant analysis (PLS-DA) to easily differentiate between adulterated and non- adulterated plant food supplements and to get a first idea of the nature of the adulterant present. The performance of PLS-DA models based on Mid-IR and NIR data were compared as well as models based on the combined data. Further three preprocessing strategies were compared. The best performance was obtained for a PLS-DA model using Mid-IR data with the second derivative as preprocessing method. This model showed a correct classification rate of 98.3% for an external test set. Also eight real samples were screened using the model and for seven of these samples a correct classification was obtained. Generally it could be concluded that the obtained model and the presented approach could be used at customs to discriminate between adulterated and non-adulterated herbal food supplements and even get a first idea of the nature of the adulterant present. The more the presented approach hardly needs sample preparation. Copyright © 2018 Elsevier B.V. All rights reserved.

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

NASA Astrophysics Data System (ADS)

Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Santos-Filho, Osvaldo A.; Esposito, Emilio X.; Hopfinger, Anton J.; Tseng, Yufeng J.

2008-06-01

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

Development of a multivariate calibration model for the determination of dry extract content in Brazilian commercial bee propolis extracts through UV-Vis spectroscopy

NASA Astrophysics Data System (ADS)

Barbeira, Paulo J. S.; Paganotti, Rosilene S. N.; Ássimos, Ariane A.

2013-10-01

This study had the objective of determining the content of dry extract of commercial alcoholic extracts of bee propolis through Partial Least Squares (PLS) multivariate calibration and electronic spectroscopy. The PLS model provided a good prediction of dry extract content in commercial alcoholic extracts of bee propolis in the range of 2.7 a 16.8% (m/v), presenting the advantage of being less laborious and faster than the traditional gravimetric methodology. The PLS model was optimized with outlier detection tests according to the ASTM E 1655-05. In this study it was possible to verify that a centrifugation stage is extremely important in order to avoid the presence of waxes, resulting in a more accurate model. Around 50% of the analyzed samples presented content of dry extract lower than the value established by Brazilian legislation, in most cases, the values found were different from the values claimed in the product's label.

Domain-Invariant Partial-Least-Squares Regression.

PubMed

Nikzad-Langerodi, Ramin; Zellinger, Werner; Lughofer, Edwin; Saminger-Platz, Susanne

2018-05-11

Multivariate calibration models often fail to extrapolate beyond the calibration samples because of changes associated with the instrumental response, environmental condition, or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical devices, while generic methods for calibration-model adaptation are largely missing. To fill this gap, we here introduce domain-invariant partial-least-squares (di-PLS) regression, which extends ordinary PLS by a domain regularizer in order to align the source and target distributions in the latent-variable space. We show that a domain-invariant weight vector can be derived in closed form, which allows the integration of (partially) labeled data from the source and target domains as well as entirely unlabeled data from the latter. We test our approach on a simulated data set where the aim is to desensitize a source calibration model to an unknown interfering agent in the target domain (i.e., unsupervised model adaptation). In addition, we demonstrate unsupervised, semisupervised, and supervised model adaptation by di-PLS on two real-world near-infrared (NIR) spectroscopic data sets.

Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy.

PubMed

De Girolamo, A; Lippolis, V; Nordkvist, E; Visconti, A

2009-06-01

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 microm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306-379 microg kg(-1)) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470-555 microg kg(-1)). Coefficients of determination (r(2)) indicated an "approximate to good" level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r(2) = 0.71-0.83), and a "good" discrimination between low and high DON contents in the PLS validation models (r(2) = 0.58-0.63). A "limited to good" practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 microg kg(-1) DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.

Comparison of Regression Methods to Compute Atmospheric Pressure and Earth Tidal Coefficients in Water Level Associated with Wenchuan Earthquake of 12 May 2008

NASA Astrophysics Data System (ADS)

He, Anhua; Singh, Ramesh P.; Sun, Zhaohua; Ye, Qing; Zhao, Gang

2016-07-01

The earth tide, atmospheric pressure, precipitation and earthquake fluctuations, especially earthquake greatly impacts water well levels, thus anomalous co-seismic changes in ground water levels have been observed. In this paper, we have used four different models, simple linear regression (SLR), multiple linear regression (MLR), principal component analysis (PCA) and partial least squares (PLS) to compute the atmospheric pressure and earth tidal effects on water level. Furthermore, we have used the Akaike information criterion (AIC) to study the performance of various models. Based on the lowest AIC and sum of squares for error values, the best estimate of the effects of atmospheric pressure and earth tide on water level is found using the MLR model. However, MLR model does not provide multicollinearity between inputs, as a result the atmospheric pressure and earth tidal response coefficients fail to reflect the mechanisms associated with the groundwater level fluctuations. On the premise of solving serious multicollinearity of inputs, PLS model shows the minimum AIC value. The atmospheric pressure and earth tidal response coefficients show close response with the observation using PLS model. The atmospheric pressure and the earth tidal response coefficients are found to be sensitive to the stress-strain state using the observed data for the period 1 April-8 June 2008 of Chuan 03# well. The transient enhancement of porosity of rock mass around Chuan 03# well associated with the Wenchuan earthquake (Mw = 7.9 of 12 May 2008) that has taken its original pre-seismic level after 13 days indicates that the co-seismic sharp rise of water well could be induced by static stress change, rather than development of new fractures.

Partial least squares based identification of Duchenne muscular dystrophy specific genes.

PubMed

An, Hui-bo; Zheng, Hua-cheng; Zhang, Li; Ma, Lin; Liu, Zheng-yan

2013-11-01

Large-scale parallel gene expression analysis has provided a greater ease for investigating the underlying mechanisms of Duchenne muscular dystrophy (DMD). Previous studies typically implemented variance/regression analysis, which would be fundamentally flawed when unaccounted sources of variability in the arrays existed. Here we aim to identify genes that contribute to the pathology of DMD using partial least squares (PLS) based analysis. We carried out PLS-based analysis with two datasets downloaded from the Gene Expression Omnibus (GEO) database to identify genes contributing to the pathology of DMD. Except for the genes related to inflammation, muscle regeneration and extracellular matrix (ECM) modeling, we found some genes with high fold change, which have not been identified by previous studies, such as SRPX, GPNMB, SAT1, and LYZ. In addition, downregulation of the fatty acid metabolism pathway was found, which may be related to the progressive muscle wasting process. Our results provide a better understanding for the downstream mechanisms of DMD.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

PubMed

Lopes, Marta B; Calado, Cecília R C; Figueiredo, Mário A T; Bioucas-Dias, José M

2017-06-01

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2015-01-01

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution.

PubMed

Koch, Cosima; Posch, Andreas E; Goicoechea, Héctor C; Herwig, Christoph; Lendl, Bernhard

2014-01-07

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution - alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L(-1) for Penicillin V and 0.32 g L(-1) for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L(-1) for Penicillin V and 0.15 g L(-1) for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Simultaneous determination of specific alpha and beta emitters by LSC-PLS in water samples.

PubMed

Fons-Castells, J; Tent-Petrus, J; Llauradó, M

2017-01-01

Liquid scintillation counting (LSC) is a commonly used technique for the determination of alpha and beta emitters. However, LSC has poor resolution and the continuous spectra for beta emitters hinder the simultaneous determination of several alpha and beta emitters from the same spectrum. In this paper, the feasibility of multivariate calibration by partial least squares (PLS) models for the determination of several alpha ( nat U, 241 Am and 226 Ra) and beta emitters ( 40 K, 60 Co, 90 Sr/ 90 Y, 134 Cs and 137 Cs) in water samples is reported. A set of alpha and beta spectra from radionuclide calibration standards were used to construct three PLS models. Experimentally mixed radionuclides and intercomparision materials were used to validate the models. The results had a maximum relative bias of 25% when all the radionuclides in the sample were included in the calibration set; otherwise the relative bias was over 100% for some radionuclides. The results obtained show that LSC-PLS is a useful approach for the simultaneous determination of alpha and beta emitters in multi-radionuclide samples. However, to obtain useful results, it is important to include all the radionuclides expected in the studied scenario in the calibration set. Copyright © 2016 Elsevier Ltd. All rights reserved.

Seasonal forecasting of high wind speeds over Western Europe

NASA Astrophysics Data System (ADS)

Palutikof, J. P.; Holt, T.

2003-04-01

As financial losses associated with extreme weather events escalate, there is interest from end users in the forestry and insurance industries, for example, in the development of seasonal forecasting models with a long lead time. This study uses exceedences of the 90th, 95th, and 99th percentiles of daily maximum wind speed over the period 1958 to present to derive predictands of winter wind extremes. The source data is the 6-hourly NCEP Reanalysis gridded surface wind field. Predictor variables include principal components of Atlantic sea surface temperature and several indices of climate variability, including the NAO and SOI. Lead times of up to a year are considered, in monthly increments. Three regression techniques are evaluated; multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS). PCR and PLS proved considerably superior to MLR with much lower standard errors. PLS was chosen to formulate the predictive model since it offers more flexibility in experimental design and gave slightly better results than PCR. The results indicate that winter windiness can be predicted with considerable skill one year ahead for much of coastal Europe, but that this deteriorates rapidly in the hinterland. The experiment succeeded in highlighting PLS as a very useful method for developing more precise forecasting models, and in identifying areas of high predictability.

Screening experiments of ecstasy street samples using near infrared spectroscopy.

PubMed

Sondermann, N; Kovar, K A

1999-12-20

Twelve different sets of confiscated ecstasy samples were analysed applying both near infrared spectroscopy in reflectance mode (1100-2500 nm) and high-performance liquid chromatography (HPLC). The sets showed a large variance in composition. A calibration data set was generated based on the theory of factorial designs. It contained 221 N-methyl-3,4-methylenedioxyamphetamine (MDMA) samples, 167 N-ethyl-3,4-methylenedioxyamphetamine (MDE), 111 amphetamine and 106 samples without a controlled substance, which will be called placebo samples thereafter. From this data set, PLS-1 models were calculated and were successfully applied for validation of various external laboratory test sets. The transferability of these results to confiscated tablets is demonstrated here. It is shown that differentiation into placebo, amphetamine and ecstasy samples is possible. Analysis of intact tablets is practicable. However, more reliable results are obtained from pulverised samples. This is due to ill-defined production procedures. The use of mathematically pretreated spectra improves the prediction quality of all the PLS-1 models studied. It is possible to improve discrimination between MDE and MDMA with the help of a second model based on raw spectra. Alternative strategies are briefly discussed.

An Improved Incremental Learning Approach for KPI Prognosis of Dynamic Fuel Cell System.

PubMed

Yin, Shen; Xie, Xiaochen; Lam, James; Cheung, Kie Chung; Gao, Huijun

2016-12-01

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

Detection of residues from explosive manipulation by near infrared hyperspectral imaging: a promising forensic tool.

PubMed

Fernández de la Ossa, Mª Ángeles; Amigo, José Manuel; García-Ruiz, Carmen

2014-09-01

In this study near infrared hyperspectral imaging (NIR-HSI) is used to provide a fast, non-contact, non-invasive and non-destructive method for the analysis of explosive residues on human handprints. Volunteers manipulated individually each of these explosives and after deposited their handprints on plastic sheets. For this purpose, classical explosives, potentially used as part of improvised explosive devices (IEDs) as ammonium nitrate, blackpowder, single- and double-base smokeless gunpowders and dynamite were studied. A partial-least squares discriminant analysis (PLS-DA) model was built to detect and classify the presence of explosive residues in handprints. High levels of sensitivity and specificity for the PLS-DA classification model created to identify ammonium nitrate, blackpowder, single- and double-base smokeless gunpowders and dynamite residues were obtained, allowing the development of a preliminary library and facilitating the direct and in situ detection of explosives by NIR-HSI. Consequently, this technique is showed as a promising forensic tool for the detection of explosive residues and other related samples. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Resolving critical dimension drift over time in plasma etching through virtual metrology based wafer-to-wafer control

NASA Astrophysics Data System (ADS)

Lee, Ho Ki; Baek, Kye Hyun; Shin, Kyoungsub

2017-06-01

As semiconductor devices are scaled down to sub-20 nm, process window of plasma etching gets extremely small so that process drift or shift becomes more significant. This study addresses one of typical process drift issues caused by consumable parts erosion over time and provides feasible solution by using virtual metrology (VM) based wafer-to-wafer control. Since erosion of a shower head has center-to-edge area dependency, critical dimensions (CDs) at the wafer center and edge area get reversed over time. That CD trend is successfully estimated on a wafer-to-wafer basis by a partial least square (PLS) model which combines variables from optical emission spectroscopy (OES), VI-probe and equipment state gauges. R 2 of the PLS model reaches 0.89 and its prediction performance is confirmed in a mass production line. As a result, the model can be exploited as a VM for wafer-to-wafer control. With the VM, advanced process control (APC) strategy is implemented to solve the CD drift. Three σ of CD across wafer is improved from the range (1.3-2.9 nm) to the range (0.79-1.7 nm). Hopefully, results introduced in this paper will contribute to accelerating implementation of VM based APC strategy in semiconductor industry.

Estimation of soil sorption coefficients of veterinary pharmaceuticals from soil properties.

PubMed

ter Laak, Thomas L; Gebbink, Wouter A; Tolls, Johannes

2006-04-01

Environmental exposure assessment of veterinary pharmaceuticals requires estimating the sorption to soil. Soil sorption coefficients of three common, ionizable, antimicrobial agents (oxytetracycline [OTC], tylosin [TYL], and sulfachloropyridazine [SCP]) were studied in relation to the soil properties of 11 different soils. The soil sorption coefficient at natural pH varied from 950 to 7,200, 10 to 370, and 0.4 to 35 L/kg for OTC, TYL, and SCP, respectively. The variation increased by almost two orders of magnitude for OTC and TYL when pH was artificially adjusted. Separate soil properties (pH, organic carbon content, clay content, cation-exchange capacity, aluminum oxyhydroxide content, and iron oxyhydroxide content) were not able to explain more than half the variation observed in soil sorption coefficients. This reflects the complexity of the sorbent-sorbate interactions. Partial-least-squares (PLS) models, integrating all the soil properties listed above, were able to explain as much as 78% of the variation in sorption coefficients. The PLS model was able to predict the sorption coefficient with an accuracy of a factor of six. Considering the pH-dependent speciation, species-specific PLS models were developed. These models were able to predict species-specific sorption coefficients with an accuracy of a factor of three to four. However, the species-specific sorption models did not improve the estimation of sorption coefficients of species mixtures, because these models were developed with a reduced data set at standardized aqueous concentrations. In conclusion, pragmatic approaches like PLS modeling might be suitable to estimate soil sorption for risk assessment purposes.

Design and Optimization of a Chemometric-Assisted Spectrophotometric Determination of Telmisartan and Hydrochlorothiazide in Pharmaceutical Dosage Form

PubMed Central

Lakshmi, KS; Lakshmi, S

2010-01-01

Two chemometric methods were developed for the simultaneous determination of telmisartan and hydrochlorothiazide. The chemometric methods applied were principal component regression (PCR) and partial least square (PLS-1). These approaches were successfully applied to quantify the two drugs in the mixture using the information included in the UV absorption spectra of appropriate solutions in the range of 200-350 nm with the intervals Δλ = 1 nm. The calibration of PCR and PLS-1 models was evaluated by internal validation (prediction of compounds in its own designed training set of calibration) and by external validation over laboratory prepared mixtures and pharmaceutical preparations. The PCR and PLS-1 methods require neither any separation step, nor any prior graphical treatment of the overlapping spectra of the two drugs in a mixture. The results of PCR and PLS-1 methods were compared with each other and a good agreement was found. PMID:21331198

Design and optimization of a chemometric-assisted spectrophotometric determination of telmisartan and hydrochlorothiazide in pharmaceutical dosage form.

PubMed

Lakshmi, Ks; Lakshmi, S

2010-01-01

Two chemometric methods were developed for the simultaneous determination of telmisartan and hydrochlorothiazide. The chemometric methods applied were principal component regression (PCR) and partial least square (PLS-1). These approaches were successfully applied to quantify the two drugs in the mixture using the information included in the UV absorption spectra of appropriate solutions in the range of 200-350 nm with the intervals Δλ = 1 nm. The calibration of PCR and PLS-1 models was evaluated by internal validation (prediction of compounds in its own designed training set of calibration) and by external validation over laboratory prepared mixtures and pharmaceutical preparations. The PCR and PLS-1 methods require neither any separation step, nor any prior graphical treatment of the overlapping spectra of the two drugs in a mixture. The results of PCR and PLS-1 methods were compared with each other and a good agreement was found.

An integrated approach utilising chemometrics and GC/MS for classification of chamomile flowers, essential oils and commercial products.

PubMed

Wang, Mei; Avula, Bharathi; Wang, Yan-Hong; Zhao, Jianping; Avonto, Cristina; Parcher, Jon F; Raman, Vijayasankar; Zweigenbaum, Jerry A; Wylie, Philip L; Khan, Ikhlas A

2014-01-01

As part of an ongoing research program on authentication, safety and biological evaluation of phytochemicals and dietary supplements, an in-depth chemical investigation of different types of chamomile was performed. A collection of chamomile samples including authenticated plants, commercial products and essential oils was analysed by GC/MS. Twenty-seven authenticated plant samples representing three types of chamomile, viz. German chamomile, Roman chamomile and Juhua were analysed. This set of data was employed to construct a sample class prediction (SCP) model based on stepwise reduction of data dimensionality followed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The model was cross-validated with samples including authenticated plants and commercial products. The model demonstrated 100.0% accuracy for both recognition and prediction abilities. In addition, 35 commercial products and 11 essential oils purported to contain chamomile were subsequently predicted by the validated PLS-DA model. Furthermore, tentative identification of the marker compounds correlated with different types of chamomile was explored. Copyright © 2013 Elsevier Ltd. All rights reserved.

A(1)H NMR-based metabonomic study on the SAMP8 and SAMR1 mice and the effect of electro-acupuncture.

PubMed

Qiao-feng, Wu; Ling-ling, Guo; Shu-guang, Yu; Qi, Zhang; Sheng-feng, Lu; Fang, Zeng; Hai-yan, Yin; Yong, Tang; Xian-zhong, Yan

2011-10-01

A (1)H NMR-based metabonomic method was used to investigate the metabolic change of plasma in senescence-prone 8 (SAMP8) mice before and after electro-acupuncture (EA). Sixteen SAMP8 male mice (aged 8 months) were randomly divided into model group and acupuncture treatment group while the later group received EA treatment for 21 days. Eight senescence-resistant 1 (SAMR1) mice were used as the control group. Morris water maze was used to evaluate the effects of EA. All mice plasma samples obtained from different groups were analyzed by using 600 MHz (1)H nuclear magnetic resonances ((1)H NMR) spectroscopy. The data sets were analyzed by Principal Components Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) to discriminate the key plasma metabolites among different groups. Results indicated that both the escape and probe tasks of SAMP8 could be improved by EA treatment. Metabonomic study showed that SAMR1 and SAMP8 were separated clearly in both CPMG_OSC_PLS and LED _OSC_PLS score plots. Interestingly, samples obtained from EA group were distributed closely to SAMR1 group in CPMG_OSC_PLS score plot, but away from SAMP8 group in LED_OSC_PLS score plot. Corresponding loading plots showed that much less lactate was seen in SAMP8 mice plasma. Other changes including higher levels of dimethylamine (DMA) Choline and α-glucose but lower levels of leucine/isoleucine, HDL, LDL/VLDL, 3-Hydroxybutyrate (3-HB), and Trimethylamine N-oxide (TMAO) were observed in the SAMP8 mice plasma than in the SAMR1. After EA treatment, the levels of lactate, DMA, choline and TMAO were improved. Results of this work can provide valuable clues to the understanding of the metabolic changes in the senile impairment of mice. It is also hoped that the methodology can be used in evaluating the effects of EA and understanding the underlying acupuncture mechanism in treating neurodegenerative diseases. Copyright © 2011 Elsevier Inc. All rights reserved.

Glucose determination in human aqueous humor with Raman spectroscopy

NASA Technical Reports Server (NTRS)

Lambert, James L.; Pelletier, Christine C.; Borchert, Mark

2005-01-01

It has been suggested that spectroscopic analysis of the aqueous humor of the eye could be used to indirectly predict blood glucose levels in diabetics noninvasively. We have been investigating this potential using Raman spectroscopy in combination with partial least squares (PLS) analysis. We have determined that glucose at clinically relevant concentrations can be accurately predicted in human aqueous humor in vitro using a PLS model based on artificial aqueous humor. We have further determined that with proper instrument design, the light energy necessary to achieve clinically acceptable prediction of glucose does not damage the retinas of rabbits and can be delivered at powers below internationally acceptable safety limits. Herein we summarize our current results and address our strategies to improve instrument design. 2005 Society of Photo-Optical Instrumentation Engineers.

Raman spectroscopy based investigation of molecular changes associated with an early stage of dengue virus infection

NASA Astrophysics Data System (ADS)

Bilal, Maria; Bilal, Muhammad; Saleem, Muhammad; Khurram, Muhammad; Khan, Saranjam; Ullah, Rahat; Ali, Hina; Ahmed, Mushtaq; Shahzada, Shaista; Ullah Khan, Ehsan

2017-04-01

Raman spectroscopy based investigations of the molecular changes associated with an early stage of dengue virus infection (DENV) using a partial least squares (PLS) regression model is presented. This study is based on non-structural protein 1 (NS1) which appears after three days of DENV infection. In total, 39 blood sera samples were collected and divided into two groups. The control group contained samples which were the negative for NS1 and antibodies and the positive group contained those samples in which NS1 is positive and antibodies were negative. Out of 39 samples, 29 Raman spectra were used for the model development while the remaining 10 were kept hidden for blind testing of the model. PLS regression yielded a vector of regression coefficients as a function of Raman shift, which were analyzed. Cytokines in the region 775-875 cm-1, lectins at 1003, 1238, 1340, 1449 and 1672 cm-1, DNA in the region 1040-1140 cm-1 and alpha and beta structures of proteins in the region 933-967 cm-1 have been identified in the regression vector for their role in an early stage of DENV infection. Validity of the model was established by its R-square value of 0.891. Sensitivity, specificity and accuracy were 100% each and the area under the receiver operator characteristic curve was found to be 1.

NIR spectroscopic measurement of moisture content in Scots pine seeds.

PubMed

Lestander, Torbjörn A; Geladi, Paul

2003-04-01

When tree seeds are used for seedling production it is important that they are of high quality in order to be viable. One of the factors influencing viability is moisture content and an ideal quality control system should be able to measure this factor quickly for each seed. Seed moisture content within the range 3-34% was determined by near-infrared (NIR) spectroscopy on Scots pine (Pinus sylvestris L.) single seeds and on bulk seed samples consisting of 40-50 seeds. The models for predicting water content from the spectra were made by partial least squares (PLS) and ordinary least squares (OLS) regression. Different conditions were simulated involving both using less wavelengths and going from samples to single seeds. Reflectance and transmission measurements were used. Different spectral pretreatment methods were tested on the spectra. Including bias, the lowest prediction errors for PLS models based on reflectance within 780-2280 nm from bulk samples and single seeds were 0.8% and 1.9%, respectively. Reduction of the single seed reflectance spectrum to 850-1048 nm gave higher biases and prediction errors in the test set. In transmission (850-1048 nm) the prediction error was 2.7% for single seeds. OLS models based on simulated 4-sensor single seed system consisting of optical filters with Gaussian transmission indicated more than 3.4% error in prediction. A practical F-test based on test sets to differentiate models is introduced.

Real-time monitoring of high-gravity corn mash fermentation using in situ raman spectroscopy.

PubMed

Gray, Steven R; Peretti, Steven W; Lamb, H Henry

2013-06-01

In situ Raman spectroscopy was employed for real-time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm(-1) C-C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high- and low-concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two-set calibration models for starch (R(2)  = 0.998, percent error = 2.5%) and ethanol (R(2)  = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS-based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman-based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present. Copyright © 2013 Wiley Periodicals, Inc.

Platelets and Plasma Proteins Are Both Required to Stimulate Collagen Gene Expression by Anterior Cruciate Ligament Cells in Three-Dimensional Culture

PubMed Central

Cheng, Mingyu; Wang, Hao; Yoshida, Ryu

2010-01-01

Collagen–platelet (PL)-rich plasma composites have shown in vivo potential to stimulate anterior cruciate ligament (ACL) healing at early time points in large animal models. However, little is known about the cellular mechanisms by which the plasma component of these composites may stimulate healing. We hypothesized that the components of PL-rich plasma (PRP), namely the PLs and PL-poor plasma (PPP), would independently significantly influence ACL cell viability and metabolic activity, including collagen gene expression. To test this hypothesis, ACL cells were cultured in a collagen type I hydrogel with PLs, PPP, or the combination of the two (PRP) for 14 days. The inclusion of PLs, PPP, and PRP all significantly reduced the rate of cell apoptosis and enhanced the metabolic activity of fibroblasts in the collagen hydrogel. PLs promoted fibroblast-mediated collagen scaffold contraction, whereas PPP inhibited this contraction. PPP and PRP both promoted cell elongation and the formation of wavy fibrous structure in the scaffolds. The addition of only PLs or only plasma proteins did not significantly enhance gene expression of collagen types I and III but the combination, as PRP, did. Our findings suggest that the addition of both PLs and plasma proteins to collagen hydrogel may be useful in stimulating ACL healing by enhancing ACL cell viability, metabolic activity, and collagen synthesis. PMID:19958169

Study on for soluble solids contents measurement of grape juice beverage based on Vis/NIRS and chemomtrics

NASA Astrophysics Data System (ADS)

Wu, Di; He, Yong

2007-11-01

The aim of this study is to investigate the potential of the visible and near infrared spectroscopy (Vis/NIRS) technique for non-destructive measurement of soluble solids contents (SSC) in grape juice beverage. 380 samples were studied in this paper. Smoothing way of Savitzky-Golay and standard normal variate were applied for the pre-processing of spectral data. Least-squares support vector machines (LS-SVM) with RBF kernel function was applied to developing the SSC prediction model based on the Vis/NIRS absorbance data. The determination coefficient for prediction (Rp2) of the results predicted by LS-SVM model was 0. 962 and root mean square error (RMSEP) was 0. 434137. It is concluded that Vis/NIRS technique can quantify the SSC of grape juice beverage fast and non-destructively.. At the same time, LS-SVM model was compared with PLS and back propagation neural network (BP-NN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SSC of grape juice beverage. In this study, the generation ability of LS-SVM, PLS and BP-NN models were also investigated. It is concluded that LS-SVM regression method is a promising technique for chemometrics in quantitative prediction.

Estimating the concentration of urea and creatinine in the human serum of normal and dialysis patients through Raman spectroscopy.

PubMed

de Almeida, Maurício Liberal; Saatkamp, Cassiano Junior; Fernandes, Adriana Barrinha; Pinheiro, Antonio Luiz Barbosa; Silveira, Landulfo

2016-09-01

Urea and creatinine are commonly used as biomarkers of renal function. Abnormal concentrations of these biomarkers are indicative of pathological processes such as renal failure. This study aimed to develop a model based on Raman spectroscopy to estimate the concentration values of urea and creatinine in human serum. Blood sera from 55 clinically normal subjects and 47 patients with chronic kidney disease undergoing dialysis were collected, and concentrations of urea and creatinine were determined by spectrophotometric methods. A Raman spectrum was obtained with a high-resolution dispersive Raman spectrometer (830 nm). A spectral model was developed based on partial least squares (PLS), where the concentrations of urea and creatinine were correlated with the Raman features. Principal components analysis (PCA) was used to discriminate dialysis patients from normal subjects. The PLS model showed r = 0.97 and r = 0.93 for urea and creatinine, respectively. The root mean square errors of cross-validation (RMSECV) for the model were 17.6 and 1.94 mg/dL, respectively. PCA showed high discrimination between dialysis and normality (95 % accuracy). The Raman technique was able to determine the concentrations with low error and to discriminate dialysis from normal subjects, consistent with a rapid and low-cost test.

Pigmented striae of the anterior lens capsule and age-associated pigment dispersion of variable degree in a group of older African-Americans: an age, race, and gender matched study.

PubMed

Roberts, D K; Winters, J E; Castells, D D; Clark, C A; Teitelbaum, B A

2001-01-01

To investigate pigmented striae of the anterior lens capsule in African-Americans, a potential indicator of significant anterior segment pigment dispersion. A group of 40 African-American subjects who exhibited pigmented lens striae (PLS) were identified from a non-referred, primary eye care population in Chicago, IL, USA. These subjects were then compared to an age, race, and gender matched control group relative to refractive error and the presence or absence of diabetes and hypertension. The PLS subjects (mean age = 65.4 +/- 8.8 years, range = 50-87 years) consisted of 36 females and 4 males. PLS were bilateral in 36 (85%) of the 40 subjects. Among the eyes with PLS, 21 (55%) of 38 right eyes and 22 (61%) of 36 left eyes also had significant corneal endothelial pigment dusting, commonly in the shape of a Krukenberg's spindle. Ten (25%) of the PLS subjects had either glaucoma or ocular hypertension (7 bilateral, 3 unilateral). The presence of trabecular meshwork pigment varied from minimal to heavy. The mean +/- SD (range) refractive error of the PLS right eyes was +1.61 +/- 1.43D (-1.50 to +5.00D) and +1.77 +/- 1.37D (-1.00 to +5.00D) for the left eyes. Based on these data, the PLS right eyes were +1.63D (Student's t, p = 0.0001; 95% CI = +0.82 to +2.44D) more hyperopic on average than the control right eyes, and the PLS left eyes were +1.77D (p = 0.0001; 95% CI = +0.92 to +2.63D) more hyperopic on average than the control left eyes. Trend analysis showed a gradually increasing likelihood of PLS with increasing magnitude of hyperopia in both eyes (Mantel-Haenszel chi-square, p = 0.001). Among PLS subjects, 24 (60%) of 40 were hypertensive and 9 (23%) of 40 were diabetic. However, these proportions were not significantly different (two-tailed Fisher's exact test; hypertension: p = 0.30; diabetes: p = 0.70) from the randomly selected controls. Among our African-American group, which consisted predominately of females >50 years of age, the likelihood of PLS increased with increasing hyperopic refractive error. This finding is consistent with the possibility that PLS may, in some circumstances, indicate a significant pigment dispersal process due to iris-lens rubbing that may be associated with crowding of anterior segment structures. Additional study is warranted to further assess the nature of PLS, their precise relationship with an age-related pigment dispersal process, and their true significance as a risk factor for development of glaucoma.

Study on feasibility of determination of glucosamine content of fermentation process using a micro NIR spectrometer.

PubMed

Sun, Zhongyu; Li, Can; Li, Lian; Nie, Lei; Dong, Qin; Li, Danyang; Gao, Lingling; Zang, Hengchang

2018-08-05

N-acetyl-d-glucosamine (GlcNAc) is a microbial fermentation product, and NIR spectroscopy is an effective process analytical technology (PAT) tool in detecting the key quality attribute: the GlcNAc content. Meanwhile, the design of NIR spectrometers is under the trend of miniaturization, portability and low-cost nowadays. The aim of this study was to explore a portable micro NIR spectrometer with the fermentation process. First, FT-NIR spectrometer and Micro-NIR 1700 spectrometer were compared with simulated fermentation process solutions. The R c 2 , R p 2 , RMSECV and RMSEP of the optimal FT-NIR and Micro-NIR 1700 models were 0.999, 0.999, 3.226 g/L, 1.388 g/L and 0.999, 0.999, 1.821 g/L, 0.967 g/L. Passing-Bablok regression method and paired t-test results showed there were no significant differences between the two instruments. Then the Micro-NIR 1700 was selected for the practical fermentation process, 135 samples from 10 batches were collected. Spectral pretreatment methods and variables selection methods (BiPLS, FiPLS, MWPLS and CARS-PLS) for PLS modeling were discussed. The R c 2 , R p 2 , RMSECV and RMSEP of the optimal GlcNAc content PLS model of the practical fermentation process were 0.994, 0.995, 2.792 g/L and 1.946 g/L. The results have a positive reference for application of the Micro-NIR spectrometer. To some extent, it could provide theoretical supports in guiding the microbial fermentation or the further assessment of bioprocess. Copyright © 2018. Published by Elsevier B.V.

Feasibility of using a miniature NIR spectrometer to measure volumic mass during alcoholic fermentation.

PubMed

Fernández-Novales, Juan; López, María-Isabel; González-Caballero, Virginia; Ramírez, Pilar; Sánchez, María-Teresa

2011-06-01

Volumic mass-a key component of must quality control tests during alcoholic fermentation-is of great interest to the winemaking industry. Transmitance near-infrared (NIR) spectra of 124 must samples over the range of 200-1,100-nm were obtained using a miniature spectrometer. The performance of this instrument to predict volumic mass was evaluated using partial least squares (PLS) regression and multiple linear regression (MLR). The validation statistics coefficient of determination (r(2)) and the standard error of prediction (SEP) were r(2) = 0.98, n = 31 and r(2) = 0.96, n = 31, and SEP = 5.85 and 7.49 g/dm(3) for PLS and MLR equations developed to fit reference data for volumic mass and spectral data. Comparison of results from MLR and PLS demonstrates that a MLR model with six significant wavelengths (P < 0.05) fit volumic mass data to transmittance (1/T) data slightly worse than a more sophisticated PLS model using the full scanning range. The results suggest that NIR spectroscopy is a suitable technique for predicting volumic mass during alcoholic fermentation, and that a low-cost NIR instrument can be used for this purpose.

Sustained prediction ability of net analyte preprocessing methods using reduced calibration sets. Theoretical and experimental study involving the spectrophotometric analysis of multicomponent mixtures.

PubMed

Goicoechea, H C; Olivieri, A C

2001-07-01

A newly developed multivariate method involving net analyte preprocessing (NAP) was tested using central composite calibration designs of progressively decreasing size regarding the multivariate simultaneous spectrophotometric determination of three active components (phenylephrine, diphenhydramine and naphazoline) and one excipient (methylparaben) in nasal solutions. Its performance was evaluated and compared with that of partial least-squares (PLS-1). Minimisation of the calibration predicted error sum of squares (PRESS) as a function of a moving spectral window helped to select appropriate working spectral ranges for both methods. The comparison of NAP and PLS results was carried out using two tests: (1) the elliptical joint confidence region for the slope and intercept of a predicted versus actual concentrations plot for a large validation set of samples and (2) the D-optimality criterion concerning the information content of the calibration data matrix. Extensive simulations and experimental validation showed that, unlike PLS, the NAP method is able to furnish highly satisfactory results when the calibration set is reduced from a full four-component central composite to a fractional central composite, as expected from the modelling requirements of net analyte based methods.

[Main Components of Xinjiang Lavender Essential Oil Determined by Partial Least Squares and Near Infrared Spectroscopy].

PubMed

Liao, Xiang; Wang, Qing; Fu, Ji-hong; Tang, Jun

2015-09-01

This work was undertaken to establish a quantitative analysis model which can rapid determinate the content of linalool, linalyl acetate of Xinjiang lavender essential oil. Totally 165 lavender essential oil samples were measured by using near infrared absorption spectrum (NIR), after analyzing the near infrared spectral absorption peaks of all samples, lavender essential oil have abundant chemical information and the interference of random noise may be relatively low on the spectral intervals of 7100~4500 cm(-1). Thus, the PLS models was constructed by using this interval for further analysis. 8 abnormal samples were eliminated. Through the clustering method, 157 lavender essential oil samples were divided into 105 calibration set samples and 52 validation set samples. Gas chromatography mass spectrometry (GC-MS) was used as a tool to determine the content of linalool and linalyl acetate in lavender essential oil. Then the matrix was established with the GC-MS raw data of two compounds in combination with the original NIR data. In order to optimize the model, different pretreatment methods were used to preprocess the raw NIR spectral to contrast the spectral filtering effect, after analysizing the quantitative model results of linalool and linalyl acetate, the root mean square error prediction (RMSEP) of orthogonal signal transformation (OSC) was 0.226, 0.558, spectrally, it was the optimum pretreatment method. In addition, forward interval partial least squares (FiPLS) method was used to exclude the wavelength points which has nothing to do with determination composition or present nonlinear correlation, finally 8 spectral intervals totally 160 wavelength points were obtained as the dataset. Combining the data sets which have optimized by OSC-FiPLS with partial least squares (PLS) to establish a rapid quantitative analysis model for determining the content of linalool and linalyl acetate in Xinjiang lavender essential oil, numbers of hidden variables of two components were 8 in the model. The performance of the model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP). In the model, RESECV of linalool and linalyl acetate were 0.170 and 0.416, respectively; RM-SEP were 0.188 and 0.364. The results indicated that raw data was pretreated by OSC and FiPLS, the NIR-PLS quantitative analysis model with good robustness, high measurement precision; it could quickly determine the content of linalool and linalyl acetate in lavender essential oil. In addition, the model has a favorable prediction ability. The study also provide a new effective method which could rapid quantitative analysis the major components of Xinjiang lavender essential oil.

Synergistic antioxidant activity of milk sphingomyeline and its sphingoid base with α-tocopherol on fish oil triacylglycerol.

PubMed

Shimajiri, Junki; Shiota, Makoto; Hosokawa, Masashi; Miyashita, Kazuo

2013-08-21

The effects of milk phospholipids (PLs), sphingolipids (SLs), and their sphingoid backbone on the oxidation of fish oil triacylglycerol (TAG) were examined with or without α-tocopherol. All compounds had little effect on the TAG oxidation in the absence of α-tocopherol. On the other hand, they could act synergistically with α-tocopherol. The highest synergistic activity was shown by sphingoid bases, followed by sphingomyelin (SPM) and other amine-containing PLs and SLs. This result showed that the synergistic activity increased with an increasing concentration of amine group of PLs, SLs, or sphingoid bases in the reaction mixture. The comparison of changes in α-tocopherol content in fish oil TAG and tricaprylin suggested that antioxidant compounds would be formed from the amine group and the lipid oxidation products in a mild oxidation condition controlled by α-tocopherol.

Effect on the partial least-squares prediction of yarn properties combining raman and infrared measurements and applying wavelength selection.

PubMed

de Groot, P J; Swierenga, H; Postma, G J; Melssen, W J; Buydens, L M C

2003-06-01

The combination of Raman and infrared spectroscopy on the one hand and wavelength selection on the other hand is used to improve the partial least-squares (PLS) prediction of seven selected yarn properties. These properties are important for on-line quality control during production. From 71 yarn samples, the Raman and infrared spectra are measured and reference methods are used to determine the selected properties. Making separate PLS models for all yarn properties using the Raman and infrared spectra, prior to wavelength selection, reveals that Raman spectroscopy outperforms infrared spectroscopy. If wavelength selection is applied, the PLS prediction error decreases and the correlation coefficient increases for all properties. However, a substantial wavelength selection effect is present for the infrared spectra compared to the Raman spectra. For the infrared spectra, wavelength selection results in PLS prediction errors comparable with the prediction performance of the Raman spectra prior to wavelength selection. Concatenating the Raman and infrared spectra does not enhance the PLS prediction performance, not even after wavelength selection. It is concluded that an infrared spectrometer, combined with a wavelength selection procedure, can be used if no (suitable) Raman instrument is available.

The Double Cone: A Mechanical Paradox or a Geometrical Constraint?

ERIC Educational Resources Information Center

Gallitto, Aurelio Agliolo; Fiordilino, Emilio

2011-01-01

In the framework of the Italian National Plan "Lauree Scientifiche" (PLS) in collaboration with secondary schools, we have investigated the mechanical paradox of the double cone. We have calculated the geometric condition for obtaining an upward movement. Based on this result, we have built a mechanical model with a double cone made of aluminum…

The Role of Safety Culture in Influencing Provider Perceptions of Patient Safety.

PubMed

Bishop, Andrea C; Boyle, Todd A

2016-12-01

To determine how provider perceptions of safety culture influence their involvement in patient safety practices. Health-care providers were surveyed in 2 tertiary hospitals located in Atlantic Canada, composed of 4 units in total. The partial least squares (PLS) approach to structural equation modeling was used to analyze the data. Latent variables provider PLS model encompassed the hypothesized relationships between provider characteristics, safety culture, perceptions of patient safety practices, and actual performance of patient safety practices, using the Health Belief Model (HBM) as a guide. Data analysis was conducted using SmartPLS. A total of 113 health-care providers completed a survey out of an eligible 318, representing a response rate of 35.5%. The final PLS model showed acceptable internal consistency with all four latent variables having a composite reliability score above the recommended 0.70 cutoff value (safety culture = 0.86, threat = 0.76, expectations = 0.83, PS practices = 0.75). Discriminant validity was established, and all path coefficients were found to be significant at the α = 0.05 level using nonparametric bootstrapping. The survey results show that safety culture accounted for 34% of the variance in perceptions of threat and 42% of the variance in expectations. This research supports the role that safety culture plays in the promotion and maintenance of patient safety activities for health-care providers. As such, it is recommended that the introduction of new patient safety strategies follow a thorough exploration of an organization's safety culture.

Determination of butter adulteration with margarine using Raman spectroscopy.

PubMed

Uysal, Reyhan Selin; Boyaci, Ismail Hakki; Genis, Hüseyin Efe; Tamer, Ugur

2013-12-15

In this study, adulteration of butter with margarine was analysed using Raman spectroscopy combined with chemometric methods (principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS)) and artificial neural networks (ANNs). Different butter and margarine samples were mixed at various concentrations ranging from 0% to 100% w/w. PCA analysis was applied for the classification of butters, margarines and mixtures. PCR, PLS and ANN were used for the detection of adulteration ratios of butter. Models were created using a calibration data set and developed models were evaluated using a validation data set. The coefficient of determination (R(2)) values between actual and predicted values obtained for PCR, PLS and ANN for the validation data set were 0.968, 0.987 and 0.978, respectively. In conclusion, a combination of Raman spectroscopy with chemometrics and ANN methods can be applied for testing butter adulteration. Copyright © 2013 Elsevier Ltd. All rights reserved.

Variable selection based cotton bollworm odor spectroscopic detection

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Gai, Shasha; Luo, Min; Zhao, Bo

2016-10-01

Aiming at rapid automatic pest detection based efficient and targeting pesticide application and shooting the trouble of reflectance spectral signal covered and attenuated by the solid plant, the possibility of near infrared spectroscopy (NIRS) detection on cotton bollworm odor is studied. Three cotton bollworm odor samples and 3 blank air gas samples were prepared. Different concentrations of cotton bollworm odor were prepared by mixing the above gas samples, resulting a calibration group of 62 samples and a validation group of 31 samples. Spectral collection system includes light source, optical fiber, sample chamber, spectrometer. Spectra were pretreated by baseline correction, modeled with partial least squares (PLS), and optimized by genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS). Minor counts differences are found among spectra of different cotton bollworm odor concentrations. PLS model of all the variables was built presenting RMSEV of 14 and RV2 of 0.89, its theory basis is insect volatilizes specific odor, including pheromone and allelochemics, which are used for intra-specific and inter-specific communication and could be detected by NIR spectroscopy. 28 sensitive variables are selected by GA, presenting the model performance of RMSEV of 14 and RV2 of 0.90. Comparably, 8 sensitive variables are selected by CARS, presenting the model performance of RMSEV of 13 and RV2 of 0.92. CARS model employs only 1.5% variables presenting smaller error than that of all variable. Odor gas based NIR technique shows the potential for cotton bollworm detection.

Analysis of pork adulteration in beef meatball using Fourier transform infrared (FTIR) spectroscopy.

PubMed

Rohman, A; Sismindari; Erwanto, Y; Che Man, Yaakob B

2011-05-01

Meatball is one of the favorite foods in Indonesia. The adulteration of pork in beef meatball is frequently occurring. This study was aimed to develop a fast and non destructive technique for the detection and quantification of pork in beef meatball using Fourier transform infrared (FTIR) spectroscopy and partial least square (PLS) calibration. The spectral bands associated with pork fat (PF), beef fat (BF), and their mixtures in meatball formulation were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure PF and BF. For quantitative analysis, PLS regression was used to develop a calibration model at the selected fingerprint regions of 1200-1000 cm(-1). The equation obtained for the relationship between actual PF value and FTIR predicted values in PLS calibration model was y = 0.999x + 0.004, with coefficient of determination (R(2)) and root mean square error of calibration are 0.999 and 0.442, respectively. The PLS calibration model was subsequently used for the prediction of independent samples using laboratory made meatball samples containing the mixtures of BF and PF. Using 4 principal components, root mean square error of prediction is 0.742. The results showed that FTIR spectroscopy can be used for the detection and quantification of pork in beef meatball formulation for Halal verification purposes. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods

PubMed Central

Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China. PMID:29410795

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods.

PubMed

Guo, Xiuhan; Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber ( Apostichopus japonicus ) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.

Year-class formation of upper St. Lawrence River northern pike

USGS Publications Warehouse

Smith, B.M.; Farrell, J.M.; Underwood, H.B.; Smith, S.J.

2007-01-01

Variables associated with year-class formation in upper St. Lawrence River northern pike Esox lucius were examined to explore population trends. A partial least-squares (PLS) regression model (PLS 1) was used to relate a year-class strength index (YCSI; 1974-1997) to explanatory variables associated with spawning and nursery areas (seasonal water level and temperature and their variability, number of ice days, and last day of ice presence). A second model (PLS 2) incorporated four additional ecological variables: potential predators (abundance of double-crested cormorants Phalacrocorax auritus and yellow perch Perca flavescens), female northern pike biomass (as a measure of stock-recruitment effects), and total phosphorus (productivity). Trends in adult northern pike catch revealed a decline (1981-2005), and year-class strength was positively related to catch per unit effort (CPUE; R2 = 0.58). The YCSI exceeded the 23-year mean in only 2 of the last 10 years. Cyclic patterns in the YCSI time series (along with strong year-classes every 4-6 years) were apparent, as was a dampening effect of amplitude beginning around 1990. The PLS 1 model explained over 50% of variation in both explanatory variables and the dependent variable, YCSI first-order moving-average residuals. Variables retained (N = 10; Wold's statistic ??? 0.8) included negative YCSI associations with high summer water levels, high variability in spring and fall water levels, and variability in fall water temperature. The YCSI exhibited positive associations with high spring, summer, and fall water temperature, variability in spring temperature, and high winter and spring water level. The PLS 2 model led to positive YCSI associations with phosphorus and yellow perch CPUE and a negative correlation with double-crested cormorant abundance. Environmental variables (water level and temperature) are hypothesized to regulate northern pike YCSI cycles, and dampening in YCSI magnitude may be related to a combination of factors, including wetland habitat changes, reduced nutrient loading, and increased predation by double-crested cormorants. ?? Copyright by the American Fisheries Society 2007.

Wastewater quality monitoring system using sensor fusion and machine learning techniques.

PubMed

Qin, Xusong; Gao, Furong; Chen, Guohua

2012-03-15

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

NIR spectroscopy for the quality control of Moringa oleifera (Lam.) leaf powders: Prediction of minerals, protein and moisture contents.

PubMed

Rébufa, Catherine; Pany, Inès; Bombarda, Isabelle

2018-09-30

A rapid methodology was developed to simultaneously predict water content and activity values (a w ) of Moringa oleifera leaf powders (MOLP) using near infrared (NIR) signatures and experimental sorption isotherms. NIR spectra of MOLP samples (n = 181) were recorded. A Partial Least Square Regression model (PLS2) was obtained with low standard errors of prediction (SEP of 1.8% and 0.07 for water content and a w respectively). Experimental sorption isotherms obtained at 20, 30 and 40 °C showed similar profiles. This result is particularly important to use MOLP in food industry. In fact, a temperature variation of the drying process will not affect their available water content (self-life). Nutrient contents based on protein and selected minerals (Ca, Fe, K) were also predicted from PLS1 models. Protein contents were well predicted (SEP of 2.3%). This methodology allowed for an improvement in MOLP safety, quality control and traceability. Published by Elsevier Ltd.

Paper spray mass spectrometry and chemometric tools for a fast and reliable identification of counterfeit blended Scottish whiskies.

PubMed

Teodoro, Janaína Aparecida Reis; Pereira, Hebert Vinicius; Sena, Marcelo Martins; Piccin, Evandro; Zacca, Jorge Jardim; Augusti, Rodinei

2017-12-15

A direct method based on the application of paper spray mass spectrometry (PS-MS) combined with a chemometric supervised method (partial least square discriminant analysis, PLS-DA) was developed and applied to the discrimination of authentic and counterfeit samples of blended Scottish whiskies. The developed methodology employed the negative ion mode MS, included 44 authentic whiskies from diverse brands and batches and 44 counterfeit samples of the same brands seized during operations of the Brazilian Federal Police, totalizing 88 samples. An exploratory principal component analysis (PCA) model showed a reasonable discrimination of the counterfeit whiskies in PC2. In spite of the samples heterogeneity, a robust, reliable and accurate PLS-DA model was generated and validated, which was able to correctly classify the samples with nearly 100% success rate. The use of PS-MS also allowed the identification of the main marker compounds associated with each type of sample analyzed: authentic or counterfeit. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Duan, Fajie; Fu, Xiao; Jiang, Jiajia; Huang, Tingting; Ma, Ling; Zhang, Cong

2018-05-01

In this work, an automatic variable selection method for quantitative analysis of soil samples using laser-induced breakdown spectroscopy (LIBS) is proposed, which is based on full spectrum correction (FSC) and modified iterative predictor weighting-partial least squares (mIPW-PLS). The method features automatic selection without artificial processes. To illustrate the feasibility and effectiveness of the method, a comparison with genetic algorithm (GA) and successive projections algorithm (SPA) for different elements (copper, barium and chromium) detection in soil was implemented. The experimental results showed that all the three methods could accomplish variable selection effectively, among which FSC-mIPW-PLS required significantly shorter computation time (12 s approximately for 40,000 initial variables) than the others. Moreover, improved quantification models were got with variable selection approaches. The root mean square errors of prediction (RMSEP) of models utilizing the new method were 27.47 (copper), 37.15 (barium) and 39.70 (chromium) mg/kg, which showed comparable prediction effect with GA and SPA.

Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products.

PubMed

Igne, Benoit; Shi, Zhenqi; Drennen, James K; Anderson, Carl A

2014-02-01

The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Prevalence of Prostatitis-Like Symptoms and Outcomes of NIH-CPSI in Outpatients with Lifelong and Acquired PE: Based on a Large Cross-Sectional Study in China.

PubMed

Zhu, Daofang; Dou, Xianming; Tang, Liang; Tang, Dongdong; Liao, Guiyi; Fang, Weihua; Zhang, Xiansheng

2017-01-01

Premature ejaculation (PE) is one of the most common sexual dysfunctions, which were associated with prostatitis-like symptoms (PLS). We intended to explore the prevalence of prostatitis-like symptoms and outcomes of National Institutes of Health-Chronic Prostatitis Symptom Index (NIH-CPSI) scores in outpatients with lifelong (LPE) and acquired premature ejaculation (APE). From December 2013 to December 2015, a total of 498 consecutive heterosexual men with PE and 322 male healthy subjects without PE were enrolled. Each of them completed a detailed questionnaire on demographics information, sexual and medical histories, and the NIH-CPSI. Assessment of NIH-CPSI and definition of PLS and PE were used to measure the PLS and NIH-CPSI scores and ejaculatory function for all subjects. Finally, a total of 820 subjects (including 498 men in PE group and 322 men in control group) were enrolled in our study. The mean ages were significantly different between PE and no PE groups. Men with PE reported worse PLS and higher NIH-CPSI scores ( P < 0.001 for all). Similar findings were also observed between men with LPE and APE. Men with APE also reported higher rates of PLS and scores of NIH-CPSI ( P < 0.001 for all). Multivariate analysis showed that PLS and NIH-CPSI scores were significantly associated with PE.

Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE PAGES

Coble, Jamie; Orton, Christopher; Schwantes, Jon

2017-01-17

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coble, Jamie; Orton, Christopher; Schwantes, Jon

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

An Electrochemical Impedance Spectroscopy System for Monitoring Pineapple Waste Saccharification.

PubMed

Conesa, Claudia; Ibáñez Civera, Javier; Seguí, Lucía; Fito, Pedro; Laguarda-Miró, Nicolás

2016-02-04

Electrochemical impedance spectroscopy (EIS) has been used for monitoring the enzymatic pineapple waste hydrolysis process. The system employed consists of a device called Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) equipped with a specific software application and a stainless steel double needle electrode. EIS measurements were conducted at different saccharification time intervals: 0, 0.75, 1.5, 6, 12 and 24 h. Partial least squares (PLS) were used to model the relationship between the EIS measurements and the sugar determination by HPAEC-PAD. On the other hand, artificial neural networks: (multilayer feed forward architecture with quick propagation training algorithm and logistic-type transfer functions) gave the best results as predictive models for glucose, fructose, sucrose and total sugars. Coefficients of determination (R²) and root mean square errors of prediction (RMSEP) were determined as R² > 0.944 and RMSEP < 1.782 for PLS and R² > 0.973 and RMSEP < 0.486 for artificial neural networks (ANNs), respectively. Therefore, a combination of both an EIS-based technique and ANN models is suggested as a promising alternative to the traditional laboratory techniques for monitoring the pineapple waste saccharification step.

6C.04: INTEGRATED SNP ANALYSIS AND METABOLOMIC PROFILES OF METABOLIC SYNDROME.

PubMed

Marrachelli, V; Monleon, D; Morales, J M; Rentero, P; Martínez, F; Chaves, F J; Martin-Escudero, J C; Redon, J

2015-06-01

Metabolic syndrome (MS) has become a health and financial burden worldwide. Susceptibility of genetically determined metabotype of MS has not yet been investigated. We aimed to identify a distinctive metabolic profile of blood serum which might correlates to the early detection of the development of MS associated to genetic polymorphism. We applied high resolution NMR spectroscopy to profile blood serum from patients without MS (n = 945) or with (n = 291). Principal component analysis (PCA) and projection to latent structures for discriminant analysis (PLS-DA) were applied to NMR spectral datasets. Results were cross-validated using the Venetian Blinds approach. Additionally, five SNPs previously associated with MS were genotyped with SNPlex and tested for associations between the metabolic profiles and the genetic variants. Statistical analysis was performed using in-house MATLAB scripts and the PLS Toolbox statistical multivariate analysis library. Our analysis provided a PLS-DA Metabolic Syndrome discrimination model based on NMR metabolic profile (AUC = 0.86) with 84% of sensitivity and 72% specificity. The model identified 11 metabolites differentially regulated in patients with MS. Among others, fatty acids, glucose, alanine, hydroxyisovalerate, acetone, trimethylamine, 2-phenylpropionate, isobutyrate and valine, significantly contributed to the model. The combined analysis of metabolomics and SNP data revealed an association between the metabolic profile of MS and genes polymorphism involved in the adiposity regulation and fatty acids metabolism: rs2272903_TT (TFAP2B), rs3803_TT (GATA2), rs174589_CC (FADS2) and rs174577_AA (FADS2). In addition, individuals with the rs2272903-TT genotype seem to develop MS earlier than general population. Our study provides new insights on the metabolic alterations associated with a MS high-risk genotype. These results could help in future development of risk assessment and predictive models for subclinical cardiovascular disease.

Analysis of multi-mode to single-mode conversion at 635 nm and 1550 nm

NASA Astrophysics Data System (ADS)

Zamora, Vanessa; Bogatzki, Angelina; Arndt-Staufenbiel, Norbert; Hofmann, Jens; Schröder, Henning

2016-03-01

We propose two low-cost and robust optical fiber systems based on the photonic lantern (PL) technology for operating at 635 nm and 1550 nm. The PL is an emerging technology that couples light from a multi-mode (MM) fiber to several single-mode (SM) fibers via a low-loss adiabatic transition. This bundle of SM fibers is observed as a MM fiber system whose spatial modes are the degenerate supermodes of the bundle. The adiabatic transition allows that those supermodes evolve into the modes of the MM fiber. Simulations of the MM fiber end structure and its taper transition have been performed via functional mode solver tools in order to understand the modal evolution in PLs. The modelled design consists of 7 SM fibers inserted into a low-index capillary. The material and geometry of the PLs are chosen such that the supermodes match to the spatial modes of the desired step-index MM fiber in a moderate loss transmission. The dispersion of materials is also considered. These parameters are studied in two PL systems in order to reach a spectral transmission from 450 nm to 1600 nm. Additionally, an analysis of the geometry and losses due to the mismatching of modes is presented. PLs are typically used in the fields of astrophotonics and space photonics. Recently, they are demonstrated as mode converters in telecommunications, especially focusing on spatial division multiplexing. In this study, we show the use of PLs as a promising interconnecting tool for the development of miniaturized spectrometers operating in a broad wavelength range.

Rapid and quantitative detection of the microbial spoilage in chicken meat by diffuse reflectance spectroscopy (600-1100 nm).

PubMed

Lin, M; Al-Holy, M; Mousavi-Hesary, M; Al-Qadiri, H; Cavinato, A G; Rasco, B A

2004-01-01

To evaluate the feasibility of visible and short-wavelength near-infrared (SW-NIR) diffuse reflectance spectroscopy (600-1100 nm) to quantify the microbial loads in chicken meat and to develop a rapid methodology for monitoring the onset of spoilage. Twenty-four prepackaged fresh chicken breast muscle samples were prepared and stored at 21 degrees C for 24 h. Visible and SW-NIR was used to detect and quantify the microbial loads in chicken breast muscle at time intervals of 0, 2, 4, 6, 8, 10, 12 and 24 h. Spectra were collected in the diffuse reflectance mode (600-1100 nm). Total aerobic plate count (APC) of each sample was determined by the spread plate method at 32 degrees C for 48 h. Principal component analysis (PCA) and partial least squares (PLS) based prediction models were developed. PCA analysis showed clear segregation of samples held 8 h or longer compared with 0-h control. An optimum PLS model required eight latent variables for chicken muscle (R = 0.91, SEP = 0.48 log CFU g(-1)). Visible and SW-NIR combined with PCA is capable of perceiving the change of the microbial loads in chicken muscle once the APC increases slightly above 1 log cycle. Accurate quantification of the bacterial loads in chicken muscle can be calculated from the PLS-based prediction method. Visible and SW-NIR spectroscopy is a technique with a considerable potential for monitoring food safety and food spoilage. Visible and SW-NIR can acquire a metabolic snapshot and quantify the microbial loads of food samples rapidly, accurately, and noninvasively. This method would allow for more expeditious applications of quality control in food industries.

[Based on the LS-SVM modeling method determination of soil available N and available K by using near-infrared spectroscopy].

PubMed

Liu, Xue-Mei; Liu, Jian-She

2012-11-01

Visible infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement accuracy of soil properties,namely, available nitrogen(N) and available potassium(K). Three types of pretreatments including standard normal variate (SNV), multiplicative scattering correction (MSC) and Savitzky-Golay smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares (PLS) and least squares-support vector machine (LS-SVM) models analysis were implemented for calibration models. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including PCA(PCs), latent variables (LVs), and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The performance of the model was evaluated by the correlation coefficient (r2) and RMSEP. The optimal EWs-LS-SVM models were achieved, and the correlation coefficient (r2) and RMSEP were 0.82 and 17.2 for N and 0.72 and 15.0 for K, respectively. The results indicated that visible and short wave-near infrared spectroscopy (Vis/SW-NIRS)(325-1 075 nm) combined with LS-SVM could be utilized as a precision method for the determination of soil properties.

Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

NASA Astrophysics Data System (ADS)

Peerbhay, Kabir Yunus; Mutanga, Onisimo; Ismail, Riyad

2013-05-01

Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393-900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user's and producer's accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user's and producer's accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393-723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases.

PubMed

Dawiskiba, Tomasz; Deja, Stanisław; Mulak, Agata; Ząbek, Adam; Jawień, Ewa; Pawełka, Dorota; Banasik, Mirosław; Mastalerz-Migas, Agnieszka; Balcerzak, Waldemar; Kaliszewski, Krzysztof; Skóra, Jan; Barć, Piotr; Korta, Krzysztof; Pormańczuk, Kornel; Szyber, Przemyslaw; Litarski, Adam; Młynarz, Piotr

2014-01-07

To evaluate the utility of serum and urine metabolomic analysis in diagnosing and monitoring of inflammatory bowel diseases (IBD). Serum and urine samples were collected from 24 patients with ulcerative colitis (UC), 19 patients with the Crohn's disease (CD) and 17 healthy controls. The activity of UC was assessed with the Simple Clinical Colitis Activity Index, while the activity of CD was determined using the Harvey-Bradshaw Index. The analysis of serum and urine samples was performed using proton nuclear magnetic resonance (NMR) spectroscopy. All spectra were exported to Matlab for preprocessing which resulted in two data matrixes for serum and urine. Prior to the chemometric analysis, both data sets were unit variance scaled. The differences in metabolite fingerprints were assessed using partial least-squares-discriminant analysis (PLS-DA). Receiver operating characteristic curves and area under curves were used to evaluate the quality and prediction performance of the obtained PLS-DA models. Metabolites responsible for separation in models were tested using STATISTICA 10 with the Mann-Whitney-Wilcoxon test and the Student's t test (α = 0.05). The comparison between the group of patients with active IBD and the group with IBD in remission provided good PLS-DA models (P value 0.002 for serum and 0.003 for urine). The metabolites that allowed to distinguish these groups were: N-acetylated compounds and phenylalanine (up-regulated in serum), low-density lipoproteins and very low-density lipoproteins (decreased in serum) as well as glycine (increased in urine) and acetoacetate (decreased in urine). The significant differences in metabolomic profiles were also found between the group of patients with active IBD and healthy control subjects providing the PLS-DA models with a very good separation (P value < 0.001 for serum and 0.003 for urine). The metabolites that were found to be the strongest biomarkers included in this case: leucine, isoleucine, 3-hydroxybutyric acid, N-acetylated compounds, acetoacetate, glycine, phenylalanine and lactate (increased in serum), creatine, dimethyl sulfone, histidine, choline and its derivatives (decreased in serum), as well as citrate, hippurate, trigonelline, taurine, succinate and 2-hydroxyisobutyrate (decreased in urine). No clear separation in PLS-DA models was found between CD and UC patients based on the analysis of serum and urine samples, although one metabolite (formate) in univariate statistical analysis was significantly lower in serum of patients with active CD, and two metabolites (alanine and N-acetylated compounds) were significantly higher in serum of patients with CD when comparing jointly patients in the remission and active phase of the diseases. Contrary to the results obtained from the serum samples, the analysis of urine samples allowed to distinguish patients with IBD in remission from healthy control subjects. The metabolites of importance included in this case up-regulated acetoacetate and down-regulated citrate, hippurate, taurine, succinate, glycine, alanine and formate. NMR-based metabolomic fingerprinting of serum and urine has the potential to be a useful tool in distinguishing patients with active IBD from those in remission.

Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases

PubMed Central

Dawiskiba, Tomasz; Deja, Stanisław; Mulak, Agata; Ząbek, Adam; Jawień, Ewa; Pawełka, Dorota; Banasik, Mirosław; Mastalerz-Migas, Agnieszka; Balcerzak, Waldemar; Kaliszewski, Krzysztof; Skóra, Jan; Barć, Piotr; Korta, Krzysztof; Pormańczuk, Kornel; Szyber, Przemyslaw; Litarski, Adam; Młynarz, Piotr

2014-01-01

AIM: To evaluate the utility of serum and urine metabolomic analysis in diagnosing and monitoring of inflammatory bowel diseases (IBD). METHODS: Serum and urine samples were collected from 24 patients with ulcerative colitis (UC), 19 patients with the Crohn’s disease (CD) and 17 healthy controls. The activity of UC was assessed with the Simple Clinical Colitis Activity Index, while the activity of CD was determined using the Harvey-Bradshaw Index. The analysis of serum and urine samples was performed using proton nuclear magnetic resonance (NMR) spectroscopy. All spectra were exported to Matlab for preprocessing which resulted in two data matrixes for serum and urine. Prior to the chemometric analysis, both data sets were unit variance scaled. The differences in metabolite fingerprints were assessed using partial least-squares-discriminant analysis (PLS-DA). Receiver operating characteristic curves and area under curves were used to evaluate the quality and prediction performance of the obtained PLS-DA models. Metabolites responsible for separation in models were tested using STATISTICA 10 with the Mann-Whitney-Wilcoxon test and the Student’s t test (α = 0.05). RESULTS: The comparison between the group of patients with active IBD and the group with IBD in remission provided good PLS-DA models (P value 0.002 for serum and 0.003 for urine). The metabolites that allowed to distinguish these groups were: N-acetylated compounds and phenylalanine (up-regulated in serum), low-density lipoproteins and very low-density lipoproteins (decreased in serum) as well as glycine (increased in urine) and acetoacetate (decreased in urine). The significant differences in metabolomic profiles were also found between the group of patients with active IBD and healthy control subjects providing the PLS-DA models with a very good separation (P value < 0.001 for serum and 0.003 for urine). The metabolites that were found to be the strongest biomarkers included in this case: leucine, isoleucine, 3-hydroxybutyric acid, N-acetylated compounds, acetoacetate, glycine, phenylalanine and lactate (increased in serum), creatine, dimethyl sulfone, histidine, choline and its derivatives (decreased in serum), as well as citrate, hippurate, trigonelline, taurine, succinate and 2-hydroxyisobutyrate (decreased in urine). No clear separation in PLS-DA models was found between CD and UC patients based on the analysis of serum and urine samples, although one metabolite (formate) in univariate statistical analysis was significantly lower in serum of patients with active CD, and two metabolites (alanine and N-acetylated compounds) were significantly higher in serum of patients with CD when comparing jointly patients in the remission and active phase of the diseases. Contrary to the results obtained from the serum samples, the analysis of urine samples allowed to distinguish patients with IBD in remission from healthy control subjects. The metabolites of importance included in this case up-regulated acetoacetate and down-regulated citrate, hippurate, taurine, succinate, glycine, alanine and formate. CONCLUSION: NMR-based metabolomic fingerprinting of serum and urine has the potential to be a useful tool in distinguishing patients with active IBD from those in remission. PMID:24415869

Study of Vis/NIR spectroscopy measurement on acidity of yogurt

NASA Astrophysics Data System (ADS)

He, Yong; Feng, Shuijuan; Wu, Di; Li, Xiaoli

2006-09-01

A fast measurement of pH of yogurt using Vis/NIR-spectroscopy techniques was established in order to measuring the acidity of yogurt rapidly. 27 samples selected separately from five different brands of yogurt were measured by Vis/NIR-spectroscopy. The pH of yogurt on positions scanned by spectrum was measured by a pH meter. The mathematical model between pH and Vis/NIR spectral measurements was established and developed based on partial least squares (PLS) by using Unscramble V9.2. Then 25 unknown samples from 5 different brands were predicted based on the mathematical model. The result shows that The correlation coefficient of pH based on PLS model is more than 0.890, and standard error of calibration (SEC) is 0.037, standard error of prediction (SEP) is 0.043. Through predicting the pH of 25 samples of yogurt from 5 different brands, the correlation coefficient between predictive value and measured value of those samples is more than 0918. The results show the good to excellent prediction performances. The Vis/NIR spectroscopy technique had a significant greater accuracy for determining the value of pH. It was concluded that the VisINIRS measurement technique can be used to measure pH of yogurt fast and accurately, and a new method for the measurement of pH of yogurt was established.

Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

PubMed

Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

2017-05-01

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

Identification and topographical characterisation of microbial nanowires in Nostoc punctiforme.

PubMed

Sure, Sandeep; Torriero, Angel A J; Gaur, Aditya; Li, Lu Hua; Chen, Ying; Tripathi, Chandrakant; Adholeya, Alok; Ackland, M Leigh; Kochar, Mandira

2016-03-01

Extracellular pili-like structures (PLS) produced by cyanobacteria have been poorly explored. We have done detailed topographical and electrical characterisation of PLS in Nostoc punctiforme PCC 73120 using transmission electron microscopy (TEM) and conductive atomic force microscopy (CAFM). TEM analysis showed that N. punctiforme produces two separate types of PLS differing in their length and diameter. The first type of PLS are 6-7.5 nm in diameter and 0.5-2 µm in length (short/thin PLS) while the second type of PLS are ~20-40 nm in diameter and more than 10 µm long (long/thick PLS). This is the first study to report long/thick PLS in N. punctiforme. Electrical characterisation of these two different PLS by CAFM showed that both are electrically conductive and can act as microbial nanowires. This is the first report to show two distinct PLS and also identifies microbial nanowires in N. punctiforme. This study paves the way for more detailed investigation of N. punctiforme nanowires and their potential role in cell physiology and symbiosis with plants.

Characteristic fingerprinting based on macamides for discrimination of maca (Lepidium meyenii) by LC/MS/MS and multivariate statistical analysis.

PubMed

Pan, Yu; Zhang, Ji; Li, Hong; Wang, Yuan-Zhong; Li, Wan-Yi

2016-10-01

Macamides with a benzylalkylamide nucleus are characteristic and major bioactive compounds in the functional food maca (Lepidium meyenii Walp). The aim of this study was to explore variations in macamide content among maca from China and Peru. Twenty-seven batches of maca hypocotyls with different phenotypes, sampled from different geographical origins, were extracted and profiled by liquid chromatography with ultraviolet detection/tandem mass spectrometry (LC-UV/MS/MS). Twelve macamides were identified by MS operated in multiple scanning modes. Similarity analysis showed that maca samples differed significantly in their macamide fingerprinting. Partial least squares discriminant analysis (PLS-DA) was used to differentiate samples according to their geographical origin and to identify the most relevant variables in the classification model. The prediction accuracy for raw maca was 91% and five macamides were selected and considered as chemical markers for sample classification. When combined with a PLS-DA model, characteristic fingerprinting based on macamides could be recommended for labelling for the authentication of maca from different geographical origins. The results provided potential evidence for the relationships between environmental or other factors and distribution of macamides. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Study on fast measurement of sugar content of yogurt using Vis/NIR spectroscopy techniques

NASA Astrophysics Data System (ADS)

He, Yong; Feng, Shuijuan; Wu, Di; Li, Xiaoli

2006-09-01

In order to measuring the sugar content of yogurt rapidly, a fast measurement of sugar content of yogurt using Vis/NIR-spectroscopy techniques was established. 25 samples selected separately from five different brands of yogurt were measured by Vis/NIR-spectroscopy. The sugar content of yogurt on positions scanned by spectrum were measured by a sugar content meter. The mathematical model between sugar content and Vis/NIR spectral measurements was established and developed based on partial least squares (PLS). The correlation coefficient of sugar content based on PLS model is more than 0.894, and standard error of calibration (SEC) is 0.356, standard error of prediction (SEP) is 0.389. Through predicting the sugar content quantitatively of 35 samples of yogurt from 5 different brands, the correlation coefficient between predictive value and measured value of those samples is more than 0.934. The results show the good to excellent prediction performance. The Vis/NIR spectroscopy technique had significantly greater accuracy for determining the sugar content. It was concluded that the Vis/NIRS measurement technique seems reliable to assess the fast measurement of sugar content of yogurt, and a new method for the measurement of sugar content of yogurt was established.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy.

PubMed

Liu, Yan-De; Ying, Yi-Bin; Fu, Xia-Ping

2005-03-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy*

PubMed Central

Liu, Yan-de; Ying, Yi-bin; Fu, Xia-ping

2005-01-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r 2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way. PMID:15682498

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS)

NASA Astrophysics Data System (ADS)

Krepper, Gabriela; Romeo, Florencia; Fernandes, David Douglas de Sousa; Diniz, Paulo Henrique Gonçalves Dias; de Araújo, Mário César Ugulino; Di Nezio, María Susana; Pistonesi, Marcelo Fabián; Centurión, María Eugenia

2018-01-01

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12 mg kg- 1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (w w- 1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59 mg kg- 1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS).

PubMed

Krepper, Gabriela; Romeo, Florencia; Fernandes, David Douglas de Sousa; Diniz, Paulo Henrique Gonçalves Dias; de Araújo, Mário César Ugulino; Di Nezio, María Susana; Pistonesi, Marcelo Fabián; Centurión, María Eugenia

2018-01-15

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12mgkg -1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (ww -1 ). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59mgkg -1 , REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved quantitative analysis of spectra using a new method of obtaining derivative spectra based on a singular perturbation technique.

PubMed

Li, Zhigang; Wang, Qiaoyun; Lv, Jiangtao; Ma, Zhenhe; Yang, Linjuan

2015-06-01

Spectroscopy is often applied when a rapid quantitative analysis is required, but one challenge is the translation of raw spectra into a final analysis. Derivative spectra are often used as a preliminary preprocessing step to resolve overlapping signals, enhance signal properties, and suppress unwanted spectral features that arise due to non-ideal instrument and sample properties. In this study, to improve quantitative analysis of near-infrared spectra, derivatives of noisy raw spectral data need to be estimated with high accuracy. A new spectral estimator based on singular perturbation technique, called the singular perturbation spectra estimator (SPSE), is presented, and the stability analysis of the estimator is given. Theoretical analysis and simulation experimental results confirm that the derivatives can be estimated with high accuracy using this estimator. Furthermore, the effectiveness of the estimator for processing noisy infrared spectra is evaluated using the analysis of beer spectra. The derivative spectra of the beer and the marzipan are used to build the calibration model using partial least squares (PLS) modeling. The results show that the PLS based on the new estimator can achieve better performance compared with the Savitzky-Golay algorithm and can serve as an alternative choice for quantitative analytical applications.

Exposure to mixtures of organohalogen contaminants and associative interactions with thyroid hormones in East Greenland polar bears (Ursus maritimus).

PubMed

Villanger, Gro D; Jenssen, Bjørn M; Fjeldberg, Rita R; Letcher, Robert J; Muir, Derek C G; Kirkegaard, Maja; Sonne, Christian; Dietz, Rune

2011-05-01

We investigated the multivariate relationships between adipose tissue residue levels of 48 individual organohalogen contaminants (OHCs) and circulating thyroid hormone (TH) levels in polar bears (Ursus maritimus) from East Greenland (1999-2001, n=62), using projection to latent structure (PLS) regression for four groupings of polar bears; subadults (SubA), adult females with cubs (AdF_N), adult females without cubs (AdF_S) and adult males (AdM). In the resulting significant PLS models for SubA, AdF_N and AdF_S, some OHCs were especially important in explaining variations in circulating TH levels: polybrominated diphenylether (PBDE)-99, PBDE-100, PBDE-153, polychlorinated biphenyl (PCB)-52, PCB-118, cis-nonachlor, trans-nonachlor, trichlorobenzene (TCB) and pentachlorobenzene (QCB), and both negative and positive relationships with THs were found. In addition, the models revealed that DDTs had a positive influence on total 3,5,3'-triiodothyronine (TT3) in AdF_S, and that a group of 17 higher chlorinated ortho-PCBs had a positive influence on total 3,5,3',5'-tetraiodothyronine (thyroxine, TT4) in AdF_N. TH levels in AdM seemed less influenced by OHCs because of non-significant PLS models. TH levels were also influenced by biological factors such as age, sex, body size, lipid content of adipose tissue and sampling date. When controlling for biological variables, the major relationships from the PLS models for SubA, AdF_N and AdF_S were found significant in partial correlations. The most important OHCs that influenced TH levels in the significant PLS models may potentially act through similar mechanisms on the hypothalamic-pituitary-thyroid (HPT) axis, suggesting that both combined effects by dose and response addition and perhaps synergistic potentiation may be a possibility in these polar bears. Statistical associations are not evidence per se of biological cause-effect relationships. Still, the results of the present study indicate that OHCs may affect circulating TH levels in East Greenland polar bears, adding to the "weight of evidence" suggesting that OHCs might interfere with thyroid homeostasis in polar bears. Copyright © 2011 Elsevier Ltd. All rights reserved.

Quantification of amine functional groups and their influence on OM/OC in the IMPROVE network

NASA Astrophysics Data System (ADS)

Kamruzzaman, Mohammed; Takahama, Satoshi; Dillner, Ann M.

2018-01-01

Recently, we developed a method using FT-IR spectroscopy coupled with partial least squares (PLS) regression to measure the four most abundant organic functional groups, aliphatic C-H, alcohol OH, carboxylic acid OH and carbonyl C=O, in atmospheric particulate matter. These functional groups are summed to estimate organic matter (OM) while the carbon from the functional groups is summed to estimate organic carbon (OC). With this method, OM and OM/OC can be estimated for each sample rather than relying on one assumed value to convert OC measurements to OM. This study continues the development of the FT-IR and PLS method for estimating OM and OM/OC by including the amine functional group. Amines are ubiquitous in the atmosphere and come from motor vehicle exhaust, animal husbandry, biomass burning, and vegetation among other sources. In this study, calibration standards for amines are produced by aerosolizing individual amine compounds and collecting them on PTFE filters using an IMPROVE sampler, thereby mimicking the filter media and collection geometry of ambient standards. The moles of amine functional group on each standard and a narrow range of amine-specific wavenumbers in the FT-IR spectra (wavenumber range 1 550-1 500 cm-1) are used to develop a PLS calibration model. The PLS model is validated using three methods: prediction of a set of laboratory standards not included in the model, a peak height analysis and a PLS model with a broader wavenumber range. The model is then applied to the ambient samples collected throughout 2013 from 16 IMPROVE sites in the USA. Urban sites have higher amine concentrations than most rural sites, but amine functional groups account for a lower fraction of OM at urban sites. Amine concentrations, contributions to OM and seasonality vary by site and sample. Amine has a small impact on the annual average OM/OC for urban sites, but for some rural sites including amine in the OM/OC calculations increased OM/OC by 0.1 or more.

PLS-LS-SVM based modeling of ATR-IR as a robust method in detection and qualification of alprazolam

NASA Astrophysics Data System (ADS)

Parhizkar, Elahehnaz; Ghazali, Mohammad; Ahmadi, Fatemeh; Sakhteman, Amirhossein

2017-02-01

According to the United States pharmacopeia (USP), Gold standard technique for Alprazolam determination in dosage forms is HPLC, an expensive and time-consuming method that is not easy to approach. In this study chemometrics assisted ATR-IR was introduced as an alternative method that produce similar results in fewer time and energy consumed manner. Fifty-eight samples containing different concentrations of commercial alprazolam were evaluated by HPLC and ATR-IR method. A preprocessing approach was applied to convert raw data obtained from ATR-IR spectra to normal matrix. Finally, a relationship between alprazolam concentrations achieved by HPLC and ATR-IR data was established using PLS-LS-SVM (partial least squares least squares support vector machines). Consequently, validity of the method was verified to yield a model with low error values (root mean square error of cross validation equal to 0.98). The model was able to predict about 99% of the samples according to R2 of prediction set. Response permutation test was also applied to affirm that the model was not assessed by chance correlations. At conclusion, ATR-IR can be a reliable method in manufacturing process in detection and qualification of alprazolam content.

Differences in chewing sounds of dry-crisp snacks by multivariate data analysis

NASA Astrophysics Data System (ADS)

De Belie, N.; Sivertsvik, M.; De Baerdemaeker, J.

2003-09-01

Chewing sounds of different types of dry-crisp snacks (two types of potato chips, prawn crackers, cornflakes and low calorie snacks from extruded starch) were analysed to assess differences in sound emission patterns. The emitted sounds were recorded by a microphone placed over the ear canal. The first bite and the first subsequent chew were selected from the time signal and a fast Fourier transformation provided the power spectra. Different multivariate analysis techniques were used for classification of the snack groups. This included principal component analysis (PCA) and unfold partial least-squares (PLS) algorithms, as well as multi-way techniques such as three-way PLS, three-way PCA (Tucker3), and parallel factor analysis (PARAFAC) on the first bite and subsequent chew. The models were evaluated by calculating the classification errors and the root mean square error of prediction (RMSEP) for independent validation sets. It appeared that the logarithm of the power spectra obtained from the chewing sounds could be used successfully to distinguish the different snack groups. When different chewers were used, recalibration of the models was necessary. Multi-way models distinguished better between chewing sounds of different snack groups than PCA on bite or chew separately and than unfold PLS. From all three-way models applied, N-PLS with three components showed the best classification capabilities, resulting in classification errors of 14-18%. The major amount of incorrect classifications was due to one type of potato chips that had a very irregular shape, resulting in a wide variation of the emitted sounds.

Identification of chilling and heat requirements of cherry trees--a statistical approach.

PubMed

Luedeling, Eike; Kunz, Achim; Blanke, Michael M

2013-09-01

Most trees from temperate climates require the accumulation of winter chill and subsequent heat during their dormant phase to resume growth and initiate flowering in the following spring. Global warming could reduce chill and hence hamper the cultivation of high-chill species such as cherries. Yet determining chilling and heat requirements requires large-scale controlled-forcing experiments, and estimates are thus often unavailable. Where long-term phenology datasets exist, partial least squares (PLS) regression can be used as an alternative, to determine climatic requirements statistically. Bloom dates of cherry cv. 'Schneiders späte Knorpelkirsche' trees in Klein-Altendorf, Germany, from 24 growing seasons were correlated with 11-day running means of daily mean temperature. Based on the output of the PLS regression, five candidate chilling periods ranging in length from 17 to 102 days, and one forcing phase of 66 days were delineated. Among three common chill models used to quantify chill, the Dynamic Model showed the lowest variation in chill, indicating that it may be more accurate than the Utah and Chilling Hours Models. Based on the longest candidate chilling phase with the earliest starting date, cv. 'Schneiders späte Knorpelkirsche' cherries at Bonn exhibited a chilling requirement of 68.6 ± 5.7 chill portions (or 1,375 ± 178 chilling hours or 1,410 ± 238 Utah chill units) and a heat requirement of 3,473 ± 1,236 growing degree hours. Closer investigation of the distinct chilling phases detected by PLS regression could contribute to our understanding of dormancy processes and thus help fruit and nut growers identify suitable tree cultivars for a future in which static climatic conditions can no longer be assumed. All procedures used in this study were bundled in an R package ('chillR') and are provided as Supplementary materials. The procedure was also applied to leaf emergence dates of walnut (cv. 'Payne') at Davis, California.

Identification of chilling and heat requirements of cherry trees—a statistical approach

NASA Astrophysics Data System (ADS)

Luedeling, Eike; Kunz, Achim; Blanke, Michael M.

2013-09-01

Most trees from temperate climates require the accumulation of winter chill and subsequent heat during their dormant phase to resume growth and initiate flowering in the following spring. Global warming could reduce chill and hence hamper the cultivation of high-chill species such as cherries. Yet determining chilling and heat requirements requires large-scale controlled-forcing experiments, and estimates are thus often unavailable. Where long-term phenology datasets exist, partial least squares (PLS) regression can be used as an alternative, to determine climatic requirements statistically. Bloom dates of cherry cv. `Schneiders späte Knorpelkirsche' trees in Klein-Altendorf, Germany, from 24 growing seasons were correlated with 11-day running means of daily mean temperature. Based on the output of the PLS regression, five candidate chilling periods ranging in length from 17 to 102 days, and one forcing phase of 66 days were delineated. Among three common chill models used to quantify chill, the Dynamic Model showed the lowest variation in chill, indicating that it may be more accurate than the Utah and Chilling Hours Models. Based on the longest candidate chilling phase with the earliest starting date, cv. `Schneiders späte Knorpelkirsche' cherries at Bonn exhibited a chilling requirement of 68.6 ± 5.7 chill portions (or 1,375 ± 178 chilling hours or 1,410 ± 238 Utah chill units) and a heat requirement of 3,473 ± 1,236 growing degree hours. Closer investigation of the distinct chilling phases detected by PLS regression could contribute to our understanding of dormancy processes and thus help fruit and nut growers identify suitable tree cultivars for a future in which static climatic conditions can no longer be assumed. All procedures used in this study were bundled in an R package (`chillR') and are provided as Supplementary materials. The procedure was also applied to leaf emergence dates of walnut (cv. `Payne') at Davis, California.

Study on rapid valid acidity evaluation of apple by fiber optic diffuse reflectance technique

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Fu, Xiaping; Jiang, Xuesong

2004-03-01

Some issues related to nondestructive evaluation of valid acidity in intact apples by means of Fourier transform near infrared (FTNIR) (800-2631nm) method were addressed. A relationship was established between the diffuse reflectance spectra recorded with a bifurcated optic fiber and the valid acidity. The data were analyzed by multivariate calibration analysis such as partial least squares (PLS) analysis and principal component regression (PCR) technique. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influence of data preprocessing and different spectra treatments were also investigated. Models based on smoothing spectra were slightly worse than models based on derivative spectra and the best result was obtained when the segment length was 5 and the gap size was 10. Depending on data preprocessing and multivariate calibration technique, the best prediction model had a correlation efficient (0.871), a low RMSEP (0.0677), a low RMSEC (0.056) and a small difference between RMSEP and RMSEC by PLS analysis. The results point out the feasibility of FTNIR spectral analysis to predict the fruit valid acidity non-destructively. The ratio of data standard deviation to the root mean square error of prediction (SDR) is better to be less than 3 in calibration models, however, the results cannot meet the demand of actual application. Therefore, further study is required for better calibration and prediction.

Development of Low Parasitic Light Sensitivity and Low Dark Current 2.8 μm Global Shutter Pixel †

PubMed Central

Yokoyama, Toshifumi; Tsutsui, Masafumi; Suzuki, Masakatsu; Nishi, Yoshiaki; Mizuno, Ikuo; Lahav, Assaf

2018-01-01

We developed a low parasitic light sensitivity (PLS) and low dark current 2.8 μm global shutter pixel. We propose a new inner lens design concept to realize both low PLS and high quantum efficiency (QE). 1/PLS is 7700 and QE is 62% at a wavelength of 530 nm. We also propose a new storage-gate based memory node for low dark current. P-type implants and negative gate biasing are introduced to suppress dark current at the surface of the memory node. This memory node structure shows the world smallest dark current of 9.5 e−/s at 60 °C. PMID:29370146

Development of Low Parasitic Light Sensitivity and Low Dark Current 2.8 μm Global Shutter Pixel.

PubMed

Yokoyama, Toshifumi; Tsutsui, Masafumi; Suzuki, Masakatsu; Nishi, Yoshiaki; Mizuno, Ikuo; Lahav, Assaf

2018-01-25

Abstract : We developed a low parasitic light sensitivity (PLS) and low dark current 2.8 μm global shutter pixel. We propose a new inner lens design concept to realize both low PLS and high quantum efficiency (QE). 1/PLS is 7700 and QE is 62% at a wavelength of 530 nm. We also propose a new storage-gate based memory node for low dark current. P-type implants and negative gate biasing are introduced to suppress dark current at the surface of the memory node. This memory node structure shows the world smallest dark current of 9.5 e - /s at 60 °C.

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors.

PubMed

Ciura, Krzesimir; Belka, Mariusz; Kawczak, Piotr; Bączek, Tomasz; Markuszewski, Michał J; Nowakowska, Joanna

2017-09-05

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R 2 and Q 2 , hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

Following a trend with an exponential moving average: Analytical results for a Gaussian model

NASA Astrophysics Data System (ADS)

Grebenkov, Denis S.; Serror, Jeremy

2014-01-01

We investigate how price variations of a stock are transformed into profits and losses (P&Ls) of a trend following strategy. In the frame of a Gaussian model, we derive the probability distribution of P&Ls and analyze its moments (mean, variance, skewness and kurtosis) and asymptotic behavior (quantiles). We show that the asymmetry of the distribution (with often small losses and less frequent but significant profits) is reminiscent to trend following strategies and less dependent on peculiarities of price variations. At short times, trend following strategies admit larger losses than one may anticipate from standard Gaussian estimates, while smaller losses are ensured at longer times. Simple explicit formulas characterizing the distribution of P&Ls illustrate the basic mechanisms of momentum trading, while general matrix representations can be applied to arbitrary Gaussian models. We also compute explicitly annualized risk adjusted P&L and strategy turnover to account for transaction costs. We deduce the trend following optimal timescale and its dependence on both auto-correlation level and transaction costs. Theoretical results are illustrated on the Dow Jones index.

Monitoring multiple components in vinegar fermentation using Raman spectroscopy.

PubMed

Uysal, Reyhan Selin; Soykut, Esra Acar; Boyaci, Ismail Hakki; Topcu, Ali

2013-12-15

In this study, the utility of Raman spectroscopy (RS) with chemometric methods for quantification of multiple components in the fermentation process was investigated. Vinegar, the product of a two stage fermentation, was used as a model and glucose and fructose consumption, ethanol production and consumption and acetic acid production were followed using RS and the partial least squares (PLS) method. Calibration of the PLS method was performed using model solutions. The prediction capability of the method was then investigated with both model and real samples. HPLC was used as a reference method. The results from comparing RS-PLS and HPLC with each other showed good correlations were obtained between predicted and actual sample values for glucose (R(2)=0.973), fructose (R(2)=0.988), ethanol (R(2)=0.996) and acetic acid (R(2)=0.983). In conclusion, a combination of RS with chemometric methods can be applied to monitor multiple components of the fermentation process from start to finish with a single measurement in a short time. Copyright © 2013 Elsevier Ltd. All rights reserved.

Diagnosis of coinfection by schistosomiasis and viral hepatitis B or C using 1H NMR-based metabonomics.

PubMed

Gouveia, Liana Ribeiro; Santos, Joelma Carvalho; Silva, Ronaldo Dionísio; Batista, Andrea Dória; Domingues, Ana Lúcia Coutinho; Lopes, Edmundo Pessoa de Almeida; Silva, Ricardo Oliveira

2017-01-01

Diagnosis of liver involvement due to schistosomiasis in asymptomatic patients from endemic areas previously diagnosed with chronic hepatitis B (HBV) or C (HCV) and periportal fibrosis is challenging. H-1 Nuclear Magnetic Resonance (NMR)-based metabonomics strategy is a powerful tool for providing a profile of endogenous metabolites of low molecular weight in biofluids in a non-invasive way. The aim of this study was to diagnose periportal fibrosis due to schistosomiasis mansoni in patients with chronic HBV or HCV infection through NMR-based metabonomics models. The study included 40 patients divided into two groups: (i) 18 coinfected patients with schistosomiasis mansoni and HBV or HCV; and (ii) 22 HBV or HCV monoinfected patients. The serum samples were analyzed through H-1 NMR spectroscopy and the models were based on Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA). Ultrasonography examination was used to ascertain the diagnosis of periportal fibrosis. Exploratory analysis showed a clear separation between coinfected and monoinfected samples. The supervised model built from PLS-DA showed accuracy, R2 and Q2 values equal to 100%, 98.1% and 97.5%, respectively. According to the variable importance in the projection plot, lactate serum levels were higher in the coinfected group, while the signals attributed to HDL serum cholesterol were more intense in the monoinfected group. The metabonomics models constructed in this study are promising as an alternative tool for diagnosis of periportal fibrosis by schistosomiasis in patients with chronic HBV or HCV infection from endemic areas for Schistosoma mansoni.

Statistical process control of cocrystallization processes: A comparison between OPLS and PLS.

PubMed

Silva, Ana F T; Sarraguça, Mafalda Cruz; Ribeiro, Paulo R; Santos, Adenilson O; De Beer, Thomas; Lopes, João Almeida

2017-03-30

Orthogonal partial least squares regression (OPLS) is being increasingly adopted as an alternative to partial least squares (PLS) regression due to the better generalization that can be achieved. Particularly in multivariate batch statistical process control (BSPC), the use of OPLS for estimating nominal trajectories is advantageous. In OPLS, the nominal process trajectories are expected to be captured in a single predictive principal component while uncorrelated variations are filtered out to orthogonal principal components. In theory, OPLS will yield a better estimation of the Hotelling's T 2 statistic and corresponding control limits thus lowering the number of false positives and false negatives when assessing the process disturbances. Although OPLS advantages have been demonstrated in the context of regression, its use on BSPC was seldom reported. This study proposes an OPLS-based approach for BSPC of a cocrystallization process between hydrochlorothiazide and p-aminobenzoic acid monitored on-line with near infrared spectroscopy and compares the fault detection performance with the same approach based on PLS. A series of cocrystallization batches with imposed disturbances were used to test the ability to detect abnormal situations by OPLS and PLS-based BSPC methods. Results demonstrated that OPLS was generally superior in terms of sensibility and specificity in most situations. In some abnormal batches, it was found that the imposed disturbances were only detected with OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing a cross-border logistics policy using a performance measurement system framework: the case of Hong Kong and the Pearl River Delta region

NASA Astrophysics Data System (ADS)

Wong, David W. C.; Choy, K. L.; Chow, Harry K. H.; Lin, Canhong

2014-06-01

For the most rapidly growing economic entity in the world, China, a new logistics operation called the indirect cross-border supply chain model has recently emerged. The primary idea of this model is to reduce logistics costs by storing goods at a bonded warehouse with low storage cost in certain Chinese regions, such as the Pearl River Delta (PRD). This research proposes a performance measurement system (PMS) framework to assess the direct and indirect cross-border supply chain models. The PMS covers four categories including cost, time, quality and flexibility in the assessment of the performance of direct and indirect models. Furthermore, a survey was conducted to investigate the logistics performance of third party logistics (3PLs) at the PRD regions, including Guangzhou, Shenzhen and Hong Kong. The significance of the proposed PMS framework allows 3PLs accurately pinpoint the weakness and strengths of it current operations policy at four major performance measurement categories. Hence, this helps 3PLs further enhance the competitiveness and operations efficiency through better resources allocation at the area of warehousing and transportation.

Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine.

PubMed

da Silva, Neirivaldo Cavalcante; Honorato, Ricardo Saldanha; Pimentel, Maria Fernanda; Garrigues, Salvador; Cervera, Maria Luisa; de la Guardia, Miguel

2015-09-01

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2% w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples. © 2015 American Academy of Forensic Sciences.

Emerging approach for analytical characterization and geographical classification of Moroccan and French honeys by means of a voltammetric electronic tongue.

PubMed

El Alami El Hassani, Nadia; Tahri, Khalid; Llobet, Eduard; Bouchikhi, Benachir; Errachid, Abdelhamid; Zine, Nadia; El Bari, Nezha

2018-03-15

Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, cost-effective sample preparation process that does not require the use of additional chemicals is implemented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Three-way analysis of the UPLC-PDA dataset for the multicomponent quantitation of hydrochlorothiazide and olmesartan medoxomil in tablets by parallel factor analysis and three-way partial least squares.

PubMed

Dinç, Erdal; Ertekin, Zehra Ceren

2016-01-01

An application of parallel factor analysis (PARAFAC) and three-way partial least squares (3W-PLS1) regression models to ultra-performance liquid chromatography-photodiode array detection (UPLC-PDA) data with co-eluted peaks in the same wavelength and time regions was described for the multicomponent quantitation of hydrochlorothiazide (HCT) and olmesartan medoxomil (OLM) in tablets. Three-way dataset of HCT and OLM in their binary mixtures containing telmisartan (IS) as an internal standard was recorded with a UPLC-PDA instrument. Firstly, the PARAFAC algorithm was applied for the decomposition of three-way UPLC-PDA data into the chromatographic, spectral and concentration profiles to quantify the concerned compounds. Secondly, 3W-PLS1 approach was subjected to the decomposition of a tensor consisting of three-way UPLC-PDA data into a set of triads to build 3W-PLS1 regression for the analysis of the same compounds in samples. For the proposed three-way analysis methods in the regression and prediction steps, the applicability and validity of PARAFAC and 3W-PLS1 models were checked by analyzing the synthetic mixture samples, inter-day and intra-day samples, and standard addition samples containing HCT and OLM. Two different three-way analysis methods, PARAFAC and 3W-PLS1, were successfully applied to the quantitative estimation of the solid dosage form containing HCT and OLM. Regression and prediction results provided from three-way analysis were compared with those obtained by traditional UPLC method. Copyright © 2015 Elsevier B.V. All rights reserved.

Energy dispersive X-ray fluorescence and scattering assessment of soil quality via partial least squares and artificial neural networks analytical modeling approaches.

PubMed

Kaniu, M I; Angeyo, K H; Mwala, A K; Mwangi, F K

2012-08-30

Soil quality assessment (SQA) calls for rapid, simple and affordable but accurate analysis of soil quality indicators (SQIs). Routine methods of soil analysis are tedious and expensive. Energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometry in conjunction with chemometrics is a potentially powerful method for rapid SQA. In this study, a 25 m Ci (109)Cd isotope source XRF spectrometer was used to realize EDXRFS spectrometry of soils. Glycerol (a simulate of "organic" soil solution) and kaolin (a model clay soil) doped with soil micro (Fe, Cu, Zn) and macro (NO(3)(-), SO(4)(2-), H(2)PO(4)(-)) nutrients were used to train multivariate chemometric calibration models for direct (non-invasive) analysis of SQIs based on partial least squares (PLS) and artificial neural networks (ANN). The techniques were compared for each SQI with respect to speed, robustness, correction ability for matrix effects, and resolution of spectral overlap. The method was then applied to perform direct rapid analysis of SQIs in field soils. A one-way ANOVA test showed no statistical difference at 95% confidence interval between PLS and ANN results compared to reference soil nutrients. PLS was more accurate analyzing C, N, Na, P and Zn (R(2)>0.9) and low SEP of (0.05%, 0.01%, 0.01%, and 1.98 μg g(-1)respectively), while ANN was better suited for analysis of Mg, Cu and Fe (R(2)>0.9 and SEP of 0.08%, 4.02 μg g(-1), and 0.88 μg g(-1) respectively). Copyright © 2012 Elsevier B.V. All rights reserved.

Near infrared spectrometric technique for testing fruit quality: optimisation of regression models using genetic algorithms

NASA Astrophysics Data System (ADS)

Isingizwe Nturambirwe, J. Frédéric; Perold, Willem J.; Opara, Umezuruike L.

2016-02-01

Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality. Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum. A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.

[Measurement of Water COD Based on UV-Vis Spectroscopy Technology].

PubMed

Wang, Xiao-ming; Zhang, Hai-liang; Luo, Wei; Liu, Xue-mei

2016-01-01

Ultraviolet/visible (UV/Vis) spectroscopy technology was used to measure water COD. A total of 135 water samples were collected from Zhejiang province. Raw spectra with 3 different pretreatment methods (Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) and 1st Derivatives were compared to determine the optimal pretreatment method for analysis. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the preprocessed spectra were then used to select sensitive wavelengths by competitive adaptive reweighted sampling (CARS), Random frog and Successive Genetic Algorithm (GA) methods. Different numbers of sensitive wavelengths were selected by different variable selection methods with SNV preprocessing method. Partial least squares (PLS) was used to build models with the full spectra, and Extreme Learning Machine (ELM) was applied to build models with the selected wavelength variables. The overall results showed that ELM model performed better than PLS model, and the ELM model with the selected wavelengths based on CARS obtained the best results with the determination coefficient (R2), RMSEP and RPD were 0.82, 14.48 and 2.34 for prediction set. The results indicated that it was feasible to use UV/Vis with characteristic wavelengths which were obtained by CARS variable selection method, combined with ELM calibration could apply for the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Near-infrared reflectance spectroscopy predicts protein, starch, and seed weight in intact seeds of common bean ( Phaseolus vulgaris L.).

PubMed

Hacisalihoglu, Gokhan; Larbi, Bismark; Settles, A Mark

2010-01-27

The objective of this study was to explore the potential of near-infrared reflectance (NIR) spectroscopy to determine individual seed composition in common bean ( Phaseolus vulgaris L.). NIR spectra and analytical measurements of seed weight, protein, and starch were collected from 267 individual bean seeds representing 91 diverse genotypes. Partial least-squares (PLS) regression models were developed with 61 bean accessions randomly assigned to a calibration data set and 30 accessions assigned to an external validation set. Protein gave the most accurate PLS regression, with the external validation set having a standard error of prediction (SEP) = 1.6%. PLS regressions for seed weight and starch had sufficient accuracy for seed sorting applications, with SEP = 41.2 mg and 4.9%, respectively. Seed color had a clear effect on the NIR spectra, with black beans having a distinct spectral type. Seed coat color did not impact the accuracy of PLS predictions. This research demonstrates that NIR is a promising technique for simultaneous sorting of multiple seed traits in single bean seeds with no sample preparation.

Rapid Elemental Analysis and Provenance Study of Blumea balsamifera DC Using Laser-Induced Breakdown Spectroscopy

PubMed Central

Liu, Xiaona; Zhang, Qiao; Wu, Zhisheng; Shi, Xinyuan; Zhao, Na; Qiao, Yanjiang

2015-01-01

Laser-induced breakdown spectroscopy (LIBS) was applied to perform a rapid elemental analysis and provenance study of Blumea balsamifera DC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were implemented to exploit the multivariate nature of the LIBS data. Scores and loadings of computed principal components visually illustrated the differing spectral data. The PLS-DA algorithm showed good classification performance. The PLS-DA model using complete spectra as input variables had similar discrimination performance to using selected spectral lines as input variables. The down-selection of spectral lines was specifically focused on the major elements of B. balsamifera samples. Results indicated that LIBS could be used to rapidly analyze elements and to perform provenance study of B. balsamifera. PMID:25558999

Adaptive object tracking via both positive and negative models matching

NASA Astrophysics Data System (ADS)

Li, Shaomei; Gao, Chao; Wang, Yawen

2015-03-01

To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as abinary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm can not only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit.

PubMed

Liu, Changhong; Liu, Wei; Lu, Xuzhong; Ma, Fei; Chen, Wei; Yang, Jianbo; Zheng, Lei

2014-01-01

Multispectral imaging with 19 wavelengths in the range of 405-970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit.

SEM-PLS Analysis of Inhibiting Factors of Cost Performance for Large Construction Projects in Malaysia: Perspective of Clients and Consultants

PubMed Central

Memon, Aftab Hameed; Rahman, Ismail Abdul

2014-01-01

This study uncovered inhibiting factors to cost performance in large construction projects of Malaysia. Questionnaire survey was conducted among clients and consultants involved in large construction projects. In the questionnaire, a total of 35 inhibiting factors grouped in 7 categories were presented to the respondents for rating significant level of each factor. A total of 300 questionnaire forms were distributed. Only 144 completed sets were received and analysed using advanced multivariate statistical software of Structural Equation Modelling (SmartPLS v2). The analysis involved three iteration processes where several of the factors were deleted in order to make the model acceptable. The result of the analysis found that R 2 value of the model is 0.422 which indicates that the developed model has a substantial impact on cost performance. Based on the final form of the model, contractor's site management category is the most prominent in exhibiting effect on cost performance of large construction projects. This finding is validated using advanced techniques of power analysis. This vigorous multivariate analysis has explicitly found the significant category which consists of several causative factors to poor cost performance in large construction projects. This will benefit all parties involved in construction projects for controlling cost overrun. PMID:24693227

SEM-PLS analysis of inhibiting factors of cost performance for large construction projects in Malaysia: perspective of clients and consultants.

PubMed

Memon, Aftab Hameed; Rahman, Ismail Abdul

2014-01-01

This study uncovered inhibiting factors to cost performance in large construction projects of Malaysia. Questionnaire survey was conducted among clients and consultants involved in large construction projects. In the questionnaire, a total of 35 inhibiting factors grouped in 7 categories were presented to the respondents for rating significant level of each factor. A total of 300 questionnaire forms were distributed. Only 144 completed sets were received and analysed using advanced multivariate statistical software of Structural Equation Modelling (SmartPLS v2). The analysis involved three iteration processes where several of the factors were deleted in order to make the model acceptable. The result of the analysis found that R(2) value of the model is 0.422 which indicates that the developed model has a substantial impact on cost performance. Based on the final form of the model, contractor's site management category is the most prominent in exhibiting effect on cost performance of large construction projects. This finding is validated using advanced techniques of power analysis. This vigorous multivariate analysis has explicitly found the significant category which consists of several causative factors to poor cost performance in large construction projects. This will benefit all parties involved in construction projects for controlling cost overrun.

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Feasibility of the Simultaneous Determination of Monomer Concentrations and Particle Size in Emulsion Polymerization Using in Situ Raman Spectroscopy

PubMed Central

2015-01-01

An immersion Raman probe was used in emulsion copolymerization reactions to measure monomer concentrations and particle sizes. Quantitative determination of monomer concentrations is feasible in two-monomer copolymerizations, but only the overall conversion could be measured by Raman spectroscopy in a four-monomer copolymerization. The feasibility of measuring monomer conversion and particle size was established using partial least-squares (PLS) calibration models. A simplified theoretical framework for the measurement of particle sizes based on photon scattering is presented, based on the elastic-sphere-vibration and surface-tension models. PMID:26900256

Characterization and relative quantification of phospholipids based on methylation and stable isotopic labeling[S

PubMed Central

Cai, Tanxi; Shu, Qingbo; Liu, Peibin; Niu, Lili; Guo, Xiaojing; Ding, Xiang; Xue, Peng; Xie, Zhensheng; Wang, Jifeng; Zhu, Nali; Wu, Peng; Niu, Lili; Yang, Fuquan

2016-01-01

Phospholipids (PLs), one of the lipid categories, are not only the primary building blocks of cellular membranes, but also can be split to produce products that function as second messengers in signal transduction and play a pivotal role in numerous cellular processes, including cell growth, survival, and motility. Here, we present an integrated novel method that combines a fast and robust TMS-diazomethane-based phosphate derivatization and isotopic labeling strategy, which enables simultaneous profiling and relative quantification of PLs from biological samples. Our results showed that phosphate methylation allows fast and sensitive identification of the six major PL classes, including their lysophospholipid counterparts, under positive ionization mode. The isotopic labeling of endogenous PLs was achieved by deuterated diazomethane, which was generated through acid-catalyzed hydrogen/deuterium (H/D) exchange and methanolysis of TMS-diazomethane during the process of phosphate derivatization. The measured H/D ratios of unlabeled and labeled PLs, which were mixed in known proportions, indicated that the isotopic labeling strategy is capable of providing relative quantitation with adequate accuracy, reproducibility, and a coefficient of variation of 9.1%, on average. This novel method offers unique advantages over existing approaches and presents a powerful tool for research of PL metabolism and signaling. PMID:26733148

Crop/weed discrimination using near-infrared reflectance spectroscopy (NIRS)

NASA Astrophysics Data System (ADS)

Zhang, Yun; He, Yong

2006-09-01

The traditional uniform herbicide application often results in an over chemical residues on soil, crop plants and agriculture produce, which have imperiled the environment and food security. Near-infrared reflectance spectroscopy (NIRS) offers a promising means for weed detection and site-specific herbicide application. In laboratory, a total of 90 samples (30 for each species) of the detached leaves of two weeds, i.e., threeseeded mercury (Acalypha australis L.) and fourleafed duckweed (Marsilea quadrfolia L.), and one crop soybean (Glycine max) was investigated for NIRS on 325- 1075 nm using a field spectroradiometer. 20 absorbance samples of each species after pretreatment were exported and the lacked Y variables were assigned independent values for partial least squares (PLS) analysis. During the combined principle component analysis (PCA) on 400-1000 nm, the PC1 and PC2 could together explain over 91% of the total variance and detect the three plant species with 98.3% accuracy. The full-cross validation results of PLS, i.e., standard error of prediction (SEP) 0.247, correlation coefficient (r) 0.954 and root mean square error of prediction (RMSEP) 0.245, indicated an optimum model for weed identification. By predicting the remaining 10 samples of each species in the PLS model, the results with deviation presented a 100% crop/weed detection rate. Thus, it could be concluded that PLS was an available alternative of for qualitative weed discrimination on NTRS.

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

Altered brainstem auditory evoked potentials in a rat central sensitization model are similar to those in migraine

PubMed Central

Arakaki, Xianghong; Galbraith, Gary; Pikov, Victor; Fonteh, Alfred N.; Harrington, Michael G.

2014-01-01

Migraine symptoms often include auditory discomfort. Nitroglycerin (NTG)-triggered central sensitization (CS) provides a rodent model of migraine, but auditory brainstem pathways have not yet been studied in this example. Our objective was to examine brainstem auditory evoked potentials (BAEPs) in rat CS as a measure of possible auditory abnormalities. We used four subdermal electrodes to record horizontal (h) and vertical (v) dipole channel BAEPs before and after injection of NTG or saline. We measured the peak latencies (PLs), interpeak latencies (IPLs), and amplitudes for detectable waveforms evoked by 8, 16, or 32 KHz auditory stimulation. At 8 KHz stimulation, vertical channel positive PLs of waves 4, 5, and 6 (vP4, vP5, and vP6), and related IPLs from earlier negative or positive peaks (vN1-vP4, vN1-vP5, vN1-vP6; vP3-vP4, vP3-vP6) increased significantly 2 hours after NTG injection compared to the saline group. However, BAEP peak amplitudes at all frequencies, PLs and IPLs from the horizontal channel at all frequencies, and the vertical channel stimulated at 16 and 32 KHz showed no significant/consistent change. For the first time in the rat CS model, we show that BAEP PLs and IPLs ranging from putative bilateral medial superior olivary nuclei (P4) to the more rostral structures such as the medial geniculate body (P6) were prolonged 2 hours after NTG administration. These BAEP alterations could reflect changes in neurotransmitters and/or hypoperfusion in the midbrain. The similarity of our results with previous human studies further validates the rodent CS model for future migraine research. PMID:24680742

Geographical provenance of palm oil by fatty acid and volatile compound fingerprinting techniques.

PubMed

Tres, A; Ruiz-Samblas, C; van der Veer, G; van Ruth, S M

2013-04-15

Analytical methods are required in addition to administrative controls to verify the geographical origin of vegetable oils such as palm oil in an objective manner. In this study the application of fatty acid and volatile organic compound fingerprinting in combination with chemometrics have been applied to verify the geographical origin of crude palm oil (continental scale). For this purpose 94 crude palm oil samples were collected from South East Asia (55), South America (11) and Africa (28). Partial least squares discriminant analysis (PLS-DA) was used to develop a hierarchical classification model by combining two consecutive binary PLS-DA models. First, a PLS-DA model was built to distinguish South East Asian from non-South East Asian palm oil samples. Then a second model was developed, only for the non-Asian samples, to discriminate African from South American crude palm oil. Models were externally validated by using them to predict the identity of new authentic samples. The fatty acid fingerprinting model revealed three misclassified samples. The volatile compound fingerprinting models showed an 88%, 100% and 100% accuracy for the South East Asian, African and American class, respectively. The verification of the geographical origin of crude palm oil is feasible by fatty acid and volatile compound fingerprinting. Further research is required to further validate the approach and to increase its spatial specificity to country/province scale. Copyright © 2012 Elsevier Ltd. All rights reserved.

Chemical spoilage extent traceability of two kinds of processed pork meats using one multispectral system developed by hyperspectral imaging combined with effective variable selection methods.

PubMed

Cheng, Weiwei; Sun, Da-Wen; Pu, Hongbin; Wei, Qingyi

2017-04-15

The feasibility of hyperspectral imaging (HSI) (400-1000nm) for tracing the chemical spoilage extent of the raw meat used for two kinds of processed meats was investigated. Calibration models established separately for salted and cooked meats using full wavebands showed good results with the determination coefficient in prediction (R 2 P ) of 0.887 and 0.832, respectively. For simplifying the calibration models, two variable selection methods were used and compared. The results showed that genetic algorithm-partial least squares (GA-PLS) with as much continuous wavebands selected as possible always had better performance. The potential of HSI to develop one multispectral system for simultaneously tracing the chemical spoilage extent of the two kinds of processed meats was also studied. Good result with an R 2 P of 0.854 was obtained using GA-PLS as the dimension reduction method, which was thus used to visualize total volatile base nitrogen (TVB-N) contents corresponding to each pixel of the image. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantification of extra virgin olive oil in dressing and edible oil blends using the representative TMS-4,4'-desmethylsterols gas-chromatographic-normalized fingerprint.

PubMed

Pérez-Castaño, Estefanía; Sánchez-Viñas, Mercedes; Gázquez-Evangelista, Domingo; Bagur-González, M Gracia

2018-01-15

This paper describes and discusses the application of trimethylsilyl (TMS)-4,4'-desmethylsterols derivatives chromatographic fingerprints (obtained from an off-line HPLC-GC-FID system) for the quantification of extra virgin olive oil in commercial vinaigrettes, dressing salad and in-house reference materials (i-HRM) using two different Partial Least Square-Regression (PLS-R) multivariate quantification methods. Different data pre-processing strategies were carried out being the whole one: (i) internal normalization; (ii) sampling based on The Nyquist Theorem; (iii) internal correlation optimized shifting, icoshift; (iv) baseline correction (v) mean centering and (vi) selecting zones. The first model corresponds to a matrix of dimensions 'n×911' variables and the second one to a matrix of dimensions 'n×431' variables. It has to be highlighted that the proposed two PLS-R models allow the quantification of extra virgin olive oil in binary blends, foodstuffs, etc., when the provided percentage is greater than 25%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Visible micro-Raman spectroscopy for determining glucose content in beverage industry.

PubMed

Delfino, I; Camerlingo, C; Portaccio, M; Ventura, B Della; Mita, L; Mita, D G; Lepore, M

2011-07-15

The potential of Raman spectroscopy with excitation in the visible as a tool for quantitative determination of single components in food industry products was investigated by focusing the attention on glucose content in commercial sport drinks. At this aim, micro-Raman spectra in the 600-1600cm(-1) wavenumber shift region of four sport drinks were recorded, showing well defined and separated vibrational fingerprints of the various contained sugars (glucose, fructose and sucrose). By profiting of the spectral separation of some peculiar peaks, glucose content was quantified by using a multivariate statistical analysis based on the interval Partial Least Square (iPLS) approach. The iPLS model needed for data analysis procedure was built by using glucose aqueous solutions at known sugar concentrations as calibration data. This model was then applied to sport drink spectra and gave predicted glucose concentrations in good agreement with the values obtained by using a biochemical assay. These results represent a significant step towards the development of a fast and simple method for the on-line glucose quantification in products of food and beverage industry. Copyright © 2011 Elsevier Ltd. All rights reserved.

An Electrochemical Impedance Spectroscopy System for Monitoring Pineapple Waste Saccharification

PubMed Central

Conesa, Claudia; Ibáñez Civera, Javier; Seguí, Lucía; Fito, Pedro; Laguarda-Miró, Nicolás

2016-01-01

Electrochemical impedance spectroscopy (EIS) has been used for monitoring the enzymatic pineapple waste hydrolysis process. The system employed consists of a device called Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) equipped with a specific software application and a stainless steel double needle electrode. EIS measurements were conducted at different saccharification time intervals: 0, 0.75, 1.5, 6, 12 and 24 h. Partial least squares (PLS) were used to model the relationship between the EIS measurements and the sugar determination by HPAEC-PAD. On the other hand, artificial neural networks: (multilayer feed forward architecture with quick propagation training algorithm and logistic-type transfer functions) gave the best results as predictive models for glucose, fructose, sucrose and total sugars. Coefficients of determination (R2) and root mean square errors of prediction (RMSEP) were determined as R2 > 0.944 and RMSEP < 1.782 for PLS and R2 > 0.973 and RMSEP < 0.486 for artificial neural networks (ANNs), respectively. Therefore, a combination of both an EIS-based technique and ANN models is suggested as a promising alternative to the traditional laboratory techniques for monitoring the pineapple waste saccharification step. PMID:26861317

The subgingival microbiota of Papillon-Lefèvre syndrome.

PubMed

Albandar, Jasim M; Khattab, Razan; Monem, Fawza; Barbuto, Sara M; Paster, Bruce J

2012-07-01

There is little information about the microbiologic profiles of periodontal lesions in Papillon-Lefèvre syndrome (PLS) and the significance of bacteria in the pathogenesis of periodontitis in these patients. This comprehensive analysis of the subgingival microbiota in patients with PLS used 16S ribosomal RNA (rRNA) clonal analysis and the 16S rRNA-based Human Oral Microbe Identification Microarray (HOMIM). Thirteen patients with PLS from seven unrelated families volunteered for this microbiologic study. Subgingival plaque was collected with sterile paper points from multiple sites with ≥5 mm probing depth, and whole genomic DNA was extracted. The 16S rRNA genes were amplified, cloned, and sequenced. The samples were then probed for ≈300 predominant oral bacterial species using the HOMIM. The most commonly detected phylotypes in the clonal analysis were Gemella morbillorum, Gemella haemolysans, Granulicatella adiacens, Lachnospiraceae OT 100 (EI074), Parvimonas micra, Selenomonas noxia, and Veillonella parvula. As a group, streptococci were commonly detected in these individuals. In the HOMIM analysis, a total of 170 bacterial species/phylotypes were detected, with a range of 40 to 80 species per patient with PLS. Of these, 12 bacterial species were detected in medium to high levels in ≥50% of the individuals. The high-frequency strains were clustered into eight groups: Aggregatibacter actinomycetemcomitans, Campylobacter spp., Capnocytophaga granulosa, G. morbillorum, P. micra, Porphyromonas endodontalis, Streptococcus spp., and Tannerella forsythia. The subgingival microbiota in PLS is diverse. Periodontal pathogens commonly associated with chronic and aggressive periodontitis and opportunistic pathogens may be associated with the development of severe periodontitis in patients with PLS.

Tissue-selective alteration of ethanolamine plasmalogen metabolism in dedifferentiated colon mucosa.

PubMed

Lopez, Daniel H; Bestard-Escalas, Joan; Garate, Jone; Maimó-Barceló, Albert; Fernández, Roberto; Reigada, Rebeca; Khorrami, Sam; Ginard, Daniel; Okazaki, Toshiro; Fernández, José A; Barceló-Coblijn, Gwendolyn

2018-08-01

Human colon lipid analysis by imaging mass spectrometry (IMS) demonstrates that the lipid fingerprint is highly sensitive to a cell's pathophysiological state. Along the colon crypt axis, and concomitant to the differentiation process, certain lipid species tightly linked to signaling (phosphatidylinositols and arachidonic acid (AA)-containing diacylglycerophospholipids), change following a rather simple mathematical expression. We extend here our observations to ethanolamine plasmalogens (PlsEtn), a unique type of glycerophospholipid presenting a vinyl ether linkage at sn-1 position. PlsEtn distribution was studied in healthy, adenomatous, and carcinomatous colon mucosa sections by IMS. In epithelium, 75% of PlsEtn changed in a highly regular manner along the crypt axis, in clear contrast with diacyl species (67% of which remained constant). Consistently, AA-containing PlsEtn species were more abundant at the base, where stem cells reside, and decreased while ascending the crypt. In turn, mono-/diunsaturated species experienced the opposite change. These gradients were accompanied by a gradual expression of ether lipid synthesis enzymes. In lamina propria, 90% of stromal PlsEtn remained unchanged despite the high content of AA and the gradient in AA-containing diacylglycerophospholipids. Finally, both lipid and protein gradients were severely affected in polyps and carcinoma. These results link PlsEtn species regulation to cell differentiation for the first time and confirm that diacyl and ether species are differently regulated. Furthermore, they reaffirm the observations on cell lipid fingerprint image sensitivity to predict cell pathophysiological status, reinforcing the translational impact both lipidome and IMS might have in clinical research. Copyright © 2018 Elsevier B.V. All rights reserved.

Human Milk Plasmalogens Are Highly Enriched in Long-Chain PUFAs.

PubMed

Moukarzel, Sara; Dyer, Roger A; Keller, Bernd O; Elango, Rajavel; Innis, Sheila M

2016-11-01

Human milk contains unique glycerophospholipids, including ethanolamine-containing plasmalogens (Pls-PEs) in the milk fat globule membrane, which have been implicated in infant brain development. Brain Pls-PEs accumulate postnatally and are enriched in long-chain polyunsaturated fatty acids (LC-PUFAs), particularly docosahexaenoic acid (DHA). Fatty acid (FA) composition of Pls-PEs in milk is poorly understood because of the analytical challenges in separating Pls-PEs from other phospholipids in the predominating presence of triacylglycerols. The variability of Pls-PE FAs and the potential role of maternal diet remain unknown. Our primary objectives were to establish improved methodology for extracting Pls-PEs from human milk, enabling FA analysis, and to compare FA composition between Pls-PEs and 2 major milk phospholipids, phosphatidylcholine and phosphatidylethanolamine. Our secondary objective was to explore associations between maternal DHA intake and DHA in milk phospholipids and variability in phospholipid-DHA within a woman. Mature milk was collected from 25 women, with 4 providing 3 milk samples on 3 separate days. Lipids were extracted, and phospholipids were removed by solid phase extraction. Pls-PEs were separated by using normal-phase HPLC, recovered and analyzed for FAs by GLC. Diet was assessed by using a validated food-frequency questionnaire. Pls-PE concentration in human milk was significantly higher in LC-PUFAs than phosphatidylethanolamine and phosphatidylcholine, including arachidonic acid (AA) and DHA. The mean ± SD concentration of AAs in Pls-PEs was ∼2.5-fold higher than in phosphatidylethanolamine (10.5 ± 1.71 and 3.82 ± 0.92 g/100 g, respectively). DHA in Pls-PEs varied across women (0.95-6.51 g/100 g), likely independent of maternal DHA intake. Pls-PE DHA also varied within a woman across days (CV ranged from 9.8% to 28%). Human milk provides the infant with LC-PUFAs from multiple lipid pools, including a source from Pls-PEs. The biological determinants of Pls-PE FAs and physiological relevance to the breastfed infant remain to be elucidated. © 2016 American Society for Nutrition.

Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, G.; Yule, I. J.

2017-06-01

New Zealand farming relies heavily on grazed pasture for feeding livestock; therefore it is important to provide high quality palatable grass in order to maintain profitable and sustainable grassland management. The presence of non-photosynthetic vegetation (NPV) such as dead vegetation in pastures severely limits the quality and productivity of pastures. Quantifying the fraction of dead vegetation in mixed pastures is a great challenge even with remote sensing approaches. In this study, a high spatial resolution with pixel resolution of 1 m and spectral resolution of 3.5-5.6 nm imaging spectroscopy data from AisaFENIX (380-2500 nm) was used to assess the fraction of dead vegetation component in mixed pastures on a hill country farm in New Zealand. We used different methods to retrieve dead vegetation fraction from the spectra; narrow band vegetation indices, full spectrum based partial least squares (PLS) regression and feature selection based PLS regression. Among all approaches, feature selection based PLS model exhibited better performance in terms of prediction accuracy (R2CV = 0.73, RMSECV = 6.05, RPDCV = 2.25). The results were consistent with validation data, and also performed well on the external test data (R2 = 0.62, RMSE = 8.06, RPD = 2.06). In addition, statistical tests were conducted to ascertain the effect of topographical variables such as slope and aspect on the accumulation of the dead vegetation fraction. Steep slopes (>25°) had a significantly (p < 0.05) higher amount of dead vegetation. In contrast, aspect showed non-significant impact on dead vegetation accumulation. The results from the study indicate that AisaFENIX imaging spectroscopy data could be a useful tool for mapping the dead vegetation fraction accurately.

Carbon Nanotube Membranes for Water Purification

NASA Astrophysics Data System (ADS)

Bakajin, Olgica

2009-03-01

Carbon nanotubes are an excellent platform for the fundamental studies of transport through channels commensurate with molecular size. Water transport through carbon nanotubes is also believed to be similar to transport in biological channels such as aquaporins. I will discuss the transport of gas, water and ions through microfabricated membranes with sub-2 nanometer aligned carbon nanotubes as ideal atomically-smooth pores. The measured gas flow through carbon nanotubes exceeded predictions of the Knudsen diffusion model by more than an order of magnitude. The measured water flow exceeded values calculated from continuum hydrodynamics models by more than three orders of magnitude and is comparable to flow rates extrapolated from molecular dynamics simulations and measured for aquaporins. More recent reverse osmosis experiments reveal ion rejection by our membranes. Based on our experimental findings, the current understanding of the fundamentals of water and gas transport and of ion rejection will be discussed. The potential application space that exploits these unique nanofluidic phenomena will be explored. The extremely high permeabilities of these membranes, combined with their small pore size will enable energy efficient filtration and eventually decrease the cost of water purification.[4pt] In collaboration with Francesco Fornasiero, Biosciences and Biotechnology Division, PLS, LLNL, Livermore, CA 94550; Sangil Kim, NSF Center for Biophotonics Science & Technology, University of California at Davis, Sacramento CA 95817; Jung Bin In, Mechanical Engineering Department, UC Berkeley, Berkeley CA 94720; Hyung Gyu Park, Jason K Holt, and Michael Stadermann, Biosciences and Biotechnology Division, PLS, LLNL; Costas P. Grigoropoulos, Mechanical Engineering Department, UC Berkeley; Aleksandr Noy, Biosciences and Biotechnology Division, PLS, LLNL and School of Natural Sciences, University of California at Merced.

A nonpolar, nonamphiphilic molecule can accelerate adsorption of phospholipids and lower their surface tension at the air/water interface.

PubMed

Nguyen, Phuc Nghia; Trinh Dang, Thuan Thao; Waton, Gilles; Vandamme, Thierry; Krafft, Marie Pierre

2011-10-04

The adsorption dynamics of a series of phospholipids (PLs) at the interface between an aqueous solution or dispersion of the PL and a gas phase containing the nonpolar, nonamphiphilic linear perfluorocarbon perfluorohexane (PFH) was studied by bubble profile analysis tensiometry. The PLs investigated were dioctanoylphosphatidylcholine (DiC(8)-PC), dilaurylphosphatidylcholine, dimyristoylphosphatidylcholine, and dipalmitoylphosphatidylcholine. The gas phase consisted of air or air saturated with PFH. The perfluorocarbon gas was found to have an unexpected, strong effect on both the adsorption rate and the equilibrium interfacial tension (γ(eq)) of the PLs. First, for all of the PLs, and at all concentrations investigated, the γ(eq) values were significantly lower (by up to 10 mN m(-1)) when PFH was present in the gas phase. The efficacy of PFH in decreasing γ(eq) depends on the ability of PLs to form micelles or vesicles in water. For vesicles, it also depends on the gel or fluid state of the membranes. Second, the adsorption rates of all the PLs at the interface (as assessed by the time required for the initial interfacial tension to be reduced by 30%) are significantly accelerated (by up to fivefold) by the presence of PFH for the lower PL concentrations. Both the surface-tension reducing effect and the adsorption rate increasing effect establish that PFH has a strong interaction with the PL monolayer and acts as a cosurfactant at the interface, despite the absence of any amphiphilic character. Fitting the adsorption profiles of DiC(8)-PC at the PFH-saturated air/aqueous solution interface with the modified Frumkin model indicated that the PFH molecule lay horizontally at the interface. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identification of a Functional Plasmodesmal Localization Signal in a Plant Viral Cell-To-Cell-Movement Protein.

PubMed

Yuan, Cheng; Lazarowitz, Sondra G; Citovsky, Vitaly

2016-01-19

Our fundamental knowledge of the protein-sorting pathways required for plant cell-to-cell trafficking and communication via the intercellular connections termed plasmodesmata has been severely limited by the paucity of plasmodesmal targeting sequences that have been identified to date. To address this limitation, we have identified the plasmodesmal localization signal (PLS) in the Tobacco mosaic virus (TMV) cell-to-cell-movement protein (MP), which has emerged as the paradigm for dissecting the molecular details of cell-to-cell transport through plasmodesmata. We report here the identification of a bona fide functional TMV MP PLS, which encompasses amino acid residues between positions 1 and 50, with residues Val-4 and Phe-14 potentially representing critical sites for PLS function that most likely affect protein conformation or protein interactions. We then demonstrated that this PLS is both necessary and sufficient for protein targeting to plasmodesmata. Importantly, as TMV MP traffics to plasmodesmata by a mechanism that is distinct from those of the three plant cell proteins in which PLSs have been reported, our findings provide important new insights to expand our understanding of protein-sorting pathways to plasmodesmata. The science of virology began with the discovery of Tobacco mosaic virus (TMV). Since then, TMV has served as an experimental and conceptual model for studies of viruses and dissection of virus-host interactions. Indeed, the TMV cell-to-cell-movement protein (MP) has emerged as the paradigm for dissecting the molecular details of cell-to-cell transport through the plant intercellular connections termed plasmodesmata. However, one of the most fundamental and key functional features of TMV MP, its putative plasmodesmal localization signal (PLS), has not been identified. Here, we fill this gap in our knowledge and identify the TMV MP PLS. Copyright © 2016 Yuan et al.

Purified human MDR 1 modulates membrane potential in reconstituted proteoliposomes.

PubMed

Howard, Ellen M; Roepe, Paul D

2003-04-01

Human multidrug resistance (hu MDR 1) cDNA was fused to a P. shermanii transcarboxylase biotin acceptor domain (TCBD), and the fusion protein was heterologously overexpressed at high yield in K(+)-uptake deficient Saccharomyces cerevisiae yeast strain 9.3, purified by avidin-biotin chromatography, and reconstituted into proteoliposomes (PLs) formed with Escherichia coli lipid. As measured by pH- dependent ATPase activity, purified, reconstituted, biotinylated MDR-TCBD protein is fully functional. Dodecyl maltoside proved to be the most effective detergent for the membrane solubilization of MDR-TCBD, and various salts were found to significantly affect reconstitution into PLs. After extensive analysis, we find that purified reconstituted MDR-TCBD protein does not catalyze measurable H(+) pumping in the presence of ATP. In the presence of physiologic [ATP], K(+)/Na(+) diffusion potentials monitored by either anionic oxonol or cationic carbocyanine are easily established upon addition of valinomycin to either control or MDR-TCBD PLs. However, in the absence of ATP, although control PLs still maintain easily measurable K(+)/Na(+) diffusion potentials upon addition of valinomycin, MDR-TCBD PLs do not. Dissipation of potential by MDR-TCBD is clearly [ATP] dependent and also appears to be Cl(-) dependent, since replacing Cl(-) with equimolar glutamate restores the ability of MDR-TCBD PLs to form a membrane potential in the absence of physiologic [ATP]. The data are difficult to reconcile with models that might propose ATP-catalyzed "pumping" of the fluorescent probes we use and are more consistent with electrically passive anion transport via MDR-TCBD protein, but only at low [ATP]. These observations may help to resolve the confusing array of data related to putative ion transport by hu MDR 1 protein.

[NIR Assignment of Magnolol by 2D-COS Technology and Model Application Huoxiangzhengqi Oral Liduid].

PubMed

Pei, Yan-ling; Wu, Zhi-sheng; Shi, Xin-yuan; Pan, Xiao-ning; Peng, Yan-fang; Qiao, Yan-jiang

2015-08-01

Near infrared (NIR) spectroscopy assignment of Magnolol was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. According to the synchronous spectra of deuterated chloroform solvent and Magnolol, 1365~1455, 1600~1720, 2000~2181 and 2275~2465 nm were the characteristic absorption of Magnolol. Connected with the structure of Magnolol, 1440 nm was the stretching vibration of phenolic group O-H, 1679 nm was the stretching vibration of aryl and methyl which connected with aryl, 2117, 2304, 2339 and 2370 nm were the combination of the stretching vibration, bending vibration and deformation vibration for aryl C-H, 2445 nm were the bending vibration of methyl which linked with aryl group, these bands attribut to the characteristics of Magnolol. Huoxiangzhengqi Oral Liduid was adopted to study the Magnolol, the characteristic band by spectral assignment and the band by interval Partial Least Squares (iPLS) and Synergy interval Partial Least Squares (SiPLS) were used to establish Partial Least Squares (PLS) quantitative model, the coefficient of determination Rcal(2) and Rpre(2) were greater than 0.99, the Root Mean of Square Error of Calibration (RM-SEC), Root Mean of Square Error of Cross Validation (RMSECV) and Root Mean of Square Error of Prediction (RMSEP) were very small. It indicated that the characteristic band by spectral assignment has the same results with the Chemometrics in PLS model. It provided a reference for NIR spectral assignment of chemical compositions in Chinese Materia Medica, and the band filters of NIR were interpreted.

Characterization of human breast cancer tissues by infrared imaging.

PubMed

Verdonck, M; Denayer, A; Delvaux, B; Garaud, S; De Wind, R; Desmedt, C; Sotiriou, C; Willard-Gallo, K; Goormaghtigh, E

2016-01-21

Fourier Transform InfraRed (FTIR) spectroscopy coupled to microscopy (IR imaging) has shown unique advantages in detecting morphological and molecular pathologic alterations in biological tissues. The aim of this study was to evaluate the potential of IR imaging as a diagnostic tool to identify characteristics of breast epithelial cells and the stroma. In this study a total of 19 breast tissue samples were obtained from 13 patients. For 6 of the patients, we also obtained Non-Adjacent Non-Tumor tissue samples. Infrared images were recorded on the main cell/tissue types identified in all breast tissue samples. Unsupervised Principal Component Analyses and supervised Partial Least Square Discriminant Analyses (PLS-DA) were used to discriminate spectra. Leave-one-out cross-validation was used to evaluate the performance of PLS-DA models. Our results show that IR imaging coupled with PLS-DA can efficiently identify the main cell types present in FFPE breast tissue sections, i.e. epithelial cells, lymphocytes, connective tissue, vascular tissue and erythrocytes. A second PLS-DA model could distinguish normal and tumor breast epithelial cells in the breast tissue sections. A patient-specific model reached particularly high sensitivity, specificity and MCC rates. Finally, we showed that the stroma located close or at distance from the tumor exhibits distinct spectral characteristics. In conclusion FTIR imaging combined with computational algorithms could be an accurate, rapid and objective tool to identify/quantify breast epithelial cells and differentiate tumor from normal breast tissue as well as normal from tumor-associated stroma, paving the way to the establishment of a potential complementary tool to ensure safe tumor margins.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

PubMed

Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-05-30

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 Elsevier B.V. All rights reserved.

Learning Leadership: A Case Study on Influences of a Leadership Training Program on the Practices of One Group of Urban School Superintendents

ERIC Educational Resources Information Center

Chace, Sarah Valentine

2013-01-01

This study undertook to examine the effects of a unique leadership-training program on one group of urban school superintendents. This two-year program, called the Program for Leading Superintendents (PLS), was largely based on concepts of Heifetz's adaptive leadership model. The purpose of the research on the effects of this program was to…

Screening method for rapid classification of psychoactive substances in illicit tablets using mid infrared spectroscopy and PLS-DA.

PubMed

Pereira, Leandro S A; Lisboa, Fernanda L C; Coelho Neto, José; Valladão, Frederico N; Sena, Marcelo M

2018-05-09

Several new psychoactive substances (NPS) have reached the illegal drug market in recent years, and ecstasy-like tablets are one of the forms affected by this change. Cathinones and tryptamines have increasingly been found in ecstasy-like seized samples as well as other amphetamine type stimulants. A presumptive method for identifying different drugs in seized ecstasy tablets (n=92) using ATR-FTIR (attenuated total reflectance - Fourier transform infrared spectroscopy) and PLS-DA (partial least squares discriminant analysis) was developed. A hierarchical strategy of sequential modeling was performed with PLS-DA. The main model discriminated four classes: 5-MeO-MIPT, methylenedioxyamphetamines (MDMA and MDA), methamphetamine, and cathinones. Two submodels were built to identify drugs present in MDs and cathinones classes. Models were validated through the estimate of figures of merit. The average reliability rate (RLR) of the main model was 96.8% and accordance (ACC) was 100%. For the submodels, RLR and ACC were 100%. The reliability of the models was corroborated through their spectral interpretation. Thus, spectral assignments were performed by associating informative vectors of each specific modeled class to the respective drugs. The developed method is simple, fast, and can be applied to the forensic laboratory routine, leading to objective results reports useful for forensic scientists and law enforcement. Copyright © 2018 Elsevier B.V. All rights reserved.

Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors.

PubMed

Zhang, Jianfeng; Han, Wenting; Huang, Lvwen; Zhang, Zhiyong; Ma, Yimian; Hu, Yamin

2016-03-25

The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis.

Characterization of postmortem biochemical changes in rabbit plasma using ATR-FTIR combined with chemometrics: A preliminary study

NASA Astrophysics Data System (ADS)

Zhang, Ji; Li, Bing; Wang, Qi; Li, Chengzhi; Zhang, Yinming; Lin, Hancheng; Wang, Zhenyuan

2017-02-01

Postmortem interval (PMI) determination is one of the most challenging tasks in forensic medicine due to a lack of accurate and reliable methods. It is especially difficult for late PMI determination. Although many attempts with various types of body fluids based on chemical methods have been made to solve this problem, few investigations are focused on blood samples. In this study, we employed an attenuated total reflection (ATR)-Fourier transform infrared (FTIR) technique coupled with principle component analysis (PCA) to monitor biochemical changes in rabbit plasma with increasing PMI. Partial least square (PLS) model was used based on the spectral data for PMI prediction in an independent sample set. Our results revealed that postmortem chemical changes in compositions of the plasma were time-dependent, and various components including proteins, lipids and nucleic acids contributed to the discrimination of the samples at different time points. A satisfactory prediction within 48 h postmortem was performed by the combined PLS model with a good fitting between actual and predicted PMI of 0.984 and with an error of ± 1.92 h. In consideration of the simplicity and portability of ATR-FTIR, our preliminary study provides an experimental and theoretical basis for application of this technique in forensic practice.

Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves

PubMed Central

Zhang, Chu; Liu, Fei; Kong, Wenwen; He, Yong

2015-01-01

Visible and near-infrared hyperspectral imaging covering spectral range of 380–1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500–900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (rp) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with rp of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves. PMID:26184198

Potential of a newly developed high-speed near-infrared (NIR) camera (Compovision) in polymer industrial analyses: monitoring crystallinity and crystal evolution of polylactic acid (PLA) and concentration of PLA in PLA/Poly-(R)-3-hydroxybutyrate (PHB) blends.

PubMed

Ishikawa, Daitaro; Nishii, Takashi; Mizuno, Fumiaki; Sato, Harumi; Kazarian, Sergei G; Ozaki, Yukihiro

2013-12-01

This study was carried out to evaluate a new high-speed hyperspectral near-infrared (NIR) camera named Compovision. Quantitative analyses of the crystallinity and crystal evolution of biodegradable polymer, polylactic acid (PLA), and its concentration in PLA/poly-(R)-3-hydroxybutyrate (PHB) blends were investigated using near-infrared (NIR) imaging. This NIR camera can measure two-dimensional NIR spectral data in the 1000-2350 nm region obtaining images with wide field of view of 150 × 250 mm(2) (approximately 100  000 pixels) at high speeds (in less than 5 s). PLA with differing crystallinities between 0 and 50% blended samples with PHB in ratios of 80/20, 60/40, 40/60, 20/80, and pure films of 100% PLA and PHB were prepared. Compovision was used to collect respective NIR spectra in the 1000-2350 nm region and investigate the crystallinity of PLA and its concentration in the blends. The partial least squares (PLS) regression models for the crystallinity of PLA were developed using absorbance, second derivative, and standard normal variate (SNV) spectra from the most informative region of the spectra, between 1600 and 2000 nm. The predicted results of PLS models achieved using the absorbance and second derivative spectra were fairly good with a root mean square error (RMSE) of less than 6.1% and a determination of coefficient (R(2)) of more than 0.88 for PLS factor 1. The results obtained using the SNV spectra yielded the best prediction with the smallest RMSE of 2.93% and the highest R(2) of 0.976. Moreover, PLS models developed for estimating the concentration of PLA in the blend polymers using SNV spectra gave good predicted results where the RMSE was 4.94% and R(2) was 0.98. The SNV-based models provided the best-predicted results, since it can reduce the effects of the spectral changes induced by the inhomogeneity and the thickness of the samples. Wide area crystal evolution of PLA on a plate where a temperature slope of 70-105 °C had occurred was also monitored using NIR imaging. An SNV-based image gave an obvious contrast of the crystallinity around the crystal growth area according to slight temperature change. Moreover, it clarified the inhomogeneity of crystal evolution over the significant wide area. These results have proved that the newly developed hyperspectral NIR camera, Compovision, can be successfully used to study polymers for industrial processes, such as monitoring the crystallinity of PLA and the different composition of PLA/PHB blends.

An Electrochemical Quartz Crystal Microbalance Multisensor System Based on Phthalocyanine Nanostructured Films: Discrimination of Musts

PubMed Central

Garcia-Hernandez, Celia; Medina-Plaza, Cristina; Garcia-Cabezon, Cristina; Martin-Pedrosa, Fernando; del Valle, Isabel; de Saja, Jose Antonio; Rodríguez-Méndez, Maria Luz

2015-01-01

An array of electrochemical quartz crystal electrodes (EQCM) modified with nanostructured films based on phthalocyanines was developed and used to discriminate musts prepared from different varieties of grapes. Nanostructured films of iron, nickel and copper phthalocyanines were deposited on Pt/quartz crystals through the Layer by Layer technique by alternating layers of the corresponding phthalocyanine and poly-allylamine hydrochloride. Simultaneous electrochemical and mass measurements were used to study the mass changes accompanying the oxidation of electroactive species present in must samples obtained from six Spanish varieties of grapes (Juan García, Prieto Picudo, Mencía Regadío, Cabernet Sauvignon, Garnacha and Tempranillo). The mass and voltammetric outputs were processed using three-way models. Parallel Factor Analysis (PARAFAC) was successfully used to discriminate the must samples according to their variety. Multi-way partial least squares (N-PLS) evidenced the correlations existing between the voltammetric data and the polyphenolic content measured by chemical methods. Similarly, N-PLS showed a correlation between mass outputs and parameters related to the sugar content. These results demonstrated that electronic tongues based on arrays of EQCM sensors can offer advantages over arrays of mass or voltammetric sensors used separately. PMID:26610494

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions between two at-line instruments installed at two liquid detergent production plants.

PubMed

Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T

2017-09-01

Calibration transfer of partial least squares (PLS) quantification models is established between two Raman spectrometers located at two liquid detergent production plants. As full recalibration of existing calibration models is time-consuming, labour-intensive and costly, it is investigated whether the use of mathematical correction methods requiring only a handful of standardization samples can overcome the dissimilarities in spectral response observed between both measurement systems. Univariate and multivariate standardization approaches are investigated, ranging from simple slope/bias correction (SBC), local centring (LC) and single wavelength standardization (SWS) to more complex direct standardization (DS) and piecewise direct standardization (PDS). The results of these five calibration transfer methods are compared reciprocally, as well as with regard to a full recalibration. Four PLS quantification models, each predicting the concentration of one of the four main ingredients in the studied liquid detergent composition, are aimed at transferring. Accuracy profiles are established from the original and transferred quantification models for validation purposes. A reliable representation of the calibration models performance before and after transfer is thus established, based on β-expectation tolerance intervals. For each transferred model, it is investigated whether every future measurement that will be performed in routine will be close enough to the unknown true value of the sample. From this validation, it is concluded that instrument standardization is successful for three out of four investigated calibration models using multivariate (DS and PDS) transfer approaches. The fourth transferred PLS model could not be validated over the investigated concentration range, due to a lack of precision of the slave instrument. Comparing these transfer results to a full recalibration on the slave instrument allows comparison of the predictive power of both Raman systems and leads to the formulation of guidelines for further standardization projects. It is concluded that it is essential to evaluate the performance of the slave instrument prior to transfer, even when it is theoretically identical to the master apparatus. Copyright © 2017 Elsevier B.V. All rights reserved.

At-line monitoring of key parameters of nisin fermentation by near infrared spectroscopy, chemometric modeling and model improvement.

PubMed

Guo, Wei-Liang; Du, Yi-Ping; Zhou, Yong-Can; Yang, Shuang; Lu, Jia-Hui; Zhao, Hong-Yu; Wang, Yao; Teng, Li-Rong

2012-03-01

An analytical procedure has been developed for at-line (fast off-line) monitoring of 4 key parameters including nisin titer (NT), the concentration of reducing sugars, cell concentration and pH during a nisin fermentation process. This procedure is based on near infrared (NIR) spectroscopy and Partial Least Squares (PLS). Samples without any preprocessing were collected at intervals of 1 h during fifteen batch of fermentations. These fermentation processes were implemented in 3 different 5 l fermentors at various conditions. NIR spectra of the samples were collected in 10 min. And then, PLS was used for modeling the relationship between NIR spectra and the key parameters which were determined by reference methods. Monte Carlo Partial Least Squares (MCPLS) was applied to identify the outliers and select the most efficacious methods for preprocessing spectra, wavelengths and the suitable number of latent variables (n (LV)). Then, the optimum models for determining NT, concentration of reducing sugars, cell concentration and pH were established. The correlation coefficients of calibration set (R (c)) were 0.8255, 0.9000, 0.9883 and 0.9581, respectively. These results demonstrated that this method can be successfully applied to at-line monitor of NT, concentration of reducing sugars, cell concentration and pH during nisin fermentation processes.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.

Graphite Based Electrode for ECG Monitoring: Evaluation under Freshwater and Saltwater Conditions.

PubMed

Thap, Tharoeun; Yoon, Kwon-Ha; Lee, Jinseok

2016-04-15

We proposed new electrodes that are applicable for electrocardiogram (ECG) monitoring under freshwater- and saltwater-immersion conditions. Our proposed electrodes are made of graphite pencil lead (GPL), a general-purpose writing pencil. We have fabricated two types of electrode: a pencil lead solid type (PLS) electrode and a pencil lead powder type (PLP) electrode. In order to assess the qualities of the PLS and PLP electrodes, we compared their performance with that of a commercial Ag/AgCl electrode, under a total of seven different conditions: dry, freshwater immersion with/without movement, post-freshwater wet condition, saltwater immersion with/without movement, and post-saltwater wet condition. In both dry and post-freshwater wet conditions, all ECG-recorded PQRST waves were clearly discernible, with all types of electrodes, Ag/AgCl, PLS, and PLP. On the other hand, under the freshwater- and saltwater-immersion conditions with/without movement, as well as post-saltwater wet conditions, we found that the proposed PLS and PLP electrodes provided better ECG waveform quality, with significant statistical differences compared with the quality provided by Ag/AgCl electrodes.

[Comparison of Three Spectroscopies for the Determination of Composition of LDPE/PP Blend with Partial Least-Squares].

PubMed

Chen, Ru-huang; Jin, Gang

2015-08-01

This paper presented an application of mid-infrared (MIR), near-infrared (NIR) and Raman spectroscopies for collecting the spectra of 31 kinds of low density polyethylene/polyprolene (LDPE/PP) samples with different proportions. The different pre-processing methods (multiplicative scatter correction, mean centering and Savitzky-Golay first derivative) and spectral region were explored to develop partial least-squares (PLS) model for LDPE, their influence on the accuracy of PLS model also being discussed. Three spectroscopies were compared about the accuracy of quantitative measurement. Consequently, the pre-processing methods and spectral region have a great impact on the accuracy of PLS model, especially the spectra with subtle difference, random noise and baseline variation. After being pre-processed and spectral region selected, the calibration model of MIR, NIR and Raman exhibited R2/RMSEC values of 0.9906/2.941, 0.9973/1.561 and 0.9972/1.598 respectively, which corrsponding to 0.8876/10.15, 0.8493/11.75 and 0.8757/10.67 before any treatment. The results also suggested MIR, NIR and Raman are three strong tools to predict the content of LDPE in LDPE/PP blend. However, NIR and Raman showed higher accuracy after being pre-processed and more suitability to fast quantitative characterization due to their high measuring speed.

Statistical variation in progressive scrambling

NASA Astrophysics Data System (ADS)

Clark, Robert D.; Fox, Peter C.

2004-07-01

The two methods most often used to evaluate the robustness and predictivity of partial least squares (PLS) models are cross-validation and response randomization. Both methods may be overly optimistic for data sets that contain redundant observations, however. The kinds of perturbation analysis widely used for evaluating model stability in the context of ordinary least squares regression are only applicable when the descriptors are independent of each other and errors are independent and normally distributed; neither assumption holds for QSAR in general and for PLS in particular. Progressive scrambling is a novel, non-parametric approach to perturbing models in the response space in a way that does not disturb the underlying covariance structure of the data. Here, we introduce adjustments for two of the characteristic values produced by a progressive scrambling analysis - the deprecated predictivity (Q_s^{ast^2}) and standard error of prediction (SDEP s * ) - that correct for the effect of introduced perturbation. We also explore the statistical behavior of the adjusted values (Q_0^{ast^2} and SDEP 0 * ) and the sensitivity to perturbation (d q 2/d r yy ' 2). It is shown that the three statistics are all robust for stable PLS models, in terms of the stochastic component of their determination and of their variation due to sampling effects involved in training set selection.

Development and Validation of a Near-Infrared Spectroscopy Method for the Prediction of Acrylamide Content in French-Fried Potato.

PubMed

Adedipe, Oluwatosin E; Johanningsmeier, Suzanne D; Truong, Van-Den; Yencho, G Craig

2016-03-02

This study investigated the ability of near-infrared spectroscopy (NIRS) to predict acrylamide content in French-fried potato. Potato flour spiked with acrylamide (50-8000 μg/kg) was used to determine if acrylamide could be accurately predicted in a potato matrix. French fries produced with various pretreatments and cook times (n = 84) and obtained from quick-service restaurants (n = 64) were used for model development and validation. Acrylamide was quantified using gas chromatography-mass spectrometry, and reflectance spectra (400-2500 nm) of each freeze-dried sample were captured on a Foss XDS Rapid Content Analyzer-NIR spectrometer. Partial least-squares (PLS) discriminant analysis and PLS regression modeling demonstrated that NIRS could accurately detect acrylamide content as low as 50 μg/kg in the model potato matrix. Prediction errors of 135 μg/kg (R(2) = 0.98) and 255 μg/kg (R(2) = 0.93) were achieved with the best PLS models for acrylamide prediction in Russet Norkotah French-fried potato and multiple samples of unknown varieties, respectively. The findings indicate that NIRS can be used as a screening tool in potato breeding and potato processing research to reduce acrylamide in the food supply.

Lactoferrin-modified PEGylated liposomes loaded with doxorubicin for targeting delivery to hepatocellular carcinoma

PubMed Central

Wei, Minyan; Guo, Xiucai; Tu, Liuxiao; Zou, Qi; Li, Qi; Tang, Chenyi; Chen, Bao; Xu, Yuehong; Wu, Chuanbin

2015-01-01

Lactoferrin (Lf) is a potential-targeting ligand for hepatocellular carcinoma (HCC) cells because of its specific binding with asialoglycoprotein receptor (ASGPR). In this present work, a doxorubicin (DOX)-loaded, Lf-modified, polyethylene glycol (PEG)ylated liposome (Lf-PLS) system was developed, and its targeting effect and antitumor efficacy to HCC was also explored. The DOX-loaded Lf-PLS system had spherical or oval vesicles, with mean particle size approximately 100 nm, and had an encapsulation efficiency of 97%. The confocal microscopy and flow cytometry indicated that the cellular uptake of Lf-PLS was significantly higher than that of PEGylated liposome (PLS) in ASGPR-positive cells (P<0.05) but not in ASGPR-negative cells (P>0.05). Cytotoxicity assay by MTT demonstrated that DOX-loaded Lf-PLS showed significantly stronger antiproliferative effects on ASGPR-positive HCC cells than did PLS without the Lf modification (P<0.05). The in vivo antitumor studies on male BALB/c nude mice bearing HepG2 xenografts demonstrated that DOX-loaded Lf-PLS had significantly stronger antitumor efficacy compared with PLS (P<0.05) and free DOX (P<0.05). All these results demonstrated that a DOX-loaded Lf-PLS might have great potential application for HCC-targeting therapy. PMID:26316745

A Thioesterase Bypasses the Requirement for Exogenous Fatty Acids in the plsX Deletion of Streptococcus pneumoniae

PubMed Central

Parsons, Joshua B.; Frank, Matthew W.; Eleveld, Marc J.; Schalkwijk, Joost; Broussard, Tyler C.; de Jonge, Marien I.; Rock, Charles O.

2015-01-01

Summary PlsX is an acyl-acyl carrier protein (ACP):phosphate transacylase that interconverts the two acyl donors in Gram-positive bacterial phospholipid synthesis. The deletion of plsX in Staphylococcus aureus results in a requirement for both exogenous fatty acids and de novo type II fatty acid biosynthesis. Deletion of plsX (SP0037) in Streptococcus pneumoniae did not result in an auxotrophic phenotype. The ΔplsX S. pneumoniae strain was refractory to myristic acid-dependent growth arrest, and unlike the wild-type strain, was susceptible to fatty acid synthesis inhibitors in the presence of exogenous oleate. The ΔplsX strain contained longer-chain saturated fatty acids imparting a distinctly altered phospholipid molecular species profile. An elevated pool of 18- and 20-carbon saturated fatty acids was detected in the ΔplsX strain. A S. pneumoniae thioesterase (TesS, SP1408) hydrolyzed acyl-ACP in vitro, and the ΔtesS ΔplsX double knockout strain was a fatty acid auxotroph. Thus, the TesS thioesterase hydrolyzed the accumulating acyl-ACP in the ΔplsX strain to liberate fatty acids that were activated by fatty acid kinase to bypass a requirement for extracellular fatty acid. This work identifies tesS as the gene responsible for the difference in exogenous fatty acid growth requirement of the ΔplsX strains of S. aureus and S. pneumoniae. PMID:25534847

Impaired corticopontocerebellar tracts underlie pseudobulbar affect in motor neuron disorders

PubMed Central

Katipally, Rohan; Kim, Meredith P.; Schanz, Olivia; Stephen, Matthew; Danielian, Laura; Wu, Tianxia; Huey, Edward D.; Meoded, Avner

2014-01-01

Objective: The objectives of the study were (1) to determine the prevalence and characteristics of pseudobulbar affect (PBA) in patients with primary lateral sclerosis (PLS) and amyotrophic lateral sclerosis (ALS) in an outpatient clinic population, and (2) to test the hypothesis that damage of inputs to the cerebellum, leading to cerebellar dysmodulation, is associated with PBA. Methods: Chart review of all patients with PLS and ALS seen between 2000 and 2013. The examining neurologist documented the presence or absence of PBA in 87 patients. Forty-seven patients also had diffusion tensor imaging (DTI) studies. Tract-based spatial statistics were used to compare DTI of patients with and without PBA to identify altered white matter tracts associated with PBA. Results: Thirty-one of 50 patients with PLS and 12 of 37 patients with ALS had PBA. Psychiatric/emotional assessment found congruence between mood and affect during episodes, but excessive magnitude of the response. DTI studies of 25 PLS and 22 ALS patient brains showed reduced fractional anisotropy of the corticospinal and callosal white matter tracts in all patients. Patients with PBA additionally had increased mean diffusivity of white matter tracts underlying the frontotemporal cortex, the transverse pontine fibers, and the middle cerebellar peduncle. Conclusions: PBA is common in PLS. Imaging findings showing disruption of corticopontocerebellar pathways support the hypothesis that PBA can be viewed as a “dysmetria” of emotional expression resulting from cerebellar dysmodulation. PMID:25008395

Impaired corticopontocerebellar tracts underlie pseudobulbar affect in motor neuron disorders.

PubMed

Floeter, Mary Kay; Katipally, Rohan; Kim, Meredith P; Schanz, Olivia; Stephen, Matthew; Danielian, Laura; Wu, Tianxia; Huey, Edward D; Meoded, Avner

2014-08-12

The objectives of the study were (1) to determine the prevalence and characteristics of pseudobulbar affect (PBA) in patients with primary lateral sclerosis (PLS) and amyotrophic lateral sclerosis (ALS) in an outpatient clinic population, and (2) to test the hypothesis that damage of inputs to the cerebellum, leading to cerebellar dysmodulation, is associated with PBA. Chart review of all patients with PLS and ALS seen between 2000 and 2013. The examining neurologist documented the presence or absence of PBA in 87 patients. Forty-seven patients also had diffusion tensor imaging (DTI) studies. Tract-based spatial statistics were used to compare DTI of patients with and without PBA to identify altered white matter tracts associated with PBA. Thirty-one of 50 patients with PLS and 12 of 37 patients with ALS had PBA. Psychiatric/emotional assessment found congruence between mood and affect during episodes, but excessive magnitude of the response. DTI studies of 25 PLS and 22 ALS patient brains showed reduced fractional anisotropy of the corticospinal and callosal white matter tracts in all patients. Patients with PBA additionally had increased mean diffusivity of white matter tracts underlying the frontotemporal cortex, the transverse pontine fibers, and the middle cerebellar peduncle. PBA is common in PLS. Imaging findings showing disruption of corticopontocerebellar pathways support the hypothesis that PBA can be viewed as a "dysmetria" of emotional expression resulting from cerebellar dysmodulation. © 2014 American Academy of Neurology.

Seminal, clinical and colour-Doppler ultrasound correlations of prostatitis-like symptoms in males of infertile couples.

PubMed

Lotti, F; Corona, G; Mondaini, N; Maseroli, E; Rossi, M; Filimberti, E; Noci, I; Forti, G; Maggi, M

2014-01-01

'Prostatitis-like symptoms' (PLS) are a cluster of bothersome conditions defined as 'perineal and/or ejaculatory pain or discomfort and National Institutes of Health-Chronic Prostatitis Symptom Index (NIH-CPSI) pain subdomain score ≥4' (Nickel's criteria). PLS may originate from the prostate or from other portions of the male genital tract. Although PLS could be associated with 'prostatitis', they should not be confused. The NIH-CPSI is considered the gold-standard for assessing PLS severity. Although previous studies investigated the impact of prostatitis, vesiculitis or epididymitis on semen parameters, correlations between their related symptoms and seminal or scrotal/transrectal colour-Doppler ultrasound (CDU) characteristics have not been carefully determined. And no previous study evaluated the CDU features of PLS in infertile men. This study was aimed at investigating possible associations among NIH-CPSI (total and subdomain) scores and PLS, with seminal, clinical and scrotal/transrectal CDU parameters in a cohort of males of infertile couples. PLS of 400 men (35.8 ± 7.2 years) with a suspected male factor were assessed by the NIH-CPSI. All patients underwent, during the same day, semen analysis, seminal plasma interleukin 8 (sIL-8, a marker of male genital tract inflammation), biochemical evaluation, urine/seminal cultures, scrotal/transrectal CDU. PLS was detected in 39 (9.8%) subjects. After adjusting for age, waist and total testosterone (TT), no association among NIH-CPSI (total or subdomain) scores or PLS and sperm parameters was observed. However, we found a positive association with current positive urine and/or seminal cultures, sIL-8 levels and CDU features suggestive of inflammation of the epididymis, seminal vesicles, prostate, but not of the testis. The aforementioned significant associations of PLS were further confirmed by comparing PLS patients with age-, waist- and TT-matched PLS-free patients (1 : 3 ratio). In conclusion, NIH-CPSI scores and PLS evaluated in males of infertile couples, are not related to sperm parameters, but mainly to clinical and CDU signs of infection/inflammation. © 2013 American Society of Andrology and European Academy of Andrology.

[Study on the detection of active ingredient contents of Paecilomyces hepiali mycelium via near infrared spectroscopy].

PubMed

Teng, Wei-Zhuo; Song, Jia; Meng, Fan-Xin; Meng, Qing-Fan; Lu, Jia-Hui; Hu, Shuang; Teng, Li-Rong; Wang, Di; Xie, Jing

2014-10-01

Partial least squares (PLS) and radial basis function neural network (RBFNN) combined with near infrared spectros- copy (NIR) were applied to develop models for cordycepic acid, polysaccharide and adenosine analysis in Paecilomyces hepialid fermentation mycelium. The developed models possess well generalization and predictive ability which can be applied for crude drugs and related productions determination. During the experiment, 214 Paecilomyces hepialid mycelium samples were obtained via chemical mutagenesis combined with submerged fermentation. The contents of cordycepic acid, polysaccharide and adenosine were determined via traditional methods and the near infrared spectroscopy data were collected. The outliers were removed and the numbers of calibration set were confirmed via Monte Carlo partial least square (MCPLS) method. Based on the values of degree of approach (Da), both moving window partial least squares (MWPLS) and moving window radial basis function neural network (MWRBFNN) were applied to optimize characteristic wavelength variables, optimum preprocessing methods and other important variables in the models. After comparison, the RBFNN, RBFNN and PLS models were developed successfully for cordycepic acid, polysaccharide and adenosine detection, and the correlation between reference values and predictive values in both calibration set (R2c) and validation set (R2p) of optimum models was 0.9417 and 0.9663, 0.9803 and 0.9850, and 0.9761 and 0.9728, respectively. All the data suggest that these models possess well fitness and predictive ability.

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

DOE PAGES

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon; ...

2017-05-19

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Liquid chromatography coupled to quadrupole-time of flight tandem mass spectrometry based quantitative structure-retention relationships of amino acid analogues derivatized via n-propyl chloroformate mediated reaction.

PubMed

Kritikos, Nikolaos; Tsantili-Kakoulidou, Anna; Loukas, Yannis L; Dotsikas, Yannis

2015-07-17

In the current study, quantitative structure-retention relationships (QSRR) were constructed based on data obtained by a LC-(ESI)-QTOF-MS/MS method for the determination of amino acid analogues, following their derivatization via chloroformate esters. Molecules were derivatized via n-propyl chloroformate/n-propanol mediated reaction. Derivatives were acquired through a liquid-liquid extraction procedure. Chromatographic separation is based on gradient elution using methanol/water mixtures from a 70/30% composition to an 85/15% final one, maintaining a constant rate of change. The group of examined molecules was diverse, including mainly α-amino acids, yet also β- and γ-amino acids, γ-amino acid analogues, decarboxylated and phosphorylated analogues and dipeptides. Projection to latent structures (PLS) method was selected for the formation of QSRRs, resulting in a total of three PLS models with high cross-validated coefficients of determination Q(2)Y. For this reason, molecular structures were previously described through the use of descriptors. Through stratified random sampling procedures, 57 compounds were split to a training set and a test set. Model creation was based on multiple criteria including principal component significance and eigenvalue, variable importance, form of residuals, etc. Validation was based on statistical metrics Rpred(2),QextF2(2),QextF3(2) for the test set and Roy's metrics rm(Av)(2) and rm(δ)(2), assessing both predictive stability and internal validity. Based on aforementioned models, simplified equivalent were then created using a multi-linear regression (MLR) method. MLR models were also validated with the same metrics. The suggested models are considered useful for the estimation of retention times of amino acid analogues for a series of applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Clinicopathologic implications of DNA mismatch repair status in endometrial carcinomas.

PubMed

Shikama, Ayumi; Minaguchi, Takeo; Matsumoto, Koji; Akiyama-Abe, Azusa; Nakamura, Yuko; Michikami, Hiroo; Nakao, Sari; Sakurai, Manabu; Ochi, Hiroyuki; Onuki, Mamiko; Satoh, Toyomi; Oki, Akinori; Yoshikawa, Hiroyuki

2016-02-01

Endometrial carcinoma is the most common malignancy in women with Lynch syndrome caused by mismatch repair (MMR) deficiency. We investigated the clinicopathologic significance of deficient MMR and Lynch syndrome presumed by MMR analyses in unselected endometrial carcinomas. We analyzed immunohistochemistry of MMR proteins (MLH1/MSH2/MSH6/PMS2) and MLH1 promoter methylation in primary endometrial carcinomas from 221 consecutive patients. Based on these results, tumors were categorized as sporadic or probable Lynch syndrome (PLS). Clinicopathologic variables and prognosis were compared according to MMR status and sporadic/PLS classification. Deficient MMR showed only trends towards favorable overall survival (OS) compared with intact MMR (p=0.13), whereas PLS showed significantly better OS than sporadic (p=0.038). Sporadic was significantly associated with older age, obesity, deep myometrial invasion, and advanced stage (p=0.008, 0.01, 0.02 and 0.03), while PLS was significantly associated with early stage and Lynch syndrome-associated multiple cancer (p=0.04 and 0.001). The trend towards favorable OS of PLS was stronger in advanced stage than in early stage (hazard ratio, 0.044 [95% CI 0-25.6] vs. 0.49 [0.063-3.8]). In the subset receiving adjuvant therapies, PLS showed trends towards favorable disease-free survival compared to sporadic by contrast with patients receiving no adjuvant therapies showing no such trend (hazard ratio, 0.045 [95% CI 0-20.3] vs. 0.81 [0.095-7.0]). The current findings suggest that analyzing MMR status and searching for Lynch syndrome may identify a subset of patients with favorable survival and high sensitivity to adjuvant therapies, providing novel and useful implications for formulating the precision medicine in endometrial carcinoma. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantitative analysis of red wine tannins using Fourier-transform mid-infrared spectrometry.

PubMed

Fernandez, Katherina; Agosin, Eduardo

2007-09-05

Tannin content and composition are critical quality components of red wines. No spectroscopic method assessing these phenols in wine has been described so far. We report here a new method using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometric techniques for the quantitative analysis of red wine tannins. Calibration models were developed using protein precipitation and phloroglucinolysis as analytical reference methods. After spectra preprocessing, six different predictive partial least-squares (PLS) models were evaluated, including the use of interval selection procedures such as iPLS and CSMWPLS. PLS regression with full-range (650-4000 cm(-1)), second derivative of the spectra and phloroglucinolysis as the reference method gave the most accurate determination for tannin concentration (RMSEC = 2.6%, RMSEP = 9.4%, r = 0.995). The prediction of the mean degree of polymerization (mDP) of the tannins also gave a reasonable prediction (RMSEC = 6.7%, RMSEP = 10.3%, r = 0.958). These results represent the first step in the development of a spectroscopic methodology for the quantification of several phenolic compounds that are critical for wine quality.

Simultaneous measurement of two enzyme activities using infrared spectroscopy: A comparative evaluation of PARAFAC, TUCKER and N-PLS modeling.

PubMed

Baum, Andreas; Hansen, Per Waaben; Meyer, Anne S; Mikkelsen, Jørn Dalgaard

2013-08-06

Enzymes are used in many processes to release fermentable sugars for green production of biofuel, or the refinery of biomass for extraction of functional food ingredients such as pectin or prebiotic oligosaccharides. The complex biomasses may, however, require a multitude of specific enzymes which are active on specific substrates generating a multitude of products. In this paper we use the plant polymer, pectin, to present a method to quantify enzyme activity of two pectolytic enzymes by monitoring their superimposed spectral evolutions simultaneously. The data is analyzed by three chemometric multiway methods, namely PARAFAC, TUCKER3 and N-PLS, to establish simultaneous enzyme activity assays for pectin lyase and pectin methyl esterase. Correlation coefficients Rpred(2) for prediction test sets are 0.48, 0.96 and 0.96 for pectin lyase and 0.70, 0.89 and 0.89 for pectin methyl esterase, respectively. The retrieved models are compared and prediction test sets show that especially TUCKER3 performs well, even in comparison to the supervised regression method N-PLS. Copyright © 2013 Elsevier B.V. All rights reserved.

Combining FT-IR spectroscopy and multivariate analysis for qualitative and quantitative analysis of the cell wall composition changes during apples development.

PubMed

Szymanska-Chargot, M; Chylinska, M; Kruk, B; Zdunek, A

2015-01-22

The aim of this work was to quantitatively and qualitatively determine the composition of the cell wall material from apples during development by means of Fourier transform infrared (FT-IR) spectroscopy. The FT-IR region of 1500-800 cm(-1), containing characteristic bands for galacturonic acid, hemicellulose and cellulose, was examined using principal component analysis (PCA), k-means clustering and partial least squares (PLS). The samples were differentiated by development stage and cultivar using PCA and k-means clustering. PLS calibration models for galacturonic acid, hemicellulose and cellulose content from FT-IR spectra were developed and validated with the reference data. PLS models were tested using the root-mean-square errors of cross-validation for contents of galacturonic acid, hemicellulose and cellulose which was 8.30 mg/g, 4.08% and 1.74%, respectively. It was proven that FT-IR spectroscopy combined with chemometric methods has potential for fast and reliable determination of the main constituents of fruit cell walls. Copyright © 2014 Elsevier Ltd. All rights reserved.

Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

PubMed

Francisco, Fabiane Lacerda; Saviano, Alessandro Morais; Almeida, Túlia de Souza Botelho; Lourenço, Felipe Rebello

2016-05-01

Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination. Copyright © 2016 Elsevier B.V. All rights reserved.

Improved discrimination between monocotyledonous and dicotyledonous plants for weed control based on the blue-green region of ultraviolet-induced fluorescence spectra.

PubMed

Panneton, Bernard; Guillaume, Serge; Roger, Jean-Michel; Samson, Guy

2010-01-01

Precision weeding by spot spraying in real time requires sensors to discriminate between weeds and crop without contact. Among the optical based solutions, the ultraviolet (UV) induced fluorescence of the plants appears as a promising alternative. In a first paper, the feasibility of discriminating between corn hybrids, monocotyledonous, and dicotyledonous weeds was demonstrated on the basis of the complete spectra. Some considerations about the different sources of fluorescence oriented the focus to the blue-green fluorescence (BGF) part, ignoring the chlorophyll fluorescence that is inherently more variable in time. This paper investigates the potential of performing weed/crop discrimination on the basis of several large spectral bands in the BGF area. A partial least squares discriminant analysis (PLS-DA) was performed on a set of 1908 spectra of corn and weed plants over 3 years and various growing conditions. The discrimination between monocotyledonous and dicotyledonous plants based on the blue-green fluorescence yielded robust models (classification error between 1.3 and 4.6% for between-year validation). On the basis of the analysis of the PLS-DA model, two large bands were chosen in the blue-green fluorescence zone (400-425 nm and 425-490 nm). A linear discriminant analysis based on the signal from these two bands also provided very robust inter-year results (classification error from 1.5% to 5.2%). The same selection process was applied to discriminate between monocotyledonous weeds and maize but yielded no robust models (up to 50% inter-year error). Further work will be required to solve this problem and provide a complete UV fluorescence based sensor for weed-maize discrimination.

Simultaneous quantification of Aroclor mixtures in soil samples by gas chromatography/mass spectrometry with solid phase microextraction using partial least-squares regression.

PubMed

Zhang, Mengliang; Harrington, Peter de B

2015-01-01

Multivariate partial least-squares (PLS) method was applied to the quantification of two complex polychlorinated biphenyls (PCBs) commercial mixtures, Aroclor 1254 and 1260, in a soil matrix. PCBs in soil samples were extracted by headspace solid phase microextraction (SPME) and determined by gas chromatography/mass spectrometry (GC/MS). Decachlorinated biphenyl (deca-CB) was used as internal standard. After the baseline correction was applied, four data representations including extracted ion chromatograms (EIC) for Aroclor 1254, EIC for Aroclor 1260, EIC for both Aroclors and two-way data sets were constructed for PLS-1 and PLS-2 calibrations and evaluated with respect to quantitative prediction accuracy. The PLS model was optimized with respect to the number of latent variables using cross validation of the calibration data set. The validation of the method was performed with certified soil samples and real field soil samples and the predicted concentrations for both Aroclors using EIC data sets agreed with the certified values. The linear range of the method was from 10μgkg(-1) to 1000μgkg(-1) for both Aroclor 1254 and 1260 in soil matrices and the detection limit was 4μgkg(-1) for Aroclor 1254 and 6μgkg(-1) for Aroclor 1260. This holistic approach for the determination of mixtures of complex samples has broad application to environmental forensics and modeling. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-01

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging.

PubMed

Moscetti, Roberto; Sturm, Barbara; Crichton, Stuart Oj; Amjad, Waseem; Massantini, Riccardo

2018-05-01

The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ 1 ]-R[λ 2 ] and R[λ 1 ]:R[λ 2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice). Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Optical scatterometry of quarter-micron patterns using neural regression

NASA Astrophysics Data System (ADS)

Bischoff, Joerg; Bauer, Joachim J.; Haak, Ulrich; Hutschenreuther, Lutz; Truckenbrodt, Horst

1998-06-01

With shrinking dimensions and increasing chip areas, a rapid and non-destructive full wafer characterization after every patterning cycle is an inevitable necessity. In former publications it was shown that Optical Scatterometry (OS) has the potential to push the attainable feature limits of optical techniques from 0.8 . . . 0.5 microns for imaging methods down to 0.1 micron and below. Thus the demands of future metrology can be met. Basically being a nonimaging method, OS combines light scatter (or diffraction) measurements with modern data analysis schemes to solve the inverse scatter issue. For very fine patterns with lambda-to-pitch ratios grater than one, the specular reflected light versus the incidence angle is recorded. Usually, the data analysis comprises two steps -- a training cycle connected the a rigorous forward modeling and the prediction itself. Until now, two data analysis schemes are usually applied -- the multivariate regression based Partial Least Squares method (PLS) and a look-up-table technique which is also referred to as Minimum Mean Square Error approach (MMSE). Both methods are afflicted with serious drawbacks. On the one hand, the prediction accuracy of multivariate regression schemes degrades with larger parameter ranges due to the linearization properties of the method. On the other hand, look-up-table methods are rather time consuming during prediction thus prolonging the processing time and reducing the throughput. An alternate method is an Artificial Neural Network (ANN) based regression which combines the advantages of multivariate regression and MMSE. Due to the versatility of a neural network, not only can its structure be adapted more properly to the scatter problem, but also the nonlinearity of the neuronal transfer functions mimic the nonlinear behavior of optical diffraction processes more adequately. In spite of these pleasant properties, the prediction speed of ANN regression is comparable with that of the PLS-method. In this paper, the viability and performance of ANN-regression will be demonstrated with the example of sub-quarter-micron resist metrology. To this end, 0.25 micrometer line/space patterns have been printed in positive photoresist by means of DUV projection lithography. In order to evaluate the total metrology chain from light scatter measurement through data analysis, a thorough modeling has been performed. Assuming a trapezoidal shape of the developed resist profile, a training data set was generated by means of the Rigorous Coupled Wave Approach (RCWA). After training the model, a second data set was computed and deteriorated by Gaussian noise to imitate real measuring conditions. Then, these data have been fed into the models established before resulting in a Standard Error of Prediction (SEP) which corresponds to the measuring accuracy. Even with putting only little effort in the design of a back-propagation network, the ANN is clearly superior to the PLS-method. Depending on whether a network with one or two hidden layers was used, accuracy gains between 2 and 5 can be achieved compared with PLS regression. Furthermore, the ANN is less noise sensitive, for there is only a doubling of the SEP at 5% noise for ANN whereas for PLS the accuracy degrades rapidly with increasing noise. The accuracy gain also depends on the light polarization and on the measured parameters. Finally, these results have been proven experimentally, where the OS-results are in good accordance with the profiles obtained from cross- sectioning micrographs.

Improved intact soil-core carbon determination applying regression shrinkage and variable selection techniques to complete spectrum laser-induced breakdown spectroscopy (LIBS).

PubMed

Bricklemyer, Ross S; Brown, David J; Turk, Philip J; Clegg, Sam M

2013-10-01

Laser-induced breakdown spectroscopy (LIBS) provides a potential method for rapid, in situ soil C measurement. In previous research on the application of LIBS to intact soil cores, we hypothesized that ultraviolet (UV) spectrum LIBS (200-300 nm) might not provide sufficient elemental information to reliably discriminate between soil organic C (SOC) and inorganic C (IC). In this study, using a custom complete spectrum (245-925 nm) core-scanning LIBS instrument, we analyzed 60 intact soil cores from six wheat fields. Predictive multi-response partial least squares (PLS2) models using full and reduced spectrum LIBS were compared for directly determining soil total C (TC), IC, and SOC. Two regression shrinkage and variable selection approaches, the least absolute shrinkage and selection operator (LASSO) and sparse multivariate regression with covariance estimation (MRCE), were tested for soil C predictions and the identification of wavelengths important for soil C prediction. Using complete spectrum LIBS for PLS2 modeling reduced the calibration standard error of prediction (SEP) 15 and 19% for TC and IC, respectively, compared to UV spectrum LIBS. The LASSO and MRCE approaches provided significantly improved calibration accuracy and reduced SEP 32-55% over UV spectrum PLS2 models. We conclude that (1) complete spectrum LIBS is superior to UV spectrum LIBS for predicting soil C for intact soil cores without pretreatment; (2) LASSO and MRCE approaches provide improved calibration prediction accuracy over PLS2 but require additional testing with increased soil and target analyte diversity; and (3) measurement errors associated with analyzing intact cores (e.g., sample density and surface roughness) require further study and quantification.

Structure-activity relationships of an antimicrobial peptide plantaricin s from two-peptide class IIb bacteriocins.

PubMed

Soliman, Wael; Wang, Liru; Bhattacharjee, Subir; Kaur, Kamaljit

2011-04-14

Class IIb bacteriocins are ribosomally synthesized antimicrobial peptides comprising two different peptides synergistically acting in equal amounts for optimal potency. In this study, we demonstrate for the first time potent (nanomolar) antimicrobial activity of a representative class IIb bacteriocin, plantaricin S (Pls), against four pathogenic gram-positive bacteria, including Listeria monocytogenes. The structure-activity relationships for Pls were studied using activity assays, circular dichroism (CD), and molecular dynamics (MD) simulations. The two Pls peptides and five Pls derived fragments were synthesized. The CD spectra of the Pls and selected fragments revealed helical conformations in aqueous 2,2,2-trifluoroethanol. The MD simulations showed that when the two Pls peptides are in antiparallel orientation, the helical regions interact and align, mediated by strong attraction between conserved GxxxG/AxxxA motifs. The results strongly correlate with the antimicrobial activity suggesting that helix-helix alignment of the two Pls peptides and interaction between the conserved motifs are crucial for interaction with the target cell membrane.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.

PubMed

Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

2007-01-01

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Application of NIRS coupled with PLS regression as a rapid, non-destructive alternative method for quantification of KBA in Boswellia sacra

NASA Astrophysics Data System (ADS)

Al-Harrasi, Ahmed; Rehman, Najeeb Ur; Mabood, Fazal; Albroumi, Muhammaed; Ali, Liaqat; Hussain, Javid; Hussain, Hidayat; Csuk, René; Khan, Abdul Latif; Alam, Tanveer; Alameri, Saif

2017-09-01

In the present study, for the first time, NIR spectroscopy coupled with PLS regression as a rapid and alternative method was developed to quantify the amount of Keto-β-Boswellic Acid (KBA) in different plant parts of Boswellia sacra and the resin exudates of the trunk. NIR spectroscopy was used for the measurement of KBA standards and B. sacra samples in absorption mode in the wavelength range from 700-2500 nm. PLS regression model was built from the obtained spectral data using 70% of KBA standards (training set) in the range from 0.1 ppm to 100 ppm. The PLS regression model obtained was having R-square value of 98% with 0.99 corelationship value and having good prediction with RMSEP value 3.2 and correlation of 0.99. It was then used to quantify the amount of KBA in the samples of B. sacra. The results indicated that the MeOH extract of resin has the highest concentration of KBA (0.6%) followed by essential oil (0.1%). However, no KBA was found in the aqueous extract. The MeOH extract of the resin was subjected to column chromatography to get various sub-fractions at different polarity of organic solvents. The sub-fraction at 4% MeOH/CHCl3 (4.1% of KBA) was found to contain the highest percentage of KBA followed by another sub-fraction at 2% MeOH/CHCl3 (2.2% of KBA). The present results also indicated that KBA is only present in the gum-resin of the trunk and not in all parts of the plant. These results were further confirmed through HPLC analysis and therefore it is concluded that NIRS coupled with PLS regression is a rapid and alternate method for quantification of KBA in Boswellia sacra. It is non-destructive, rapid, sensitive and uses simple methods of sample preparation.

Application of Multispectral Imaging to Determine Quality Attributes and Ripeness Stage in Strawberry Fruit

PubMed Central

Liu, Changhong; Liu, Wei; Lu, Xuzhong; Ma, Fei; Chen, Wei; Yang, Jianbo; Zheng, Lei

2014-01-01

Multispectral imaging with 19 wavelengths in the range of 405–970 nm has been evaluated for nondestructive determination of firmness, total soluble solids (TSS) content and ripeness stage in strawberry fruit. Several analysis approaches, including partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN), were applied to develop theoretical models for predicting the firmness and TSS of intact strawberry fruit. Compared with PLS and SVM, BPNN considerably improved the performance of multispectral imaging for predicting firmness and total soluble solids content with the correlation coefficient (r) of 0.94 and 0.83, SEP of 0.375 and 0.573, and bias of 0.035 and 0.056, respectively. Subsequently, the ability of multispectral imaging technology to classify fruit based on ripeness stage was tested using SVM and principal component analysis-back propagation neural network (PCA-BPNN) models. The higher classification accuracy of 100% was achieved using SVM model. Moreover, the results of all these models demonstrated that the VIS parts of the spectra were the main contributor to the determination of firmness, TSS content estimation and classification of ripeness stage in strawberry fruit. These results suggest that multispectral imaging, together with suitable analysis model, is a promising technology for rapid estimation of quality attributes and classification of ripeness stage in strawberry fruit. PMID:24505317

[Exploration of rapidly determining quality of traditional Chinese medicines by (NIR) spectroscopy based on internet sharing mode].

PubMed

Ni, Li-Jun; Luan, Shao-Rong; Zhang, Li-Guo

2016-10-01

Because of the numerous varieties of herbal species and active ingredients in the traditional Chinese medicine(TCM),the traditional methods employed could hardly satisfy the current determination requirements of TCM.The present work proposed an idea to realize rapid determination of the quality of TCM based on near infrared(NIR)spectroscopy and internet sharing mode. Low cost and portable multi-source composite spectrometer was invented by our group for in-site fast measurement of spectra of TCM samples. The database could be set up by sharing spectra and quality detection data of TCM samples among TCM enterprises based on the internet platform.A novel method called as keeping same relationship between X and Y space based on K nearest neighbors(KNN-KSR for short)was applied to predict the contents of effective compounds of the samples. In addition,a comparative study between KNN-KSR and partial least squares(PLS)was conducted. Two datasets were applied to validate above idea:one was about 58 Ginkgo Folium samples samples measured with four near-infrared spectroscopy instruments and two multi-source composite spectrometers,another one was about 80 corn samples available online measured with three NIR instruments. The results show that the KNN-KSR method could obtain more reliable outcomes without correcting spectrum.However transforming the PLS models to other instruments could hardly acquire better predictive results until spectral calibration is performed. Meanwhile,the similar analysis results of total flavonoids and total lactones of Ginkgo Folium samples are achieved on the multi-source composite spectrometers and near-infrared spectroscopy instruments,and the prediction results of KNN-KSR are better than PLS. The idea proposed in present study is in urgent need of more samples spectra, and then to be verified by more case studies. Copyright© by the Chinese Pharmaceutical Association.

Texture analysis of pulmonary parenchymateous changes related to pulmonary thromboembolism in dogs - a novel approach using quantitative methods.

PubMed

Marschner, C B; Kokla, M; Amigo, J M; Rozanski, E A; Wiinberg, B; McEvoy, F J

2017-07-11

Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses. CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between -1024 and -250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images. Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM). The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models' poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.

Characterization of potential plasma biomarkers related to cognitive impairment by untargeted profiling of phospholipids using the HILIC-ESI-IT-TOF-MS system.

PubMed

Song, Shuang; Cheong, Ling-Zhi; Man, Qing-Qing; Pang, Shao-Jie; Li, Yue-Qi; Ren, Biao; Zhang, Jian

2018-05-01

Early diagnosis of neural changes causing cognitive impairment is critical for development of preventive therapies for dementia. Biomarkers currently characterized cannot be extensively applied due to the invasive sampling of cerebrospinal fluid. The other imaging approaches are either expensive or require a high technique. Phospholipids (PLs), which are basic constituents of neurons, might be a key variable in the pathogenesis of cognitive impairment. Changes in plasma PL provide the possibility for development of novel biomarkers with minimal invasion and high patient acceptance. In this work, a HILIC-ESI-IT-TOF-MS system was introduced for untargeted profiling of plasma PLs to investigate the relationship between changes of plasma PL profiles and cognitive impairment. A total of 272 types of PL molecular structures were characterized in human plasma and quantified through the internal standard method. Univariate analysis shows 29 PLs were significantly different between the control (n = 41) and the cognitive impairment (CI) group (n = 41). Multivariate analysis (PCA and OPLS-DA) was conducted based on these 29 potential PL biomarkers. Both univariate and multivariate analyses show abnormality of PL metabolism in the CI group, and the downregulation of ethanolamine plasmalogen (pPE) supply, especially those with PUFAs, in the circulation system should be strongly associated with neurodegeneration. A discriminative model was established with satisfied fit (R2) and prediction (Q2) abilities, and the classification test showed better recognition of the CI group than the control group indicating that this model of PL biomarkers could be used as indicators for screening of CI. Graphical abstract Characterization of potential plasma biomarkers related to cognitive impairment by untargeted profiling of phospholipids.

Metabolomic Profile of Ards by Nuclear Magnetic Resonance Spectroscopy in Patients with H1N1 Influenza Virus Pneumonia.

PubMed

Izquierdo-Garcia, Jose L; Nin, Nicolas; Jimenez-Clemente, Jorge; Horcajada, Juan P; Arenas-Miras, Maria Del Mar; Gea, Joaquim; Esteban, Andres; Ruiz-Cabello, Jesus; Lorente, Jose A

2017-12-29

The integrated analysis of changes in the metabolic profile could be critical for the discovery of biomarkers of lung injury, and also for generating new pathophysiological hypotheses and designing novel therapeutic targets for the acute respiratory distress syndrome (ARDS). This study aimed at developing a Nuclear Magnetic Resonance (NMR)-based approach for the identification of the metabolomic profile of ARDS in patients with H1N1 influenza virus pneumonia. Serum samples from 30 patients (derivation set) diagnosed of H1N1 influenza virus pneumonia were analysed by unsupervised Principal Component Analysis (PCA) to identify metabolic differences between patients with and without ARDS by NMR-spectroscopy. A predictive model of partial least squares discriminant analysis (PLS-DA) was developed for the identification of ARDS. PLS-DA was trained with the derivation set and tested in another set of samples from 26 patients also diagnosed of H1N1 influenza virus pneumonia (validation set). Decreased serum glucose, alanine, glutamine, methylhistidine and fatty acids concentrations, and elevated serum phenylalanine and methylguanidine concentrations, discriminated patients with ARDS versus patients without ARDS. PLS-DA model successfully identified the presence of ARDS in the validation set with a success rate of 92% (sensitivity 100% and specificity 91%). The classification functions showed a good correlation with the Sequential Organ Failure Assessment (SOFA) score (R = 0.74, p < 0.0001) and the Pa02/Fi02 ratio (R = 0.41, p = 0.03). The serum metabolomic profile is sensitive and specific to identify ARDS in patients with H1N1 influenza A pneumonia. Future studies are needed to determine the role of NMR-spectroscopy as a biomarker of ARDS.

Quantification of trace metals in infant formula premixes using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Cama-Moncunill, Raquel; Casado-Gavalda, Maria P.; Cama-Moncunill, Xavier; Markiewicz-Keszycka, Maria; Dixit, Yash; Cullen, Patrick J.; Sullivan, Carl

2017-09-01

Infant formula is a human milk substitute generally based upon fortified cow milk components. In order to mimic the composition of breast milk, trace elements such as copper, iron and zinc are usually added in a single operation using a premix. The correct addition of premixes must be verified to ensure that the target levels in infant formulae are achieved. In this study, a laser-induced breakdown spectroscopy (LIBS) system was assessed as a fast validation tool for trace element premixes. LIBS is a promising emission spectroscopic technique for elemental analysis, which offers real-time analyses, little to no sample preparation and ease of use. LIBS was employed for copper and iron determinations of premix samples ranging approximately from 0 to 120 mg/kg Cu/1640 mg/kg Fe. LIBS spectra are affected by several parameters, hindering subsequent quantitative analyses. This work aimed at testing three matrix-matched calibration approaches (simple-linear regression, multi-linear regression and partial least squares regression (PLS)) as means for precision and accuracy enhancement of LIBS quantitative analysis. All calibration models were first developed using a training set and then validated with an independent test set. PLS yielded the best results. For instance, the PLS model for copper provided a coefficient of determination (R2) of 0.995 and a root mean square error of prediction (RMSEP) of 14 mg/kg. Furthermore, LIBS was employed to penetrate through the samples by repetitively measuring the same spot. Consequently, LIBS spectra can be obtained as a function of sample layers. This information was used to explore whether measuring deeper into the sample could reduce possible surface-contaminant effects and provide better quantifications.

PLS-based quantitative structure-activity relationship for substituted benzamides of clebopride type. Application of experimental design in drug design.

PubMed

Norinder, U; Högberg, T

1992-04-01

The advantageous approach of using an experimentally designed training set as the basis for establishing a quantitative structure-activity relationship with good predictive capability is described. The training set was selected from a fractional factorial design scheme based on a principal component description of physico-chemical parameters of aromatic substituents. The derived model successfully predicts the activities of additional substituted benzamides of 6-methoxy-N-(4-piperidyl)salicylamide type. The major influence on activity of the 3-substituent is demonstrated.

The crucial role of the Pls1 tetraspanin during ascospore germination in Podospora anserina provides an example of the convergent evolution of morphogenetic processes in fungal plant pathogens and saprobes.

PubMed

Lambou, Karine; Malagnac, Fabienne; Barbisan, Crystel; Tharreau, Didier; Lebrun, Marc-Henri; Silar, Philippe

2008-10-01

Pls1 tetraspanins were shown for some pathogenic fungi to be essential for appressorium-mediated penetration into their host plants. We show here that Podospora anserina, a saprobic fungus lacking appressorium, contains PaPls1, a gene orthologous to known PLS1 genes. Inactivation of PaPls1 demonstrates that this gene is specifically required for the germination of ascospores in P. anserina. These ascospores are heavily melanized cells that germinate under inducing conditions through a specific pore. On the contrary, MgPLS1, which fully complements a DeltaPaPls1 ascospore germination defect, has no role in the germination of Magnaporthe grisea nonmelanized ascospores but is required for the formation of the penetration peg at the pore of its melanized appressorium. P. anserina mutants with mutation of PaNox2, which encodes the NADPH oxidase of the NOX2 family, display the same ascospore-specific germination defect as the DeltaPaPls1 mutant. Both mutant phenotypes are suppressed by the inhibition of melanin biosynthesis, suggesting that they are involved in the same cellular process required for the germination of P. anserina melanized ascospores. The analysis of the distribution of PLS1 and NOX2 genes in fungal genomes shows that they are either both present or both absent. These results indicate that the germination of P. anserina ascospores and the formation of the M. grisea appressorium penetration peg use the same molecular machinery that includes Pls1 and Nox2. This machinery is specifically required for the emergence of polarized hyphae from reinforced structures such as appressoria and ascospores. Its recurrent recruitment during fungal evolution may account for some of the morphogenetic convergence observed in fungi.

Xylella taiwanensis sp. nov., causing pear leaf scorch disease.

PubMed

Su, C-C; Deng, W-L; Jan, F-J; Chang, C-J; Huang, H; Shih, H-T; Chen, J

2016-11-01

A Gram-stain-negative, nutritionally fastidious bacterium (PLS229T) causing pear leaf scorch was identified in Taiwan and previously grouped into Xylella fastidiosa. Yet, significant variations between PLS229T and Xylellafastidiosa were noted. In this study, PLS229T was evaluated phenotypically and genotypically against representative strains of Xylellafastidiosa, including strains of the currently known subspecies of Xylellafastidiosa, Xylella fastidiosa subsp. multiplex and 'Xylella fastidiosasubsp.pauca'. Because of the difficulty of in vitro culture characterization, emphases were made to utilize the available whole-genome sequence information. The average nucleotide identity (ANI) values, an alternative for DNA-DNA hybridization relatedness, between PLS229T and Xylellafastidiosa were 83.4-83.9 %, significantly lower than the bacterial species threshold of 95 %. In contrast, sequence similarity of 16S rRNA genes was greater than 98 %, higher than the 97 % threshold to justify if two bacterial strains belong to different species. The uniqueness of PLS229T was also evident by observing only about 87 % similarity in the sequence of the 16S-23S internal transcribed spacer (ITS) between PLS229T and strains of Xylellafastidiosa, discovering significant single nucleotide polymorphisms at 18 randomly selected housekeeping gene loci, observing a distinct fatty acid profile for PLS229T compared with Xylellafastidiosa, and PLS229T having different observable phenotypes, such as different susceptibility to antibiotics. A phylogenetic tree derived from 16S rRNA gene sequences showed a distinct PLS229T phyletic lineage positioning it between Xylellafastidiosa and members of the genus Xanthomonas. On the basis of these data, a novel species, Xylella taiwanensis sp. nov. is proposed. The type strain is PLS229T (=BCRC 80915T=JCM 31187T).

Calibration Transfer Between a Bench Scanning and a Submersible Diode Array Spectrophotometer for In Situ Wastewater Quality Monitoring in Sewer Systems.

PubMed

Brito, Rita S; Pinheiro, Helena M; Ferreira, Filipa; Matos, José S; Pinheiro, Alexandre; Lourenço, Nídia D

2016-03-01

Online monitoring programs based on spectroscopy have a high application potential for the detection of hazardous wastewater discharges in sewer systems. Wastewater hydraulics poses a challenge for in situ spectroscopy, especially when the system includes storm water connections leading to rapid changes in water depth, velocity, and in the water quality matrix. Thus, there is a need to optimize and fix the location of in situ instruments, limiting their availability for calibration. In this context, the development of calibration models on bench spectrophotometers to estimate wastewater quality parameters from spectra acquired with in situ instruments could be very useful. However, spectra contain information not only from the samples, but also from the spectrophotometer generally invalidating this approach. The use of calibration transfer methods is a promising solution to this problem. In this study, calibration models were developed using interval partial least squares (iPLS), for the estimation of total suspended solids (TSS) and chemical oxygen demand (COD) in sewage from Ultraviolet-visible spectra acquired in a bench scanning spectrophotometer. The feasibility of calibration transfer to a submersible, diode array equipment, to be subsequently operated in situ, was assessed using three procedures: slope and bias correction (SBC); single wavelength standardization (SWS) on mean spectra; and local centering (LC). The results showed that SBC was the most adequate for the available data, adding insignificant error to the base model estimates. Single wavelength standardization was a close second best, potentially more robust, and independent of the base iPLS model. Local centering was shown to be inadequate for the samples and instruments used. © The Author(s) 2016.

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy.

PubMed

Oliveira, Flavia C C; Brandão, Christian R R; Ramalho, Hugo F; da Costa, Leonardo A F; Suarez, Paulo A Z; Rubim, Joel C

2007-03-28

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.

LIBS data analysis using a predictor-corrector based digital signal processor algorithm

NASA Astrophysics Data System (ADS)

Sanders, Alex; Griffin, Steven T.; Robinson, Aaron

2012-06-01

There are many accepted sensor technologies for generating spectra for material classification. Once the spectra are generated, communication bandwidth limitations favor local material classification with its attendant reduction in data transfer rates and power consumption. Transferring sensor technologies such as Cavity Ring-Down Spectroscopy (CRDS) and Laser Induced Breakdown Spectroscopy (LIBS) require effective material classifiers. A result of recent efforts has been emphasis on Partial Least Squares - Discriminant Analysis (PLS-DA) and Principle Component Analysis (PCA). Implementation of these via general purpose computers is difficult in small portable sensor configurations. This paper addresses the creation of a low mass, low power, robust hardware spectra classifier for a limited set of predetermined materials in an atmospheric matrix. Crucial to this is the incorporation of PCA or PLS-DA classifiers into a predictor-corrector style implementation. The system configuration guarantees rapid convergence. Software running on multi-core Digital Signal Processor (DSPs) simulates a stream-lined plasma physics model estimator, reducing Analog-to-Digital (ADC) power requirements. This paper presents the results of a predictorcorrector model implemented on a low power multi-core DSP to perform substance classification. This configuration emphasizes the hardware system and software design via a predictor corrector model that simultaneously decreases the sample rate while performing the classification.

Raman spectroscopy: in vivo quick response code of skin physiological status

NASA Astrophysics Data System (ADS)

Vyumvuhore, Raoul; Tfayli, Ali; Piot, Olivier; Le Guillou, Maud; Guichard, Nathalie; Manfait, Michel; Baillet-Guffroy, Arlette

2014-11-01

Dermatologists need to combine different clinically relevant characteristics for a better understanding of skin health. These characteristics are usually measured by different techniques, and some of them are highly time consuming. Therefore, a predicting model based on Raman spectroscopy and partial least square (PLS) regression was developed as a rapid multiparametric method. The Raman spectra collected from the five uppermost micrometers of 11 healthy volunteers were fitted to different skin characteristics measured by independent appropriate methods (transepidermal water loss, hydration, pH, relative amount of ceramides, fatty acids, and cholesterol). For each parameter, the obtained PLS model presented correlation coefficients higher than R2=0.9. This model enables us to obtain all the aforementioned parameters directly from the unique Raman signature. In addition to that, in-depth Raman analyses down to 20 μm showed different balances between partially bound water and unbound water with depth. In parallel, the increase of depth was followed by an unfolding process of the proteins. The combinations of all these information led to a multiparametric investigation, which better characterizes the skin status. Raman signal can thus be used as a quick response code (QR code). This could help dermatologic diagnosis of physiological variations and presents a possible extension to pathological characterization.

Raman spectroscopy: in vivo quick response code of skin physiological status.

PubMed

Vyumvuhore, Raoul; Tfayli, Ali; Piot, Olivier; Le Guillou, Maud; Guichard, Nathalie; Manfait, Michel; Baillet-Guffroy, Arlette

2014-01-01

Dermatologists need to combine different clinically relevant characteristics for a better understanding of skin health. These characteristics are usually measured by different techniques, and some of them are highly time consuming. Therefore, a predicting model based on Raman spectroscopy and partial least square (PLS) regression was developed as a rapid multiparametric method. The Raman spectra collected from the five uppermost micrometers of 11 healthy volunteers were fitted to different skin characteristics measured by independent appropriate methods (transepidermal water loss, hydration, pH, relative amount of ceramides, fatty acids, and cholesterol). For each parameter, the obtained PLS model presented correlation coefficients higher than R2=0.9. This model enables us to obtain all the aforementioned parameters directly from the unique Raman signature. In addition to that, in-depth Raman analyses down to 20 μm showed different balances between partially bound water and unbound water with depth. In parallel, the increase of depth was followed by an unfolding process of the proteins. The combinations of all these information led to a multiparametric investigation, which better characterizes the skin status. Raman signal can thus be used as a quick response code (QR code). This could help dermatologic diagnosis of physiological variations and presents a possible extension to pathological characterization.

Long-term change of disease behavior in Papillon-Lefèvre syndrome: seven years follow-up.

PubMed

Wang, Xinwen; Liu, Yang; Liu, Yuan; Dong, Guangying; Kenney, E Barrie; Liu, Qing; Ma, Zhiwei; Wang, Qingtao

2015-03-01

Papillon-Lefèvre syndrome (PLS) is an autosomal recessive disease, characterized by severe periodontitis and palmoplantar hyperkeratosis. Mutations in the cathepsin C (CTSC) gene are the causative genetic factor. PLS starts at very early age, however, the age associated change of PLS has never been characterized. In this report, four PLS patients with CTSC mutations were followed up for seven years, periodontal condition and serum immunoglobulins (Igs) were recorded. Results showed that periodontal inflammation of PLS peaked at teenage years, but declined with time. At the same time the serum IgE change was consistent with the change, suggesting the possibility of using IgE as a monitoring index for PLS inflammation level, or to develop new target for therapy. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Exclusion of phospholipases (PLs)-producing bacteria in raw milk flushed with nitrogen gas (N(2)).

PubMed

Munsch-Alatossava, Patricia; Gursoy, Oguz; Alatossava, Tapani

2010-01-01

Prolonged cold storage of raw milks favors the growth of psychrotrophs, which produce heat-resistant exoenzymes of considerable spoilage potential; the bacterial proteases and lipases affect raw milk quality; among them phospholipases (PLs) may target the milk fat globule. More importantly, bacterial PLs are key virulence factors for numerous species. Two studies examined the use of nitrogen (N(2)) gas and examined its effect on psychrotrophs, proteases and lipase producers when the milk was stored in closed vessels; however, the effect on PLs producers is unknown. Here we show that by considering an open system the PLs producers were sooner or later excluded in raw milk (whereas the PLs producers in the non-treated controls culminated at 10(8)CFU/ml), by effective gas treatments that bring oxygen (O(2)) levels in milk lower than 0.1ppm. No increase of the PLs producers among the anaerobes was noticed during the course of the experiments. In the experiments performed at 6.0 degrees C, the delay after which the PLs producers were no longer detectable seemed independent of the initial level of PLs producers in raw milk (lower than 10(3)CFU/ml). We anticipate that flushing pure N(2) gas in raw milk tanks, considered as open systems, along the cold chain of raw milk storage and transportation, may be an additional technique to control psychrotrophs, and may also constitute an interesting perspective for limiting their spoilage and pathogenic potential in food materials in general.

Nurses' Own Birth Experiences Influence Labor Support Attitudes and Behaviors.

PubMed

Aschenbrenner, Ann P; Hanson, Lisa; Johnson, Teresa S; Kelber, Sheryl T

2016-01-01

To describe the attitudes of intrapartum nurses about the importance of and intent to provide professional labor support (PLS); barriers to PLS, such as perceived subjective norms and perceived behavioral control; and relationships among attitudes, behaviors, and nurse and site characteristics. A cross-sectional, mixed-methods, descriptive design was guided by the Theory of Planned Behavior. Three hospital sites in one region of a single Midwestern state. Sixty intrapartum nurses participated. The Labor Support Questionnaire and demographic questionnaire were administered online. The Labor Support Questionnaire is used to measure attitudes about the importance of and intended behaviors associated with labor support. Nurse Caring Behaviors was the highest rated PLS dimension. Participants' own personal birth experiences and length of current intrapartum experience were positively correlated with attitudes about and intent to provide PLS. Barriers to PLS included staffing, documentation, physicians, use of epidural analgesia, doulas, and birth plans. Personal birth and work experience influenced attitudes about and intent to provide PLS and demonstrated the relationships described in the Theory of Planned Behavior. Intrapartum nurses may benefit from an examination of their personal experiences to see how they might influence attitudes about PLS. Enhanced training and expanded labor and birth experience for novice nurses or students may improve attitudes and intended behavior with regard to PLS. Further investigations of the factors that affect integration of PLS into care are important to promote healthy birth outcomes. Copyright © 2016 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses. Published by Elsevier Inc. All rights reserved.

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms.

PubMed

Gonzalez Viejo, Claudia; Fuentes, Sigfredo; Torrico, Damir; Howell, Kate; Dunshea, Frank R

2018-01-01

Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam. The ANN method was able to create more accurate models (R 2  = 0.95) compared to PLS. Principal components analysis using RoboBEER parameters and NIR overtones related to protein explained 67% of total data variability. Additionally, a sub-space discriminant model using the absorbance values from NIR wavelengths resulted in the successful classification of 85% of beers according to fermentation type. The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Influence of metal substrates on the detection of explosive residues with laser-induced breakdown spectroscopy.

PubMed

Gottfried, Jennifer L

2013-02-01

Laser-induced breakdown spectroscopy is a promising approach for explosive residue detection, but several limitations to its widespread use remain. One issue is that the emission spectra of the residues are dependent on the substrate composition because some of the substrate is usually entrained in the laser-induced plasma and the laser-material interaction can be significantly affected by the substrate type. Here, we have demonstrated that despite the strong spectral variation in cyclotrimethylenetrinitramine (RDX) residues applied to various metal substrates, classification of the RDX residue independent of substrate type is feasible. Several approaches to improving the chemometric models based on partial least squares discriminant analysis (PLS-DA) have been described: classifying the RDX residue spectra together in one class independent of substrate, using selected emission intensities and ratios to increase the true positive rate (TPR) and decrease the false positive rate (FPR), and fusing the results from two PLS-DA models generated using the full broadband spectra and selected intensities and ratios. The combination of these approaches resulted in a TPR of 97.5% and a FPR of 1.0% for RDX classification on metal substrates.

Relationships between digestive efficiency and metabolomic profiles of serum and intestinal contents in chickens.

PubMed

Beauclercq, Stéphane; Nadal-Desbarats, Lydie; Hennequet-Antier, Christelle; Gabriel, Irène; Tesseraud, Sophie; Calenge, Fanny; Le Bihan-Duval, Elisabeth; Mignon-Grasteau, Sandrine

2018-04-27

The increasing cost of conventional feedstuffs has bolstered interest in genetic selection for digestive efficiency (DE), a component of feed efficiency, assessed by apparent metabolisable energy corrected to zero nitrogen retention (AMEn). However, its measurement is time-consuming and constraining, and its relationship with metabolic efficiency poorly understood. To simplify selection for this trait, we searched for indirect metabolic biomarkers through an analysis of the serum metabolome using nuclear magnetic resonance ( 1 H NMR). A partial least squares (PLS) model including six amino acids and two derivatives from butyrate predicted 59% of AMEn variability. Moreover, to increase our knowledge of the molecular mechanisms controlling DE, we investigated 1 H NMR metabolomes of ileal, caecal, and serum contents by fitting canonical sparse PLS. This analysis revealed strong associations between metabolites and DE. Models based on the ileal, caecal, and serum metabolome respectively explained 77%, 78%, and 74% of the variability of AMEn and its constitutive components (utilisation of starch, lipids, and nitrogen). In our conditions, the metabolites presenting the strongest associations with AMEn were proline in the serum, fumarate in the ileum and glucose in caeca. This study shows that serum metabolomics offers new opportunities to predict chicken DE.

An augmented classical least squares method for quantitative Raman spectral analysis against component information loss.

PubMed

Zhou, Yan; Cao, Hui

2013-01-01

We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.

Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes.

PubMed

Diaz, Sílvia O; Barros, António S; Goodfellow, Brian J; Duarte, Iola F; Galhano, Eulália; Pita, Cristina; Almeida, Maria do Céu; Carreira, Isabel M; Gil, Ana M

2013-06-07

Given the recognized lack of prenatal clinical methods for the early diagnosis of preterm delivery, intrauterine growth restriction, preeclampsia and gestational diabetes mellitus, and the continuing need for optimized diagnosis methods for specific chromosomal disorders (e.g., trisomy 21) and fetal malformations, this work sought specific metabolic signatures of these conditions in second trimester maternal urine, using (1)H Nuclear Magnetic Resonance ((1)H NMR) metabolomics. Several variable importance to the projection (VIP)- and b-coefficient-based variable selection methods were tested, both individually and through their intersection, and the resulting data sets were analyzed by partial least-squares discriminant analysis (PLS-DA) and submitted to Monte Carlo cross validation (MCCV) and permutation tests to evaluate model predictive power. The NMR data subsets produced significantly improved PLS-DA models for all conditions except for pre-premature rupture of membranes. Specific urinary metabolic signatures were unveiled for central nervous system malformations, trisomy 21, preterm delivery, gestational diabetes, intrauterine growth restriction and preeclampsia, and biochemical interpretations were proposed. This work demonstrated, for the first time, the value of maternal urine profiling as a complementary means of prenatal diagnostics and early prediction of several poor pregnancy outcomes.

Pyogenic liver abscess and peritonitis due to Rhizopus oryzae in a child with Papillon-Lefevre syndrome.

PubMed

Dalgic, Buket; Bukulmez, Aysegul; Sari, Sinan

2011-06-01

Papillon-Lefevre syndrome (PLS) is an autosomal recessive disease that is characterized by symmetric palmoplantar keratodermatitis and severe periodontal destruction. Mutations in the cathepsin C gene (CTSC) have recently been detected in PLS. Immune dysregulation, due to a mutation in CTSC, increases the risk of pyogenic infections in PLS patients. A child with PLS is presented here with liver abscesses and peritonitis caused by Rhizopus oryzae. His liver abscess and peritonitis were cured with amphotericin B without surgical care. This is the first case in the literature liver abscess due to Rhizopus oryzae in a child with PLS.

Robust Ultraviolet-Visible (UV-Vis) Partial Least-Squares (PLS) Models for Tannin Quantification in Red Wine.

PubMed

Aleixandre-Tudo, José Luis; Nieuwoudt, Helené; Aleixandre, José Luis; Du Toit, Wessel J

2015-02-04

The validation of ultraviolet-visible (UV-vis) spectroscopy combined with partial least-squares (PLS) regression to quantify red wine tannins is reported. The methylcellulose precipitable (MCP) tannin assay and the bovine serum albumin (BSA) tannin assay were used as reference methods. To take the high variability of wine tannins into account when the calibration models were built, a diverse data set was collected from samples of South African red wines that consisted of 18 different cultivars, from regions spanning the wine grape-growing areas of South Africa with their various sites, climates, and soils, ranging in vintage from 2000 to 2012. A total of 240 wine samples were analyzed, and these were divided into a calibration set (n = 120) and a validation set (n = 120) to evaluate the predictive ability of the models. To test the robustness of the PLS calibration models, the predictive ability of the classifying variables cultivar, vintage year, and experimental versus commercial wines was also tested. In general, the statistics obtained when BSA was used as a reference method were slightly better than those obtained with MCP. Despite this, the MCP tannin assay should also be considered as a valid reference method for developing PLS calibrations. The best calibration statistics for the prediction of new samples were coefficient of correlation (R 2 val) = 0.89, root mean standard error of prediction (RMSEP) = 0.16, and residual predictive deviation (RPD) = 3.49 for MCP and R 2 val = 0.93, RMSEP = 0.08, and RPD = 4.07 for BSA, when only the UV region (260-310 nm) was selected, which also led to a faster analysis time. In addition, a difference in the results obtained when the predictive ability of the classifying variables vintage, cultivar, or commercial versus experimental wines was studied suggests that tannin composition is highly affected by many factors. This study also discusses the correlations in tannin values between the methylcellulose and protein precipitation methods.

Linked functional network abnormalities during intrinsic and extrinsic activity in schizophrenia as revealed by a data-fusion approach.

PubMed

Hashimoto, Ryu-Ichiro; Itahashi, Takashi; Okada, Rieko; Hasegawa, Sayaka; Tani, Masayuki; Kato, Nobumasa; Mimura, Masaru

2018-01-01

Abnormalities in functional brain networks in schizophrenia have been studied by examining intrinsic and extrinsic brain activity under various experimental paradigms. However, the identified patterns of abnormal functional connectivity (FC) vary depending on the adopted paradigms. Thus, it is unclear whether and how these patterns are inter-related. In order to assess relationships between abnormal patterns of FC during intrinsic activity and those during extrinsic activity, we adopted a data-fusion approach and applied partial least square (PLS) analyses to FC datasets from 25 patients with chronic schizophrenia and 25 age- and sex-matched normal controls. For the input to the PLS analyses, we generated a pair of FC maps during the resting state (REST) and the auditory deviance response (ADR) from each participant using the common seed region in the left middle temporal gyrus, which is a focus of activity associated with auditory verbal hallucinations (AVHs). PLS correlation (PLS-C) analysis revealed that patients with schizophrenia have significantly lower loadings of a component containing positive FCs in default-mode network regions during REST and a component containing positive FCs in the auditory and attention-related networks during ADR. Specifically, loadings of the REST component were significantly correlated with the severities of positive symptoms and AVH in patients with schizophrenia. The co-occurrence of such altered FC patterns during REST and ADR was replicated using PLS regression, wherein FC patterns during REST are modeled to predict patterns during ADR. These findings provide an integrative understanding of altered FCs during intrinsic and extrinsic activity underlying core schizophrenia symptoms.

Chemometric classification of casework arson samples based on gasoline content.

PubMed

Sinkov, Nikolai A; Sandercock, P Mark L; Harynuk, James J

2014-02-01

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools. Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric. In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Rapid Analysis of Deoxynivalenol in Durum Wheat by FT-NIR Spectroscopy

PubMed Central

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-01-01

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation. PMID:25384107

Rapid analysis of deoxynivalenol in durum wheat by FT-NIR spectroscopy.

PubMed

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-11-06

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50-16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%-90% and 3%-7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.

Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques.

PubMed

Philip Ye, X; Liu, Lu; Hayes, Douglas; Womac, Alvin; Hong, Kunlun; Sokhansanj, Shahab

2008-10-01

The objectives of this research were to determine the variation of chemical composition across botanical fractions of cornstover, and to probe the potential of Fourier transform near-infrared (FT-NIR) techniques in qualitatively classifying separated cornstover fractions and in quantitatively analyzing chemical compositions of cornstover by developing calibration models to predict chemical compositions of cornstover based on FT-NIR spectra. Large variations of cornstover chemical composition for wide calibration ranges, which is required by a reliable calibration model, were achieved by manually separating the cornstover samples into six botanical fractions, and their chemical compositions were determined by conventional wet chemical analyses, which proved that chemical composition varies significantly among different botanical fractions of cornstover. Different botanic fractions, having total saccharide content in descending order, are husk, sheath, pith, rind, leaf, and node. Based on FT-NIR spectra acquired on the biomass, classification by Soft Independent Modeling of Class Analogy (SIMCA) was employed to conduct qualitative classification of cornstover fractions, and partial least square (PLS) regression was used for quantitative chemical composition analysis. SIMCA was successfully demonstrated in classifying botanical fractions of cornstover. The developed PLS model yielded root mean square error of prediction (RMSEP %w/w) of 0.92, 1.03, 0.17, 0.27, 0.21, 1.12, and 0.57 for glucan, xylan, galactan, arabinan, mannan, lignin, and ash, respectively. The results showed the potential of FT-NIR techniques in combination with multivariate analysis to be utilized by biomass feedstock suppliers, bioethanol manufacturers, and bio-power producers in order to better manage bioenergy feedstocks and enhance bioconversion.

1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas.

PubMed

Son, Hong-Seok; Kim, Ki Myong; van den Berg, Frans; Hwang, Geum-Sook; Park, Won-Mok; Lee, Cherl-Ho; Hong, Young-Shick

2008-09-10

(1)H NMR spectroscopy was used to investigate the metabolic differences in wines produced from different grape varieties and different regions. A significant separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was observed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The metabolites contributing to the separation were assigned to be 2,3-butanediol, lactate, acetate, proline, succinate, malate, glycerol, tartarate, glucose, and phenolic compounds by PCA and PLS-DA loading plots. Wines produced from Cabernet Sauvignon grapes harvested in the continental areas of Australia, France, and California were also separated. PLS-DA loading plots revealed that the level of proline in Californian Cabernet Sauvignon wines was higher than that in Australian and French Cabernet Sauvignon, Australian Shiraz, and Korean Campbell Early wines, showing that the chemical composition of the grape berries varies with the variety and growing area. This study highlights the applicability of NMR-based metabolomics with multivariate statistical data sets in determining wine quality and product origin.

Selection of molecular descriptors with artificial intelligence for the understanding of HIV-1 protease peptidomimetic inhibitors-activity.

PubMed

Sirois, S; Tsoukas, C M; Chou, Kuo-Chen; Wei, Dongqing; Boucher, C; Hatzakis, G E

2005-03-01

Quantitative Structure Activity Relationship (QSAR) techniques are used routinely by computational chemists in drug discovery and development to analyze datasets of compounds. Quantitative numerical methods like Partial Least Squares (PLS) and Artificial Neural Networks (ANN) have been used on QSAR to establish correlations between molecular properties and bioactivity. However, ANN may be advantageous over PLS because it considers the interrelations of the modeled variables. This study focused on the HIV-1 Protease (HIV-1 Pr) inhibitors belonging to the peptidomimetic class of compounds. The main objective was to select molecular descriptors with the best predictive value for antiviral potency (Ki). PLS and ANN were used to predict Ki activity of HIV-1 Pr inhibitors and the results were compared. To address the issue of dimensionality reduction, Genetic Algorithms (GA) were used for variable selection and their performance was compared against that of ANN. Finally, the structure of the optimum ANN achieving the highest Pearson's-R coefficient was determined. On the basis of Pearson's-R, PLS and ANN were compared to determine which exhibits maximum performance. Training and validation of models was performed on 15 random split sets of the master dataset consisted of 231 compounds. For each compound 192 molecular descriptors were considered. The molecular structure and constant of inhibition (Ki) were selected from the NIAID database. Study findings suggested that non-covalent interactions such as hydrophobicity, shape and hydrogen bonding describe well the antiviral activity of the HIV-1 Pr compounds. The significance of lipophilicity and relationship to HIV-1 associated hyperlipidemia and lipodystrophy syndrome warrant further investigation.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures.

PubMed

Hegazy, Maha A; Lotfy, Hayam M; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-05

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative real-time monitoring of dryer effluent using fiber optic near-infrared spectroscopy.

PubMed

Harris, S C; Walker, D S

2000-09-01

This paper describes a method for real-time quantitation of the solvents evaporating from a dryer. The vapor stream in the vacuum line of a dryer was monitored in real time using a fiber optic-coupled acousto-optic tunable filter near-infrared (AOTF-NIR) spectrometer. A balance was placed in the dryer, and mass readings were recorded for every scan of the AOTF-NIR. A partial least-squares (PLS) calibration was subsequently built based on change in mass over change in time for solvents typically used in a chemical manufacturing plant. Controlling software for the AOTF-NIR was developed. The software collects spectra, builds the PLS calibration model, and continuously fits subsequently collected spectra to the calibration, allowing the operator to follow the mass loss of solvent from the dryer. The results indicate that solvent loss can be monitored and quantitated in real time using NIR for the optimization of drying times. These time-based mass loss values have also been used to calculate "dynamic" vapor density values for the solvents. The values calculated are in agreement with values determined from the ideal gas law and could prove valuable as tools to measure temperature or pressure indirectly.

A component prediction method for flue gas of natural gas combustion based on nonlinear partial least squares method.

PubMed

Cao, Hui; Yan, Xingyu; Li, Yaojiang; Wang, Yanxia; Zhou, Yan; Yang, Sanchun

2014-01-01

Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.

PubMed

Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

2018-03-08

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds

PubMed Central

Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

2018-01-01

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400–1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides’ spectra of every seed), and mixture datasets (two sides’ spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner. PMID:29517991

Simultaneous determination of vitamin B12 and its derivatives using some of multivariate calibration 1 (MVC1) techniques

NASA Astrophysics Data System (ADS)

Samadi-Maybodi, Abdolraouf; Darzi, S. K. Hassani Nejad

2008-10-01

Resolution of binary mixtures of vitamin B12, methylcobalamin and B12 coenzyme with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable (PLS1), orthogonal signal correction/partial least squares (OSC/PLS), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV-vis spectra. The UV-vis spectra of the vitamin B12, methylcobalamin and B12 coenzyme were recorded in the same spectral conditions. The method of central composite design was used in the ranges of 10-80 mg L -1 for vitamin B12 and methylcobalamin and 20-130 mg L -1 for B12 coenzyme. The models refinement procedure and validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 2.26 mg L -1 for vitamin B12 with PLS1, 1.33 mg L -1 for methylcobalamin with OSC/PLS and 3.24 mg L -1 for B12 coenzyme with HLA techniques. Figures of merit such as selectivity, sensitivity, analytical sensitivity and LOD were determined for three compounds. The procedure was successfully applied to simultaneous determination of three compounds in synthetic mixtures and in a pharmaceutical formulation.

Tablet potency of Tianeptine in coated tablets by near infrared spectroscopy: model optimisation, calibration transfer and confidence intervals.

PubMed

Boiret, Mathieu; Meunier, Loïc; Ginot, Yves-Michel

2011-02-20

A near infrared (NIR) method was developed for determination of tablet potency of active pharmaceutical ingredient (API) in a complex coated tablet matrix. The calibration set contained samples from laboratory and production scale batches. The reference values were obtained by high performance liquid chromatography (HPLC) and partial least squares (PLS) regression was used to establish a model. The model was challenged by calculating tablet potency of two external test sets. Root mean square errors of prediction were respectively equal to 2.0% and 2.7%. To use this model with a second spectrometer from the production field, a calibration transfer method called piecewise direct standardisation (PDS) was used. After the transfer, the root mean square error of prediction of the first test set was 2.4% compared to 4.0% without transferring the spectra. A statistical technique using bootstrap of PLS residuals was used to estimate confidence intervals of tablet potency calculations. This method requires an optimised PLS model, selection of the bootstrap number and determination of the risk. In the case of a chemical analysis, the tablet potency value will be included within the confidence interval calculated by the bootstrap method. An easy to use graphical interface was developed to easily determine if the predictions, surrounded by minimum and maximum values, are within the specifications defined by the regulatory organisation. Copyright © 2010 Elsevier B.V. All rights reserved.

Paper spray mass spectrometry and PLS-DA improved by variable selection for the forensic discrimination of beers.

PubMed

Pereira, Hebert Vinicius; Amador, Victória Silva; Sena, Marcelo Martins; Augusti, Rodinei; Piccin, Evandro

2016-10-12

Paper spray mass spectrometry (PS-MS) combined with partial least squares discriminant analysis (PLS-DA) was applied for the first time in a forensic context to a fast and effective differentiation of beers. Eight different brands of American standard lager beers produced by four different breweries (141 samples from 55 batches) were studied with the aim at performing a differentiation according to their market prices. The three leader brands in the Brazilian beer market, which have been subject to fraud, were modeled as the higher-price class, while the five brands most used for counterfeiting were modeled as the lower-price class. Parameters affecting the paper spray ionization were examined and optimized. The best MS signal stability and intensity was obtained while using the positive ion mode, with PS(+) mass spectra characterized by intense pairs of signals corresponding to sodium and potassium adducts of malto-oligosaccharides. Discrimination was not apparent neither by using visual inspection nor principal component analysis (PCA). However, supervised classification models provided high rates of sensitivity and specificity. A PLS-DA model using full scan mass spectra were improved by variable selection with ordered predictors selection (OPS), providing 100% of reliability rate and reducing the number of variables from 1701 to 60. This model was interpreted by detecting fifteen variables as the most significant VIP (variable importance in projection) scores, which were therefore considered diagnostic ions for this type of beer counterfeit. Copyright © 2016 Elsevier B.V. All rights reserved.

Model observer design for multi-signal detection in the presence of anatomical noise

NASA Astrophysics Data System (ADS)

Wen, Gezheng; Markey, Mia K.; Park, Subok

2017-02-01

As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Model observers are typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g. multifocal multicentric (MFMC) breast cancer), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g. digital breast tomosynthesis may be more effective for diagnosis of MFMC than mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model observer to detect multiple signals in an image dataset. A novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. The PLS channels are adaptive to the characteristics of signals and the background, and they capture the interactions among signal locations. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our results show that: (1) the model observer can achieve high performance with a reasonably small number of channels; (2) the model observer with PLS channels outperforms that with benchmark modified Laguerre-Gauss channels, especially when realistic signal shapes and complex background statistics are involved; (3) the tasks of clinical interest, and other constraints such as sample size would alter the optimal design of the model observer.

Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning.

PubMed

Maraschin, Marcelo; Somensi-Zeggio, Amélia; Oliveira, Simone K; Kuhnen, Shirley; Tomazzoli, Maíra M; Raguzzoni, Josiane C; Zeri, Ana C M; Carreira, Rafael; Correia, Sara; Costa, Christopher; Rocha, Miguel

2016-01-22

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Development and validation of an in-line NIR spectroscopic method for continuous blend potency determination in the feed frame of a tablet press.

PubMed

De Leersnyder, Fien; Peeters, Elisabeth; Djalabi, Hasna; Vanhoorne, Valérie; Van Snick, Bernd; Hong, Ke; Hammond, Stephen; Liu, Angela Yang; Ziemons, Eric; Vervaet, Chris; De Beer, Thomas

2018-03-20

A calibration model for in-line API quantification based on near infrared (NIR) spectra collection during tableting in the tablet press feed frame was developed and validated. First, the measurement set-up was optimised and the effect of filling degree of the feed frame on the NIR spectra was investigated. Secondly, a predictive API quantification model was developed and validated by calculating the accuracy profile based on the analysis results of validation experiments. Furthermore, based on the data of the accuracy profile, the measurement uncertainty was determined. Finally, the robustness of the API quantification model was evaluated. An NIR probe (SentroPAT FO) was implemented into the feed frame of a rotary tablet press (Modul™ P) to monitor physical mixtures of a model API (sodium saccharine) and excipients with two different API target concentrations: 5 and 20% (w/w). Cutting notches into the paddle wheel fingers did avoid disturbances of the NIR signal caused by the rotating paddle wheel fingers and hence allowed better and more complete feed frame monitoring. The effect of the design of the notched paddle wheel fingers was also investigated and elucidated that straight paddle wheel fingers did cause less variation in NIR signal compared to curved paddle wheel fingers. The filling degree of the feed frame was reflected in the raw NIR spectra. Several different calibration models for the prediction of the API content were developed, based on the use of single spectra or averaged spectra, and using partial least squares (PLS) regression or ratio models. These predictive models were then evaluated and validated by processing physical mixtures with different API concentrations not used in the calibration models (validation set). The β-expectation tolerance intervals were calculated for each model and for each of the validated API concentration levels (β was set at 95%). PLS models showed the best predictive performance. For each examined saccharine concentration range (i.e., between 4.5 and 6.5% and between 15 and 25%), at least 95% of future measurements will not deviate more than 15% from the true value. Copyright © 2018 Elsevier B.V. All rights reserved.

Primary Lateral Sclerosis

PubMed Central

Statland, Jeffrey M.; Barohn, Richard J.; Dimachkie, Mazen M.; Floeter, Mary Kay; Mitsumoto, Hiroshi

2015-01-01

Synopsis Primary lateral sclerosis (PLS) is characterized by insidious onset of progressive upper motor neuron dysfunction in the absence of clinical signs of lower motor neuron involvement. Patients experience stiffness, decreased balance and coordination, and mild weakness, and if the bulbar region is affected, difficulty speaking and swallowing, and emotional lability. The diagnosis is made based on clinical history, typical exam findings, and diagnostic testing negative for other causes of upper motor neuron dysfunction. EMG is normal, or only shows mild neurogenic findings in a few muscles, not meeting El Escorial criteria. Although no test is specific for PLS, some neurodiagnostic tests are supportive: including absent or delayed central motor conduction times; and changes in the precentral gyrus or corticospinal tracts on MRI, DTI or MR Spectroscopy. Treatment is largely supportive, and includes medications for spasticity, baclofen pump, and treatment for pseudobulbar affect. The prognosis in PLS is more benign than ALS, making this a useful diagnostic category. PMID:26515619

The Detection and Quantification of Adulteration in Ground Roasted Asian Palm Civet Coffee Using Near-Infrared Spectroscopy in Tandem with Chemometrics

NASA Astrophysics Data System (ADS)

Suhandy, D.; Yulia, M.; Ogawa, Y.; Kondo, N.

2018-05-01

In the present research, an evaluation of using near infrared (NIR) spectroscopy in tandem with full spectrum partial least squares (FS-PLS) regression for quantification of degree of adulteration in civet coffee was conducted. A number of 126 ground roasted coffee samples with degree of adulteration 0-51% were prepared. Spectral data were acquired using a NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement in the range of 1300-2500 nm. The samples were divided into two groups calibration sample set (84 samples) and prediction sample set (42 samples). The calibration model was developed on original spectra using FS-PLS regression with full-cross validation method. The calibration model exhibited the determination coefficient R2=0.96 for calibration and R2=0.92 for validation. The prediction resulted in low root mean square error of prediction (RMSEP) (4.67%) and high ratio prediction to deviation (RPD) (3.75). In conclusion, the degree of adulteration in civet coffee have been quantified successfully by using NIR spectroscopy and FS-PLS regression in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation.

Prediction of ethanol in bottled Chinese rice wine by NIR spectroscopy

NASA Astrophysics Data System (ADS)

Ying, Yibin; Yu, Haiyan; Pan, Xingxiang; Lin, Tao

2006-10-01

To evaluate the applicability of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining ethanol concentration of Chinese rice wine in square brown glass bottle, transmission spectra of 100 bottled Chinese rice wine samples were collected in the spectral range of 350-1200 nm. Statistical equations were established between the reference data and VIS-NIR spectra by partial least squares (PLS) regression method. Performance of three kinds of mathematical treatment of spectra (original spectra, first derivative spectra and second derivative spectra) were also discussed. The PLS models of original spectra turned out better results, with higher correlation coefficient in calibration (R cal) of 0.89, lower root mean standard error of calibration (RMSEC) of 0.165, and lower root mean standard error of cross validation (RMSECV) of 0.179. Using original spectra, PLS models for ethanol concentration prediction were developed. The R cal and the correlation coefficient in validation (R val) were 0.928 and 0.875, respectively; and the RMSEC and the root mean standard error of validation (RMSEP) were 0.135 (%, v v -1) and 0.177 (%, v v -1), respectively. The results demonstrated that VIS-NIR spectroscopy could be used to predict ethanol concentration in bottled Chinese rice wine.

[Determination of wine original regions using information fusion of NIR and MIR spectroscopy].

PubMed

Xiang, Ling-Li; Li, Meng-Hua; Li, Jing-Mingz; Li, Jun-Hui; Zhang, Lu-Da; Zhao, Long-Lian

2014-10-01

Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters' evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spe trometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which areall typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR). After using the method proposed in this paper, the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately, which all achieved better results of determination than individual spectroscopy. These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions.

Status of PLS-II Upgrade Program

NASA Astrophysics Data System (ADS)

Kim, Kyung-Ryul; Wiedemann, Helmut; Park, Sung-Ju; Kim, Dong-Eon; Park, Chong-Do; Park, Sung-Soo; Kim, Seong-Hwan; Kim, Bongsoo; Namkung, Won; Nam, Sanghoon; Ree, Moonhor

2010-06-01

The Pohang Light Source (PLS) at the Pohang Accelerator Laboratory has been operated first at 2.0 GeV since 1995, and later was upgraded to 2.5 GeV. During this time, 6 insertion devices like undulators and multipole wigglers have been put into operation to produce special photon beams, with a total of 27 beamlines installed and 3 beamlines under construction. Recently, Korea synchrotron user's community is demanding high beam stability, higher photon energies as well as more straight sections for insertion devices in the PLS. To meet the user requirements, the PLS-II upgrade program has been launched in January, 2009, incorporating a modified chromatic version of Double Bend Achromat (DBA) to achieve almost twice as many straight sections as the current PLS with a design goal of the relatively low emittance, ɛ, of 5.9 nmṡrad. In the PLS-II, the top-up injection using full energy linac is planned for much higher stable beam as well and thus the production of hard x-ray undulator radiation of 8 to 13 keV is anticipated to allow for the successful research program namely Protein Crystallography. The PLS-II machine components of storage ring, linear accelerator and photon beamlines will be partly dismantled and reinstalled in a 6-months shutdown beginning January, 2011 and then the PLS-II upgrade be started the initial commissioning with a 100 mA beam current from July in 2011.

Structure, Antimicrobial Activities and Mode of Interaction with Membranes of Bovel Phylloseptins from the Painted-Belly Leaf Frog, Phyllomedusa sauvagii

PubMed Central

Raja, Zahid; André, Sonia; Piesse, Christophe; Sereno, Denis; Nicolas, Pierre; Foulon, Thierry

2013-01-01

Transcriptomic and peptidomic analysis of skin secretions from the Painted-belly leaf frog Phyllomedusa sauvagii led to the identification of 5 novel phylloseptins (PLS-S2 to -S6) and also of phylloseptin-1 (PSN-1, here renamed PLS-S1), the only member of this family previously isolated in this frog. Synthesis and characterization of these phylloseptins revealed differences in their antimicrobial activities. PLS-S1, -S2, and -S4 (79–95% amino acid sequence identity; net charge  = +2) were highly potent and cidal against Gram-positive bacteria, including multidrug resistant S. aureus strains, and killed the promastigote stage of Leishmania infantum, L. braziliensis and L. major. By contrast, PLS-S3 (95% amino acid identity with PLS-S2; net charge  = +1) and -S5 (net charge  = +2) were found to be almost inactive against bacteria and protozoa. PLS-S6 was not studied as this peptide was closely related to PLS-S1. Differential scanning calorimetry on anionic and zwitterionic multilamellar vesicles combined with circular dichroism spectroscopy and membrane permeabilization assays on bacterial cells indicated that PLS-S1, -S2, and -S4 are structured in an amphipathic α-helix that disrupts the acyl chain packing of anionic lipid bilayers. As a result, regions of two coexisting phases could be formed, one phase rich in peptide and the other lipid-rich. After reaching a threshold peptide concentration, the disruption of lipid packing within the bilayer may lead to local cracks and disintegration of the microbial membrane. Differences in the net charge, α-helical folding propensity, and/or degree of amphipathicity between PLS-S1, -S2 and -S4, and between PLS-S3 and -S5 appear to be responsible for their marked differences in their antimicrobial activities. In addition to the detailed characterization of novel phylloseptins from P. sauvagii, our study provides additional data on the previously isolated PLS-S1 and on the mechanism of action of phylloseptins. PMID:23967105

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

The Crucial Role of the Pls1 Tetraspanin during Ascospore Germination in Podospora anserina Provides an Example of the Convergent Evolution of Morphogenetic Processes in Fungal Plant Pathogens and Saprobes▿ †

PubMed Central

Lambou, Karine; Malagnac, Fabienne; Barbisan, Crystel; Tharreau, Didier; Lebrun, Marc-Henri; Silar, Philippe

2008-01-01

Pls1 tetraspanins were shown for some pathogenic fungi to be essential for appressorium-mediated penetration into their host plants. We show here that Podospora anserina, a saprobic fungus lacking appressorium, contains PaPls1, a gene orthologous to known PLS1 genes. Inactivation of PaPls1 demonstrates that this gene is specifically required for the germination of ascospores in P. anserina. These ascospores are heavily melanized cells that germinate under inducing conditions through a specific pore. On the contrary, MgPLS1, which fully complements a ΔPaPls1 ascospore germination defect, has no role in the germination of Magnaporthe grisea nonmelanized ascospores but is required for the formation of the penetration peg at the pore of its melanized appressorium. P. anserina mutants with mutation of PaNox2, which encodes the NADPH oxidase of the NOX2 family, display the same ascospore-specific germination defect as the ΔPaPls1 mutant. Both mutant phenotypes are suppressed by the inhibition of melanin biosynthesis, suggesting that they are involved in the same cellular process required for the germination of P. anserina melanized ascospores. The analysis of the distribution of PLS1 and NOX2 genes in fungal genomes shows that they are either both present or both absent. These results indicate that the germination of P. anserina ascospores and the formation of the M. grisea appressorium penetration peg use the same molecular machinery that includes Pls1 and Nox2. This machinery is specifically required for the emergence of polarized hyphae from reinforced structures such as appressoria and ascospores. Its recurrent recruitment during fungal evolution may account for some of the morphogenetic convergence observed in fungi. PMID:18757568

Familiarity breeds contempt: combining proximity loggers and GPS reveals female white-tailed deer (Odocoileus virginianus) avoiding close contact with neighbors.

PubMed

Tosa, Marie I; Schauber, Eric M; Nielsen, Clayton K

2015-01-01

Social interactions can influence infectious disease dynamics, particularly for directly transmitted pathogens. Therefore, reliable information on contact frequency within and among groups can better inform disease modeling and management. We compared three methods of assessing contact patterns: (1) space-use overlap (volume of interaction [VI]), (2) direct contact rates measured by simultaneous global positioning system (GPS) locations (<10 m apart), and (3) direct contact rates measured by proximity loggers (PLs; 1-m detection) among female white-tailed deer (Odocoileus virginianus). We calculated the PL∶GPS contact ratios to see whether both devices reveal similar contact patterns and thus predict similar pathogen transmission patterns. Contact rates measured by GPS and PLs were similarly high for two within-group dyads (pairs of deer in the same social groups). Dyads representing separate but neighboring groups (high VI) had PL∶GPS contact ratios near zero, whereas dyads further apart (intermediate VI) had higher PL∶GPS contact ratios. Social networks based on PL contacts showed the fewest connected individuals and lowest mean centrality measures; network metrics were intermediate when based on GPS contacts and greatest when based on VI. Thus, the VI network portrayed animals to be more uniformly and strongly connected than did the PL network. We conclude that simultaneous GPS locations, compared with PLs, substantially underestimate the impact of group membership on direct contact rates of female deer and make networks appear more connected. We also present evidence that deer coming within the general vicinity of each other are less likely to come in close contact if they are in neighboring social groups than deer whose home ranges overlap little if at all. Combined, these results provide evidence that direct transmission of disease agents among female and juvenile white-tailed deer is likely to be constrained both spatially and by social structure, more so than GPS data alone would suggest.

Classification of Brazilian and foreign gasolines adulterated with alcohol using infrared spectroscopy.

PubMed

da Silva, Neirivaldo C; Pimentel, Maria Fernanda; Honorato, Ricardo S; Talhavini, Marcio; Maldaner, Adriano O; Honorato, Fernanda A

2015-08-01

The smuggling of products across the border regions of many countries is a practice to be fought. Brazilian authorities are increasingly worried about the illicit trade of fuels along the frontiers of the country. In order to confirm this as a crime, the Federal Police must have a means of identifying the origin of the fuel. This work describes the development of a rapid and nondestructive methodology to classify gasoline as to its origin (Brazil, Venezuela and Peru), using infrared spectroscopy and multivariate classification. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling Class Analogy (SIMCA) models were built. Direct standardization (DS) was employed aiming to standardize the spectra obtained in different laboratories of the border units of the Federal Police. Two approaches were considered in this work: (1) local and (2) global classification models. When using Approach 1, the PLS-DA achieved 100% correct classification, and the deviation of the predicted values for the secondary instrument considerably decreased after performing DS. In this case, SIMCA models were not efficient in the classification, even after standardization. Using a global model (Approach 2), both PLS-DA and SIMCA techniques were effective after performing DS. Considering that real situations may involve questioned samples from other nations (such as Peru), the SIMCA method developed according to Approach 2 is a more adequate, since the sample will be classified neither as Brazil nor Venezuelan. This methodology could be applied to other forensic problems involving the chemical classification of a product, provided that a specific modeling is performed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Genetic and Physiological Studies of Bacillus anthracis Related to Development of an Improved Vaccine

DTIC Science & Technology

1987-07-01

nontransformable Bacillus species such as B. anthracis. Our results suggest that plasmid pLS20 of Bacillus subtilis ( natto ), which promotes transfer of the...mobilizing pBC16, pLS20 mediates transfer of the B. subtills ( natto ) plasmid pLS19 and the Staphylococcus aureus plasmid pUB110. To facilitate direct...and (v) transformation of B. cereus and B. anthracis with plasmid DNA. The 55-kb plasmid, pLS20, of Bacillus subtilis ( natto ) 3335 promotes tr msfer

Effective apatinib treatment of pleomorphic liposarcoma: A case report.

PubMed

Yan, Peng; Sun, Mei-Li; Sun, Yu-Ping; Liu, Chuan-Yong

2017-08-01

Pleomorphic liposarcoma (PLS) is a rare and aggressive malignant tumor, and both radiation and conventional cytotoxic chemotherapy remain controversial for metastatic or unresectable disease. We presented an 81-year-old Chinese woman with advanced PLS who received apatinib after failure chemotherapy. The patient was diagnosed as having PLS by biopsy. After a failed chemotherapy, apatinib started to be taken orally 425 mg per day. This patient achieved 3-month progression-free survival (PFS) and a higher quality of life. Meanwhile, this patient suffered grade 2 hypertension and grade 3 hand-foot syndrome (HFS). In this case, apatinib presented good efficacy and safety to treat PLS. Randomized clinical studies are required to confirm the efficacy and safety of apatinib in the treatment of PLS.

Monitoring of substrate and product concentrations in acetic fermentation processes for onion vinegar production by NIR spectroscopy: value addition to worthless onions.

PubMed

González-Sáiz, J M; Esteban-Díez, I; Sánchez-Gallardo, C; Pizarro, C

2008-08-01

Wastes and by-products of the onion-processing industry pose an increasing disposal and environmental problem and represent a loss of valuable sources of nutrients. The present study focused on the production of vinegar from worthless onions as a potential valorisation route which could provide a viable solution to multiple disposal and environmental problems, simultaneously offering the possibility of converting waste materials into a useful food-grade product and of exploiting the unique properties and health benefits of onions. This study deals specifically with the second and definitive step of the onion vinegar production process: the efficient production of vinegar from onion waste by transforming onion ethanol, previously produced by alcoholic fermentation, into acetic acid via acetic fermentation. Near-infrared spectroscopy (NIRS), coupled with multivariate calibration methods, has been used to monitor the concentrations of both substrates and products in acetic fermentation. Separate partial least squares (PLS) regression models, correlating NIR spectral data of fermentation samples with each kinetic parameter studied, were developed. Wavelength selection was also performed applying the iterative predictor weighting-PLS (IPW-PLS) method in order to only consider significant spectral features in each model development to improve the quality of the final models constructed. Biomass, substrate (ethanol) and product (acetic acid) concentration were predicted in the acetic fermentation of onion alcohol with high accuracy using IPW-PLS models with a root-mean-square error of the residuals in external prediction (RMSEP) lower than 2.5% for both ethanol and acetic acid, and an RMSEP of 6.1% for total biomass concentration (a very satisfactory result considering the relatively low precision and accuracy associated with the reference method used for determining the latter). Thus, the simple and reliable calibration models proposed in this study suggest that they could be implemented in routine applications to monitor and predict the key species involved in the acetic fermentation of onion alcohol, allowing the onion vinegar production process to be controlled in real time.

Computerized pigment design based on property hypersurfaces

NASA Astrophysics Data System (ADS)

Halova, Jaroslava; Sulcova, Petra; Kupka, Karel

2007-05-01

Competition is tough in the pigment market. Rational pigment design has therefore a competitive advantage, saving time and money. The aim of this work is to provide methods that can assist in designing pigments with defined properties. These methods include partial least squares regression (PLSR), neural network (NN) and generalized regression ANOVA model. Authors show how PLS bi-plot can be used to identify market gaps poorly covered by pigment manufacturers, thus giving an opportunity to develop pigments with potentially profitable properties.

A transverse bunch by bunch feedback system for Pohang Light Source upgrade

NASA Astrophysics Data System (ADS)

Lee, E.-H.; Kim, D.-T.; Huang, J.-Y.; Shin, S.; Nakamura, T.; Kobayashi, K.

2014-12-01

The Pohang Light Source upgrade (PLS-II) project has successfully upgraded the Pohang Light Source (PLS). The main goals of the PLS-II project are to increase the beam energy to 3 GeV, increase the number of insertion devices by a factor of two (20 IDs), increase the beam current to 400 mA, and at the same time reduce the beam emittance to below 10 nm by using the existing PLS tunnel and injection system. Among 20 insertion devices, 10 narrow gap in-vacuum undulators are in operation now and two more in-vacuum undulators are to be installed later. Since these narrow gap in-vacuum undulators are most likely to produce coupled bunch instability by the resistive wall impedance and limit the stored beam current, a bunch by bunch feedback system is implemented to suppress coupled bunch instability in the PLS-II. This paper describes the scheme and performance of the PLS-II bunch by bunch feedback system.

aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events

PubMed Central

Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B.; Ben-Hur, Asa; Boucher, Christina

2016-01-01

Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The P class contains tandem P-type motif sequences, and the PLS class contains alternating P, L and S type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a PLS-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the PLS class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for PLS-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

Dysgraphia in Patients with Primary Lateral Sclerosis: A Speech-Based Rehearsal Deficit?

PubMed Central

Zago, S.; Poletti, B.; Corbo, M.; Adobbati, L.; Silani, V.

2008-01-01

The present study aims to demonstrate that errors when writing are more common than expected in patients affected by primary lateral sclerosis (PLS) with severe dysarthria or complete mutism, independent of spasticity. Sixteen patients meeting Pringle’s et al. [34] criteria for PLS underwent standard neuropsychological tasks and evaluation of writing. We assessed writing abilities in spelling through dictation in which a set of words, non-words and short phrases were presented orally and by composing words using a set of preformed letters. Finally, a written copying task was performed with the same words. Relative to controls, PLS patients made a greater number of spelling errors in all writing conditions, but not in copy task. The error types included: omissions, transpositions, insertions and letter substitutions. These were equally distributed on the writing task and the composition of words with a set of preformed letters. This pattern of performance is consistent with a spelling impairment. The results are consistent with the concept that written production is critically dependent on the subvocal articulatory mechanism of rehearsal, perhaps at the level of retaining the sequence of graphemes in a graphemic buffer. In PLS patients a disturbance in rehearsal opportunity may affect the correct sequencing/assembly of an orthographic representation in the written process. PMID:19096141

Experiments on Supervised Learning Algorithms for Text Categorization

NASA Technical Reports Server (NTRS)

Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.

2005-01-01

Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

The effect of thermal treatment on the enhancement of detection of adulteration in extra virgin olive oils by synchronous fluorescence spectroscopy and chemometric analysis.

PubMed

Mabood, F; Boqué, R; Folcarelli, R; Busto, O; Jabeen, F; Al-Harrasi, Ahmed; Hussain, J

2016-05-15

In this study the effect of thermal treatment on the enhancement of synchronous fluorescence spectroscopic method for discrimination and quantification of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with refined oil was investigated. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8h, in contact with air and with light exposure, to favor oxidation. All the samples were then measured with synchronous fluorescence spectroscopy. Synchronous fluorescence spectra were acquired by varying the wavelength in the region from 250 to 720 nm at 20 nm wavelength differential interval of excitation and emission. Pure and adulterated olive oils were discriminated by using partial least-squares discriminant analysis (PLS-DA). It was found that the best PLS-DA models were those built with the difference spectra (75 °C-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration of refined olive oils. Furthermore, PLS regression models were also built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 3.18% of adulteration. Copyright © 2016 Elsevier B.V. All rights reserved.

Laser-Induced Breakdown Spectroscopy (LIBS) Measurement of Uranium in Molten Salt.

PubMed

Williams, Ammon; Phongikaroon, Supathorn

2018-01-01

In this current study, the molten salt aerosol-laser-induced breakdown spectroscopy (LIBS) system was used to measure the uranium (U) content in a ternary UCl 3 -LiCl-KCl salt to investigate and assess a near real-time analytical approach for material safeguards and accountability. Experiments were conducted using five different U concentrations to determine the analytical figures of merit for the system with respect to U. In the analysis, three U lines were used to develop univariate calibration curves at the 367.01 nm, 385.96 nm, and 387.10 nm lines. The 367.01 nm line had the lowest limit of detection (LOD) of 0.065 wt% U. The 385.96 nm line had the best root mean square error of cross-validation (RMSECV) of 0.20 wt% U. In addition to the univariate calibration approach, a multivariate partial least squares (PLS) model was developed to further analyze the data. Using partial least squares (PLS) modeling, an RMSECV of 0.085 wt% U was determined. The RMSECV from the multivariate approach was significantly better than the univariate case and the PLS model is recommended for future LIBS analysis. Overall, the aerosol-LIBS system performed well in monitoring the U concentration and it is expected that the system could be used to quantitatively determine the U compositions within the normal operational concentrations of U in pyroprocessing molten salts.

Importance-Performance Matrix Analysis (IPMA) Of Transport Disadvantage Variables on Social Exclusion in a Rural Context

NASA Astrophysics Data System (ADS)

Larasati, Ophilia; Puspita Dirgahayani, Eng., Dr.

2018-05-01

Transport services are essential to support daily life. A lack of transport supply leads to the existence of transport disadvantaged (TDA) groups who are vulnerable to social exclusion, which happens when a particular group or individual is having difficulties to access certain activities that are considered normal in society. To tackle this phenomenon, the understanding of the influence of TDA variables on social exclusion is needed. The aim of this study is to analyze the influences of TDA variables on social exclusion in a rural context, with Cibeureum Village (Bandung Barat Regency) and Bunikasih Village (Subang Regency) as the study case. Both case studies provide different characteristics of accessibility. Partial Least Squares (PLS) Structural Equation Modeling (SEM) is chosen as the method to analyze the influences of TDA variables on social exclusion. The PLS-SEM model is developed according to the social exclusion variable and four TDA variables, i.e., accessibility, individual characteristics, private vehicle existence, and travel behavior. IPMA is done after the PLS-SEM model is evaluated. The study reveals that among four of the TDA variables, accessibility has the most influence on social exclusion, hence interventions related to improving accessibility are needed to tackle social exclusion. More specifically, the provision of alternative modes is needed in both study areas, while in Bunikasih Village the cost of travel is also an important variable to consider.

The effect of thermal treatment on the enhancement of detection of adulteration in extra virgin olive oils by synchronous fluorescence spectroscopy and chemometric analysis

NASA Astrophysics Data System (ADS)

Mabood, F.; Boqué, R.; Folcarelli, R.; Busto, O.; Jabeen, F.; Al-Harrasi, Ahmed; Hussain, J.

2016-05-01

In this study the effect of thermal treatment on the enhancement of synchronous fluorescence spectroscopic method for discrimination and quantification of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with refined oil was investigated. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8 h, in contact with air and with light exposure, to favor oxidation. All the samples were then measured with synchronous fluorescence spectroscopy. Synchronous fluorescence spectra were acquired by varying the wavelength in the region from 250 to 720 nm at 20 nm wavelength differential interval of excitation and emission. Pure and adulterated olive oils were discriminated by using partial least-squares discriminant analysis (PLS-DA). It was found that the best PLS-DA models were those built with the difference spectra (75 °C-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration of refined olive oils. Furthermore, PLS regression models were also built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 3.18% of adulteration.

Classification and Identification of Plant Fibrous Material with Different Species Using near Infrared Technique—A New Way to Approach Determining Biomass Properties Accurately within Different Species

PubMed Central

Jiang, Wei; Zhou, Chengfeng; Han, Guangting; Via, Brian; Swain, Tammy; Fan, Zhaofei; Liu, Shaoyang

2017-01-01

Plant fibrous material is a good resource in textile and other industries. Normally, several kinds of plant fibrous materials used in one process are needed to be identified and characterized in advance. It is easy to identify them when they are in raw condition. However, most of the materials are semi products which are ground, rotted or pre-hydrolyzed. To classify these samples which include different species with high accuracy is a big challenge. In this research, both qualitative and quantitative analysis methods were chosen to classify six different species of samples, including softwood, hardwood, bast, and aquatic plant. Soft Independent Modeling of Class Analogy (SIMCA) and partial least squares (PLS) were used. The algorithm to classify different species of samples using PLS was created independently in this research. Results found that the six species can be successfully classified using SIMCA and PLS methods, and these two methods show similar results. The identification rates of kenaf, ramie and pine are 100%, and the identification rates of lotus, eucalyptus and tallow are higher than 94%. It is also found that spectra loadings can help pick up best wavenumber ranges for constructing the NIR model. Inter material distance can show how close between two species. Scores graph is helpful to choose the principal components numbers during the model construction. PMID:28105037

Hyperspectral imaging using near infrared spectroscopy to monitor coat thickness uniformity in the manufacture of a transdermal drug delivery system.

PubMed

Pavurala, Naresh; Xu, Xiaoming; Krishnaiah, Yellela S R

2017-05-15

Hyperspectral imaging using near infrared spectroscopy (NIRS) integrates spectroscopy and conventional imaging to obtain both spectral and spatial information of materials. The non-invasive and rapid nature of hyperspectral imaging using NIRS makes it a valuable process analytical technology (PAT) tool for in-process monitoring and control of the manufacturing process for transdermal drug delivery systems (TDS). The focus of this investigation was to develop and validate the use of Near Infra-red (NIR) hyperspectral imaging to monitor coat thickness uniformity, a critical quality attribute (CQA) for TDS. Chemometric analysis was used to process the hyperspectral image and a partial least square (PLS) model was developed to predict the coat thickness of the TDS. The goodness of model fit and prediction were 0.9933 and 0.9933, respectively, indicating an excellent fit to the training data and also good predictability. The % Prediction Error (%PE) for internal and external validation samples was less than 5% confirming the accuracy of the PLS model developed in the present study. The feasibility of the hyperspectral imaging as a real-time process analytical tool for continuous processing was also investigated. When the PLS model was applied to detect deliberate variation in coating thickness, it was able to predict both the small and large variations as well as identify coating defects such as non-uniform regions and presence of air bubbles. Published by Elsevier B.V.

Thermal oxidation process accelerates degradation of the olive oil mixed with sunflower oil and enables its discrimination using synchronous fluorescence spectroscopy and chemometric analysis

NASA Astrophysics Data System (ADS)

Mabood, Fazal; Boqué, Ricard; Folcarelli, Rita; Busto, Olga; Al-Harrasi, Ahmed; Hussain, Javid

2015-05-01

We have investigated the effect of thermal treatment on the discrimination of pure extra virgin olive oil (EVOO) samples from EVOO samples adulterated with sunflower oil. Two groups of samples were used. One group was analyzed at room temperature (25 °C) and the other group was thermally treated in a thermostatic water bath at 75 °C for 8 h, in contact with air and with light exposure, to favor oxidation. All samples were then measured with synchronous fluorescence spectroscopy. Fluorescence spectra were acquired by varying the excitation wavelength in the region from 250 to 720 nm. In order to optimize the differences between excitation and emission wavelengths, four constant differential wavelengths, i.e., 20 nm, 40 nm, 60 nm and 80 nm, were tried. Partial least-squares discriminant analysis (PLS-DA) was used to discriminate between pure and adulterated oils. It was found that the 20 nm difference was the optimal, at which the discrimination models showed the best results. The best PLS-DA models were those built with the difference spectra (75-25 °C), which were able to discriminate pure from adulterated oils at a 2% level of adulteration. Furthermore, PLS regression models were built to quantify the level of adulteration. Again, the best model was the one built with the difference spectra, with a prediction error of 1.75% of adulteration.

Variables selection methods in near-infrared spectroscopy.

PubMed

Xiaobo, Zou; Jiewen, Zhao; Povey, Malcolm J W; Holmes, Mel; Hanpin, Mao

2010-05-14

Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical, agricultural, food and biomedical sectors during the past 15 years. A NIR spectrum of a sample is typically measured by modern scanning instruments at hundreds of equally spaced wavelengths. The large number of spectral variables in most data sets encountered in NIR spectral chemometrics often renders the prediction of a dependent variable unreliable. Recently, considerable effort has been directed towards developing and evaluating different procedures that objectively identify variables which contribute useful information and/or eliminate variables containing mostly noise. This review focuses on the variable selection methods in NIR spectroscopy. Selection methods include some classical approaches, such as manual approach (knowledge based selection), "Univariate" and "Sequential" selection methods; sophisticated methods such as successive projections algorithm (SPA) and uninformative variable elimination (UVE), elaborate search-based strategies such as simulated annealing (SA), artificial neural networks (ANN) and genetic algorithms (GAs) and interval base algorithms such as interval partial least squares (iPLS), windows PLS and iterative PLS. Wavelength selection with B-spline, Kalman filtering, Fisher's weights and Bayesian are also mentioned. Finally, the websites of some variable selection software and toolboxes for non-commercial use are given. Copyright 2010 Elsevier B.V. All rights reserved.

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

PubMed

Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

2012-02-01

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Effective apatinib treatment of pleomorphic liposarcoma

PubMed Central

Yan, Peng; Sun, Mei-Li; Sun, Yu-Ping; Liu, Chuan-Yong

2017-01-01

Abstract Rationale: Pleomorphic liposarcoma (PLS) is a rare and aggressive malignant tumor, and both radiation and conventional cytotoxic chemotherapy remain controversial for metastatic or unresectable disease. Patient Concerns: We presented an 81-year-old Chinese woman with advanced PLS who received apatinib after failure chemotherapy. Diagnoses: The patient was diagnosed as having PLS by biopsy. Interventions: After a failed chemotherapy, apatinib started to be taken orally 425 mg per day. Outcomes: This patient achieved 3-month progression-free survival (PFS) and a higher quality of life. Meanwhile, this patient suffered grade 2 hypertension and grade 3 hand–foot syndrome (HFS). Lessons: In this case, apatinib presented good efficacy and safety to treat PLS. Randomized clinical studies are required to confirm the efficacy and safety of apatinib in the treatment of PLS. PMID:28816958

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

Characterization of a nonribosomal peptide antibiotic solid dispersion formulation by process analytical technologies sensors.

PubMed

Rahman, Ziyaur; Siddiqui, Akhtar; Khan, Mansoor A

2013-12-01

The focus of present investigation was to characterize and evaluate the variability of solid dispersion (SD) of amorphous vancomycin (VCM), utilizing crystalline polyethylene glycol (PEG-6000) as a carrier and subsequently, determining their percentage composition by nondestructive method of process analytical technology (PAT) sensors. The SD were prepared by heat fusion method and characterized for physicochemical and spectral properties. Enhanced dissolution was shown by the SD formulations. Decreased crystallinity of PEG-6000 was observed indicating that the drug was present as solution and dispersed form within the polymer. The SD formulations were homogenous as shown by near infrared (NIR) chemical imaging data. Principal component analysis (PCA) and partial least square (PLS) method were applied to NIR and PXRD (powder X-ray diffraction) data to develop model for quantification of drug and carrier. PLS of both data showed correlation coefficient >0.9934 with good prediction capability as revealed by smaller value of root mean square and standard error. The model based on NIR and PXRD were two folds more accurate in estimating PEG-6000 than VCM. In conclusion, the drug dissolution from the SD increased by decreasing crystallinity of PEG-6000, and the chemometric models showed usefulness of PAT sensor in estimating percentage of both VCM and PEG-600 simultaneously. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

Development of a NIR-based blend uniformity method for a drug product containing multiple structurally similar actives by using the quality by design principles.

PubMed

Lin, Yiqing; Li, Weiyong; Xu, Jin; Boulas, Pierre

2015-07-05

The aim of this study is to develop an at-line near infrared (NIR) method for the rapid and simultaneous determination of four structurally similar active pharmaceutical ingredients (APIs) in powder blends intended for the manufacturing of tablets. Two of the four APIs in the formula are present in relatively small amounts, one at 0.95% and the other at 0.57%. Such small amounts in addition to the similarity in structures add significant complexity to the blend uniformity analysis. The NIR method is developed using spectra from six laboratory-created calibration samples augmented by a small set of spectra from a large-scale blending sample. Applying the quality by design (QbD) principles, the calibration design included concentration variations of the four APIs and a main excipient, microcrystalline cellulose. A bench-top FT-NIR instrument was used to acquire the spectra. The obtained NIR spectra were analyzed by applying principal component analysis (PCA) before calibration model development. Score patterns from the PCA were analyzed to reveal relationship between latent variables and concentration variations of the APIs. In calibration model development, both PLS-1 and PLS-2 models were created and evaluated for their effectiveness in predicting API concentrations in the blending samples. The final NIR method shows satisfactory specificity and accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

[Estimating Winter Wheat Nitrogen Vertical Distribution Based on Bidirectional Canopy Reflected Spectrum].

PubMed

Yang, Shao-yuan; Huang, Wen-jiang; Liang, Dong; Uang, Lin-sheng; Yang, Gui-jun; Zhang, Gui-jan; Cai, Shu-Hong

2015-07-01

The vertical distribution of crop nitrogen is increased with plant height, timely and non-damaging measurement of crop nitrogen vertical distribution is critical for the crop production and quality, improving fertilizer utilization and reducing environmental impact. The objective of this study was to discuss the method of estimating winter wheat nitrogen vertical distribution by exploring bidirectional reflectance distribution function (BRDF) data using partial least square (PLS) algorithm. The canopy reflectance at nadir, +/-50 degrees and +/- 60 degrees; at nadir, +/- 30 degrees and +/- 40 degrees; and at nadir, +/- 20 degrees and +/- 30 degrees were selected to estimate foliage nitrogen density (FND) at upper layer, middle layer and bottom layer, respectively. Three PLS analysis models with FND as the dependent variable and vegetation indices at corresponding angles as the explicative variables were. established. The impact of soil reflectance and the canopy non-photosynthetic materials, was minimized by seven kinds of modifying vegetation indices with the ratio R700/R670. The estimated accuracy is significant raised at upper layer, middle layer and bottom layer in modeling experiment. Independent model verification selected the best three vegetation indices for further research. The research result showed that the modified Green normalized difference vegetation index (GNDVI) shows better performance than other vegetation indices at each layer, which means modified GNDVI could be used in estimating winter wheat nitrogen vertical distribution

Statistical shape modelling to aid surgical planning: associations between surgical parameters and head shapes following spring-assisted cranioplasty.

PubMed

Rodriguez-Florez, Naiara; Bruse, Jan L; Borghi, Alessandro; Vercruysse, Herman; Ong, Juling; James, Greg; Pennec, Xavier; Dunaway, David J; Jeelani, N U Owase; Schievano, Silvia

2017-10-01

Spring-assisted cranioplasty is performed to correct the long and narrow head shape of children with sagittal synostosis. Such corrective surgery involves osteotomies and the placement of spring-like distractors, which gradually expand to widen the skull until removal about 4 months later. Due to its dynamic nature, associations between surgical parameters and post-operative 3D head shape features are difficult to comprehend. The current study aimed at applying population-based statistical shape modelling to gain insight into how the choice of surgical parameters such as craniotomy size and spring positioning affects post-surgical head shape. Twenty consecutive patients with sagittal synostosis who underwent spring-assisted cranioplasty at Great Ormond Street Hospital for Children (London, UK) were prospectively recruited. Using a nonparametric statistical modelling technique based on mathematical currents, a 3D head shape template was computed from surface head scans of sagittal patients after spring removal. Partial least squares (PLS) regression was employed to quantify and visualise trends of localised head shape changes associated with the surgical parameters recorded during spring insertion: anterior-posterior and lateral craniotomy dimensions, anterior spring position and distance between anterior and posterior springs. Bivariate correlations between surgical parameters and corresponding PLS shape vectors demonstrated that anterior-posterior (Pearson's [Formula: see text]) and lateral craniotomy dimensions (Spearman's [Formula: see text]), as well as the position of the anterior spring ([Formula: see text]) and the distance between both springs ([Formula: see text]) on average had significant effects on head shapes at the time of spring removal. Such effects were visualised on 3D models. Population-based analysis of 3D post-operative medical images via computational statistical modelling tools allowed for detection of novel associations between surgical parameters and head shape features achieved following spring-assisted cranioplasty. The techniques described here could be extended to other cranio-maxillofacial procedures in order to assess post-operative outcomes and ultimately facilitate surgical decision making.

Nondestructive prediction of the drug content of an aspirin suppository by near-infrared spectroscopy.

PubMed

Otsuka, Eri; Abe, Hiroyuki; Aburada, Masaki; Otsuka, Makoto

2010-07-01

A suppository dosage form has a rapid effect on therapeutics, because it dissolves in the rectum, is absorbed in the bloodstream, and passes the hepatic metabolism. However, the dosage form is unstable, because a suppository is made in a semisolid form, and so it is not easy to mix the bulk drug powder in the base. This article describes a nondestructive method of determining the drug content of suppositories using near-infrared spectrometry (NIR) combined with chemometrics. Suppositories (aspirin content: 1.8, 2.7, 4.5, 7.3, and 9.1%, w/w) were produced by mixing an aspirin bulk powder with hard fat at 50 degrees C and pouring the melt mixture into a plastic mold (2.25 mL). NIR spectra of 12 calibration and 12 validation sample sets were recorded 5 times. A total of 60 spectral data were used as a calibration set to establish a calibration model to predict drug content with a partial least-squares (PLS) regression analysis. NIR data of the suppository samples were divided into two wave number ranges, 4000-12500 cm(-1) (LR), and 5900-6300 cm(-1) (SR). Calibration models for the aspirin content of the suppositories were calculated based on LR and SR ranges of second-derivative NIR spectra using PLS. The models for LR and SR consisted of five and one principal components (PC), respectively. The plots of predicted values against actual values gave a straight line with regression coefficient constants of 0.9531 and 0.9749, respectively. The mean bias and mean accuracy of the calibration models were calculated based on the SR of variation data sets, and were lower than those of LR, respectively. Limiting the wave number of spectral data sets is useful to help understand the calibration model because of noise cancellation and to measure objective functions.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy

NASA Astrophysics Data System (ADS)

Tewari, Jagdish C.; Dixit, Vivechana; Cho, Byoung-Kwan; Malik, Kamal A.

2008-12-01

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1100 to 2500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient ( R2). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R2 value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy.

PubMed

Tewari, Jagdish C; Dixit, Vivechana; Cho, Byoung-Kwan; Malik, Kamal A

2008-12-01

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1,100 to 2,500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient (R(2)). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R(2) value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Simultaneous spectrophotometric determination of valsartan and hydrochlorothiazide by H-point standard addition method and partial least squares regression.

PubMed

Lakshmi, Karunanidhi Santhana; Lakshmi, Sivasubramanian

2011-03-01

Simultaneous determination of valsartan and hydrochlorothiazide by the H-point standard additions method (HPSAM) and partial least squares (PLS) calibration is described. Absorbances at a pair of wavelengths, 216 and 228 nm, were monitored with the addition of standard solutions of valsartan. Results of applying HPSAM showed that valsartan and hydrochlorothiazide can be determined simultaneously at concentration ratios varying from 20:1 to 1:15 in a mixed sample. The proposed PLS method does not require chemical separation and spectral graphical procedures for quantitative resolution of mixtures containing the titled compounds. The calibration model was based on absorption spectra in the 200-350 nm range for 25 different mixtures of valsartan and hydrochlorothiazide. Calibration matrices contained 0.5-3 μg mL-1 of both valsartan and hydrochlorothiazide. The standard error of prediction (SEP) for valsartan and hydrochlorothiazide was 0.020 and 0.038 μg mL-1, respectively. Both proposed methods were successfully applied to the determination of valsartan and hydrochlorothiazide in several synthetic and real matrix samples.

Spectroscopic signature of mouse embryonic stem cell-derived hepatocytes using synchrotron Fourier transform infrared microspectroscopy

NASA Astrophysics Data System (ADS)

Thumanu, Kanjana; Tanthanuch, Waraporn; Ye, Danna; Sangmalee, Anawat; Lorthongpanich, Chanchao; Parnpai, Rangsun; Heraud, Philip

2011-05-01

Stem cell-based therapy for liver regeneration has been proposed to overcome the persistent shortage in the supply of suitable donor organs. A requirement for this to succeed is to find a rapid method to detect functional hepatocytes, differentiated from embryonic stem cells. We propose Fourier transform infrared (FTIR) microspectroscopy as a versatile method to identify the early and last stages of the differentiation process leading to the formation of hepatocytes. Using synchrotron-FTIR microspectroscopy, the means of identifying hepatocytes at the single-cell level is possible and explored. Principal component analysis and subsequent partial least-squares (PLS) discriminant analysis is applied to distinguish endoderm induction from hepatic progenitor cells and matured hepatocyte-like cells. The data are well modeled by PLS with endoderm induction, hepatic progenitor cells, and mature hepatocyte-like cells able to be discriminated with very high sensitivity and specificity. This method provides a practical tool to monitor endoderm induction and has the potential to be applied for quality control of cell differentiation leading to hepatocyte formation.

An effective approach to quantitative analysis of ternary amino acids in foxtail millet substrate based on terahertz spectroscopy.

PubMed

Lu, Shao Hua; Li, Bao Qiong; Zhai, Hong Lin; Zhang, Xin; Zhang, Zhuo Yong

2018-04-25

Terahertz time-domain spectroscopy has been applied to many fields, however, it still encounters drawbacks in multicomponent mixtures analysis due to serious spectral overlapping. Here, an effective approach to quantitative analysis was proposed, and applied on the determination of the ternary amino acids in foxtail millet substrate. Utilizing three parameters derived from the THz-TDS, the images were constructed and the Tchebichef image moments were used to extract the information of target components. Then the quantitative models were obtained by stepwise regression. The correlation coefficients of leave-one-out cross-validation (R loo-cv 2 ) were more than 0.9595. As for external test set, the predictive correlation coefficients (R p 2 ) were more than 0.8026 and the root mean square error of prediction (RMSE p ) were less than 1.2601. Compared with the traditional methods (PLS and N-PLS methods), our approach is more accurate, robust and reliable, and can be a potential excellent approach to quantify multicomponent with THz-TDS spectroscopy. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fast determination of total ginsenosides content in ginseng powder by near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Hua-cai; Chen, Xing-dan; Lu, Yong-jun; Cao, Zhi-qiang

2006-01-01

Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.

Prediction of anticancer activity of diterpenes isolated from the paraiban flora through a PLS model and molecular surfaces.

PubMed

Scotti, Luciana; Scotti, Marcus T; Ishiki, Hamilton; Junior, Francisco J B M; dos, Santos Paula F; Tavares, Josean F; da Silva, Marcelo S

2014-05-01

The aim of this work was to predict the anticancer potential of 3 atisane, and 3 trachylobane diterpene compounds extracted from the roots of Xylopia langsdorffiana. The prediction of anticancer activity as expressed against PC-3 tumor cells was made using a PLS model built with 26 diterpenes in the training set. Significant statistical measures were obtained. The six investigated diterpenes were applied to the model and their activities against PC-3 cells were calculated. All the diterpenes were active, with atisane diterpenes showing the higher pICso values. In human prostate carcinoma PC-3 cells, the apoptosis mechanism is related to an inhibition of IKK/NF-KB. Antioxidant potential implies a greater electronic molecular atmosphere (increased donor electron capacity), which can reduce radical reactivity, and facilitate post donation charge accommodation. Molecular surfaces indicated a much greater electronic cloud over atisane diterpenes.

A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares

PubMed Central

Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

2018-01-01

To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future. PMID:29410849

A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares.

PubMed

Li, Xu; Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

2018-01-01

To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.

Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks.

PubMed

Özbalci, Beril; Boyaci, İsmail Hakkı; Topcu, Ali; Kadılar, Cem; Tamer, Uğur

2013-02-15

The aim of this study was to quantify glucose, fructose, sucrose and maltose contents of honey samples using Raman spectroscopy as a rapid method. By performing a single measurement, quantifications of sugar contents have been said to be unaffordable according to the molecular similarities between sugar molecules in honey matrix. This bottleneck was overcome by coupling Raman spectroscopy with chemometric methods (principal component analysis (PCA) and partial least squares (PLS)) and an artificial neural network (ANN). Model solutions of four sugars were processed with PCA and significant separation was observed. This operation, done with the spectral features by using PLS and ANN methods, led to the discriminant analysis of sugar contents. Models/trained networks were created using a calibration data set and evaluated using a validation data set. The correlation coefficient values between actual and predicted values of glucose, fructose, sucrose and maltose were determined as 0.964, 0.965, 0.968 and 0.949 for PLS and 0.965, 0.965, 0.978 and 0.956 for ANN, respectively. The requirement of rapid analysis of sugar contents of commercial honeys has been met by the data processed within this article. Copyright © 2012 Elsevier Ltd. All rights reserved.

Prediction of pH of cola beverage using Vis/NIR spectroscopy and least squares-support vector machine

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-02-01

Visible and near infrared (Vis/NIR) transmission spectroscopy and chemometric methods were utilized to predict the pH values of cola beverages. Five varieties of cola were prepared and 225 samples (45 samples for each variety) were selected for the calibration set, while 75 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay and standard normal variate (SNV) followed by first-derivative were used as the pre-processing methods. Partial least squares (PLS) analysis was employed to extract the principal components (PCs) which were used as the inputs of least squares-support vector machine (LS-SVM) model according to their accumulative reliabilities. Then LS-SVM with radial basis function (RBF) kernel function and a two-step grid search technique were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias were 0.961, 0.040 and 0.012 for PLS, while 0.975, 0.031 and 4.697x10 -3 for LS-SVM, respectively. Both methods obtained a satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be applied as an alternative way for the prediction of pH of cola beverages.

Spectrophotometric determination of ternary mixtures of thiamin, riboflavin and pyridoxal in pharmaceutical and human plasma by least-squares support vector machines.

PubMed

Niazi, Ali; Zolgharnein, Javad; Afiuni-Zadeh, Somaie

2007-11-01

Ternary mixtures of thiamin, riboflavin and pyridoxal have been simultaneously determined in synthetic and real samples by applications of spectrophotometric and least-squares support vector machines. The calibration graphs were linear in the ranges of 1.0 - 20.0, 1.0 - 10.0 and 1.0 - 20.0 microg ml(-1) with detection limits of 0.6, 0.5 and 0.7 microg ml(-1) for thiamin, riboflavin and pyridoxal, respectively. The experimental calibration matrix was designed with 21 mixtures of these chemicals. The concentrations were varied between calibration graph concentrations of vitamins. The simultaneous determination of these vitamin mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares (PLS) modeling and least-squares support vector machines were used for the multivariate calibration of the spectrophotometric data. An excellent model was built using LS-SVM, with low prediction errors and superior performance in relation to PLS. The root mean square errors of prediction (RMSEP) for thiamin, riboflavin and pyridoxal with PLS and LS-SVM were 0.6926, 0.3755, 0.4322 and 0.0421, 0.0318, 0.0457, respectively. The proposed method was satisfactorily applied to the rapid simultaneous determination of thiamin, riboflavin and pyridoxal in commercial pharmaceutical preparations and human plasma samples.

Prediction of Caffeine Content in Java Preanger Coffee Beans by NIR Spectroscopy Using PLS and MLR Method

NASA Astrophysics Data System (ADS)

Budiastra, I. W.; Sutrisno; Widyotomo, S.; Ayu, P. C.

2018-05-01

Caffeine is one of important components in coffee that contributes to the coffee beverages flavor. Caffeine concentration in coffee bean is usually determined by chemical method which is time consuming and destructive method. A nondestructive method using NIR spectroscopy was successfully applied to determine the caffeine concentration of Arabica gayo coffee bean. In this study, NIR Spectroscopy was assessed to determine the caffeine concentration of java preanger coffee bean. A hundred samples, each consist of 96 g coffee beans were prepared for reflectance and chemical measurement. Reflectance of the sample was measured by FT-NIR spectrometer in the wavelength of 1000-2500 nm (10000-4000 cm-1) followed by determination of caffeine content using LCMS method. Calibration of NIR spectra and the caffeine content was carried out using PLS and MLR methods. Several spectra data processing was conducted to increase the accuracy of prediction. The result of the study showed that caffeine content could be determined by PLS model using 7 factors and spectra data processing of combination of the first derivative and MSC of spectra absorbance (r = 0.946; CV = 1.54 %; RPD = 2.28). A lower accuracy was obtained by MLR model consisted of three caffeine and other four absorption wavelengths (r = 0.683; CV = 3.31%; RPD = 1.18).

Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition

NASA Astrophysics Data System (ADS)

Hu, Leqian; Ma, Shuai; Yin, Chunling

2018-03-01

In this work, fluorescence spectroscopy combined with multi-way pattern recognition techniques were developed for determining the geographical origin of kudzu root and detection and quantification of adulterants in kudzu root. Excitation-emission (EEM) spectra were obtained for 150 pure kudzu root samples of different geographical origins and 150 fake kudzu roots with different adulteration proportions by recording emission from 330 to 570 nm with excitation in the range of 320-480 nm, respectively. Multi-way principal components analysis (M-PCA) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods were used to decompose the excitation-emission matrices datasets. 150 pure kudzu root samples could be differentiated exactly from each other according to their geographical origins by M-PCA and N-PLS-DA models. For the adulteration kudzu root samples, N-PLS-DA got better and more reliable classification result comparing with the M-PCA model. The results obtained in this study indicated that EEM spectroscopy coupling with multi-way pattern recognition could be used as an easy, rapid and novel tool to distinguish the geographical origin of kudzu root and detect adulterated kudzu root. Besides, this method was also suitable for determining the geographic origin and detection the adulteration of the other foodstuffs which can produce fluorescence.

Is Efficacy of the Anti-Cd20 Antibody Rituximab Preventing Hemolysis Due to Passenger Lymphocyte Syndrome?

PubMed

Tsujimura, Kazuma; Ishida, Hideki; Tanabe, Kazunari

2017-02-01

Passenger lymphocyte syndrome (PLS) often occurs after ABO-mismatched solid organ and/or bone marrow transplantation between a donor and recipient. Viable donor B-lymphocytes transferred during organ transplantation produce antibodies against recipient red cell antigens, leading to hemolysis. The incidence of PLS has been reported to be around 9% after renal transplantation. A previous report showed that rituximab (Rit) was useful for treatment of PLS in allogeneic stem cell transplantation, bowel transplant and severe cases of hemolysis. However, the effectiveness of Rit in preventing PLS after renal transplantation has not yet been evaluated. The participants in this study were 85 patients who had undergone ABO-mismatched renal transplantation from January 2005 to April 2013. Rit was administered to these patients before transplantation. None of the patients that received Rit treatment developed PLS. Thus administration of Rit before transplantation effectively controlled the production of antibodies by B-lymphocytes, which probably prevented the development of PLS. © 2016 International Society for Apheresis, Japanese Society for Apheresis, and Japanese Society for Dialysis Therapy.

Generation and Biological Activities of Oxidized Phospholipids

PubMed Central

Oskolkova, Olga V.; Birukov, Konstantin G.; Levonen, Anna-Liisa; Binder, Christoph J.; Stöckl, Johannes

2010-01-01

Abstract Glycerophospholipids represent a common class of lipids critically important for integrity of cellular membranes. Oxidation of esterified unsaturated fatty acids dramatically changes biological activities of phospholipids. Apart from impairment of their structural function, oxidation makes oxidized phospholipids (OxPLs) markers of “modified-self” type that are recognized by soluble and cell-associated receptors of innate immunity, including scavenger receptors, natural (germ line-encoded) antibodies, and C-reactive protein, thus directing removal of senescent and apoptotic cells or oxidized lipoproteins. In addition, OxPLs acquire novel biological activities not characteristic of their unoxidized precursors, including the ability to regulate innate and adaptive immune responses. Effects of OxPLs described in vitro and in vivo suggest their potential relevance in different pathologies, including atherosclerosis, acute inflammation, lung injury, and many other conditions. This review summarizes current knowledge on the mechanisms of formation, structures, and biological activities of OxPLs. Furthermore, potential applications of OxPLs as disease biomarkers, as well as experimental therapies targeting OxPLs, are described, providing a broad overview of an emerging class of lipid mediators. Antioxid. Redox Signal. 12, 1009–1059. PMID:19686040

A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling.

PubMed

Sato, Takako; Zaitsu, Kei; Tsuboi, Kento; Nomura, Masakatsu; Kusano, Maiko; Shima, Noriaki; Abe, Shuntaro; Ishii, Akira; Tsuchihashi, Hitoshi; Suzuki, Koichi

2015-05-01

Estimation of postmortem interval (PMI) is an important goal in judicial autopsy. Although many approaches can estimate PMI through physical findings and biochemical tests, accurate PMI calculation by these conventional methods remains difficult because PMI is readily affected by surrounding conditions, such as ambient temperature and humidity. In this study, Sprague-Dawley (SD) rats (10 weeks) were sacrificed by suffocation, and blood was collected by dissection at various time intervals (0, 3, 6, 12, 24, and 48 h; n = 6) after death. A total of 70 endogenous metabolites were detected in plasma by gas chromatography-tandem mass spectrometry (GC-MS/MS). Each time group was separated from each other on the principal component analysis (PCA) score plot, suggesting that the various endogenous metabolites changed with time after death. To prepare a prediction model of a PMI, a partial least squares (or projection to latent structure, PLS) regression model was constructed using the levels of significantly different metabolites determined by variable importance in the projection (VIP) score and the Kruskal-Wallis test (P < 0.05). Because the constructed PLS regression model could successfully predict each PMI, this model was validated with another validation set (n = 3). In conclusion, plasma metabolic profiling demonstrated its ability to successfully estimate PMI under a certain condition. This result can be considered to be the first step for using the metabolomics method in future forensic casework.

Screening soy hydrolysates for the production of a recombinant therapeutic protein in commercial cell line by combined approach of near-infrared spectroscopy and chemometrics.

PubMed

Li, Guiyang; Wen, Zai-Qing

2013-03-01

Soy hydrolysates are widely used as the major nutrient sources for cell culture processes for industrial manufacturing of therapeutic recombinant proteins. The primary goal of this study was to develop a spectroscopy based chemometric method, a partial least squares (PLS), to screen soy hydrolysates for better yield of protein production (titers) in cell culture medium. Harvest titer values of 29 soy hydrolysate lots with production yield between 490 and 1,350 mg/L were obtained from shake flask models or from manufacture engineering runs. The soy hydrolysate samples were measured by near-infrared (NIR) in reflectance mode using an infrared fiber optic probe. The fiber optic probe could easily enable in situ measurement of the soy hydrolysates for convenient raw material screening. The best PLS calibration has a determination coefficient of R (2) = 0.887 utilizing no spectral preprocessing, the two spectral ranges of 10,000-5,376 cm(-1) and 4,980-4,484 cm(-1), and a rank of 6 factors. The cross-validation of the model resulted in a determination coefficient of R (2) = 0.741 between the predicted and actual titer values with an average standard deviation of 72 mg/L. Compared with the resource demanding shake flask model, the combination of NIR and chemometric modeling provides a convenient method for soy hydrolysate screening with the advantage of fast speed, low cost and non-destructive.

Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shao Yongni; He Yong; Mao Jingyuan

Visible and near-infrared (Vis/NIR) reflectance spectroscopy has been investigated for its ability to nondestructively detect acidity in bayberry juice. What we believe to be a new, better mathematic model is put forward, which we have named principal component analysis-stepwise regression analysis-backpropagation neural network (PCA-SRA-BPNN), to build a correlation between the spectral reflectivity data and the acidity of bayberry juice. In this model, the optimum network parameters,such as the number of input nodes, hidden nodes, learning rate, and momentum, are chosen by the value of root-mean-square (rms) error. The results show that its prediction statistical parameters are correlation coefficient (r) ofmore » 0.9451 and root-mean-square error of prediction(RMSEP) of 0.1168. Partial least-squares (PLS) regression is also established to compare with this model. Before doing this, the influences of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S. Golay first derivative, and wavelet package transform) are compared. The PLS approach with wavelet package transform preprocessing spectra is found to provide the best results, and its prediction statistical parameters are correlation coefficient (r) of 0.9061 and RMSEP of 0.1564. Hence, these two models are both desirable to analyze the data from Vis/NIR spectroscopy and to solve the problem of the acidity prediction of bayberry juice. This supplies basal research to ultimately realize the online measurements of the juice's internal quality through this Vis/NIR spectroscopy technique.« less

Use of partial least squares regression for the multivariate calibration of hazardous air pollutants in open-path FT-IR spectrometry

NASA Astrophysics Data System (ADS)

Hart, Brian K.; Griffiths, Peter R.

1998-06-01

Partial least squares (PLS) regression has been evaluated as a robust calibration technique for over 100 hazardous air pollutants (HAPs) measured by open path Fourier transform infrared (OP/FT-IR) spectrometry. PLS has the advantage over the current recommended calibration method of classical least squares (CLS), in that it can look at the whole useable spectrum (700-1300 cm-1, 2000-2150 cm-1, and 2400-3000 cm-1), and detect several analytes simultaneously. Up to one hundred HAPs synthetically added to OP/FT-IR backgrounds have been simultaneously calibrated and detected using PLS. PLS also has the advantage in requiring less preprocessing of spectra than that which is required in CLS calibration schemes, allowing PLS to provide user independent real-time analysis of OP/FT-IR spectra.

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.

PubMed

Flassig, Robert J; Migal, Iryna; der Zalm, Esther van; Rihko-Struckmann, Liisa; Sundmacher, Kai

2015-01-16

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort. In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts. Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

Classification of type 2 diabetes rats based on urine amino acids metabolic profiling by liquid chromatography coupled with tandem mass spectrometry.

PubMed

Wang, Chunyan; Zhu, Hongbin; Pi, Zifeng; Song, Fengrui; Liu, Zhiqiang; Liu, Shuying

2013-09-15

An analytical method for quantifying underivatized amino acids (AAs) in urine samples of rats was developed by using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Classification of type 2 diabetes rats was based on urine amino acids metabolic profiling. LC-MS/MS analysis was applied through chromatographic separation and multiple reactions monitoring (MRM) transitions of MS/MS. Multivariate profile-wide predictive models were constructed using partial least squares discriminant analysis (PLS-DA) by SIMAC-P 11.5 version software package and hierarchical cluster analysis (HCA) by SPSS 18.0 version software. Some amino acids in urine of rats have significant change. The results of the present study prove that this method could perform the quantification of free AAs in urine of rats by using LC-MS/MS. In summary, the PLS-DA and HCA statistical analysis in our research were preferable to differentiate healthy rats and type 2 diabetes rats by the quantification of AAs in their urine samples. In addition, comparing with health group the seven increased amino acids in urine of type 2 rats were returned to normal under the treatment of acarbose. Copyright © 2013 Elsevier B.V. All rights reserved.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

PubMed

Yang, Yan-Qin; Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

PubMed Central

Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties. PMID:29494626

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS).

PubMed

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS. Copyright © 2013 Elsevier B.V. All rights reserved.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS)

NASA Astrophysics Data System (ADS)

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P.; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS.

Structure-based characterization of the binding of peptide to the human endophilin-1 Src homology 3 domain using position-dependent noncovalent potential analysis.

PubMed

Fu, Chunjiang; Wu, Gang; Lv, Fenglin; Tian, Feifei

2012-05-01

Many protein-protein interactions are mediated by a peptide-recognizing domain, such as WW, PDZ, or SH3. In the present study, we describe a new method called position-dependent noncovalent potential analysis (PDNPA), which can accurately characterize the nonbonding profile between the human endophilin-1 Src homology 3 (hEndo1 SH3) domain and its peptide ligands and quantitatively predict the binding affinity of peptide to hEndo1 SH3. In this procedure, structure models of diverse peptides in complex with the hEndo1 SH3 domain are constructed by molecular dynamics simulation and a virtual mutagenesis protocol. Subsequently, three noncovalent interactions associated with each position of the peptide ligand in the complexed state are analyzed using empirical potential functions, and the resulting potential descriptors are then correlated with the experimentally measured affinity on the basis of 1997 hEndo1 SH3-binding peptides with known activities, using linear partial least squares regression (PLS) and the nonlinear support vector machine (SVM). The results suggest that: (i) the electrostatics appears to be more important than steric properties and hydrophobicity in the formation of the hEndo1 SH3-peptide complex; (ii) P(-4) of the core decapeptide ligand with the sequence pattern P(-6)P(-5)P(-4)P(-3)P(-2)P(-1)P(0)P(1)P(2)P(3) is the most important position in terms of determining both the stability and specificity of the architecture of the complex, and; (iii) nonlinear SVM appears to be more effective than linear PLS for accurately predicting the binding affinity of a peptide ligand to hEndo1 SH3, whereas PLS models are straightforward and easy to interpret as compared to those built by SVM.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling.

PubMed

Kornecki, Martin; Strube, Jochen

2018-03-16

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network-either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.