Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy.

PubMed

Kuligowski, Julia; Carrión, David; Quintás, Guillermo; Garrigues, Salvador; de la Guardia, Miguel

2011-01-01

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).

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.

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.

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.

The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling

NASA Astrophysics Data System (ADS)

Aimran, Ahmad Nazim; Ahmad, Sabri; Afthanorhan, Asyraf; Awang, Zainudin

2017-05-01

Structural equation modeling (SEM) is the second generation statistical analysis technique developed for analyzing the inter-relationships among multiple variables in a model. Previous studies have shown that there seemed to be at least an implicit agreement about the factors that should drive the choice between covariance-based structural equation modeling (CB-SEM) and partial least square path modeling (PLS-PM). PLS-PM appears to be the preferred method by previous scholars because of its less stringent assumption and the need to avoid the perceived difficulties in CB-SEM. Along with this issue has been the increasing debate among researchers on the use of CB-SEM and PLS-PM in studies. The present study intends to assess the performance of CB-SEM and PLS-PM as a confirmatory study in which the findings will contribute to the body of knowledge of SEM. Maximum likelihood (ML) was chosen as the estimator for CB-SEM and was expected to be more powerful than PLS-PM. Based on the balanced experimental design, the multivariate normal data with specified population parameter and sample sizes were generated using Pro-Active Monte Carlo simulation, and the data were analyzed using AMOS for CB-SEM and SmartPLS for PLS-PM. Comparative Bias Index (CBI), construct relationship, average variance extracted (AVE), composite reliability (CR), and Fornell-Larcker criterion were used to study the consequence of each estimator. The findings conclude that CB-SEM performed notably better than PLS-PM in estimation for large sample size (100 and above), particularly in terms of estimations accuracy and consistency.

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.

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.

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

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

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.

Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging

NASA Astrophysics Data System (ADS)

Meksiarun, Phiranuphon; Ishigaki, Mika; Huck-Pezzei, Verena A. C.; Huck, Christian W.; Wongravee, Kanet; Sato, Hidetoshi; Ozaki, Yukihiro

2017-03-01

This study aimed to extract the paraffin component from paraffin-embedded oral cancer tissue spectra using three multivariate analysis (MVA) methods; Independent Component Analysis (ICA), Partial Least Squares (PLS) and Independent Component - Partial Least Square (IC-PLS). The estimated paraffin components were used for removing the contribution of paraffin from the tissue spectra. These three methods were compared in terms of the efficiency of paraffin removal and the ability to retain the tissue information. It was found that ICA, PLS and IC-PLS could remove the paraffin component from the spectra at almost the same level while Principal Component Analysis (PCA) was incapable. In terms of retaining cancer tissue spectral integrity, effects of PLS and IC-PLS on the non-paraffin region were significantly less than that of ICA where cancer tissue spectral areas were deteriorated. The paraffin-removed spectra were used for constructing Raman images of oral cancer tissue and compared with Hematoxylin and Eosin (H&E) stained tissues for verification. This study has demonstrated the capability of Raman spectroscopy together with multivariate analysis methods as a diagnostic tool for the paraffin-embedded tissue section.

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

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.

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.

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.

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.

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%.

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.

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.

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

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.

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

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.

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

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.

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.

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.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

Detection of Butter Adulteration with Lard by Employing (1)H-NMR Spectroscopy and Multivariate Data Analysis.

PubMed

Fadzillah, Nurrulhidayah Ahmad; Man, Yaakob bin Che; Rohman, Abdul; Rosman, Arieff Salleh; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2015-01-01

The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.

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

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.

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.

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.

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.

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

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.

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.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations.

PubMed

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

2014-09-15

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components' mixtures using easy and widely used UV spectrophotometer. Copyright © 2014 Elsevier B.V. All rights reserved.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

NASA Astrophysics Data System (ADS)

Elkhoudary, Mahmoud M.; Abdel Salam, Randa A.; Hadad, Ghada M.

2014-09-01

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components’ mixtures using easy and widely used UV spectrophotometer.

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.

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.

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.

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.

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.

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

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].

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.

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

PubMed

Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

2016-05-15

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples. Published by Elsevier B.V.

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.

Simultaneous chemometric determination of pyridoxine hydrochloride and isoniazid in tablets by multivariate regression methods.

PubMed

Dinç, Erdal; Ustündağ, Ozgür; Baleanu, Dumitru

2010-08-01

The sole use of pyridoxine hydrochloride during treatment of tuberculosis gives rise to pyridoxine deficiency. Therefore, a combination of pyridoxine hydrochloride and isoniazid is used in pharmaceutical dosage form in tuberculosis treatment to reduce this side effect. In this study, two chemometric methods, partial least squares (PLS) and principal component regression (PCR), were applied to the simultaneous determination of pyridoxine (PYR) and isoniazid (ISO) in their tablets. A concentration training set comprising binary mixtures of PYR and ISO consisting of 20 different combinations were randomly prepared in 0.1 M HCl. Both multivariate calibration models were constructed using the relationships between the concentration data set (concentration data matrix) and absorbance data matrix in the spectral region 200-330 nm. The accuracy and the precision of the proposed chemometric methods were validated by analyzing synthetic mixtures containing the investigated drugs. The recovery results obtained by applying PCR and PLS calibrations to the artificial mixtures were found between 100.0 and 100.7%. Satisfactory results obtained by applying the PLS and PCR methods to both artificial and commercial samples were obtained. The results obtained in this manuscript strongly encourage us to use them for the quality control and the routine analysis of the marketing tablets containing PYR and ISO drugs. Copyright © 2010 John Wiley & Sons, Ltd.

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

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.

Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration.

PubMed

Inácio, Maria Raquel Cavalcanti; de Lima, Kássio Michell Gomes; Lopes, Valquiria Garcia; Pessoa, José Dalton Cruz; de Almeida Teixeira, Gustavo Henrique

2013-02-15

The aim of this study was to evaluate near-infrared reflectance spectroscopy (NIR), and multivariate calibration potential as a rapid method to determinate anthocyanin content in intact fruit (açaí and palmitero-juçara). Several multivariate calibration techniques, including partial least squares (PLS), interval partial least squares, genetic algorithm, successive projections algorithm, and net analyte signal were compared and validated by establishing figures of merit. Suitable results were obtained with the PLS model (four latent variables and 5-point smoothing) with a detection limit of 6.2 g kg(-1), limit of quantification of 20.7 g kg(-1), accuracy estimated as root mean square error of prediction of 4.8 g kg(-1), mean selectivity of 0.79 g kg(-1), sensitivity of 5.04×10(-3) g kg(-1), precision of 27.8 g kg(-1), and signal-to-noise ratio of 1.04×10(-3) g kg(-1). These results suggest NIR spectroscopy and multivariate calibration can be effectively used to determine anthocyanin content in intact açaí and palmitero-juçara fruit. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

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.

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.

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.

Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis

PubMed Central

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Yoo, Hyeonchae; Ham, Hyeonheui; Kim, Moon S.

2017-01-01

The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium-infected hulled barley. Both normal hulled barley and Fusarium-infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium-infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium-infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method. PMID:28974012

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.

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.

Discrimination of Gastrodia elata from Different Geographical Origin for Quality Evaluation Using Newly-Build Near Infrared Spectrum Coupled with Multivariate Analysis.

PubMed

Zuo, Yamin; Deng, Xuehua; Wu, Qing

2018-05-04

Discrimination of Gastrodia elata ( G. elata ) geographical origin is of great importance to pharmaceutical companies and consumers in China. this paper focuses on the feasibility of near infrared spectrum (NIRS) combined multivariate analysis as a rapid and non-destructive method to prove its fit for this purpose. Firstly, 16 batches of G. elata samples from four main-cultivation regions in China were quantified by traditional HPLC method. It showed that samples from different origins could not be efficiently differentiated by the contents of four phenolic compounds in this study. Secondly, the raw near infrared (NIR) spectra of those samples were acquired and two different pattern recognition techniques were used to classify the geographical origins. The results showed that with spectral transformation optimized, discriminant analysis (DA) provided 97% and 99% correct classification for the calibration and validation sets of samples from discriminating of four different main-cultivation regions, and provided 98% and 99% correct classifications for the calibration and validation sets of samples from eight different cities, respectively, which all performed better than the principal component analysis (PCA) method. Thirdly, as phenolic compounds content (PCC) is highly related with the quality of G. elata , synergy interval partial least squares (Si-PLS) was applied to build the PCC prediction model. The coefficient of determination for prediction (R p ²) of the Si-PLS model was 0.9209, and root mean square error for prediction (RMSEP) was 0.338. The two regions (4800 cm −1 ⁻5200 cm −1 , and 5600 cm −1 ⁻6000 cm −1 ) selected by Si-PLS corresponded to the absorptions of aromatic ring in the basic phenolic structure. It can be concluded that NIR spectroscopy combined with PCA, DA and Si-PLS would be a potential tool to provide a reference for the quality control of G. elata.

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

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.

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...

Quantitative analysis of binary polymorphs mixtures of fusidic acid by diffuse reflectance FTIR spectroscopy, diffuse reflectance FT-NIR spectroscopy, Raman spectroscopy and multivariate calibration.

PubMed

Guo, Canyong; Luo, Xuefang; Zhou, Xiaohua; Shi, Beijia; Wang, Juanjuan; Zhao, Jinqi; Zhang, Xiaoxia

2017-06-05

Vibrational spectroscopic techniques such as infrared, near-infrared and Raman spectroscopy have become popular in detecting and quantifying polymorphism of pharmaceutics since they are fast and non-destructive. This study assessed the ability of three vibrational spectroscopy combined with multivariate analysis to quantify a low-content undesired polymorph within a binary polymorphic mixture. Partial least squares (PLS) regression and support vector machine (SVM) regression were employed to build quantitative models. Fusidic acid, a steroidal antibiotic, was used as the model compound. It was found that PLS regression performed slightly better than SVM regression in all the three spectroscopic techniques. Root mean square errors of prediction (RMSEP) were ranging from 0.48% to 1.17% for diffuse reflectance FTIR spectroscopy and 1.60-1.93% for diffuse reflectance FT-NIR spectroscopy and 1.62-2.31% for Raman spectroscopy. The results indicate that diffuse reflectance FTIR spectroscopy offers significant advantages in providing accurate measurement of polymorphic content in the fusidic acid binary mixtures, while Raman spectroscopy is the least accurate technique for quantitative analysis of polymorphs. Copyright © 2017 Elsevier B.V. All rights reserved.

Application of correlation constrained multivariate curve resolution alternating least-squares methods for determination of compounds of interest in biodiesel blends using NIR and UV-visible spectroscopic data.

PubMed

de Oliveira, Rodrigo Rocha; de Lima, Kássio Michell Gomes; Tauler, Romà; de Juan, Anna

2014-07-01

This study describes two applications of a variant of the multivariate curve resolution alternating least squares (MCR-ALS) method with a correlation constraint. The first application describes the use of MCR-ALS for the determination of biodiesel concentrations in biodiesel blends using near infrared (NIR) spectroscopic data. In the second application, the proposed method allowed the determination of the synthetic antioxidant N,N'-Di-sec-butyl-p-phenylenediamine (PDA) present in biodiesel mixtures from different vegetable sources using UV-visible spectroscopy. Well established multivariate regression algorithm, partial least squares (PLS), were calculated for comparison of the quantification performance in the models developed in both applications. The correlation constraint has been adapted to handle the presence of batch-to-batch matrix effects due to ageing effects, which might occur when different groups of samples were used to build a calibration model in the first application. Different data set configurations and diverse modes of application of the correlation constraint are explored and guidelines are given to cope with different type of analytical problems, such as the correction of matrix effects among biodiesel samples, where MCR-ALS outperformed PLS reducing the relative error of prediction RE (%) from 9.82% to 4.85% in the first application, or the determination of minor compound with overlapped weak spectroscopic signals, where MCR-ALS gave higher (RE (%)=3.16%) for prediction of PDA compared to PLS (RE (%)=1.99%), but with the advantage of recovering the related pure spectral profile of analytes and interferences. The obtained results show the potential of the MCR-ALS method with correlation constraint to be adapted to diverse data set configurations and analytical problems related to the determination of biodiesel mixtures and added compounds therein. Copyright © 2014 Elsevier B.V. All rights reserved.

Use of chemometrics to compare NIR and HPLC for the simultaneous determination of drug levels in fixed-dose combination tablets employed in tuberculosis treatment.

PubMed

Teixeira, Kelly Sivocy Sampaio; da Cruz Fonseca, Said Gonçalves; de Moura, Luís Carlos Brigido; de Moura, Mario Luís Ribeiro; Borges, Márcia Herminia Pinheiro; Barbosa, Euzébio Guimaraes; De Lima E Moura, Túlio Flávio Accioly

2018-02-05

The World Health Organization recommends that TB treatment be administered using combination therapy. The methodologies for quantifying simultaneously associated drugs are highly complex, being costly, extremely time consuming and producing chemical residues harmful to the environment. The need to seek alternative techniques that minimize these drawbacks is widely discussed in the pharmaceutical industry. Therefore, the objective of this study was to develop and validate a multivariate calibration model in association with the near infrared spectroscopy technique (NIR) for the simultaneous determination of rifampicin, isoniazid, pyrazinamide and ethambutol. These models allow the quality control of these medicines to be optimized using simple, fast, low-cost techniques that produce no chemical waste. In the NIR - PLS method, spectra readings were acquired in the 10,000-4000cm -1 range using an infrared spectrophotometer (IRPrestige - 21 - Shimadzu) with a resolution of 4cm -1 , 20 sweeps, under controlled temperature and humidity. For construction of the model, the central composite experimental design was employed on the program Statistica 13 (StatSoft Inc.). All spectra were treated by computational tools for multivariate analysis using partial least squares regression (PLS) on the software program Pirouette 3.11 (Infometrix, Inc.). Variable selections were performed by the QSAR modeling program. The models developed by NIR in association with multivariate analysis provided good prediction of the APIs for the external samples and were therefore validated. For the tablets, however, the slightly different quantitative compositions of excipients compared to the mixtures prepared for building the models led to results that were not statistically similar, despite having prediction errors considered acceptable in the literature. Copyright © 2017 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

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.

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.

Multi-element fingerprinting as a tool in origin authentication of four east China marine species.

PubMed

Guo, Lipan; Gong, Like; Yu, Yanlei; Zhang, Hong

2013-12-01

The contents of 25 elements in 4 types of commercial marine species from the East China Sea were determined by inductively coupled plasma mass spectrometry and atomic absorption spectrometry. The elemental composition was used to differentiate marine species according to geographical origin by multivariate statistical analysis. The results showed that principal component analysis could distinguish samples from different areas and reveal the elements which played the most important role in origin diversity. The established models by partial least squares discriminant analysis (PLS-DA) and by probabilistic neural network (PNN) can both precisely predict the origin of the marine species. Further study indicated that PLS-DA and PNN were efficacious in regional discrimination. The models from these 2 statistical methods, with an accuracy of 97.92% and 100%, respectively, could both distinguish samples from different areas without the need for species differentiation. © 2013 Institute of Food Technologists®

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.

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.

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.

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.

Rapid analysis of glucose, fructose, sucrose, and maltose in honeys from different geographic regions using fourier transform infrared spectroscopy and multivariate analysis.

PubMed

Wang, Jun; Kliks, Michael M; Jun, Soojin; Jackson, Mel; Li, Qing X

2010-03-01

Quantitative analysis of glucose, fructose, sucrose, and maltose in different geographic origin honey samples in the world using the Fourier transform infrared (FTIR) spectroscopy and chemometrics such as partial least squares (PLS) and principal component regression was studied. The calibration series consisted of 45 standard mixtures, which were made up of glucose, fructose, sucrose, and maltose. There were distinct peak variations of all sugar mixtures in the spectral "fingerprint" region between 1500 and 800 cm(-1). The calibration model was successfully validated using 7 synthetic blend sets of sugars. The PLS 2nd-derivative model showed the highest degree of prediction accuracy with a highest R(2) value of 0.999. Along with the canonical variate analysis, the calibration model further validated by high-performance liquid chromatography measurements for commercial honey samples demonstrates that FTIR can qualitatively and quantitatively determine the presence of glucose, fructose, sucrose, and maltose in multiple regional honey samples.

Associations between complex OHC mixtures and thyroid and cortisol hormone levels in East Greenland polar bears

PubMed Central

TØ, Bechshøft; Sonne, C; Dietz, R; Born, EW; Muir, DCG; Letcher, RJ; Novak, MA; Henchey, E; Meyer, JS; Jenssen, BM; Villanger, GD

2012-01-01

The multivariate relationship between hair cortisol, whole blood thyroid hormones, and the complex mixtures of organohalogen contaminant (OHC) levels measured in subcutaneous adipose of 23 East Greenland polar bears (eight males and 15 females, all sampled between the years 1999 and 2001) was analyzed using projection to latent structure (PLS) regression modeling. In the resulting PLS model, most important variables with a negative influence on cortisol levels were particularly BDE-99, but also CB-180, -201, BDE-153, and CB-170/190. The most important variables with a positive influence on cortisol were CB-66/95, α-HCH, TT3, as well as heptachlor epoxide, dieldrin, BDE-47, p,p′-DDD. Although statistical modeling does not necessarily fully explain biological cause-effect relationships, relationships indicate that (1) the hypothalamic-pituitary-adrenal (HPA) axis in East Greenland polar bears is likely to be affected by OHC-contaminants and (2) the association between OHCs and cortisol may be linked with the hypothalamus-pituitary-thyroid (HPT) axis. PMID:22575327

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.

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.

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.

Measurement of pH in whole blood by near-infrared spectroscopy

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

Alam, M. Kathleen; Maynard, John D.; Robinson, M. Ries

1999-03-01

Whole blood pH has been determined {ital in vitro} by using near-infrared spectroscopy over the wavelength range of 1500 to 1785 nm with multivariate calibration modeling of the spectral data obtained from two different sample sets. In the first sample set, the pH of whole blood was varied without controlling cell size and oxygen saturation (O{sub 2} Sat) variation. The result was that the red blood cell (RBC) size and O{sub 2} Sat correlated with pH. Although the partial least-squares (PLS) multivariate calibration of these data produced a good pH prediction cross-validation standard error of prediction (CVSEP)=0.046, R{sup 2}=0.982, themore » spectral data were dominated by scattering changes due to changing RBC size that correlated with the pH changes. A second experiment was carried out where the RBC size and O{sub 2} Sat were varied orthogonally to the pH variation. A PLS calibration of the spectral data obtained from these samples produced a pH prediction with an R{sup 2} of 0.954 and a cross-validated standard error of prediction of 0.064 pH units. The robustness of the PLS calibration models was tested by predicting the data obtained from the other sets. The predicted pH values obtained from both data sets yielded R{sup 2} values greater than 0.9 once the data were corrected for differences in hemoglobin concentration. For example, with the use of the calibration produced from the second sample set, the pH values from the first sample set were predicted with an R{sup 2} of 0.92 after the predictions were corrected for bias and slope. It is shown that spectral information specific to pH-induced chemical changes in the hemoglobin molecule is contained within the PLS loading vectors developed for both the first and second data sets. It is this pH specific information that allows the spectra dominated by pH-correlated scattering changes to provide robust pH predictive ability in the uncorrelated data, and visa versa. {copyright} {ital 1999} {ital Society for Applied Spectroscopy}« less

A Combined Approach of Infrared Spectroscopy and Multivariate Analysis for the Simultaneous Determination of Sugars and Fructans in Strawberry Juices During Storage.

PubMed

Cassani, Lucía; Santos, Mauricio; Gerbino, Esteban; Del Rosario Moreira, María; Gómez-Zavaglia, Andrea

2018-03-01

In this work, a Fourier transform mid-infrared spectroscopy (FTIR)-based method was developed for simultaneously quantifying simple sugars and exogenously added fructooligosaccharides (FOS) in strawberry juices preserved for up to 14 d using nonthermal techniques (geraniol and vanillin+ultrasound). The main spectral differences were observed in the 1200 to 900 cm -1 region. The presence of FOS was identified by the typical bands at 1134, 1034, and 935 cm -1 . During storage, a significant decrease of sucrose was concomitant to an increase of glucose and fructose in juices stored without any previous preservation treatment, as determined by high-performance liquid chromatography (HPLC). A principal component analysis was performed on the FTIR spectra corresponding to the different treatments. The groups observed explained more than 94% of the variance and were related to changes in the carbohydrate composition during storage. Then, different partial least square models (PLS) were defined to determine the concentrations of glucose, sucrose, fructose, and those of exogenously added FOS with degrees of polymerization within 3 and 5. The carbohydrates' concentrations determined by HPLC were used as reference method. The models were validated with independent sets of data. The mean of predicted values fitted nicely those obtained by HPLC (correlation and R 2  > 0.97), thus supporting the use of the PLS models to monitor the quality of strawberry juices in unknown samples. In conclusion, FTIR spectroscopy appears as an adequate analytical tool to quick assess whether juice formulations meet specifications in terms of authenticity, contamination and/or deterioration. FTIR spectroscopy provided a method potentially transferable to the food industry when associated with the multivariate analysis. The robust 21 PLS models defined in this work provided reliable tools for the rapid monitoring of juices' authenticity and/or deterioration. In this regard, FTIR associated to multivariate analysis enabled the determination of different sugars in a single measurement without the need of pure sugars as standards. This experimental simplicity supports the use of FTIR at the production line, and also contributes to save time in determining carbohydrates' composition and stability, in an environmentally friendly way. © 2017 Institute of Food Technologists®.

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.

Metabolite profiling of Clinacanthus nutans leaves extracts obtained from different drying methods by 1H NMR-based metabolomics

NASA Astrophysics Data System (ADS)

Hashim, Noor Haslinda Noor; Latip, Jalifah; Khatib, Alfi

2016-11-01

The metabolites of Clinacanthus nutans leaves extracts and their dependence on drying process were systematically characterized using 1H nuclear magnetic resonance spectroscopy (NMR) multivariate data analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were able to distinguish the leaves extracts obtained from different drying methods. The identified metabolites were carbohydrates, amino acid, flavonoids and sulfur glucoside compounds. The major metabolites responsible for the separation in PLS-DA loading plots were lupeol, cycloclinacosides, betulin, cerebrosides and choline. The results showed that the combination of 1H NMR spectroscopy and multivariate data analyses could act as an efficient technique to understand the C. nutans composition and its variation.

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.

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.

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.

New spectrophotometric/chemometric assisted methods for the simultaneous determination of imatinib, gemifloxacin, nalbuphine and naproxen in pharmaceutical formulations and human urine

NASA Astrophysics Data System (ADS)

Belal, F.; Ibrahim, F.; Sheribah, Z. A.; Alaa, H.

2018-06-01

In this paper, novel univariate and multivariate regression methods along with model-updating technique were developed and validated for the simultaneous determination of quaternary mixture of imatinib (IMB), gemifloxacin (GMI), nalbuphine (NLP) and naproxen (NAP). The univariate method is extended derivative ratio (EDR) which depends on measuring every drug in the quaternary mixture by using a ternary mixture of the other three drugs as divisor. Peak amplitudes were measured at 294 nm, 250 nm, 283 nm and 239 nm within linear concentration ranges of 4.0-17.0, 3.0-15.0, 4.0-80.0 and 1.0-6.0 μg mL-1 for IMB, GMI, NLP and NAB, respectively. Multivariate methods adopted are partial least squares (PLS) in original and derivative mode. These models were constructed for simultaneous determination of the studied drugs in the ranges of 4.0-8.0, 3.0-11.0, 10.0-18.0 and 1.0-3.0 μg mL-1 for IMB, GMI, NLP and NAB, respectively, by using eighteen mixtures as a calibration set and seven mixtures as a validation set. The root mean square error of predication (RMSEP) were 0.09 and 0.06 for IMB, 0.14 and 0.13 for GMI, 0.07 and 0.02 for NLP and 0.64 and 0.27 for NAP by PLS in original and derivative mode, respectively. Both models were successfully applied for analysis of IMB, GMI, NLP and NAP in their dosage forms. Updated PLS in derivative mode and EDR were applied for determination of the studied drugs in spiked human urine. The obtained results were statistically compared with those obtained by the reported methods giving a conclusion that there is no significant difference regarding accuracy and precision.

New spectrophotometric/chemometric assisted methods for the simultaneous determination of imatinib, gemifloxacin, nalbuphine and naproxen in pharmaceutical formulations and human urine.

PubMed

Belal, F; Ibrahim, F; Sheribah, Z A; Alaa, H

2018-06-05

In this paper, novel univariate and multivariate regression methods along with model-updating technique were developed and validated for the simultaneous determination of quaternary mixture of imatinib (IMB), gemifloxacin (GMI), nalbuphine (NLP) and naproxen (NAP). The univariate method is extended derivative ratio (EDR) which depends on measuring every drug in the quaternary mixture by using a ternary mixture of the other three drugs as divisor. Peak amplitudes were measured at 294nm, 250nm, 283nm and 239nm within linear concentration ranges of 4.0-17.0, 3.0-15.0, 4.0-80.0 and 1.0-6.0μgmL -1 for IMB, GMI, NLP and NAB, respectively. Multivariate methods adopted are partial least squares (PLS) in original and derivative mode. These models were constructed for simultaneous determination of the studied drugs in the ranges of 4.0-8.0, 3.0-11.0, 10.0-18.0 and 1.0-3.0μgmL -1 for IMB, GMI, NLP and NAB, respectively, by using eighteen mixtures as a calibration set and seven mixtures as a validation set. The root mean square error of predication (RMSEP) were 0.09 and 0.06 for IMB, 0.14 and 0.13 for GMI, 0.07 and 0.02 for NLP and 0.64 and 0.27 for NAP by PLS in original and derivative mode, respectively. Both models were successfully applied for analysis of IMB, GMI, NLP and NAP in their dosage forms. Updated PLS in derivative mode and EDR were applied for determination of the studied drugs in spiked human urine. The obtained results were statistically compared with those obtained by the reported methods giving a conclusion that there is no significant difference regarding accuracy and precision. Copyright © 2018 Elsevier B.V. All rights reserved.

A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization.

PubMed

Lozano, Valeria A; Ibañez, Gabriela A; Olivieri, Alejandro C

2009-10-05

In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.

Evaluation of multivariate calibration models with different pre-processing and processing algorithms for a novel resolution and quantitation of spectrally overlapped quaternary mixture in syrup

NASA Astrophysics Data System (ADS)

Moustafa, Azza A.; Hegazy, Maha A.; Mohamed, Dalia; Ali, Omnia

2016-02-01

A novel approach for the resolution and quantitation of severely overlapped quaternary mixture of carbinoxamine maleate (CAR), pholcodine (PHL), ephedrine hydrochloride (EPH) and sunset yellow (SUN) in syrup was demonstrated utilizing different spectrophotometric assisted multivariate calibration methods. The applied methods have used different processing and pre-processing algorithms. The proposed methods were partial least squares (PLS), concentration residuals augmented classical least squares (CRACLS), and a novel method; continuous wavelet transforms coupled with partial least squares (CWT-PLS). These methods were applied to a training set in the concentration ranges of 40-100 μg/mL, 40-160 μg/mL, 100-500 μg/mL and 8-24 μg/mL for the four components, respectively. The utilized methods have not required any preliminary separation step or chemical pretreatment. The validity of the methods was evaluated by an external validation set. The selectivity of the developed methods was demonstrated by analyzing the drugs in their combined pharmaceutical formulation without any interference from additives. The obtained results were statistically compared with the official and reported methods where no significant difference was observed regarding both accuracy and precision.

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

PubMed

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

PubMed

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

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.

A new technique for spectrophotometric determination of pseudoephedrine and guaifenesin in syrup and synthetic mixture.

PubMed

Riahi, Siavash; Hadiloo, Farshad; Milani, Seyed Mohammad R; Davarkhah, Nazila; Ganjali, Mohammad R; Norouzi, Parviz; Seyfi, Payam

2011-05-01

The accuracy in predicting different chemometric methods was compared when applied on ordinary UV spectra and first order derivative spectra. Principal component regression (PCR) and partial least squares with one dependent variable (PLS1) and two dependent variables (PLS2) were applied on spectral data of pharmaceutical formula containing pseudoephedrine (PDP) and guaifenesin (GFN). The ability to derivative in resolved overlapping spectra chloropheniramine maleate was evaluated when multivariate methods are adopted for analysis of two component mixtures without using any chemical pretreatment. The chemometrics models were tested on an external validation dataset and finally applied to the analysis of pharmaceuticals. Significant advantages were found in analysis of the real samples when the calibration models from derivative spectra were used. It should also be mentioned that the proposed method is a simple and rapid way requiring no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories. Copyright © 2011 John Wiley & Sons, Ltd.

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.

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.

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.

Quality-by-design III: application of near-infrared spectroscopy to monitor roller compaction in-process and product quality attributes of immediate release tablets.

PubMed

Kona, Ravikanth; Fahmy, Raafat M; Claycamp, Gregg; Polli, James E; Martinez, Marilyn; Hoag, Stephen W

2015-02-01

The objective of this study is to use near-infrared spectroscopy (NIRS) coupled with multivariate chemometric models to monitor granule and tablet quality attributes in the formulation development and manufacturing of ciprofloxacin hydrochloride (CIP) immediate release tablets. Critical roller compaction process parameters, compression force (CFt), and formulation variables identified from our earlier studies were evaluated in more detail. Multivariate principal component analysis (PCA) and partial least square (PLS) models were developed during the development stage and used as a control tool to predict the quality of granules and tablets. Validated models were used to monitor and control batches manufactured at different sites to assess their robustness to change. The results showed that roll pressure (RP) and CFt played a critical role in the quality of the granules and the finished product within the range tested. Replacing binder source did not statistically influence the quality attributes of the granules and tablets. However, lubricant type has significantly impacted the granule size. Blend uniformity, crushing force, disintegration time during the manufacturing was predicted using validated PLS regression models with acceptable standard error of prediction (SEP) values, whereas the models resulted in higher SEP for batches obtained from different manufacturing site. From this study, we were able to identify critical factors which could impact the quality attributes of the CIP IR tablets. In summary, we demonstrated the ability of near-infrared spectroscopy coupled with chemometrics as a powerful tool to monitor critical quality attributes (CQA) identified during formulation development.

Use of metabolomics and lipidomics to evaluate the hypocholestreolemic effect of Proanthocyanidins from grape seed in a pig model.

PubMed

Quifer-Rada, Paola; Choy, Ying Yng; Calvert, Christopher C; Waterhouse, Andrew L; Lamuela-Raventos, Rosa M

2016-10-01

This work aims to evaluate changes in the fecal metabolomic profile due to grape seed extract (GSE) intake by untargeted and targeted analysis using high resolution mass spectrometry in conjunction with multivariate statistics. An intervention study with six crossbred female pigs was performed. The pigs followed a standard diet for 3 days, then they were fed with a supplemented diet containing 1% (w/w) of MegaNatural® Gold grape seed extract for 6 days. Fresh pig fecal samples were collected daily. A combination of untargeted high resolution mass spectrometry, multivariate analysis (PLS-DA), data-dependent MS/MS scan, and accurate mass database matching was used to measure the effect of the treatment on fecal composition. The resultant PLS-DA models showed a good discrimination among classes with great robustness and predictability. A total of 14 metabolites related to the GSE consumption were identified including biliary acid, dicarboxylic fatty acid, cholesterol metabolites, purine metabolites, and eicosanoid metabolites among others. Moreover, targeted metabolomics using GC-MS showed that cholesterol and its metabolites fecal excretion was increased due to the proanthocyanidins from grape seed extract. The results show that oligomeric procyanidins from GSE modifies bile acid and steroid excretion, which could exert a hypocholesterolemic effect. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Simultaneous Determination of Metamizole, Thiamin and Pyridoxin Using UV-Spectroscopy in Combination with Multivariate Calibration

PubMed Central

Chotimah, Chusnul; Sudjadi; Riyanto, Sugeng; Rohman, Abdul

2015-01-01

Purpose: Analysis of drugs in multicomponent system officially is carried out using chromatographic technique, however, this technique is too laborious and involving sophisticated instrument. Therefore, UV-VIS spectrophotometry coupled with multivariate calibration of partial least square (PLS) for quantitative analysis of metamizole, thiamin and pyridoxin is developed in the presence of cyanocobalamine without any separation step. Methods: The calibration and validation samples are prepared. The calibration model is prepared by developing a series of sample mixture consisting these drugs in certain proportion. Cross validation of calibration sample using leave one out technique is used to identify the smaller set of components that provide the greatest predictive ability. The evaluation of calibration model was based on the coefficient of determination (R2) and root mean square error of calibration (RMSEC). Results: The results showed that the coefficient of determination (R2) for the relationship between actual values and predicted values for all studied drugs was higher than 0.99 indicating good accuracy. The RMSEC values obtained were relatively low, indicating good precision. The accuracy and presision results of developed method showed no significant difference compared to those obtained by official method of HPLC. Conclusion: The developed method (UV-VIS spectrophotometry in combination with PLS) was succesfully used for analysis of metamizole, thiamin and pyridoxin in tablet dosage form. PMID:26819934

Modified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach

NASA Astrophysics Data System (ADS)

Yuniarto, Budi; Kurniawan, Robert

2017-03-01

PLS Path Modeling (PLS-PM) is different from covariance based SEM, where PLS-PM use an approach based on variance or component, therefore, PLS-PM is also known as a component based SEM. Multiblock Partial Least Squares (MBPLS) is a method in PLS regression which can be used in PLS Path Modeling which known as Multiblock PLS Path Modeling (MBPLS-PM). This method uses an iterative procedure in its algorithm. This research aims to modify MBPLS-PM with Back Propagation Neural Network approach. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. By modifying the MBPLS-PM algorithm using Back Propagation Neural Network approach, the model parameters obtained are relatively not significantly different compared to model parameters obtained by original MBPLS-PM algorithm.

Associations between complex OHC mixtures and thyroid and cortisol hormone levels in East Greenland polar bears.

PubMed

Bechshøft, T Ø; Sonne, C; Dietz, R; Born, E W; Muir, D C G; Letcher, R J; Novak, M A; Henchey, E; Meyer, J S; Jenssen, B M; Villanger, G D

2012-07-01

The multivariate relationship between hair cortisol, whole blood thyroid hormones, and the complex mixtures of organohalogen contaminant (OHC) levels measured in subcutaneous adipose of 23 East Greenland polar bears (eight males and 15 females, all sampled between the years 1999 and 2001) was analyzed using projection to latent structure (PLS) regression modeling. In the resulting PLS model, most important variables with a negative influence on cortisol levels were particularly BDE-99, but also CB-180, -201, BDE-153, and CB-170/190. The most important variables with a positive influence on cortisol were CB-66/95, α-HCH, TT3, as well as heptachlor epoxide, dieldrin, BDE-47, p,p'-DDD. Although statistical modeling does not necessarily fully explain biological cause-effect relationships, relationships indicate that (1) the hypothalamic-pituitary-adrenal (HPA) axis in East Greenland polar bears is likely to be affected by OHC-contaminants and (2) the association between OHCs and cortisol may be linked with the hypothalamus-pituitary-thyroid (HPT) axis. Copyright © 2012 Elsevier Inc. All rights reserved.

Simultaneous determination of penicillin G salts by infrared spectroscopy: Evaluation of combining orthogonal signal correction with radial basis function-partial least squares regression

NASA Astrophysics Data System (ADS)

Talebpour, Zahra; Tavallaie, Roya; Ahmadi, Seyyed Hamid; Abdollahpour, Assem

2010-09-01

In this study, a new method for the simultaneous determination of penicillin G salts in pharmaceutical mixture via FT-IR spectroscopy combined with chemometrics was investigated. The mixture of penicillin G salts is a complex system due to similar analytical characteristics of components. Partial least squares (PLS) and radial basis function-partial least squares (RBF-PLS) were used to develop the linear and nonlinear relation between spectra and components, respectively. The orthogonal signal correction (OSC) preprocessing method was used to correct unexpected information, such as spectral overlapping and scattering effects. In order to compare the influence of OSC on PLS and RBF-PLS models, the optimal linear (PLS) and nonlinear (RBF-PLS) models based on conventional and OSC preprocessed spectra were established and compared. The obtained results demonstrated that OSC clearly enhanced the performance of both RBF-PLS and PLS calibration models. Also in the case of some nonlinear relation between spectra and component, OSC-RBF-PLS gave satisfactory results than OSC-PLS model which indicated that the OSC was helpful to remove extrinsic deviations from linearity without elimination of nonlinear information related to component. The chemometric models were tested on an external dataset and finally applied to the analysis commercialized injection product of penicillin G salts.

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.

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.

Laser ablation molecular isotopic spectroscopy (LAMIS) towards the determination of multivariate LODs via PLS calibration model of 10B and 11B Boric acid mixtures

NASA Astrophysics Data System (ADS)

Harris, C. D.; Profeta, Luisa T. M.; Akpovo, Codjo A.; Johnson, Lewis; Stowe, Ashley C.

2017-05-01

A calibration model was created to illustrate the detection capabilities of laser ablation molecular isotopic spectroscopy (LAMIS) discrimination in isotopic analysis. The sample set contained boric acid pellets that varied in isotopic concentrations of 10B and 11B. Each sample set was interrogated with a Q-switched Nd:YAG ablation laser operating at 532 nm. A minimum of four band heads of the β system B2∑ -> Χ2∑transitions were identified and verified with previous literature on BO molecular emission lines. Isotopic shifts were observed in the spectra for each transition and used as the predictors in the calibration model. The spectra along with their respective 10/11B isotopic ratios were analyzed using Partial Least Squares Regression (PLSR). An IUPAC novel approach for determining a multivariate Limit of Detection (LOD) interval was used to predict the detection of the desired isotopic ratios. The predicted multivariate LOD is dependent on the variation of the instrumental signal and other composites in the calibration model space.

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.

Mixture quantification using PLS in plastic scintillation measurements.

PubMed

Bagán, H; Tarancón, A; Rauret, G; García, J F

2011-06-01

This article reports the capability of plastic scintillation (PS) combined with multivariate calibration (Partial least squares; PLS) to detect and quantify alpha and beta emitters in mixtures. While several attempts have been made with this purpose in mind using liquid scintillation (LS), no attempt was done using PS that has the great advantage of not producing mixed waste after the measurements are performed. Following this objective, ternary mixtures of alpha and beta emitters ((241)Am, (137)Cs and (90)Sr/(90)Y) have been quantified. Procedure optimisation has evaluated the use of the net spectra or the sample spectra, the inclusion of different spectra obtained at different values of the Pulse Shape Analysis parameter and the application of the PLS1 or PLS2 algorithms. The conclusions show that the use of PS+PLS2 applied to the sample spectra, without the use of any pulse shape discrimination, allows quantification of the activities with relative errors less than 10% in most of the cases. This procedure not only allows quantification of mixtures but also reduces measurement time (no blanks are required) and the application of this procedure does not require detectors that include the pulse shape analysis parameter. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

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

Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares.

PubMed

Wang, Pingyue; Chen, Kewei; Yao, Li; Hu, Bin; Wu, Xia; Zhang, Jiacai; Ye, Qing; Guo, Xiaojuan

2016-08-10

In recent years, increasing attention has been given to the identification of the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Brain neuroimaging techniques have been widely used to support the classification or prediction of MCI. The present study combined magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose PET (FDG-PET), and 18F-florbetapir PET (florbetapir-PET) to discriminate MCI converters (MCI-c, individuals with MCI who convert to AD) from MCI non-converters (MCI-nc, individuals with MCI who have not converted to AD in the follow-up period) based on the partial least squares (PLS) method. Two types of PLS models (informed PLS and agnostic PLS) were built based on 64 MCI-c and 65 MCI-nc from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the three-modality informed PLS model achieved better classification accuracy of 81.40%, sensitivity of 79.69%, and specificity of 83.08% compared with the single-modality model, and the three-modality agnostic PLS model also achieved better classification compared with the two-modality model. Moreover, combining the three modalities with clinical test score (ADAS-cog), the agnostic PLS model (independent data: florbetapir-PET; dependent data: FDG-PET and MRI) achieved optimal accuracy of 86.05%, sensitivity of 81.25%, and specificity of 90.77%. In addition, the comparison of PLS, support vector machine (SVM), and random forest (RF) showed greater diagnostic power of PLS. These results suggested that our multimodal PLS model has the potential to discriminate MCI-c from the MCI-nc and may therefore be helpful in the early diagnosis of AD.

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.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

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.

External characteristic determination of eggs and cracked eggs identification using spectral signature

PubMed Central

Xie, Chuanqi; He, Yong

2016-01-01

This study was carried out to use hyperspectral imaging technique for determining color (L*, a* and b*) and eggshell strength and identifying cracked chicken eggs. Partial least squares (PLS) models based on full and selected wavelengths suggested by regression coefficient (RC) method were established to predict the four parameters, respectively. Partial least squares-discriminant analysis (PLS-DA) and RC-partial least squares-discriminant analysis (RC-PLS-DA) models were applied to identify cracked eggs. PLS models performed well with the correlation coefficient (rp) of 0.788 for L*, 0.810 for a*, 0.766 for b* and 0.835 for eggshell strength. RC-PLS models also obtained the rp of 0.771 for L*, 0.806 for a*, 0.767 for b* and 0.841 for eggshell strength. The classification results were 97.06% in PLS-DA model and 88.24% in RC-PLS-DA model. It demonstrated that hyperspectral imaging technique has the potential to be used to detect color and eggshell strength values and identify cracked chicken eggs. PMID:26882990

Application of Fluorescence Spectrometry With Multivariate Calibration to the Enantiomeric Recognition of Fluoxetine in Pharmaceutical Preparations.

PubMed

Poláček, Roman; Májek, Pavel; Hroboňová, Katarína; Sádecká, Jana

2016-04-01

Fluoxetine is the most prescribed antidepressant chiral drug worldwide. Its enantiomers have a different duration of serotonin inhibition. A novel simple and rapid method for determination of the enantiomeric composition of fluoxetine in pharmaceutical pills is presented. Specifically, emission, excitation, and synchronous fluorescence techniques were employed to obtain the spectral data, which with multivariate calibration methods, namely, principal component regression (PCR) and partial least square (PLS), were investigated. The chiral recognition of fluoxetine enantiomers in the presence of β-cyclodextrin was based on diastereomeric complexes. The results of the multivariate calibration modeling indicated good prediction abilities. The obtained results for tablets were compared with those from chiral HPLC and no significant differences are shown by Fisher's (F) test and Student's t-test. The smallest residuals between reference or nominal values and predicted values were achieved by multivariate calibration of synchronous fluorescence spectral data. This conclusion is supported by calculated values of the figure of merit.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

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.

The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study.

PubMed

Sandoval, S; Torres, A; Pawlowsky-Reusing, E; Riechel, M; Caradot, N

2013-01-01

The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.

Comparison of Portable and Bench-Top Spectrometers for Mid-Infrared Diffuse Reflectance Measurements of Soils.

PubMed

Hutengs, Christopher; Ludwig, Bernard; Jung, András; Eisele, Andreas; Vohland, Michael

2018-03-27

Mid-infrared (MIR) spectroscopy has received widespread interest as a method to complement traditional soil analysis. Recently available portable MIR spectrometers additionally offer potential for on-site applications, given sufficient spectral data quality. We therefore tested the performance of the Agilent 4300 Handheld FTIR (DRIFT spectra) in comparison to a Bruker Tensor 27 bench-top instrument in terms of (i) spectral quality and measurement noise quantified by wavelet analysis; (ii) accuracy of partial least squares (PLS) calibrations for soil organic carbon (SOC), total nitrogen (N), pH, clay and sand content with a repeated cross-validation analysis; and (iii) key spectral regions for these soil properties identified with a Monte Carlo spectral variable selection approach. Measurements and multivariate calibrations with the handheld device were as good as or slightly better than Bruker equipped with a DRIFT accessory, but not as accurate as with directional hemispherical reflectance (DHR) data collected with an integrating sphere. Variations in noise did not markedly affect the accuracy of multivariate PLS calibrations. Identified key spectral regions for PLS calibrations provided a good match between Agilent and Bruker DHR data, especially for SOC and N. Our findings suggest that portable FTIR instruments are a viable alternative for MIR measurements in the laboratory and offer great potential for on-site applications.

Simultaneous determination of potassium guaiacolsulfonate, guaifenesin, diphenhydramine HCl and carbetapentane citrate in syrups by using HPLC-DAD coupled with partial least squares multivariate calibration.

PubMed

Dönmez, Ozlem Aksu; Aşçi, Bürge; Bozdoğan, Abdürrezzak; Sungur, Sidika

2011-02-15

A simple and rapid analytical procedure was proposed for the determination of chromatographic peaks by means of partial least squares multivariate calibration (PLS) of high-performance liquid chromatography with diode array detection (HPLC-DAD). The method is exemplified with analysis of quaternary mixtures of potassium guaiacolsulfonate (PG), guaifenesin (GU), diphenhydramine HCI (DP) and carbetapentane citrate (CP) in syrup preparations. In this method, the area does not need to be directly measured and predictions are more accurate. Though the chromatographic and spectral peaks of the analytes were heavily overlapped and interferents coeluted with the compounds studied, good recoveries of analytes could be obtained with HPLC-DAD coupled with PLS calibration. This method was tested by analyzing the synthetic mixture of PG, GU, DP and CP. As a comparison method, a classsical HPLC method was used. The proposed methods were applied to syrups samples containing four drugs and the obtained results were statistically compared with each other. Finally, the main advantage of HPLC-PLS method over the classical HPLC method tried to emphasized as the using of simple mobile phase, shorter analysis time and no use of internal standard and gradient elution. Copyright © 2010 Elsevier B.V. All rights reserved.

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.

Fourier transform infrared spectroscopy for Kona coffee authentication.

PubMed

Wang, Jun; Jun, Soojin; Bittenbender, H C; Gautz, Loren; Li, Qing X

2009-06-01

Kona coffee, the variety of "Kona typica" grown in the north and south districts of Kona-Island, carries a unique stamp of the region of Big Island of Hawaii, U.S.A. The excellent quality of Kona coffee makes it among the best coffee products in the world. Fourier transform infrared (FTIR) spectroscopy integrated with an attenuated total reflectance (ATR) accessory and multivariate analysis was used for qualitative and quantitative analysis of ground and brewed Kona coffee and blends made with Kona coffee. The calibration set of Kona coffee consisted of 10 different blends of Kona-grown original coffee mixture from 14 different farms in Hawaii and a non-Kona-grown original coffee mixture from 3 different sampling sites in Hawaii. Derivative transformations (1st and 2nd), mathematical enhancements such as mean centering and variance scaling, multivariate regressions by partial least square (PLS), and principal components regression (PCR) were implemented to develop and enhance the calibration model. The calibration model was successfully validated using 9 synthetic blend sets of 100% Kona coffee mixture and its adulterant, 100% non-Kona coffee mixture. There were distinct peak variations of ground and brewed coffee blends in the spectral "fingerprint" region between 800 and 1900 cm(-1). The PLS-2nd derivative calibration model based on brewed Kona coffee with mean centering data processing showed the highest degree of accuracy with the lowest standard error of calibration value of 0.81 and the highest R(2) value of 0.999. The model was further validated by quantitative analysis of commercial Kona coffee blends. Results demonstrate that FTIR can be a rapid alternative to authenticate Kona coffee, which only needs very quick and simple sample preparations.

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.

A multiple hold-out framework for Sparse Partial Least Squares.

PubMed

Monteiro, João M; Rao, Anil; Shawe-Taylor, John; Mourão-Miranda, Janaina

2016-09-15

Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain's mechanisms by finding relationships between neuroimaging and clinical/demographic data. The identification of these relationships has the potential to improve the current understanding of disease mechanisms, refine clinical assessment tools, and stratify patients. SPLS finds multivariate associative effects in the data by computing pairs of sparse weight vectors, where each pair is used to remove its corresponding associative effect from the data by matrix deflation, before computing additional pairs. We propose a novel SPLS framework which selects the adequate number of voxels and clinical variables to describe each associative effect, and tests their reliability by fitting the model to different splits of the data. As a proof of concept, the approach was applied to find associations between grey matter probability maps and individual items of the Mini-Mental State Examination (MMSE) in a clinical sample with various degrees of dementia. The framework found two statistically significant associative effects between subsets of brain voxels and subsets of the questions/tasks. SPLS was compared with its non-sparse version (PLS). The use of projection deflation versus a classical PLS deflation was also tested in both PLS and SPLS. SPLS outperformed PLS, finding statistically significant effects and providing higher correlation values in hold-out data. Moreover, projection deflation provided better results. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

[Spectral quantitative analysis by nonlinear partial least squares based on neural network internal model for flue gas of thermal power plant].

PubMed

Cao, Hui; Li, Yao-Jiang; Zhou, Yan; Wang, Yan-Xia

2014-11-01

To deal with nonlinear characteristics of spectra data for the thermal power plant flue, a nonlinear partial least square (PLS) analysis method with internal model based on neural network is adopted in the paper. The latent variables of the independent variables and the dependent variables are extracted by PLS regression firstly, and then they are used as the inputs and outputs of neural network respectively to build the nonlinear internal model by train process. For spectra data of flue gases of the thermal power plant, PLS, the nonlinear PLS with the internal model of back propagation neural network (BP-NPLS), the non-linear PLS with the internal model of radial basis function neural network (RBF-NPLS) and the nonlinear PLS with the internal model of adaptive fuzzy inference system (ANFIS-NPLS) are compared. The root mean square error of prediction (RMSEP) of sulfur dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 16.96%, 16.60% and 19.55% than that of PLS, respectively. The RMSEP of nitric oxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 8.60%, 8.47% and 10.09% than that of PLS, respectively. The RMSEP of nitrogen dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 2.11%, 3.91% and 3.97% than that of PLS, respectively. Experimental results show that the nonlinear PLS is more suitable for the quantitative analysis of glue gas than PLS. Moreover, by using neural network function which can realize high approximation of nonlinear characteristics, the nonlinear partial least squares method with internal model mentioned in this paper have well predictive capabilities and robustness, and could deal with the limitations of nonlinear partial least squares method with other internal model such as polynomial and spline functions themselves under a certain extent. ANFIS-NPLS has the best performance with the internal model of adaptive fuzzy inference system having ability to learn more and reduce the residuals effectively. Hence, ANFIS-NPLS is an accurate and useful quantitative thermal power plant flue gas analysis method.

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.

Discrimination of edible oils and fats by combination of multivariate pattern recognition and FT-IR spectroscopy: A comparative study between different modeling methods

NASA Astrophysics Data System (ADS)

Javidnia, Katayoun; Parish, Maryam; Karimi, Sadegh; Hemmateenejad, Bahram

2013-03-01

By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm-1. Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity.

Metabolomics fingerprint of coffee species determined by untargeted-profiling study using LC-HRMS.

PubMed

Souard, Florence; Delporte, Cédric; Stoffelen, Piet; Thévenot, Etienne A; Noret, Nausicaa; Dauvergne, Bastien; Kauffmann, Jean-Michel; Van Antwerpen, Pierre; Stévigny, Caroline

2018-04-15

Coffee bean extracts are consumed all over the world as beverage and there is a growing interest in coffee leaf extracts as food supplements. The wild diversity in Coffea (Rubiaceae) genus is large and could offer new opportunities and challenges. In the present work, a metabolomics approach was implemented to examine leaf chemical composition of 9 Coffea species grown in the same environmental conditions. Leaves were analyzed by LC-HRMS and a comprehensive statistical workflow was designed. It served for univariate hypothesis testing and multivariate modeling by PCA and partial PLS-DA on the Workflow4Metabolomics infrastructure. The first two axes of PCA and PLS-DA describes more than 40% of variances with good values of explained variances. This strategy permitted to investigate the metabolomics data and their relation with botanic and genetic informations. Finally, the identification of several key metabolites for the discrimination between species was further characterized. Copyright © 2017 Elsevier Ltd. 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 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.

An In Silico Method for Screening Nicotine Derivatives as Cytochrome P450 2A6 Selective Inhibitors Based on Kernel Partial Least Squares

PubMed Central

Wang, Yonghua; Li, Yan; Wang, Bin

2007-01-01

Nicotine and a variety of other drugs and toxins are metabolized by cytochrome P450 (CYP) 2A6. The aim of the present study was to build a quantitative structure-activity relationship (QSAR) model to predict the activities of nicotine analogues on CYP2A6. Kernel partial least squares (K-PLS) regression was employed with the electro-topological descriptors to build the computational models. Both the internal and external predictabilities of the models were evaluated with test sets to ensure their validity and reliability. As a comparison to K-PLS, a standard PLS algorithm was also applied on the same training and test sets. Our results show that the K-PLS produced reasonable results that outperformed the PLS model on the datasets. The obtained K-PLS model will be helpful for the design of novel nicotine-like selective CYP2A6 inhibitors.

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

Role of physicochemical properties in the activation of peroxisome proliferator-activated receptor δ.

PubMed

Maltarollo, Vinícius G; Homem-de-Mello, Paula; Honorio, Káthia M

2011-10-01

Current researches on treatments for metabolic diseases involve a class of biological receptors called peroxisome proliferator-activated receptors (PPARs), which control the metabolism of carbohydrates and lipids. A subclass of these receptors, PPARδ, regulates several metabolic processes, and the substances that activate them are being studied as new drug candidates for the treatment of diabetes mellitus and metabolic syndrome. In this study, several PPARδ agonists with experimental biological activity were selected for a structural and chemical study. Electronic, stereochemical, lipophilic and topological descriptors were calculated for the selected compounds using various theoretical methods, such as density functional theory (DFT). Fisher's weight and principal components analysis (PCA) methods were employed to select the most relevant variables for this study. The partial least squares (PLS) method was used to construct the multivariate statistical model, and the best model obtained had 4 PCs, q ( 2 ) = 0.80 and r ( 2 ) = 0.90, indicating a good internal consistency. The prediction residues calculated for the compounds in the test set had low values, indicating the good predictive capability of our PLS model. The model obtained in this study is reliable and can be used to predict the biological activity of new untested compounds. Docking studies have also confirmed the importance of the molecular descriptors selected for this system.

Augmented classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2004-02-03

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-07-26

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-01-11

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation.

PubMed

Lourenço, Vera; Herdling, Thorsten; Reich, Gabriele; Menezes, José C; Lochmann, Dirk

2011-08-01

A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles. Copyright © 2011. Published by Elsevier B.V.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

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

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from whichmore » unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.« less

[Determination of Cu in Shell of Preserved Egg by LIBS Coupled with PLS].

PubMed

Hu, Hui-qin; Xu, Xue-hong; Liu, Mu-hua; Tu, Jian-ping; Huang, Le; Huang, Lin; Yao, Ming-yin; Chen, Tian-bing; Yang, Ping

2015-12-01

In this work, the content of copper in the shell of preserved eggs were determined directly by Laser induced breakdown spectroscopy (LIBS), and the characteristics lines of Cu was obtained. The samples of eggshell were pretreated by acid wet digestion, and the real content of Cu was obtained by atomic absorption spectrophotometer (AAS). Due to the test precision and accuracy of LIBS was influenced by a serious of factors, for example, the complex matrix effect of sample, the enviro nment noise, the system noise of the instrument, the stability of laser energy and so on. And the conventional unvariate linear calibration curve between LIBS intensity and content of element of sample, such as by use of Schiebe G-Lomakin equation, can not meet the requirement of quantitative analysis. In account of that, a kind of multivariate calibration method is needed. In this work, the data of LIBS spectra were processed by partial least squares (PLS), the precision and accuracy of PLS model were compared by different smoothing treatment and five pretreatment methods. The result showed that the correlation coefficient and the accuracy of the PLS model were improved, and the root mean square error and the average relative error were reduced effectively by 11 point smoothing with Multiplicative scatter correction (MSC) pretreatment. The results of the study show that, heavy metal Cu in preserved egg shells can be direct detected accurately by laser induced breakdown spectroscopy, and the next step batch tests will been conducted to find out the relationship of heavy metal Cu content in the preserved egg between the eggshell, egg white and egg yolk. And the goal of the contents of heavy metals in the egg white, egg yolk can be knew through determinate the eggshell by the LIBS can be achieved, to provide new method for rapid non-destructive testing technology for quality and satety of agricultural products.

Kernel Partial Least Squares for Nonlinear Regression and Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Clancy, Daniel (Technical Monitor)

2002-01-01

This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

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.

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

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.

Application of FTIR-ATR spectroscopy coupled with multivariate analysis for rapid estimation of butter adulteration.

PubMed

Fadzlillah, Nurrulhidayah Ahmad; Rohman, Abdul; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2013-01-01

In dairy product sector, butter is one of the potential sources of fat soluble vitamins, namely vitamin A, D, E, K; consequently, butter is taken into account as high valuable price from other dairy products. This fact has attracted unscrupulous market players to blind butter with other animal fats to gain economic profit. Animal fats like mutton fat (MF) are potential to be mixed with butter due to the similarity in terms of fatty acid composition. This study focused on the application of FTIR-ATR spectroscopy in conjunction with chemometrics for classification and quantification of MF as adulterant in butter. The FTIR spectral region of 3910-710 cm⁻¹ was used for classification between butter and butter blended with MF at various concentrations with the aid of discriminant analysis (DA). DA is able to classify butter and adulterated butter without any mistakenly grouped. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the frequency regions of 3910-710 cm⁻¹. The equation obtained for the relationship between actual value of MF and FTIR predicted values of MF in PLS calibration model was y = 0.998x + 1.033, with the values of coefficient of determination (R²) and root mean square error of calibration are 0.998 and 0.046% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with MF. Using 9 principal components, root mean square error of prediction (RMSEP) is 1.68% (v/v). The results showed that FTIR spectroscopy can be used for the classification and quantification of MF in butter formulation for verification purposes.

Determination of enantiomeric composition of ibuprofen in pharmaceutical formulations by partial least-squares regression of strongly overlapped chromatographic profiles.

PubMed

Grisales, Jaiver Osorio; Arancibia, Juan A; Castells, Cecilia B; Olivieri, Alejandro C

2012-12-01

In this report, we demonstrate how chiral liquid chromatography combined with multivariate chemometric techniques, specifically unfolded-partial least-squares regression (U-PLS), provides a powerful analytical methodology. Using U-PLS, strongly overlapped enantiomer profiles in a sample could be successfully processed and enantiomeric purity could be accurately determined without requiring baseline enantioresolution between peaks. The samples were partially enantioseparated with a permethyl-β-cyclodextrin chiral column under reversed-phase conditions. Signals detected with a diode-array detector within a wavelength range from 198 to 241 nm were recorded, and the data were processed by a second-order multivariate algorithm to decrease detection limits. The R-(-)-enantiomer of ibuprofen in tablet formulation samples could be determined at the level of 0.5 mg L⁻¹ in the presence of 99.9% of the S-(+)-enantiomorph with relative prediction error within ±3%. Copyright © 2012 Elsevier B.V. All rights reserved.

Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration.

PubMed

Wood, Clive; Alwati, Abdolati; Halsey, Sheelagh; Gough, Tim; Brown, Elaine; Kelly, Adrian; Paradkar, Anant

2016-09-10

The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen-nicotinamide (IBU-NIC) and 1:1 carbamazepine-nicotinamide (CBZ-NIC) has been evaluated. A partial least squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

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.

Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Kong, Wenwen; Liu, Fei; Zhang, Chu; Bao, Yidan; Yu, Jiajia; He, Yong

2014-01-01

Tomatoes are cultivated around the world and gray mold is one of its most prominent and destructive diseases. An early disease detection method can decrease losses caused by plant diseases and prevent the spread of diseases. The activity of peroxidase (POD) is very important indicator of disease stress for plants. The objective of this study is to examine the possibility of fast detection of POD activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging data. Five pre-treatment methods were investigated. Genetic algorithm-partial least squares (GA-PLS) was applied to select optimal wavelengths. A new fast learning neural algorithm named extreme learning machine (ELM) was employed as multivariate analytical tool in this study. 21 optimal wavelengths were selected by GA-PLS and used as inputs of three calibration models. The optimal prediction result was achieved by ELM model with selected wavelengths, and the r and RMSEP in validation were 0.8647 and 465.9880 respectively. The results indicated that hyperspectral imaging could be considered as a valuable tool for POD activity prediction. The selected wavelengths could be potential resources for instrument development.

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

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%).

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.

Improved detection of highly energetic materials traces on surfaces by standoff laser-induced thermal emission incorporating neural networks

NASA Astrophysics Data System (ADS)

Figueroa-Navedo, Amanda; Galán-Freyle, Nataly Y.; Pacheco-Londoño, Leonardo C.; Hernández-Rivera, Samuel P.

2013-05-01

Terrorists conceal highly energetic materials (HEM) as Improvised Explosive Devices (IED) in various types of materials such as PVC, wood, Teflon, aluminum, acrylic, carton and rubber to disguise them from detection equipment used by military and security agency personnel. Infrared emissions (IREs) of substrates, with and without HEM, were measured to generate models for detection and discrimination. Multivariable analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and neural networks (NN) were employed to generate models, in which the emission of IR light from heated samples was stimulated using a CO2 laser giving rise to laser induced thermal emission (LITE) of HEMs. Traces of a specific target threat chemical explosive: PETN in surface concentrations of 10 to 300 ug/cm2 were studied on the surfaces mentioned. Custom built experimental setup used a CO2 laser as a heating source positioned with a telescope, where a minimal loss in reflective optics was reported, for the Mid-IR at a distance of 4 m and 32 scans at 10 s. SVM-DA resulted in the best statistical technique for a discrimination performance of 97%. PLS-DA accurately predicted over 94% and NN 88%.

Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics.

PubMed

Villar, Alberto; Vadillo, Julen; Santos, Jose I; Gorritxategi, Eneko; Mabe, Jon; Arnaiz, Aitor; Fernández, Luis A

2017-04-15

Optimization of a multivariate calibration process has been undertaken for a Visible-Near Infrared (400-1100nm) sensor system, applied in the monitoring of the fermentation process of the cider produced in the Basque Country (Spain). The main parameters that were monitored included alcoholic proof, l-lactic acid content, glucose+fructose and acetic acid content. The multivariate calibration was carried out using a combination of different variable selection techniques and the most suitable pre-processing strategies were selected based on the spectra characteristics obtained by the sensor system. The variable selection techniques studied in this work include Martens Uncertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA). This procedure arises from the need to improve the calibration models prediction ability for cider monitoring. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale.

PubMed

Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T

2018-03-01

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer. Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models. An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R 2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses. It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach. Copyright © 2017 Elsevier B.V. All rights reserved.

[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.

FT-Raman and chemometric tools for rapid determination of quality parameters in milk powder: Classification of samples for the presence of lactose and fraud detection by addition of maltodextrin.

PubMed

Rodrigues Júnior, Paulo Henrique; de Sá Oliveira, Kamila; de Almeida, Carlos Eduardo Rocha; De Oliveira, Luiz Fernando Cappa; Stephani, Rodrigo; Pinto, Michele da Silva; de Carvalho, Antônio Fernandes; Perrone, Ítalo Tuler

2016-04-01

FT-Raman spectroscopy has been explored as a quick screening method to evaluate the presence of lactose and identify milk powder samples adulterated with maltodextrin (2.5-50% w/w). Raman measurements can easily differentiate samples of milk powder, without the need for sample preparation, while traditional quality control methods, including high performance liquid chromatography, are cumbersome and slow. FT-Raman spectra were obtained from samples of whole lactose and low-lactose milk powder, both without and with addition of maltodextrin. Differences were observed between the spectra involved in identifying samples with low lactose content, as well as adulterated samples. Exploratory data analysis using Raman spectroscopy and multivariate analysis was also developed to classify samples with PCA and PLS-DA. The PLS-DA models obtained allowed to correctly classify all samples. These results demonstrate the utility of FT-Raman spectroscopy in combination with chemometrics to infer about the quality of milk powder. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discrimination of edible oils and fats by combination of multivariate pattern recognition and FT-IR spectroscopy: a comparative study between different modeling methods.

PubMed

Javidnia, Katayoun; Parish, Maryam; Karimi, Sadegh; Hemmateenejad, Bahram

2013-03-01

By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm(-1). Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity. Copyright © 2012 Elsevier B.V. All rights reserved.

Multivariate methods for evaluating the efficiency of electrodialytic removal of heavy metals from polluted harbour sediments.

PubMed

Pedersen, Kristine Bondo; Kirkelund, Gunvor M; Ottosen, Lisbeth M; Jensen, Pernille E; Lejon, Tore

2015-01-01

Chemometrics was used to develop a multivariate model based on 46 previously reported electrodialytic remediation experiments (EDR) of five different harbour sediments. The model predicted final concentrations of Cd, Cu, Pb and Zn as a function of current density, remediation time, stirring rate, dry/wet sediment, cell set-up as well as sediment properties. Evaluation of the model showed that remediation time and current density had the highest comparative influence on the clean-up levels. Individual models for each heavy metal showed variance in the variable importance, indicating that the targeted heavy metals were bound to different sediment fractions. Based on the results, a PLS model was used to design five new EDR experiments of a sixth sediment to achieve specified clean-up levels of Cu and Pb. The removal efficiencies were up to 82% for Cu and 87% for Pb and the targeted clean-up levels were met in four out of five experiments. The clean-up levels were better than predicted by the model, which could hence be used for predicting an approximate remediation strategy; the modelling power will however improve with more data included. Copyright © 2014 Elsevier B.V. All rights reserved.

Strategies for multivariate modeling of moisture content in freeze-dried mannitol-containing products by near-infrared spectroscopy.

PubMed

Yip, Wai Lam; Gausemel, Ingvil; Sande, Sverre Arne; Dyrstad, Knut

2012-11-01

Accurate determination of residual moisture content of a freeze-dried (FD) pharmaceutical product is critical for prediction of its quality. Near-infrared (NIR) spectroscopy is a fast and non-invasive method routinely used for quantification of moisture. However, several physicochemical properties of the FD product may interfere with absorption bands related to the water content. A commonly used stabilizer and bulking agent in FD known for variation in physicochemical properties, is mannitol. To minimize this physicochemical interference, different approaches for multivariate correlation between NIR spectra of a FD product containing mannitol and the corresponding moisture content measured by Karl Fischer (KF) titration have been investigated. A novel method, MIPCR (Main and Interactions of Individual Principal Components Regression), was found to have significantly increased predictive ability of moisture content compared to a traditional PLS approach. The philosophy behind the MIPCR is that the interference from a variety of particle and morphology attributes has interactive effects on the water related absorption bands. The transformation of original wavelength variables to orthogonal scores gives a new set of variables (scores) without covariance structure, and the possibility of inclusion of interaction terms in the further modeling. The residual moisture content of the FD product investigated is in the range from 0.7% to 2.6%. The mean errors of cross validated prediction of models developed in the investigated NIR regions were reduced from a range of 24.1-27.6% for traditional PLS method to 15.7-20.5% for the MIPCR method. Improved model quality by application of MIPCR, without the need for inclusion of a large number of calibration samples, might increase the use of NIR in early phase product development, where availability of calibration samples is often limited. Copyright © 2012 Elsevier B.V. All rights reserved.

Real-time Raman spectroscopy for automatic in vivo skin cancer detection: an independent validation.

PubMed

Zhao, Jianhua; Lui, Harvey; Kalia, Sunil; Zeng, Haishan

2015-11-01

In a recent study, we have demonstrated that real-time Raman spectroscopy could be used for skin cancer diagnosis. As a translational study, the objective of this study is to validate previous findings through a completely independent clinical test. In total, 645 confirmed cases were included in the analysis, including a cohort of 518 cases from a previous study, and an independent cohort of 127 new cases. Multi-variant statistical data analyses including principal component with general discriminant analysis (PC-GDA) and partial least squares (PLS) were used separately for lesion classification, which generated similar results. When the previous cohort (n = 518) was used as training and the new cohort (n = 127) was used as testing, the area under the receiver operating characteristic curve (ROC AUC) was found to be 0.889 (95 % CI 0.834-0.944; PLS); when the two cohorts were combined, the ROC AUC was 0.894 (95 % CI 0.870-0.918; PLS) with the narrowest confidence intervals. Both analyses were comparable to the previous findings, where the ROC AUC was 0.896 (95 % CI 0.846-0.946; PLS). The independent study validates that real-time Raman spectroscopy could be used for automatic in vivo skin cancer diagnosis with good accuracy.

Noninvasive Recognition and Biomarkers of Early Allergic Asthma in Cats Using Multivariate Statistical Analysis of NMR Spectra of Exhaled Breath Condensate

PubMed Central

Fulcher, Yan G.; Fotso, Martial; Chang, Chee-Hoon; Rindt, Hans; Reinero, Carol R.

2016-01-01

Asthma is prevalent in children and cats, and needs means of noninvasive diagnosis. We sought to distinguish noninvasively the differences in 53 cats before and soon after induction of allergic asthma, using NMR spectra of exhaled breath condensate (EBC). Statistical pattern recognition was improved considerably by preprocessing the spectra with probabilistic quotient normalization and glog transformation. Classification of the 106 preprocessed spectra by principal component analysis and partial least squares with discriminant analysis (PLS-DA) appears to be impaired by variances unrelated to eosinophilic asthma. By filtering out confounding variances, orthogonal signal correction (OSC) PLS-DA greatly improved the separation of the healthy and early asthmatic states, attaining 94% specificity and 94% sensitivity in predictions. OSC enhancement of multi-level PLS-DA boosted the specificity of the prediction to 100%. OSC-PLS-DA of the normalized spectra suggest the most promising biomarkers of allergic asthma in cats to include increased acetone, metabolite(s) with overlapped NMR peaks near 5.8 ppm, and a hydroxyphenyl-containing metabolite, as well as decreased phthalate. Acetone is elevated in the EBC of 74% of the cats with early asthma. The noninvasive detection of early experimental asthma, biomarkers in EBC, and metabolic perturbation invite further investigation of the diagnostic potential in humans. PMID:27764146

Classification of cassava starch films by physicochemical properties and water vapor permeability quantification by FTIR and PLS.

PubMed

Henrique, C M; Teófilo, R F; Sabino, L; Ferreira, M M C; Cereda, M P

2007-05-01

Cassava starches are widely used in the production of biodegradable films, but their resistance to humidity migration is very low. In this work, commercial cassava starch films were studied and classified according to their physicochemical properties. A nondestructive method for water vapor permeability determination, which combines with infrared spectroscopy and multivariate calibration, is also presented. The following commercial cassava starches were studied: pregelatinized (amidomax 3550), carboxymethylated starch (CMA) of low and high viscosities, and esterified starches. To make the films, 2 different starch concentrations were evaluated, consisting of water suspensions with 3% and 5% starch. The filmogenic solutions were dried and characterized for their thickness, grammage, water vapor permeability, water activity, tensile strength (deformation force), water solubility, and puncture strength (deformation). The minimum thicknesses were 0.5 to 0.6 mm in pregelatinized starch films. The results were treated by means of the following chemometric methods: principal component analysis (PCA) and partial least squares (PLS) regression. PCA analysis on the physicochemical properties of the films showed that the differences in concentration of the dried material (3% and 5% starch) and also in the type of starch modification were mainly related to the following properties: permeability, solubility, and thickness. IR spectra collected in the region of 4000 to 600 cm(-1) were used to build a PLS model with good predictive power for water vapor permeability determination, with mean relative errors of 10.0% for cross-validation and 7.8% for the prediction set.

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.

Advanced modelling, monitoring, and process control of bioconversion systems

NASA Astrophysics Data System (ADS)

Schmitt, Elliott C.

Production of fuels and chemicals from lignocellulosic biomass is an increasingly important area of research and industrialization throughout the world. In order to be competitive with fossil-based fuels and chemicals, maintaining cost-effectiveness is critical. Advanced process control (APC) and optimization methods could significantly reduce operating costs in the biorefining industry. Two reasons APC has previously proven challenging to implement for bioprocesses include: lack of suitable online sensor technology of key system components, and strongly nonlinear first principal models required to predict bioconversion behavior. To overcome these challenges batch fermentations with the acetogen Moorella thermoacetica were monitored with Raman spectroscopy for the conversion of real lignocellulosic hydrolysates and a kinetic model for the conversion of synthetic sugars was developed. Raman spectroscopy was shown to be effective in monitoring the fermentation of sugarcane bagasse and sugarcane straw hydrolysate, where univariate models predicted acetate concentrations with a root mean square error of prediction (RMSEP) of 1.9 and 1.0 g L-1 for bagasse and straw, respectively. Multivariate partial least squares (PLS) models were employed to predict acetate, xylose, glucose, and total sugar concentrations for both hydrolysate fermentations. The PLS models were more robust than univariate models, and yielded a percent error of approximately 5% for both sugarcane bagasse and sugarcane straw. In addition, a screening technique was discussed for improving Raman spectra of hydrolysate samples prior to collecting fermentation data. Furthermore, a mechanistic model was developed to predict batch fermentation of synthetic glucose, xylose, and a mixture of the two sugars to acetate. The models accurately described the bioconversion process with an RMSEP of approximately 1 g L-1 for each model and provided insights into how kinetic parameters changed during dual substrate fermentation with diauxic growth. Model predictive control (MPC), an advanced process control strategy, is capable of utilizing nonlinear models and sensor feedback to provide optimal input while ensuring critical process constraints are met. Using the microorganism Saccharomyces cerevisiae, a commonly used microorganism for biofuel production, and work performed with M. thermoacetica, a nonlinear MPC was implemented on a continuous membrane cell-recycle bioreactor (MCRB) for the conversion of glucose to ethanol. The dilution rate was used to control the ethanol productivity of the system will maintaining total substrate conversion above the constraint of 98%. PLS multivariate models for glucose (RMSEP 1.5 g L-1) and ethanol (RMSEP 0.4 g L-1) were robust in predicting concentrations and a mechanistic kinetic model built accurately predicted continuous fermentation behavior. A setpoint trajectory, ranging from 2 - 4.5 g L-1 h-1 for productivity was closely tracked by the fermentation system using Raman measurements and an extended Kalman filter to estimate biomass concentrations. Overall, this work was able to demonstrate an effective approach for real-time monitoring and control of a complex fermentation system.

Simultaneous quantitative analysis of olmesartan, amlodipine and hydrochlorothiazide in their combined dosage form utilizing classical and alternating least squares based chemometric methods.

PubMed

Darwish, Hany W; Bakheit, Ahmed H; Abdelhameed, Ali S

2016-03-01

Simultaneous spectrophotometric analysis of a multi-component dosage form of olmesartan, amlodipine and hydrochlorothiazide used for the treatment of hypertension has been carried out using various chemometric methods. Multivariate calibration methods include classical least squares (CLS) executed by net analyte processing (NAP-CLS), orthogonal signal correction (OSC-CLS) and direct orthogonal signal correction (DOSC-CLS) in addition to multivariate curve resolution-alternating least squares (MCR-ALS). Results demonstrated the efficiency of the proposed methods as quantitative tools of analysis as well as their qualitative capability. The three analytes were determined precisely using the aforementioned methods in an external data set and in a dosage form after optimization of experimental conditions. Finally, the efficiency of the models was validated via comparison with the partial least squares (PLS) method in terms of accuracy and precision.

Simultaneous determination of Nifuroxazide and Drotaverine hydrochloride in pharmaceutical preparations by bivariate and multivariate spectral analysis

NASA Astrophysics Data System (ADS)

Metwally, Fadia H.

2008-02-01

The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12 μg ml -1 of NIF and 2-8 μg ml -1 of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.

[Quality evaluation of American ginseng using UPLC coupled with multivariate analysis].

PubMed

Tang, Yan; Yan, Shu-Mo; Wang, Jing-Jing; Yuan, Yuan; Yang, Bin

2016-05-01

An ultra performance liquid chromatography (UPLC)method combined with multivariate data analysis was developed to evaluate the quality of American ginseng by simultaneously determining the concentrations of six ginsenosides (Rg₁, Re, Rb₁, Rc, Ro and Rd)in the samples. For UPLC, acetonitrile with 0.01% formic acid and water with 0.01% formic acid were used as the mobile phase with gradient elution. Under the established chromatographic conditions, the six ginsenosides could be well separated and the results of linearity, stability, precision, repeatability, and recovery rate all reached the requirement of quantification analysis, respectively. The total contents of Rg₁, Re, and Rb₁ in 57 samples all reached the requirement of the 2015 edition of Chinese Pharmacopoeia. At the same time, the experimental data were analyzed by principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The crude drugs and the decoction pieces can be discriminated by a PCA method and the samples with different age can be distinguished by a PLS-DA method. Copyright© by the Chinese Pharmaceutical Association.

Analysis of Lard in Lipstick Formulation Using FTIR Spectroscopy and Multivariate Calibration: A Comparison of Three Extraction Methods.

PubMed

Waskitho, Dri; Lukitaningsih, Endang; Sudjadi; Rohman, Abdul

2016-01-01

Analysis of lard extracted from lipstick formulation containing castor oil has been performed using FTIR spectroscopic method combined with multivariate calibration. Three different extraction methods were compared, namely saponification method followed by liquid/liquid extraction with hexane/dichlorometane/ethanol/water, saponification method followed by liquid/liquid extraction with dichloromethane/ethanol/water, and Bligh & Dyer method using chloroform/methanol/water as extracting solvent. Qualitative and quantitative analysis of lard were performed using principle component (PCA) and partial least square (PLS) analysis, respectively. The results showed that, in all samples prepared by the three extraction methods, PCA was capable of identifying lard at wavelength region of 1200-800 cm -1 with the best result was obtained by Bligh & Dyer method. Furthermore, PLS analysis at the same wavelength region used for qualification showed that Bligh and Dyer was the most suitable extraction method with the highest determination coefficient (R 2 ) and the lowest root mean square error of calibration (RMSEC) as well as root mean square error of prediction (RMSEP) values.

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.

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.

[On-site evaluation of raw milk qualities by portable Vis/NIR transmittance technique].

PubMed

Wang, Jia-Hua; Zhang, Xiao-Wei; Wang, Jun; Han, Dong-Hai

2014-10-01

To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with che- mometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a porta- ble Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) meth- ods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also cal- ibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk.

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.

Comparison of univariate and multivariate models for prediction of major and minor elements from laser-induced breakdown spectra with and without masking

NASA Astrophysics Data System (ADS)

Dyar, M. Darby; Fassett, Caleb I.; Giguere, Stephen; Lepore, Kate; Byrne, Sarah; Boucher, Thomas; Carey, CJ; Mahadevan, Sridhar

2016-09-01

This study uses 1356 spectra from 452 geologically-diverse samples, the largest suite of LIBS rock spectra ever assembled, to compare the accuracy of elemental predictions in models that use only spectral regions thought to contain peaks arising from the element of interest versus those that use information in the entire spectrum. Results show that for the elements Si, Al, Ti, Fe, Mg, Ca, Na, K, Ni, Mn, Cr, Co, and Zn, univariate predictions based on single emission lines are by far the least accurate, no matter how carefully the region of channels/wavelengths is chosen and despite the prominence of the selected emission lines. An automated iterative algorithm was developed to sweep through all 5485 channels of data and select the single region that produces the optimal prediction accuracy for each element using univariate analysis. For the eight major elements, use of this technique results in a 35% improvement in prediction accuracy; for minors, the improvement is 13%. The best wavelength region choice for any given univariate analysis is likely to be an inherent property of the specific training set that cannot be generalized. In comparison, multivariate analysis using partial least-squares (PLS) almost universally outperforms univariate analysis. PLS using all the same wavelength regions from the univariate analysis produces results that improve in accuracy by 63% for major elements and 3% for minor element. This difference is likely a reflection of signal to noise ratios, which are far better for major elements than for minor elements, and likely limit their prediction accuracy by any technique. We also compare predictions using specific wavelength ranges for each element against those employing all channels. Masking out channels to focus on emission lines from a specific element that occurs decreases prediction accuracy for major elements but is useful for minor elements with low signals and proportionally much higher noise; use of PLS rather than univariate analysis is still recommended. Finally, we tested the generalizability of our results by analyzing a second data set from a different instrument. Overall prediction accuracies for the mixed data sets are higher than for either set alone for all major and minor elements except Ni, Cr, and Co, where results are roughly comparable.

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

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.

Effects of Flavor and Texture on the Sensory Perception of Gouda-Type Cheese Varieties during Ripening Using Multivariate Analysis.

PubMed

Shiota, Makoto; Iwasawa, Ai; Suzuki-Iwashima, Ai; Iida, Fumiko

2015-12-01

The impact of flavor composition, texture, and other factors on desirability of different commercial sources of Gouda-type cheese using multivariate analyses on the basis of sensory and instrumental analyses were investigated. Volatile aroma compounds were measured using headspace solid-phase microextraction gas chromatography/mass spectrometry (GC/MS) and steam distillation extraction (SDE)-GC/MS, and fatty acid composition, low-molecular-weight compounds, including amino acids, and organic acids, as well pH, texture, and color were measured to determine their relationship with sensory perception. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to discriminate between 2 different ripening periods in 7 sample sets, revealing that ethanol, ethyl acetate, hexanoic acid, and octanoic acid increased with increasing sensory attribute scores for sweetness, fruity, and sulfurous. A partial least squares (PLS) regression model was constructed to predict the desirability of cheese using these parameters. We showed that texture and buttery flavors are important factors affecting the desirability of Gouda-type cheeses for Japanese consumers using these multivariate analyses. © 2015 Institute of Food Technologists®

[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.

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.

[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.

Determination of benzo[a]pyrene in cigarette mainstream smoke by using mid-infrared spectroscopy associated with a novel chemometric algorithm.

PubMed

Zhang, Yan; Zou, Hong-Yan; Shi, Pei; Yang, Qin; Tang, Li-Juan; Jiang, Jian-Hui; Wu, Hai-Long; Yu, Ru-Qin

2016-01-01

Determination of benzo[a]pyrene (BaP) in cigarette smoke can be very important for the tobacco quality control and the assessment of its harm to human health. In this study, mid-infrared spectroscopy (MIR) coupled to chemometric algorithm (DPSO-WPT-PLS), which was based on the wavelet packet transform (WPT), discrete particle swarm optimization algorithm (DPSO) and partial least squares regression (PLS), was used to quantify harmful ingredient benzo[a]pyrene in the cigarette mainstream smoke with promising result. Furthermore, the proposed method provided better performance compared to several other chemometric models, i.e., PLS, radial basis function-based PLS (RBF-PLS), PLS with stepwise regression variable selection (Stepwise-PLS) as well as WPT-PLS with informative wavelet coefficients selected by correlation coefficient test (rtest-WPT-PLS). It can be expected that the proposed strategy could become a new effective, rapid quantitative analysis technique in analyzing the harmful ingredient BaP in cigarette mainstream smoke. Copyright © 2015 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.

Non-targeted analyses of animal plasma: betaine and choline represent the nutritional and metabolic status.

PubMed

Katayama, K; Sato, T; Arai, T; Amao, H; Ohta, Y; Ozawa, T; Kenyon, P R; Hickson, R E; Tazaki, H

2013-02-01

Simple liquid chromatography-mass spectrometry (LC-MS) was applied to non-targeted metabolic analyses to discover new metabolic markers in animal plasma. Principle component analysis (PCA) and partial least squares-discriminate analysis (PLS-DA) were used to analyse LC-MS multivariate data. PCA clearly generated two separate clusters for artificially induced diabetic mice and healthy control mice. PLS-DA of time-course changes in plasma metabolites of chicks after feeding generated three clusters (pre- and immediately after feeding, 0.5-3 h after feeding and 4 h after feeding). Two separate clusters were also generated for plasma metabolites of pregnant Angus heifers with differing live-weight change profiles (gaining or losing). The accompanying PLS-DA loading plot detailed the metabolites that contribute the most to the cluster separation. In each case, the same highly hydrophilic metabolite was strongly correlated to the group separation. The metabolite was identified as betaine by LC-MS/MS. This result indicates that betaine and its metabolic precursor, choline, may be useful biomarkers to evaluate the nutritional and metabolic status of animals. © 2011 Blackwell Verlag GmbH.

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.

Rapid classification of pharmaceutical ingredients with Raman spectroscopy using compressive detection strategy with PLS-DA multivariate filters.

PubMed

Cebeci Maltaş, Derya; Kwok, Kaho; Wang, Ping; Taylor, Lynne S; Ben-Amotz, Dor

2013-06-01

Identifying pharmaceutical ingredients is a routine procedure required during industrial manufacturing. Here we show that a recently developed Raman compressive detection strategy can be employed to classify various widely used pharmaceutical materials using a hybrid supervised/unsupervised strategy in which only two ingredients are used for training and yet six other ingredients can also be distinguished. More specifically, our liquid crystal spatial light modulator (LC-SLM) based compressive detection instrument is trained using only the active ingredient, tadalafil, and the excipient, lactose, but is tested using these and various other excipients; microcrystalline cellulose, magnesium stearate, titanium (IV) oxide, talc, sodium lauryl sulfate and hydroxypropyl cellulose. Partial least squares discriminant analysis (PLS-DA) is used to generate the compressive detection filters necessary for fast chemical classification. Although the filters used in this study are trained on only lactose and tadalafil, we show that all the pharmaceutical ingredients mentioned above can be differentiated and classified using PLS-DA compressive detection filters with an accumulation time of 10ms per filter. Copyright © 2013 Elsevier B.V. All rights reserved.

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano.

PubMed

Abbatangelo, Marco; Núñez-Carmona, Estefanía; Sberveglieri, Veronica; Zappa, Dario; Comini, Elisabetta; Sberveglieri, Giorgio

2018-05-18

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.

The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance.

PubMed

Heidema, A Geert; Thissen, Uwe; Boer, Jolanda M A; Bouwman, Freek G; Feskens, Edith J M; Mariman, Edwin C M

2009-06-01

In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

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).

Multicomponent kinetic spectrophotometric determination of pefloxacin and norfloxacin in pharmaceutical preparations and human plasma samples with the aid of chemometrics

NASA Astrophysics Data System (ADS)

Ni, Yongnian; Wang, Yong; Kokot, Serge

2008-10-01

A spectrophotometric method for the simultaneous determination of the important pharmaceuticals, pefloxacin and its structurally similar metabolite, norfloxacin, is described for the first time. The analysis is based on the monitoring of a kinetic spectrophotometric reaction of the two analytes with potassium permanganate as the oxidant. The measurement of the reaction process followed the absorbance decrease of potassium permanganate at 526 nm, and the accompanying increase of the product, potassium manganate, at 608 nm. It was essential to use multivariate calibrations to overcome severe spectral overlaps and similarities in reaction kinetics. Calibration curves for the individual analytes showed linear relationships over the concentration ranges of 1.0-11.5 mg L -1 at 526 and 608 nm for pefloxacin, and 0.15-1.8 mg L -1 at 526 and 608 nm for norfloxacin. Various multivariate calibration models were applied, at the two analytical wavelengths, for the simultaneous prediction of the two analytes including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), radial basis function-artificial neural network (RBF-ANN) and principal component-radial basis function-artificial neural network (PC-RBF-ANN). PLS and PC-RBF-ANN calibrations with the data collected at 526 nm, were the preferred methods—%RPE T ˜ 5, and LODs for pefloxacin and norfloxacin of 0.36 and 0.06 mg L -1, respectively. Then, the proposed method was applied successfully for the simultaneous determination of pefloxacin and norfloxacin present in pharmaceutical and human plasma samples. The results compared well with those from the alternative analysis by HPLC.

Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study.

PubMed

Hicks, Lucy C; Huang, Juzheng; Kumar, Sacheen; Powles, Sam T; Orchard, Timothy R; Hanna, George B; Williams, Horace R T

2015-09-01

Distinguishing between the inflammatory bowel diseases [IBD], Crohn's disease [CD] and ulcerative colitis [UC], is important for determining management and prognosis. Selected ion flow tube mass spectrometry [SIFT-MS] may be used to analyse volatile organic compounds [VOCs] in exhaled breath: these may be altered in disease states, and distinguishing breath VOC profiles can be identified. The aim of this pilot study was to identify, quantify, and analyse VOCs present in the breath of IBD patients and controls, potentially providing insights into disease pathogenesis and complementing current diagnostic algorithms. SIFT-MS breath profiling of 56 individuals [20 UC, 18 CD, and 18 healthy controls] was undertaken. Multivariate analysis included principal components analysis and partial least squares discriminant analysis with orthogonal signal correction [OSC-PLS-DA]. Receiver operating characteristic [ROC] analysis was performed for each comparative analysis using statistically significant VOCs. OSC-PLS-DA modelling was able to distinguish both CD and UC from healthy controls and from one other with good sensitivity and specificity. ROC analysis using combinations of statistically significant VOCs [dimethyl sulphide, hydrogen sulphide, hydrogen cyanide, ammonia, butanal, and nonanal] gave integrated areas under the curve of 0.86 [CD vs healthy controls], 0.74 [UC vs healthy controls], and 0.83 [CD vs UC]. Exhaled breath VOC profiling was able to distinguish IBD patients from controls, as well as to separate UC from CD, using both multivariate and univariate statistical techniques. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Application of Multivariable Analysis and FTIR-ATR Spectroscopy to the Prediction of Properties in Campeche Honey

PubMed Central

Pat, Lucio; Ali, Bassam; Guerrero, Armando; Córdova, Atl V.; Garduza, José P.

2016-01-01

Attenuated total reflectance-Fourier transform infrared spectrometry and chemometrics model was used for determination of physicochemical properties (pH, redox potential, free acidity, electrical conductivity, moisture, total soluble solids (TSS), ash, and HMF) in honey samples. The reference values of 189 honey samples of different botanical origin were determined using Association Official Analytical Chemists, (AOAC), 1990; Codex Alimentarius, 2001, International Honey Commission, 2002, methods. Multivariate calibration models were built using partial least squares (PLS) for the measurands studied. The developed models were validated using cross-validation and external validation; several statistical parameters were obtained to determine the robustness of the calibration models: (PCs) optimum number of components principal, (SECV) standard error of cross-validation, (R 2 cal) coefficient of determination of cross-validation, (SEP) standard error of validation, and (R 2 val) coefficient of determination for external validation and coefficient of variation (CV). The prediction accuracy for pH, redox potential, electrical conductivity, moisture, TSS, and ash was good, while for free acidity and HMF it was poor. The results demonstrate that attenuated total reflectance-Fourier transform infrared spectrometry is a valuable, rapid, and nondestructive tool for the quantification of physicochemical properties of honey. PMID:28070445

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.

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.

Dissolution testing of isoniazid, rifampicin, pyrazinamide and ethambutol tablets using near-infrared spectroscopy (NIRS) and multivariate calibration.

PubMed

de Oliveira Neves, Ana Carolina; Soares, Gustavo Mesquita; de Morais, Stéphanie Cavalcante; da Costa, Fernanda Saadna Lopes; Porto, Dayanne Lopes; de Lima, Kássio Michell Gomes

2012-01-05

This work utilized the near-infrared spectroscopy (NIRS) and multivariate calibration to measure the percentage drug dissolution of four active pharmaceutical ingredients (APIs) (isoniazid, rifampicin, pyrazinamide and ethambutol) in finished pharmaceutical products produced in the Federal University of Rio Grande do Norte (Brazil). The conventional analytical method employed in quality control tests of the dissolution by the pharmaceutical industry is high-performance liquid chromatography (HPLC). The NIRS is a reliable method that offers important advantages for the large-scale production of tablets and for non-destructive analysis. NIR spectra of 38 samples (in triplicate) were measured using a Bomen FT-NIR 160 MB in the range 1100-2500nm. Each spectrum was the average of 50 scans obtained in the diffuse reflectance mode. The dissolution test, which was initially carried out in 900mL of 0.1N hydrochloric acid at 37±0.5°C, was used to determine the percentage a drug that dissolved from each tablet measured at the same time interval (45min) at pH 6.8. The measurement of the four API was performed by HPLC (Shimadzu, Japan) in the gradiente mode. The influence of various spectral pretreatments (Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and Savitzky-Golay derivatives) and multivariate analysis using the partial least squares (PLS) regression algorithm was calculated by the Unscrambler 9.8 (Camo) software. The correlation coefficient (R(2)) for the HPLC determination versus predicted values (NIRS) ranged from 0.88 to 0.98. The root-mean-square error of prediction (RMSEP) obtained from PLS models were 9.99%, 8.63%, 8.57% and 9.97% for isoniazid, rifampicin, ethambutol and pyrazinamide, respectively, indicating that the NIR method is an effective and non-destructive tool for measurement of drug dissolution from tablets. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Comparison of partial least squares and lasso regression techniques as applied to laser-induced breakdown spectroscopy of geological samples

NASA Astrophysics Data System (ADS)

Dyar, M. D.; Carmosino, M. L.; Breves, E. A.; Ozanne, M. V.; Clegg, S. M.; Wiens, R. C.

2012-04-01

A remote laser-induced breakdown spectrometer (LIBS) designed to simulate the ChemCam instrument on the Mars Science Laboratory Rover Curiosity was used to probe 100 geologic samples at a 9-m standoff distance. ChemCam consists of an integrated remote LIBS instrument that will probe samples up to 7 m from the mast of the rover and a remote micro-imager (RMI) that will record context images. The elemental compositions of 100 igneous and highly-metamorphosed rocks are determined with LIBS using three variations of multivariate analysis, with a goal of improving the analytical accuracy. Two forms of partial least squares (PLS) regression are employed with finely-tuned parameters: PLS-1 regresses a single response variable (elemental concentration) against the observation variables (spectra, or intensity at each of 6144 spectrometer channels), while PLS-2 simultaneously regresses multiple response variables (concentrations of the ten major elements in rocks) against the observation predictor variables, taking advantage of natural correlations between elements. Those results are contrasted with those from the multivariate regression technique of the least absolute shrinkage and selection operator (lasso), which is a penalized shrunken regression method that selects the specific channels for each element that explain the most variance in the concentration of that element. To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units. Results demonstrate that the lasso, PLS-1, and PLS-2 all yield comparable results in terms of accuracy for this dataset. However, the interpretability of these methods differs greatly in terms of fundamental understanding of LIBS emissions. PLS techniques generate principal components, linear combinations of intensities at any number of spectrometer channels, which explain as much variance in the response variables as possible while avoiding multicollinearity between principal components. When the selected number of principal components is projected back into the original feature space of the spectra, 6144 correlation coefficients are generated, a small fraction of which are mathematically significant to the regression. In contrast, the lasso models require only a small number (< 24) of non-zero correlation coefficients (β values) to determine the concentration of each of the ten major elements. Causality between the positively-correlated emission lines chosen by the lasso and the elemental concentration was examined. In general, the higher the lasso coefficient (β), the greater the likelihood that the selected line results from an emission of that element. Emission lines with negative β values should arise from elements that are anti-correlated with the element being predicted. For elements except Fe, Al, Ti, and P, the lasso-selected wavelength with the highest β value corresponds to the element being predicted, e.g. 559.8 nm for neutral Ca. However, the specific lines chosen by the lasso with positive β values are not always those from the element being predicted. Other wavelengths and the elements that most strongly correlate with them to predict concentration are obviously related to known geochemical correlations or close overlap of emission lines, while others must result from matrix effects. Use of the lasso technique thus directly informs our understanding of the underlying physical processes that give rise to LIBS emissions by determining which lines can best represent concentration, and which lines from other elements are causing matrix effects.

Partial least squares density modeling (PLS-DM) - a new class-modeling strategy applied to the authentication of olives in brine by near-infrared spectroscopy.

PubMed

Oliveri, Paolo; López, M Isabel; Casolino, M Chiara; Ruisánchez, Itziar; Callao, M Pilar; Medini, Luca; Lanteri, Silvia

2014-12-03

A new class-modeling method, referred to as partial least squares density modeling (PLS-DM), is presented. The method is based on partial least squares (PLS), using a distance-based sample density measurement as the response variable. Potential function probability density is subsequently calculated on PLS scores and used, jointly with residual Q statistics, to develop efficient class models. The influence of adjustable model parameters on the resulting performances has been critically studied by means of cross-validation and application of the Pareto optimality criterion. The method has been applied to verify the authenticity of olives in brine from cultivar Taggiasca, based on near-infrared (NIR) spectra recorded on homogenized solid samples. Two independent test sets were used for model validation. The final optimal model was characterized by high efficiency and equilibrate balance between sensitivity and specificity values, if compared with those obtained by application of well-established class-modeling methods, such as soft independent modeling of class analogy (SIMCA) and unequal dispersed classes (UNEQ). Copyright © 2014 Elsevier B.V. All rights reserved.

Multivariate modelling and personality organization: a comparative study of the Defense Mechanism Test and linguistic expressions.

PubMed

Sundbom, E; Jeanneau, M

1996-03-01

The main aim of the study is to establish an empirical connection between perceptual defences as measured by the Defense Mechanism Test (DMT)--a projective percept-genetic method--and manifest linguistic expressions based on word pattern analyses. The subjects were 25 psychiatric patients with the diagnoses neurotic personality organization (NPO), borderline personality organization (BPO) and psychotic personality organization (PPO) in accordance with Kernberg's theory. A set of 130 DMT variables and 40 linguistic variables were analyzed by means of partial least squares (PLS) discriminant analysis separately and then pooled together. The overall hypothesis was that it would be possible to define the personality organization of the patients in terms of an amalgam of perceptual defences and word patterns, and that these two kinds of data would confirm each other. The result of the combined PLS analysis revealed a very good separation between the diagnostic groups as measured by the pooled variable sets. Among other things, it was shown that NPO patients are principally characterized by linguistic variables, whereas BPO and PPO patients are better defined by perceptual defences as measured by the DMT method.

The employment of FTIR spectroscopy in combination with chemometrics for analysis of rat meat in meatball formulation.

PubMed

Rahmania, Halida; Sudjadi; Rohman, Abdul

2015-02-01

For Indonesian community, meatball is one of the favorite meat food products. In order to gain economical benefits, the substitution of beef meat with rat meat can happen due to the different prices between rat meat and beef. In this present research, the feasibility of FTIR spectroscopy in combination with multivariate calibration of partial least square (PLS) was used for the quantitative analysis of rat meat in the binary mixture of beef in meatball formulation. Meanwhile, the chemometrics of principal component analysis (PCA) was used for the classification between rat meat and beef meatballs. Some frequency regions in mid infrared region were optimized, and finally, the frequency region of 750-1000 cm(-1) was selected during PLS and PCA modeling.For quantitative analysis, the relationship between actual values (x-axis) and FTIR predicted values (y-axis) of rat meat is described by the equation of y= 0.9417x+ 2.8410 with coefficient of determination (R2) of 0.993, and root mean square error of calibration (RMSEC) of 1.79%. Furthermore, PCA was successfully used for the classification of rat meat meatball and beef meatball.

Experiences of stigma and discrimination of people with schizophrenia in India

PubMed Central

Koschorke, Mirja; Padmavati, R.; Kumar, Shuba; Cohen, Alex; Weiss, Helen A.; Chatterjee, Sudipto; Pereira, Jesina; Naik, Smita; John, Sujit; Dabholkar, Hamid; Balaji, Madhumitha; Chavan, Animish; Varghese, Mathew; Thara, R.; Thornicroft, Graham; Patel, Vikram

2014-01-01

Stigma contributes greatly to the burden of schizophrenia and is a major obstacle to recovery, yet, little is known about the subjective experiences of those directly affected in low and middle income countries. This paper aims to describe the experiences of stigma and discrimination of people living with schizophrenia (PLS) in three sites in India and to identify factors influencing negative discrimination. The study used mixed methods and was nested in a randomised controlled trial of community care for schizophrenia. Between November 2009 and October 2010, data on four aspects of stigma experienced by PLS and several clinical variables were collected from 282 PLS and 282 caregivers and analysed using multivariate regression. In addition, in-depth-interviews with PLS and caregivers (36 each) were carried out and analysed using thematic analysis. Quantitative findings indicate that experiences of negative discrimination were reported less commonly (42%) than more internalised forms of stigma experience such as a sense of alienation (79%) and significantly less often than in studies carried out elsewhere. Experiences of negative discrimination were independently predicted by higher levels of positive symptoms of schizophrenia, lower levels of negative symptoms of schizophrenia, higher caregiver knowledge about symptomatology, lower PLS age and not having a source of drinking water in the home. Qualitative findings illustrate the major impact of stigma on ‘what matters most’ in the lives of PLS and highlight three key domains influencing the themes of 'negative reactions' and ‘negative views and feelings about the self’, i.e., ‘others finding out’, ‘behaviours and manifestations of the illness’ and ‘reduced ability to meet role expectations’. Findings have implications for conceptualising and measuring stigma and add to the rationale for enhancing psycho-social interventions to support those facing discrimination. Findings also highlight the importance of addressing public stigma and achieving higher level social and political structural change. PMID:25462616

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.

Overlap in the functional neural systems involved in semantic and episodic memory retrieval.

PubMed

Rajah, M N; McIntosh, A R

2005-03-01

Neuroimaging and neuropsychological data suggest that episodic and semantic memory may be mediated by distinct neural systems. However, an alternative perspective is that episodic and semantic memory represent different modes of processing within a single declarative memory system. To examine whether the multiple or the unitary system view better represents the data we conducted a network analysis using multivariate partial least squares (PLS ) activation analysis followed by covariance structural equation modeling (SEM) of positron emission tomography data obtained while healthy adults performed episodic and semantic verbal retrieval tasks. It is argued that if performance of episodic and semantic retrieval tasks are mediated by different memory systems, then there should differences in both regional activations and interregional correlations related to each type of retrieval task, respectively. The PLS results identified brain regions that were differentially active during episodic retrieval versus semantic retrieval. Regions that showed maximal differences in regional activity between episodic retrieval tasks were used to construct separate functional models for episodic and semantic retrieval. Omnibus tests of these functional models failed to find a significant difference across tasks for both functional models. The pattern of path coefficients for the episodic retrieval model were not different across tasks, nor were the path coefficients for the semantic retrieval model. The SEM results suggest that the same memory network/system was engaged across tasks, given the similarities in path coefficients. Therefore, activation differences between episodic and semantic retrieval may ref lect variation along a continuum of processing during task performance within the context of a single memory system.

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.

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.

Biodiesel content determination in diesel fuel blends using near infrared (NIR) spectroscopy and support vector machines (SVM).

PubMed

Alves, Julio Cesar L; Poppi, Ronei J

2013-01-30

This work verifies the potential of support vector machine (SVM) algorithm applied to near infrared (NIR) spectroscopy data to develop multivariate calibration models for determination of biodiesel content in diesel fuel blends that are more effective and appropriate for analytical determinations of this type of fuel nowadays, providing the usual extended analytical range with required accuracy. Considering the difficulty to develop suitable models for this type of determination in an extended analytical range and that, in practice, biodiesel/diesel fuel blends are nowadays most often used between 0 and 30% (v/v) of biodiesel content, a calibration model is suggested for the range 0-35% (v/v) of biodiesel in diesel blends. The possibility of using a calibration model for the range 0-100% (v/v) of biodiesel in diesel fuel blends was also investigated and the difficulty in obtaining adequate results for this full analytical range is discussed. The SVM models are compared with those obtained with PLS models. The best result was obtained by the SVM model using the spectral region 4400-4600 cm(-1) providing the RMSEP value of 0.11% in 0-35% biodiesel content calibration model. This model provides the determination of biodiesel content in agreement with the accuracy required by ABNT NBR and ASTM reference methods and without interference due to the presence of vegetable oil in the mixture. The best SVM model fit performance for the relationship studied is also verified by providing similar prediction results with the use of 4400-6200 cm(-1) spectral range while the PLS results are much worse over this spectral region. Copyright © 2012 Elsevier B.V. All rights reserved.

Stability indicating methods for the analysis of cefprozil in the presence of its alkaline induced degradation product

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2016-04-01

Three simple, specific, accurate and precise spectrophotometric methods were developed for the determination of cefprozil (CZ) in the presence of its alkaline induced degradation product (DCZ). The first method was the bivariate method, while the two other multivariate methods were partial least squares (PLS) and spectral residual augmented classical least squares (SRACLS). The multivariate methods were applied with and without variable selection procedure (genetic algorithm GA). These methods were tested by analyzing laboratory prepared mixtures of the above drug with its alkaline induced degradation product and they were applied to its commercial pharmaceutical products.

Application of multivariate chemometric techniques for simultaneous determination of five parameters of cottonseed oil by single bounce attenuated total reflectance Fourier transform infrared spectroscopy.

PubMed

Talpur, M Younis; Kara, Huseyin; Sherazi, S T H; Ayyildiz, H Filiz; Topkafa, Mustafa; Arslan, Fatma Nur; Naz, Saba; Durmaz, Fatih; Sirajuddin

2014-11-01

Single bounce attenuated total reflectance (SB-ATR) Fourier transform infrared (FTIR) spectroscopy in conjunction with chemometrics was used for accurate determination of free fatty acid (FFA), peroxide value (PV), iodine value (IV), conjugated diene (CD) and conjugated triene (CT) of cottonseed oil (CSO) during potato chips frying. Partial least square (PLS), stepwise multiple linear regression (SMLR), principal component regression (PCR) and simple Beer׳s law (SBL) were applied to develop the calibrations for simultaneous evaluation of five stated parameters of cottonseed oil (CSO) during frying of French frozen potato chips at 170°C. Good regression coefficients (R(2)) were achieved for FFA, PV, IV, CD and CT with value of >0.992 by PLS, SMLR, PCR, and SBL. Root mean square error of prediction (RMSEP) was found to be less than 1.95% for all determinations. Result of the study indicated that SB-ATR FTIR in combination with multivariate chemometrics could be used for accurate and simultaneous determination of different parameters during the frying process without using any toxic organic solvent. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Determination of Rare Earth Elements in Geological Samples Using Laser-Induced Breakdown Spectroscopy (LIBS).

PubMed

Bhatt, Chet R; Jain, Jinesh C; Goueguel, Christian L; McIntyre, Dustin L; Singh, Jagdish P

2018-01-01

Laser-induced breakdown spectroscopy (LIBS) was used to detect rare earth elements (REEs) in natural geological samples. Low and high intensity emission lines of Ce, La, Nd, Y, Pr, Sm, Eu, Gd, and Dy were identified in the spectra recorded from the samples to claim the presence of these REEs. Multivariate analysis was executed by developing partial least squares regression (PLS-R) models for the quantification of Ce, La, and Nd. Analysis of unknown samples indicated that the prediction results of these samples were found comparable to those obtained by inductively coupled plasma mass spectrometry analysis. Data support that LIBS has potential to quantify REEs in geological minerals/ores.

Analysis of spreadable cheese by Raman spectroscopy and chemometric tools.

PubMed

Oliveira, Kamila de Sá; Callegaro, Layce de Souza; Stephani, Rodrigo; Almeida, Mariana Ramos; de Oliveira, Luiz Fernando Cappa

2016-03-01

In this work, FT-Raman spectroscopy was explored to evaluate spreadable cheese samples. A partial least squares discriminant analysis was employed to identify the spreadable cheese samples containing starch. To build the models, two types of samples were used: commercial samples and samples manufactured in local industries. The method of supervised classification PLS-DA was employed to classify the samples as adulterated or without starch. Multivariate regression was performed using the partial least squares method to quantify the starch in the spreadable cheese. The limit of detection obtained for the model was 0.34% (w/w) and the limit of quantification was 1.14% (w/w). The reliability of the models was evaluated by determining the confidence interval, which was calculated using the bootstrap re-sampling technique. The results show that the classification models can be used to complement classical analysis and as screening methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Study of cuttings identification using laser-induced breakdown spectroscopy].

PubMed

Tian, Ye; Wang, Zhen-nan; Hou, Hua-ming; Zhai, Xiao-wei; Ci, Xing-hua; Zheng, Rong-er

2012-08-01

Cutting identification is one of the most important links in the course of cutting logging which is very significant in the process of oil drilling. In the present paper, LIBS was used for identification of four kinds of cutting samples coming from logging field, and then multivariate analysis was used in data processing. The whole spectra model and the feature model were built for cuttings identification using PLS-DA method. The accuracy of the whole spectra model was 88.3%, a little more than the feature model with an accuracy of 86.7%. While in the aspect of data size, the variables were decreased from 24,041 to 27 by feature extraction, which increased the efficiency of data processing observably. The obtained results demonstrate that LIBS combined with chemometrics method could be developed as a rapid and valid approach to cutting identification and has great potential to be used in logging field.

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

Sui, Jing; Adali, Tülay; Li, Yi-Ou; Yang, Honghui; Calhoun, Vince D.

2010-03-01

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.

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.

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.

Crystalline phase transition of ezetimibe in final product, after packing, promoted by the humidity of excipients: Monitoring and quantification by Raman spectroscopy.

PubMed

Farias, Marco Antônio Dos Santos; Soares, Frederico Luis Felipe; Carneiro, Renato Lajarim

2016-03-20

Ezetimibe (EZT), in its anhydrous form, is a drug used for cholesterol and lipids reduction in blood plasma. The presence of EZT monohydrate in commercial tablets can change the solubility rate of the API, decreasing its activity. The objective of this work was to verify if the humidity present in the excipients could promote the phase transition from EZT anhydrous to hydrate. Initially the stability of the pure anhydrous form was monitored by Raman, at room temperature (23°C) and relative humidity (75%). The MCR-ALS method showed that almost all EZT changed to hydrated form in 30 min. Then tablets of ezetimibe in the presence of its excipients were prepared and vacuum packed using a polyethylene film. Such tablet was monitored by Raman spectroscopy for 24h in order to quantify the mixture of the crystalline forms. A multivariate calibration model using Raman spectroscopy and Partial Least Square (PLS) regression was built, with validation and cross validation errors around 0.6% (wt/wt), for both crystalline forms, and R(2) higher than 0.96. The PLS model was used to quantify the crystalline mixture of ezetimibe in the monitored tablet, after 24h more than 70% of ezetimibe changed to the hydrated form. Copyright © 2016 Elsevier B.V. All rights reserved.

PGI chicory (Cichorium intybus L.) traceability by means of HRMAS-NMR spectroscopy: a preliminary study.

PubMed

Ritota, Mena; Casciani, Lorena; Valentini, Massimiliano

2013-05-01

Analytical traceability of PGI and PDO foods (Protected Geographical Indication and Protected Denomination Origin respectively) is one of the most challenging tasks of current applied research. Here we proposed a metabolomic approach based on the combination of (1)H high-resolution magic angle spinning-nuclear magnetic resonance (HRMAS-NMR) spectroscopy with multivariate analysis, i.e. PLS-DA, as a reliable tool for the traceability of Italian PGI chicories (Cichorium intybus L.), i.e. Radicchio Rosso di Treviso and Radicchio Variegato di Castelfranco, also known as red and red-spotted, respectively. The metabolic profile was gained by means of HRMAS-NMR, and multivariate data analysis allowed us to build statistical models capable of providing clear discrimination among the two varieties and classification according to the geographical origin. Based on Variable Importance in Projection values, the molecular markers for classifying the different types of red chicories analysed were found accounting for both the cultivar and the place of origin. © 2012 Society of Chemical Industry.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Fast and simultaneously determination of light and heavy rare earth elements in monazite using combination of ultraviolet-visible spectrophotometry and multivariate analysis

NASA Astrophysics Data System (ADS)

Anggraeni, Anni; Arianto, Fernando; Mutalib, Abdul; Pratomo, Uji; Bahti, Husein H.

2017-05-01

Rare Earth Elements (REE) are elements that a lot of function for life, such as metallurgy, optical devices, and manufacture of electronic devices. Sources of REE is present in the mineral, in which each element has similar properties. Currently, to determining the content of REE is used instruments such as ICP-OES, ICP-MS, XRF, and HPLC. But in each instruments, there are still have some weaknesses. Therefore we need an alternative analytical method for the determination of rare earth metal content, one of them is by a combination of UV-Visible spectrophotometry and multivariate analysis, including Principal Component Analysis (PCA), Principal Component Regression (PCR), and Partial Least Square Regression (PLS). The purpose of this experiment is to determine the content of light and medium rare earth elements in the mineral monazite without chemical separation by using a combination of multivariate analysis and UV-Visible spectrophotometric methods. Training set created 22 variations of concentration and absorbance was measured using a UV-Vis spectrophotometer, then the data is processed by PCA, PCR, and PLSR. The results were compared and validated to obtain the mathematical equation with the smallest percent error. From this experiment, mathematical equation used PLS methods was better than PCR after validated, which has RMSE value for La, Ce, Pr, Nd, Gd, Sm, Eu, and Tb respectively 0.095; 0.573; 0.538; 0.440; 3.387; 1.240; 1.870; and 0.639.

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.

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.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Biological variation of Vanilla planifolia leaf metabolome.

PubMed

Palama, Tony Lionel; Fock, Isabelle; Choi, Young Hae; Verpoorte, Robert; Kodja, Hippolyte

2010-04-01

The metabolomic analysis of Vanilla planifolia leaves collected at different developmental stages was carried out using (1)H-nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis in order to evaluate their variation. Ontogenic changes of the metabolome were considered since leaves of different ages were collected at two different times of the day and in two different seasons. Principal component analysis (PCA) and partial least square modeling discriminate analysis (PLS-DA) of (1)H NMR data provided a clear separation according to leaf age, time of the day and season of collection. Young leaves were found to have higher levels of glucose, bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-isopropyltartrate (glucoside A) and bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-(2-butyl)-tartrate (glucoside B), whereas older leaves had more sucrose, acetic acid, homocitric acid and malic acid. Results obtained from PLS-DA analysis showed that leaves collected in March 2008 had higher levels of glucosides A and B as compared to those collected in August 2007. However, the relative standard deviation (RSD) exhibited by the individual values of glucosides A and B showed that those compounds vary more according to their developmental stage (50%) than to the time of day or the season in which they were collected (19%). Although morphological variations of the V. planifolia accessions were observed, no clear separation of the accessions was determined from the analysis of the NMR spectra. The results obtained in this study, show that this method based on the use of (1)H NMR spectroscopy in combination with multivariate analysis has a great potential for further applications in the study of vanilla leaf metabolome. Copyright 2009 Elsevier Ltd. All rights reserved.

Discerning some Tylenol brands using attenuated total reflection Fourier transform infrared data and multivariate analysis techniques.

PubMed

Msimanga, Huggins Z; Ollis, Robert J

2010-06-01

Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify acetaminophen-containing medicines using their attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectra. Four formulations of Tylenol (Arthritis Pain Relief, Extra Strength Pain Relief, 8 Hour Pain Relief, and Extra Strength Pain Relief Rapid Release) along with 98% pure acetaminophen were selected for this study because of the similarity of their spectral features, with correlation coefficients ranging from 0.9857 to 0.9988. Before acquiring spectra for the predictor matrix, the effects on spectral precision with respect to sample particle size (determined by sieve size opening), force gauge of the ATR accessory, sample reloading, and between-tablet variation were examined. Spectra were baseline corrected and normalized to unity before multivariate analysis. Analysis of variance (ANOVA) was used to study spectral precision. The large particles (35 mesh) showed large variance between spectra, while fine particles (120 mesh) indicated good spectral precision based on the F-test. Force gauge setting did not significantly affect precision. Sample reloading using the fine particle size and a constant force gauge setting of 50 units also did not compromise precision. Based on these observations, data acquisition for the predictor matrix was carried out with the fine particles (sieve size opening of 120 mesh) at a constant force gauge setting of 50 units. After removing outliers, PCA successfully classified the five samples in the first and second components, accounting for 45.0% and 24.5% of the variances, respectively. The four-component PLS-DA model (R(2)=0.925 and Q(2)=0.906) gave good test spectra predictions with an overall average of 0.961 +/- 7.1% RSD versus the expected 1.0 prediction for the 20 test spectra used.

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.

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.

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.

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

PubMed

Osterberg, T; Norinder, U

2001-01-01

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

PubMed Central

Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

2014-01-01

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%. PMID:25301508

Multivariate statistical analysis of a high rate biofilm process treating kraft mill bleach plant effluent.

PubMed

Goode, C; LeRoy, J; Allen, D G

2007-01-01

This study reports on a multivariate analysis of the moving bed biofilm reactor (MBBR) wastewater treatment system at a Canadian pulp mill. The modelling approach involved a data overview by principal component analysis (PCA) followed by partial least squares (PLS) modelling with the objective of explaining and predicting changes in the BOD output of the reactor. Over two years of data with 87 process measurements were used to build the models. Variables were collected from the MBBR control scheme as well as upstream in the bleach plant and in digestion. To account for process dynamics, a variable lagging approach was used for variables with significant temporal correlations. It was found that wood type pulped at the mill was a significant variable governing reactor performance. Other important variables included flow parameters, faults in the temperature or pH control of the reactor, and some potential indirect indicators of biomass activity (residual nitrogen and pH out). The most predictive model was found to have an RMSEP value of 606 kgBOD/d, representing a 14.5% average error. This was a good fit, given the measurement error of the BOD test. Overall, the statistical approach was effective in describing and predicting MBBR treatment performance.

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.

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.

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.

Quantitation of active pharmaceutical ingredients and excipients in powder blends using designed multivariate calibration models by near-infrared spectroscopy.

PubMed

Li, Weiyong; Worosila, Gregory D

2005-05-13

This research note demonstrates the simultaneous quantitation of a pharmaceutical active ingredient and three excipients in a simulated powder blend containing acetaminophen, Prosolv and Crospovidone. An experimental design approach was used in generating a 5-level (%, w/w) calibration sample set that included 125 samples. The samples were prepared by weighing suitable amount of powders into separate 20-mL scintillation vials and were mixed manually. Partial least squares (PLS) regression was used in calibration model development. The models generated accurate results for quantitation of Crospovidone (at 5%, w/w) and magnesium stearate (at 0.5%, w/w). Further testing of the models demonstrated that the 2-level models were as effective as the 5-level ones, which reduced the calibration sample number to 50. The models had a small bias for quantitation of acetaminophen (at 30%, w/w) and Prosolv (at 64.5%, w/w) in the blend. The implication of the bias is discussed.

Quantitative analysis of virgin coconut oil in cream cosmetics preparations using fourier transform infrared (FTIR) spectroscopy.

PubMed

Rohman, A; Man, Yb Che; Sismindari

2009-10-01

Today, virgin coconut oil (VCO) is becoming valuable oil and is receiving an attractive topic for researchers because of its several biological activities. In cosmetics industry, VCO is excellent material which functions as a skin moisturizer and softener. Therefore, it is important to develop a quantitative analytical method offering a fast and reliable technique. Fourier transform infrared (FTIR) spectroscopy with sample handling technique of attenuated total reflectance (ATR) can be successfully used to analyze VCO quantitatively in cream cosmetic preparations. A multivariate analysis using calibration of partial least square (PLS) model revealed the good relationship between actual value and FTIR-predicted value of VCO with coefficient of determination (R2) of 0.998.

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.

Rationalizing context-dependent performance of dynamic RNA regulatory devices.

PubMed

Kent, Ross; Halliwell, Samantha; Young, Kate; Swainston, Neil; Dixon, Neil

2018-06-21

The ability of RNA to sense, regulate and store information is an attractive attribute for a variety of functional applications including the development of regulatory control devices for synthetic biology. RNA folding and function is known to be highly context sensitive, which limits the modularity and reuse of RNA regulatory devices to control different heterologous sequences and genes. We explored the cause and effect of sequence context sensitivity for translational ON riboswitches located in the 5' UTR, by constructing and screening a library of N-terminal synonymous codon variants. By altering the N-terminal codon usage we were able to obtain RNA devices with a broad range of functional performance properties (ON, OFF, fold-change). Linear regression and calculated metrics were used to rationalize the major determining features leading to optimal riboswitch performance, and to identify multiple interactions between the explanatory metrics. Finally, partial least squared (PLS) analysis was employed in order to understand the metrics and their respective effect on performance. This PLS model was shown to provide good explanation of our library. This study provides a novel multi-variant analysis framework by which to rationalize the codon context performance of allosteric RNA-devices. The framework will also serve as a platform for future riboswitch context engineering endeavors.

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.

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.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

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.

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.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

USGS Publications Warehouse

Anderson, Ryan; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott M.; Morris, Richard V.; Ehlmann, Bethany L.; Dyar, M. Darby

2017-01-01

Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.

Fast-multivariate optimization of chiral separations in capillary electrophoresis: anticipative strategies.

PubMed

Escuder-Gilabert, L; Martín-Biosca, Y; Sagrado, S; Medina-Hernández, M J

2014-10-10

The design of experiments (DOE) is a good option for rationally limiting the number of experiments required to achieve the enantioresolution (Rs) of a chiral compound in capillary electrophoresis. In some cases, the modeled Rs after DOE analysis can be unsatisfactory, maybe because the range of the explored factors (DOE domain) was not the adequate. In these cases, anticipative strategies can be an alternative to the repetition of the process (e.g. a new DOE), to save time and money. In this work, multiple linear regression (MLR)-steepest ascent and a new anticipative strategy based on a multiple response-partial least squares model (called PLS2-prediction) are examined as post-DOE strategies to anticipate new experimental conditions providing satisfactory Rs values. The new anticipative strategy allows to include the analysis time (At) and uncertainty limits into the decision making process. To demonstrate their efficiency, the chiral separation of hexaconazole and penconazole, as model compounds, is studied using highly sulfated-β-cyclodextrin (HS-β-CD) in electrokinetic chromatography (EKC). Box-Behnken DOE for three factors (background electrolyte pH, separation temperature and HS-β-CD concentration) and two responses (Rs and At) is used. Using commercially available software, the whole modeling and anticipative process is automatic, simple and requires minimal skills from the researcher. Both strategies studied have proven to successfully anticipate Rs values close to the experimental ones for EKC conditions outside the DOE domain for the two model compounds. The results in this work suggest that PLS2-prediction approach could be the strategy of choice to obtain secure anticipations in EKC. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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

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.

HPLC-based metabolic profiling and quality control of leaves of different Panax species

PubMed Central

Yang, Seung-Ok; Lee, Sang Won; Kim, Young Ock; Sohn, Sang-Hyun; Kim, Young Chang; Hyun, Dong Yoon; Hong, Yoon Pyo; Shin, Yu Su

2013-01-01

Leaves from Panax ginseng Meyer (Korean origin and Chinese origin of Korean ginseng) and P. quinquefolius (American ginseng) were harvested in Haenam province, Korea, and were analyzed to investigate patterns in major metabolites using HPLC-based metabolic profiling. Partial least squares discriminant analysis (PLS-DA) was used to analyze the HPLC chromatogram data. There was a clear separation between Panax species and/or origins from different countries in the PLS-DA score plots. The ginsenoside compounds of Rg1, Re, Rg2, Rb2, Rb3, and Rd in Korean leaves were higher than in Chinese and American ginseng leaves, and the Rb1 level in P. quinquefolius leaves was higher than in P. ginseng (Korean origin or Chinese origin). HPLC chromatogram data coupled with multivariate statistical analysis can be used to profile the metabolite content and undertake quality control of Panax products. PMID:23717177

Classical vs. evolved quenching parameters and procedures in scintillation measurements: The importance of ionization quenching

NASA Astrophysics Data System (ADS)

Bagán, H.; Tarancón, A.; Rauret, G.; García, J. F.

2008-07-01

The quenching parameters used to model detection efficiency variations in scintillation measurements have not evolved since the decade of 1970s. Meanwhile, computer capabilities have increased enormously and ionization quenching has appeared in practical measurements using plastic scintillation. This study compares the results obtained in activity quantification by plastic scintillation of 14C samples that contain colour and ionization quenchers, using classical (SIS, SCR-limited, SCR-non-limited, SIS(ext), SQP(E)) and evolved (MWA-SCR and WDW) parameters and following three calibration approaches: single step, which does not take into account the quenching mechanism; two steps, which takes into account the quenching phenomena; and multivariate calibration. Two-step calibration (ionization followed by colour) yielded the lowest relative errors, which means that each quenching phenomenon must be specifically modelled. In addition, the sample activity was quantified more accurately when the evolved parameters were used. Multivariate calibration-PLS also yielded better results than those obtained using classical parameters, which confirms that the quenching phenomena must be taken into account. The detection limits for each calibration method and each parameter were close to those obtained theoretically using the Currie approach.

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.

Exploration of attenuated total reflectance mid-infrared spectroscopy and multivariate calibration to measure immunoglobulin G in human sera.

PubMed

Hou, Siyuan; Riley, Christopher B; Mitchell, Cynthia A; Shaw, R Anthony; Bryanton, Janet; Bigsby, Kathryn; McClure, J Trenton

2015-09-01

Immunoglobulin G (IgG) is crucial for the protection of the host from invasive pathogens. Due to its importance for human health, tools that enable the monitoring of IgG levels are highly desired. Consequently there is a need for methods to determine the IgG concentration that are simple, rapid, and inexpensive. This work explored the potential of attenuated total reflectance (ATR) infrared spectroscopy as a method to determine IgG concentrations in human serum samples. Venous blood samples were collected from adults and children, and from the umbilical cord of newborns. The serum was harvested and tested using ATR infrared spectroscopy. Partial least squares (PLS) regression provided the basis to develop the new analytical methods. Three PLS calibrations were determined: one for the combined set of the venous and umbilical cord serum samples, the second for only the umbilical cord samples, and the third for only the venous samples. The number of PLS factors was chosen by critical evaluation of Monte Carlo-based cross validation results. The predictive performance for each PLS calibration was evaluated using the Pearson correlation coefficient, scatter plot and Bland-Altman plot, and percent deviations for independent prediction sets. The repeatability was evaluated by standard deviation and relative standard deviation. The results showed that ATR infrared spectroscopy is potentially a simple, quick, and inexpensive method to measure IgG concentrations in human serum samples. The results also showed that it is possible to build a united calibration curve for the umbilical cord and the venous samples. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pharmaceutical Pellets.

PubMed

Naidu, Venkata Ramana; Deshpande, Rucha S; Syed, Moinuddin R; Deoghare, Piyush; Singh, Dharamvir; Wakte, Pravin S

2017-08-01

Current endeavor was aimed towards monitoring percent weight build-up during functional coating process on drug-layered pellets. Near-infrared (NIR) spectroscopy is an emerging process analytical technology (PAT) tool which was employed here within quality by design (QbD) framework. Samples were withdrawn after spraying every 15-Kg cellulosic coating material during Wurster coating process of drug-loaded pellets. NIR spectra of these samples were acquired using cup spinner assembly of Thermoscientific Antaris II, followed by multivariate analysis using partial least squares (PLS) calibration model. PLS model was built by selecting various absorption regions of NIR spectra for Ethyl cellulose, drug and correlating the absorption values with actual percent weight build up determined by HPLC. The spectral regions of 8971.04 to 8250.77 cm -1 , 7515.24 to 7108.33 cm -1 , and 5257.00 to 5098.87 cm -1 were found to be specific to cellulose, where as the spectral region of 6004.45 to 5844.14 cm -1 was found to be specific to drug. The final model gave superb correlation co-efficient value of 0.9994 for calibration and 0.9984 for validation with low root mean square of error (RMSE) values of 0.147 for calibration and 0.371 for validation using 6 factors. The developed correlation between the NIR spectra and cellulose content is useful in precise at-line prediction of functional coat value and can be used for monitoring the Wurster coating process.

Novel method for the determination of average molecular weight of natural polymers based on 2D DOSY NMR and chemometrics: Example of heparin.

PubMed

Monakhova, Yulia B; Diehl, Bernd W K; Do, Tung X; Schulze, Margit; Witzleben, Steffen

2018-02-05

Apart from the characterization of impurities, the full characterization of heparin and low molecular weight heparin (LMWH) also requires the determination of average molecular weight, which is closely related to the pharmaceutical properties of anticoagulant drugs. To determine average molecular weight of these animal-derived polymer products, partial least squares regression (PLS) was utilized for modelling of diffused-ordered spectroscopy NMR data (DOSY) of a representative set of heparin (n=32) and LMWH (n=30) samples. The same sets of samples were measured by gel permeation chromatography (GPC) to obtain reference data. The application of PLS to the data led to calibration models with root mean square error of prediction of 498Da and 179Da for heparin and LMWH, respectively. The average coefficients of variation (CVs) did not exceed 2.1% excluding sample preparation (by successive measuring one solution, n=5) and 2.5% including sample preparation (by preparing and analyzing separate samples, n=5). An advantage of the method is that the sample after standard 1D NMR characterization can be used for the molecular weight determination without further manipulation. The accuracy of multivariate models is better than the previous results for other matrices employing internal standards. Therefore, DOSY experiment is recommended to be employed for the calculation of molecular weight of heparin products as a complementary measurement to standard 1D NMR quality control. The method can be easily transferred to other matrices as well. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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.

Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data

NASA Astrophysics Data System (ADS)

Ogulei, David; Hopke, Philip K.; Zhou, Liming; Patrick Pancras, J.; Nair, Narayanan; Ondov, John M.

Several multivariate data analysis methods have been applied to a combination of particle size and composition measurements made at the Baltimore Supersite. Partial least squares (PLS) was used to investigate the relationship (linearity) between number concentrations and the measured PM2.5 mass concentrations of chemical species. The data were obtained at the Ponca Street site and consisted of six days' measurements: 6, 7, 8, 18, 19 July, and 21 August 2002. The PLS analysis showed that the covariance between the data could be explained by 10 latent variables (LVs), but only the first four of these were sufficient to establish the linear relationship between the two data sets. More LVs could not make the model better. The four LVs were found to better explain the covariance between the large sized particles and the chemical species. A bilinear receptor model, PMF2, was then used to simultaneously analyze the size distribution and chemical composition data sets. The resolved sources were identified using information from number and mass contributions from each source (source profiles) as well as meteorological data. Twelve sources were identified: oil-fired power plant emissions, secondary nitrate I, local gasoline traffic, coal-fired power plant, secondary nitrate II, secondary sulfate, diesel emissions/bus maintenance, Quebec wildfire episode, nucleation, incinerator, airborne soil/road-way dust, and steel plant emissions. Local sources were mostly characterized by bi-modal number distributions. Regional sources were characterized by transport mode particles (0.2- 0.5μm).

Simultaneous estimation of ramipril, acetylsalicylic acid and atorvastatin calcium by chemometrics assisted UV-spectrophotometric method in capsules.

PubMed

Sankar, A S Kamatchi; Vetrichelvan, Thangarasu; Venkappaya, Devashya

2011-09-01

In the present work, three different spectrophotometric methods for simultaneous estimation of ramipril, aspirin and atorvastatin calcium in raw materials and in formulations are described. Overlapped data was quantitatively resolved by using chemometric methods, viz. inverse least squares (ILS), principal component regression (PCR) and partial least squares (PLS). Calibrations were constructed using the absorption data matrix corresponding to the concentration data matrix. The linearity range was found to be 1-5, 10-50 and 2-10 μg mL-1 for ramipril, aspirin and atorvastatin calcium, respectively. The absorbance matrix was obtained by measuring the zero-order absorbance in the wavelength range between 210 and 320 nm. A training set design of the concentration data corresponding to the ramipril, aspirin and atorvastatin calcium mixtures was organized statistically to maximize the information content from the spectra and to minimize the error of multivariate calibrations. By applying the respective algorithms for PLS 1, PCR and ILS to the measured spectra of the calibration set, a suitable model was obtained. This model was selected on the basis of RMSECV and RMSEP values. The same was applied to the prediction set and capsule formulation. Mean recoveries of the commercial formulation set together with the figures of merit (calibration sensitivity, selectivity, limit of detection, limit of quantification and analytical sensitivity) were estimated. Validity of the proposed approaches was successfully assessed for analyses of drugs in the various prepared physical mixtures and formulations.

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.

Chemometric-assisted spectrophotometric methods and high performance liquid chromatography for simultaneous determination of seven β-blockers in their pharmaceutical products: A comparative study

NASA Astrophysics Data System (ADS)

Abdel Hameed, Eman A.; Abdel Salam, Randa A.; Hadad, Ghada M.

2015-04-01

Chemometric-assisted spectrophotometric methods and high performance liquid chromatography (HPLC) were developed for the simultaneous determination of the seven most commonly prescribed β-blockers (atenolol, sotalol, metoprolol, bisoprolol, propranolol, carvedilol and nebivolol). Principal component regression PCR, partial least square PLS and PLS with previous wavelength selection by genetic algorithm (GA-PLS) were used for chemometric analysis of spectral data of these drugs. The compositions of the mixtures used in the calibration set were varied to cover the linearity ranges 0.7-10 μg ml-1 for AT, 1-15 μg ml-1 for ST, 1-15 μg ml-1 for MT, 0.3-5 μg ml-1 for BS, 0.1-3 μg ml-1 for PR, 0.1-3 μg ml-1 for CV and 0.7-5 μg ml-1 for NB. The analytical performances of these chemometric methods were characterized by relative prediction errors and were compared with each other. GA-PLS showed superiority over the other applied multivariate methods due to the wavelength selection. A new gradient HPLC method had been developed using statistical experimental design. Optimum conditions of separation were determined with the aid of central composite design. The developed HPLC method was found to be linear in the range of 0.2-20 μg ml-1 for AT, 0.2-20 μg ml-1 for ST, 0.1-15 μg ml-1 for MT, 0.1-15 μg ml-1 for BS, 0.1-13 μg ml-1 for PR, 0.1-13 μg ml-1 for CV and 0.4-20 μg ml-1 for NB. No significant difference between the results of the proposed GA-PLS and HPLC methods with respect to accuracy and precision. The proposed analytical methods did not show any interference of the excipients when applied to pharmaceutical products.

Optical and laser spectroscopic diagnostics for energy applications

NASA Astrophysics Data System (ADS)

Tripathi, Markandey Mani

The continuing need for greater energy security and energy independence has motivated researchers to develop new energy technologies for better energy resource management and efficient energy usage. The focus of this dissertation is the development of optical (spectroscopic) sensing methodologies for various fuels, and energy applications. A fiber-optic NIR sensing methodology was developed for predicting water content in bio-oil. The feasibility of using the designed near infrared (NIR) system for estimating water content in bio-oil was tested by applying multivariate analysis to NIR spectral data. The calibration results demonstrated that the spectral information can successfully predict the bio-oil water content (from 16% to 36%). The effect of ultraviolet (UV) light on the chemical stability of bio-oil was studied by employing laser-induced fluorescence (LIF) spectroscopy. To simulate the UV light exposure, a laser in the UV region (325 nm) was employed for bio-oil excitation. The LIF, as a signature of chemical change, was recorded from bio-oil. From this study, it was concluded that phenols present in the bio-oil show chemical instability, when exposed to UV light. A laser-induced breakdown spectroscopy (LIBS)-based optical sensor was designed, developed, and tested for detection of four important trace impurities in rocket fuel (hydrogen). The sensor can simultaneously measure the concentrations of nitrogen, argon, oxygen, and helium in hydrogen from storage tanks and supply lines. The sensor had estimated lower detection limits of 80 ppm for nitrogen, 97 ppm for argon, 10 ppm for oxygen, and 25 ppm for helium. A chemiluminescence-based spectroscopic diagnostics were performed to measure equivalence ratios in methane-air premixed flames. A partial least-squares regression (PLS-R)-based multivariate sensing methodology was investigated. It was found that the equivalence ratios predicted with the PLS-R-based multivariate calibration model matched with the experimentally measured equivalence ratios within 7 %. A comparative study was performed for equivalence ratios measurement in atmospheric premixed methane-air flames with ungated LIBS and chemiluminescence spectroscopy. It was reported that LIBS-based calibration, which carries spectroscopic information from a "point-like-volume," provides better predictions of equivalence ratios compared to chemiluminescence-based calibration, which is essentially a "line-of-sight" measurement.

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

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.

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.

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.

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.

Solving matrix effects exploiting the second-order advantage in the resolution and determination of eight tetracycline antibiotics in effluent wastewater by modelling liquid chromatography data with multivariate curve resolution-alternating least squares and unfolded-partial least squares followed by residual bilinearization algorithms II. Prediction and figures of merit.

PubMed

García, M D Gil; Culzoni, M J; De Zan, M M; Valverde, R Santiago; Galera, M Martínez; Goicoechea, H C

2008-02-01

A new powerful algorithm (unfolded-partial least squares followed by residual bilinearization (U-PLS/RBL)) was applied for first time on second-order liquid chromatography with diode array detection (LC-DAD) data and compared with a well-known established method (multivariate curve resolution-alternating least squares (MCR-ALS)) for the simultaneous determination of eight tetracyclines (tetracycline, oxytetracycline, meclocycline, minocycline, metacycline, chlortetracycline, demeclocycline and doxycycline) in wastewaters. Tetracyclines were pre-concentrated using Oasis Max C18 cartridges and then separated on a Thermo Aquasil C18 (150 mm x 4.6mm, 5 microm) column. The whole method was validated using Milli-Q water samples and both univariate and multivariate analytical figures of merit were obtained. Additionally, two data pre-treatment were applied (baseline correction and piecewise direct standardization), which allowed to correct the effect of breakthrough and to reduce the total interferences retained after pre-concentration of wastewaters. The results showed that the eight tetracycline antibiotics can be successfully determined in wastewaters, the drawbacks due to matrix interferences being adequately handled and overcome by using U-PSL/RBL.

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.

Near and mid infrared spectroscopy and multivariate data analysis in studies of oxidation of edible oils.

PubMed

Wójcicki, Krzysztof; Khmelinskii, Igor; Sikorski, Marek; Sikorska, Ewa

2015-11-15

Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR--based on NIR (R(2) = 0.890), MIR (R(2) = 0.707) and combined NIR and MIR (R(2) = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR (R(2) = 0.701, RPD = 1.7), NIR (R(2) = 0.970, RPD = 5.3), and combined NIR and MIR data (R(2) = 0.954, RPD = 3.1). Copyright © 2015 Elsevier Ltd. 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.

Authentication of whisky due to its botanical origin and way of production by instrumental analysis and multivariate classification methods

NASA Astrophysics Data System (ADS)

Wiśniewska, Paulina; Boqué, Ricard; Borràs, Eva; Busto, Olga; Wardencki, Waldemar; Namieśnik, Jacek; Dymerski, Tomasz

2017-02-01

Headspace mass-spectrometry (HS-MS), mid infrared (MIR) and UV-vis spectroscopy were used to authenticate whisky samples from different origins and ways of production ((Irish, Spanish, Bourbon, Tennessee Whisky and Scotch). The collected spectra were processed with partial least-squares discriminant analysis (PLS-DA) to build the classification models. In all cases the five groups of whiskies were distinguished, but the best results were obtained by HS-MS, which indicates that the biggest differences between different types of whisky are due to their aroma. Differences were also found inside groups, showing that not only raw material is important to discriminate samples but also the way of their production. The methodology is quick, easy and does not require sample preparation.

Assessment of Fecal Microbiota and Fecal Metabolome in Symptomatic Uncomplicated Diverticular Disease of the Colon.

PubMed

Tursi, Antonio; Mastromarino, Paola; Capobianco, Daniela; Elisei, Walter; Miccheli, Alfredo; Capuani, Giorgio; Tomassini, Alberta; Campagna, Giuseppe; Picchio, Marcello; Giorgetti, GianMarco; Fabiocchi, Federica; Brandimarte, Giovanni

2016-10-01

The aim of this study was to assess fecal microbiota and metabolome in a population with symptomatic uncomplicated diverticular disease (SUDD). Whether intestinal microbiota and metabolic profiling may be altered in patients with SUDD is unknown. Stool samples from 44 consecutive women [15 patients with SUDD, 13 with asymptomatic diverticulosis (AD), and 16 healthy controls (HCs)] were analyzed. Real-time polymerase chain reaction was used to quantify targeted microorganisms. High-resolution proton nuclear magnetic resonance spectroscopy associated with multivariate analysis with partial least-square discriminant analysis (PLS-DA) was applied on the metabolite data set. The overall bacterial quantity did not differ among the 3 groups (P=0.449), with no difference in Bacteroides/Prevotella, Clostridium coccoides, Bifidobacterium, Lactobacillus, and Escherichia coli subgroups. The amount of Akkermansia muciniphila species was significantly different between HC, AD, and SUDD subjects (P=0.017). PLS-DA analysis of nuclear magnetic resonance -based metabolomics associated with microbiological data showed significant discrimination between HCs and AD patients (R=0.733; Q=0.383; P<0.05, LV=2). PLS analysis showed lower N-acetyl compound and isovalerate levels in AD, associated with higher levels of A. municiphila, as compared with the HC group. PLS-DA applied on AD and SUDD samples showed a good discrimination between these 2 groups (R=0.69; Q=0.35; LV=2). SUDD patients were characterized by low levels of valerate, butyrate, and choline and by high levels of N-acetyl derivatives and U1. SUDD and AD do not show colonic bacterial overgrowth, but a significant difference in the levels of fecal A. muciniphila was observed. Moreover, increasing expression of some metabolites as expression of different AD and SUDD metabolic activity was found.

Multivariate classification of the infrared spectra of cell and tissue samples

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

Haaland, D.M.; Jones, H.D.; Thomas, E.V.

1997-03-01

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

Simultaneous determination of rifampicin, isoniazid and pyrazinamide in tablet preparations by multivariate spectrophotometric calibration.

PubMed

Goicoechea, H C; Olivieri, A C

1999-08-01

The use of multivariate spectrophotometric calibration is presented for the simultaneous determination of the active components of tablets used in the treatment of pulmonary tuberculosis. The resolution of ternary mixtures of rifampicin, isoniazid and pyrazinamide has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy and precision, rapidly and with no need of nonaqueous solvents for dissolving the samples. No interference has been observed from the tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap among the studied compounds. This is highlighted in the case of isoniazid, due to the small absorbances measured for this component.

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.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

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

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

DOE PAGES

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; ...

2016-12-15

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

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.

Improved chemometric methodologies for the assessment of soil carbon sequestration mechanisms

NASA Astrophysics Data System (ADS)

Jiménez-González, Marco A.; Almendros, Gonzalo; Álvarez, Ana M.; González-Vila, Francisco J.

2016-04-01

The factors involved soil C sequestration, which is reflected in the highly variable content of organic matter in the soils, are not yet well defined. Therefore, their identification is crucial for understanding Earth's biogeochemical cycle and global change. The main objective of this work is to contribute to a better qualitative and quantitative assessment of the mechanisms of organic C sequestration in the soil, using omic approaches not requiring the detailed knowledge of the structure of the material under study. With this purpose, we have carried out a series of chemometric approaches on a set of widely differing soils (35 representative ecosystems). In an exploratory phase, we used multivariate statistical models (e.g., multidimensional scaling, discriminant analysis with automatic backward variable selection…) to analyze arrays of more than 200 independent soil variables (physicochemical, spectroscopic, pyrolytic...) in order to select those factors (descriptors or proxies) that explain most of the total system variance (content and stability of the different C forms). These models showed that the factors determining the stabilization of organic material are greatly dependent on the soil type. In some cases, the molecular structure of organic matter seemed strongly correlated with their resilience, while in other soil types the organo-mineral interactions played a significant bearing on the accumulation of selectively preserved C forms. In any case, it was clear that the factors driving the resilience of organic matter are manifold and not exclusive. Consequently, in a second stage, prediction models of the soil C content and their biodegradability (laboratory incubation experiments) were carried out by massive data processing by partial least squares (PLS) regression of data from Py-GC-MS and Py-MS. In some models, PLS was applied to a matrix of 150 independent variables corresponding to major pyrolysis compounds (peak areas) from the 35 samples of whole soils. The variable importance in the projection (VIP) histogram obtained from this treatment (total C and total mineralization coefficients as dependent variables) illustrated the contribution of the individual compounds to the total inertia of the models (e.g., carbohydrate-derived compounds, methoxyphenols, or specific alkylbenzenes were relevant in explaining the total quality and the biodegradation rates of the organic matter). Further simplified models consisting of direct PLS analysis of the debugged ion matrix calculated by averaging all ions (45 - 250 amu) in the whole chromatographic area in the 5-60 min range (here referred to as 'rebuilt MS spectra' or 'Py-MS spectra' when obtained connecting directly the pyrolyser to the MS detector through suitable interfaces) were carried out. The above three approaches coincided in pointing out that C sequestration behave as an emergent soil property depending on the complexity of its progressive molecular levels. Most of the total variance is explained by specific assemblages of variables, strongly depending on the soil types. On the other hand, chemical biodiversity (e.g., Shannon indices or coefficients from multivariate data models) behaved as a common background in the prediction models including very different soil types. In fact, assessment of chemodiversity of the pyrolytic compound assemblages (or the Py-MS ion data) would represent a valid clue for the assessment of the extent to which the original biomass has been diagenetically reworked into chaotic structures with non-repeatable units, providing a useful proxy to forecast at least a portion of the total variance in the soil organic matter biodegradability.

Quality by design case study: an integrated multivariate approach to drug product and process development.

PubMed

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

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.

Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters

NASA Astrophysics Data System (ADS)

de Santana, Felipe Bachion; de Souza, André Marcelo; Poppi, Ronei Jesus

2018-02-01

This study evaluates the use of visible and near infrared spectroscopy (Vis-NIRS) combined with multivariate regression based on random forest to quantify some quality soil parameters. The parameters analyzed were soil cation exchange capacity (CEC), sum of exchange bases (SB), organic matter (OM), clay and sand present in the soils of several regions of Brazil. Current methods for evaluating these parameters are laborious, timely and require various wet analytical methods that are not adequate for use in precision agriculture, where faster and automatic responses are required. The random forest regression models were statistically better than PLS regression models for CEC, OM, clay and sand, demonstrating resistance to overfitting, attenuating the effect of outlier samples and indicating the most important variables for the model. The methodology demonstrates the potential of the Vis-NIR as an alternative for determination of CEC, SB, OM, sand and clay, making possible to develop a fast and automatic analytical procedure.

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.

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.

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.

[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.

Determination of the main solid-state form of albendazole in bulk drug, employing Raman spectroscopy coupled to multivariate analysis.

PubMed

Calvo, Natalia L; Arias, Juan M; Altabef, Aída Ben; Maggio, Rubén M; Kaufman, Teodoro S

2016-09-10

Albendazole (ALB) is a broad-spectrum anthelmintic, which exhibits two solid-state forms (Forms I and II). The Form I is the metastable crystal at room temperature, while Form II is the stable one. Because the drug has poor aqueous solubility and Form II is less soluble than Form I, it is desirable to have a method to assess the solid-state form of the drug employed for manufacturing purposes. Therefore, a Partial Least Squares (PLS) model was developed for the determination of Form I of ALB in its mixtures with Form II. For model development, both solid-state forms of ALB were prepared and characterized by microscopic (optical and with normal and polarized light), thermal (DSC) and spectroscopic (ATR-FTIR, Raman) techniques. Mixtures of solids in different ratios were prepared by weighing and mechanical mixing of the components. Their Raman spectra were acquired, and subjected to peak smoothing, normalization, standard normal variate correction and de-trending, before performing the PLS calculations. The optimal spectral region (1396-1280cm(-1)) and number of latent variables (LV=3) were obtained employing a moving window of variable size strategy. The method was internally validated by means of the leave one out procedure, providing satisfactory statistics (r(2)=0.9729 and RMSD=5.6%) and figures of merit (LOD=9.4% and MDDC=1.4). Furthermore, the method's performance was also evaluated by analysis of two validation sets. Validation set I was used for assessment of linearity and range and Validation set II, to demonstrate accuracy and precision (Recovery=101.4% and RSD=2.8%). Additionally, a third set of spiked commercial samples was evaluated, exhibiting excellent recoveries (94.2±6.4%). The results suggest that the combination of Raman spectroscopy with multivariate analysis could be applied to the assessment of the main crystal form and its quantitation in samples of ALB bulk drug, in the routine quality control laboratory. Copyright © 2016 Elsevier B.V. All rights reserved.

Assessment of temporal and spatial water quality in international Gomishan Lagoon, Iran, using multivariate analysis.

PubMed

Basatnia, Nabee; Hossein, Seyed Abbas; Rodrigo-Comino, Jesús; Khaledian, Yones; Brevik, Eric C; Aitkenhead-Peterson, Jacqueline; Natesan, Usha

2018-04-29

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO 2 ), nitrate (NO 3 ), biological oxygen demand (BOD), phosphate (PO 4 ), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R 2 , RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.

Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry.

PubMed

Ahn, Joong Kyong; Kim, Jungyeon; Hwang, Jiwon; Song, Juhwan; Kim, Kyoung Heon; Cha, Hoon-Suk

2018-05-01

Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform. Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses. A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R 2 X of 0.246, R 2 Y of 0.913 and Q 2 of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set. This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS. Copyright © 2017. Published by Elsevier SAS.

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.

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

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.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S. A.; Ferré, Joan; Boqué, Ricard

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.

Authentication and Quantitation of Fraud in Extra Virgin Olive Oils Based on HPLC-UV Fingerprinting and Multivariate Calibration

PubMed Central

Carranco, Núria; Farrés-Cebrián, Mireia; Saurina, Javier

2018-01-01

High performance liquid chromatography method with ultra-violet detection (HPLC-UV) fingerprinting was applied for the analysis and characterization of olive oils, and was performed using a Zorbax Eclipse XDB-C8 reversed-phase column under gradient elution, employing 0.1% formic acid aqueous solution and methanol as mobile phase. More than 130 edible oils, including monovarietal extra-virgin olive oils (EVOOs) and other vegetable oils, were analyzed. Principal component analysis results showed a noticeable discrimination between olive oils and other vegetable oils using raw HPLC-UV chromatographic profiles as data descriptors. However, selected HPLC-UV chromatographic time-window segments were necessary to achieve discrimination among monovarietal EVOOs. Partial least square (PLS) regression was employed to tackle olive oil authentication of Arbequina EVOO adulterated with Picual EVOO, a refined olive oil, and sunflower oil. Highly satisfactory results were obtained after PLS analysis, with overall errors in the quantitation of adulteration in the Arbequina EVOO (minimum 2.5% adulterant) below 2.9%. PMID:29561820

Predictive modeling in Clostridium acetobutylicum fermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

NASA Astrophysics Data System (ADS)

Zu, Theresah N. K.; Liu, Sanchao; Germane, Katherine L.; Servinsky, Matthew D.; Gerlach, Elliot S.; Mackie, David M.; Sund, Christian J.

2016-05-01

The coupling of optical fibers with Raman instrumentation has proven to be effective for real-time monitoring of chemical reactions and fermentations when combined with multivariate statistical data analysis. Raman spectroscopy is relatively fast, with little interference from the water peak present in fermentation media. Medical research has explored this technique for analysis of mammalian cultures for potential diagnosis of some cancers. Other organisms studied via this route include Escherichia coli, Saccharomyces cerevisiae, and some Bacillus sp., though very little work has been performed on Clostridium acetobutylicum cultures. C. acetobutylicum is a gram-positive anaerobic bacterium, which is highly sought after due to its ability to use a broad spectrum of substrates and produce useful byproducts through the well-known Acetone-Butanol-Ethanol (ABE) fermentation. In this work, real-time Raman data was acquired from C. acetobutylicum cultures grown on glucose. Samples were collected concurrently for comparative off-line product analysis. Partial-least squares (PLS) models were built both for agitated cultures and for static cultures from both datasets. Media components and metabolites monitored include glucose, butyric acid, acetic acid, and butanol. Models were cross-validated with independent datasets. Experiments with agitation were more favorable for modeling with goodness of fit (QY) values of 0.99 and goodness of prediction (Q2Y) values of 0.98. Static experiments did not model as well as agitated experiments. Raman results showed the static experiments were chaotic, especially during and shortly after manual sampling.

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.

Building global models for fat and total protein content in raw milk based on historical spectroscopic data in the visible and short-wave near infrared range.

PubMed

Melenteva, Anastasiia; Galyanin, Vladislav; Savenkova, Elena; Bogomolov, Andrey

2016-07-15

A large set of fresh cow milk samples collected from many suppliers over a large geographical area in Russia during a year has been analyzed by optical spectroscopy in the range 400-1100 nm in accordance with previously developed scatter-based technique. The global (i.e. resistant to seasonal, genetic, regional and other variations of the milk composition) models for fat and total protein content, which were built using partial least-squares (PLS) regression, exhibit satisfactory prediction performances enabling their practical application in the dairy. The root mean-square errors of prediction (RMSEP) were 0.09 and 0.10 for fat and total protein content, respectively. The issues of raw milk analysis and multivariate modelling based on the historical spectroscopic data have been considered and approaches to the creation of global models and their transfer between the instruments have been proposed. Availability of global models should significantly facilitate the dissemination of optical spectroscopic methods for the laboratory and in-line quantitative milk analysis. Copyright © 2016. Published by Elsevier Ltd.

A QSAR study of integrase strand transfer inhibitors based on a large set of pyrimidine, pyrimidone, and pyridopyrazine carboxamide derivatives

NASA Astrophysics Data System (ADS)

de Campos, Luana Janaína; de Melo, Eduardo Borges

2017-08-01

In the present study, 199 compounds derived from pyrimidine, pyrimidone and pyridopyrazine carboxamides with inhibitory activity against HIV-1 integrase were modeled. Subsequently, a multivariate QSAR study was conducted with 54 molecules employed by Ordered Predictors Selection (OPS) and Partial Least Squares (PLS) for the selection of variables and model construction, respectively. Topological, electrotopological, geometric, and molecular descriptors were used. The selected real model was robust and free from chance correlation; in addition, it demonstrated favorable internal and external statistical quality. Once statistically validated, the training model was used to predict the activity of a second data set (n = 145). The root mean square deviation (RMSD) between observed and predicted values was 0.698. Although it is a value outside of the standards, only 15 (10.34%) of the samples exhibited higher residual values than 1 log unit, a result considered acceptable. Results of Williams and Euclidean applicability domains relative to the prediction showed that the predictions did not occur by extrapolation and that the model is representative of the chemical space of test compounds.

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

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 Ladle Vibration

NASA Astrophysics Data System (ADS)

Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle

2016-08-01

The homogeneity of composition and uniformity of temperature of the steel melt before it is transferred to the tundish are crucial in making high-quality steel product. The homogenization process is performed by stirring the melt using inert gas in ladles. Continuous monitoring of this process is important to make sure the action of stirring is constant throughout the ladle. Currently, the stirring process is monitored by process operators who largely rely on visual and acoustic phenomena from the ladle. However, due to lack of measurable signals, the accuracy and suitability of this manual monitoring are problematic. The actual flow of argon gas to the ladle may not be same as the flow gage reading due to leakage along the gas line components. As a result, the actual degree of stirring may not be correctly known. Various researchers have used one-dimensional vibration, and sound and image signals measured from the ladle to predict the degree of stirring inside. They developed online sensors which are indeed to monitor the online stirring phenomena. In this investigation, triaxial vibration signals have been measured from a cold water model which is a model of an industrial ladle. Three flow rate ranges and varying bath heights were used to collect vibration signals. The Fast Fourier Transform was applied to the dataset before it has been analyzed using principal component analysis (PCA) and partial least squares (PLS). PCA was used to unveil the structure in the experimental data. PLS was mainly applied to predict the stirring from the vibration response. It was found that for each flow rate range considered in this study, the informative signals reside in different frequency ranges. The first latent variables in these frequency ranges explain more than 95 pct of the variation in the stirring process for the entire single layer and the double layer data collected from the cold model. PLS analysis in these identified frequency ranges demonstrated that the latent variables of the response and predictor variables are highly correlated. The predicted variable has shown linear relationship with the stirring energy and bath recirculation speed. This outcome can improve the predictability of the mixing status in ladle metallurgy and make the online control of the process easier. Industrial testing of this input will follow.

A novel in-line NIR spectroscopy application for the monitoring of tablet film coating in an industrial scale process.

PubMed

Möltgen, C-V; Puchert, T; Menezes, J C; Lochmann, D; Reich, G

2012-04-15

Film coating of tablets is a multivariate pharmaceutical unit operation. In this study an innovative in-line Fourier-Transform Near-Infrared Spectroscopy (FT-NIRS) application is described which enables real-time monitoring of a full industrial scale pan coating process of heart-shaped tablets. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film of up to approx. 28 μm on the tablet face as determined by SEM, corresponding to a weight gain of 2.26%. For a better understanding of the aqueous coating process the NIR probe was positioned inside the rotating tablet bed. Five full scale experimental runs have been performed to evaluate the impact of process variables such as pan rotation, exhaust air temperature, spray rate and pan load and elaborate robust and selective quantitative calibration models for the real-time determination of both coating growth and tablet moisture content. Principal Component (PC) score plots allowed each coating step, namely preheating, spraying and drying to be distinguished and the dominating factors and their spectral effects to be identified (e.g. temperature, moisture, coating growth, change of tablet bed density, and core/coat interactions). The distinct separation of HPMC coating growth and tablet moisture in different PCs enabled a real-time in-line monitoring of both attributes. A PLS calibration model based on Karl Fischer reference values allowed the tablet moisture trajectory to be determined throughout the entire coating process. A 1-latent variable iPLS weight gain calibration model with calibration samples from process stages dominated by the coating growth (i.e. ≥ 30% of the theoretically applied amount of coating) was sufficiently selective and accurate to predict the progress of the thin HPMC coating layer. At-line NIR Chemical Imaging (NIR-CI) in combination with PLS Discriminant Analysis (PLSDA) verified the HPMC coating growth and physical changes at the core/coat interface during the initial stages of the coating process. In addition, inter- and intra-tablet coating variability throughout the process could be assessed. These results clearly demonstrate that in-line NIRS and at-line NIR-CI can be applied as complimentary PAT tools to monitor a challenging pan coating process. Copyright © 2012 Elsevier B.V. All rights reserved.

Authentication of whisky due to its botanical origin and way of production by instrumental analysis and multivariate classification methods.

PubMed

Wiśniewska, Paulina; Boqué, Ricard; Borràs, Eva; Busto, Olga; Wardencki, Waldemar; Namieśnik, Jacek; Dymerski, Tomasz

2017-02-15

Headspace mass-spectrometry (HS-MS), mid infrared (MIR) and UV-vis spectroscopy were used to authenticate whisky samples from different origins and ways of production ((Irish, Spanish, Bourbon, Tennessee Whisky and Scotch). The collected spectra were processed with partial least-squares discriminant analysis (PLS-DA) to build the classification models. In all cases the five groups of whiskies were distinguished, but the best results were obtained by HS-MS, which indicates that the biggest differences between different types of whisky are due to their aroma. Differences were also found inside groups, showing that not only raw material is important to discriminate samples but also the way of their production. The methodology is quick, easy and does not require sample preparation. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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

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.

Integrated environmental monitoring and multivariate data analysis-A case study.

PubMed

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

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.

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.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

PubMed Central

2014-01-01

Background In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). Results The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. Conclusion Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks. PMID:24955114

Northern Russian chironomid-based modern summer temperature data set and inference models

NASA Astrophysics Data System (ADS)

Nazarova, Larisa; Self, Angela E.; Brooks, Stephen J.; van Hardenbroek, Maarten; Herzschuh, Ulrike; Diekmann, Bernhard

2015-11-01

West and East Siberian data sets and 55 new sites were merged based on the high taxonomic similarity, and the strong relationship between mean July air temperature and the distribution of chironomid taxa in both data sets compared with other environmental parameters. Multivariate statistical analysis of chironomid and environmental data from the combined data set consisting of 268 lakes, located in northern Russia, suggests that mean July air temperature explains the greatest amount of variance in chironomid distribution compared with other measured variables (latitude, longitude, altitude, water depth, lake surface area, pH, conductivity, mean January air temperature, mean July air temperature, and continentality). We established two robust inference models to reconstruct mean summer air temperatures from subfossil chironomids based on ecological and geographical approaches. The North Russian 2-component WA-PLS model (RMSEPJack = 1.35 °C, rJack2 = 0.87) can be recommended for application in palaeoclimatic studies in northern Russia. Based on distinctive chironomid fauna and climatic regimes of Kamchatka the Far East 2-component WAPLS model (RMSEPJack = 1.3 °C, rJack2 = 0.81) has potentially better applicability in Kamchatka.

Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses.

PubMed

Longobardi, Francesco; Innamorato, Valentina; Di Gioia, Annalisa; Ventrella, Andrea; Lippolis, Vincenzo; Logrieco, Antonio F; Catucci, Lucia; Agostiano, Angela

2017-12-15

Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted 1 H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data.

PubMed

Nielsen, Allan Aasbjerg

2002-01-01

This paper describes two- and multiset canonical correlations analysis (CCA) for data fusion, multisource, multiset, or multitemporal exploratory data analysis. These techniques transform multivariate multiset data into new orthogonal variables called canonical variates (CVs) which, when applied in remote sensing, exhibit ever-decreasing similarity (as expressed by correlation measures) over sets consisting of 1) spectral variables at fixed points in time (R-mode analysis), or 2) temporal variables with fixed wavelengths (T-mode analysis). The CVs are invariant to linear and affine transformations of the original variables within sets which means, for example, that the R-mode CVs are insensitive to changes over time in offset and gain in a measuring device. In a case study, CVs are calculated from Landsat Thematic Mapper (TM) data with six spectral bands over six consecutive years. Both Rand T-mode CVs clearly exhibit the desired characteristic: they show maximum similarity for the low-order canonical variates and minimum similarity for the high-order canonical variates. These characteristics are seen both visually and in objective measures. The results from the multiset CCA R- and T-mode analyses are very different. This difference is ascribed to the noise structure in the data. The CCA methods are related to partial least squares (PLS) methods. This paper very briefly describes multiset CCA-based multiset PLS. Also, the CCA methods can be applied as multivariate extensions to empirical orthogonal functions (EOF) techniques. Multiset CCA is well-suited for inclusion in geographical information systems (GIS).

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.

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Mohamed, Heba M.

2016-01-01

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference.

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.

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.

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.

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

PubMed

Guelpa, Anina; Bevilacqua, Marta; Marini, Federico; O'Kennedy, Kim; Geladi, Paul; Manley, Marena

2015-04-15

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quality control of the paracetamol drug by chemometrics and imaging spectroscopy in the near infrared region

NASA Astrophysics Data System (ADS)

Baptistao, Mariana; Rocha, Werickson Fortunato de Carvalho; Poppi, Ronei Jesus

2011-09-01

In this work, it was used imaging spectroscopy and chemometric tools for the development and analysis of paracetamol and excipients in pharmaceutical formulations. It was also built concentration maps to study the distribution of the drug in the tablets surface. Multivariate models based on PLS regression were developed for paracetamol and excipients concentrations prediction. For the construction of the models it was used 31 samples in the tablet form containing the active principle in a concentration range of 30.0-90.0% (w/w) and errors below to 5% were obtained for validation samples. Finally, the study of the distribution in the drug was performed through the distribution maps of concentration of active principle and excipients. The analysis of maps showed the complementarity between the active principle and excipients in the tablets. The region with a high concentration of a constituent must have, necessarily, absence or low concentration of the other one. Thus, an alternative method for the paracetamol drug quality monitoring is presented.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

PubMed

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

2016-07-15

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluation of 1H NMR metabolic profiling using biofluid mixture design.

PubMed

Athersuch, Toby J; Malik, Shahid; Weljie, Aalim; Newton, Jack; Keun, Hector C

2013-07-16

A strategy for evaluating the performance of quantitative spectral analysis tools in conditions that better approximate background variation in a metabonomics experiment is presented. Three different urine samples were mixed in known proportions according to a {3, 3} simplex lattice experimental design and analyzed in triplicate by 1D (1)H NMR spectroscopy. Fifty-four urinary metabolites were subsequently quantified from the sample spectra using two methods common in metabolic profiling studies: (1) targeted spectral fitting and (2) targeted spectral integration. Multivariate analysis using partial least-squares (PLS) regression showed the latent structure of the spectral set recapitulated the experimental mixture design. The goodness-of-prediction statistic (Q(2)) of each metabolite variable in a PLS model was calculated as a metric for the reliability of measurement, across the sample compositional space. Several metabolites were observed to have low Q(2) values, largely as a consequence of their spectral resonances having low s/n or strong overlap with other sample components. This strategy has the potential to allow evaluation of spectral features obtained from metabolic profiling platforms in the context of the compositional background found in real biological sample sets, which may be subject to considerable variation. We suggest that it be incorporated into metabolic profiling studies to improve the estimation of matrix effects that confound accurate metabolite measurement. This novel method provides a rational basis for exploiting information from several samples in an efficient manner and avoids the use of multiple spike-in authentic standards, which may be difficult to obtain.

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

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.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

PubMed

Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

2014-07-15

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration.

PubMed

Dabkiewicz, Vanessa Emídio; de Mello Pereira Abrantes, Shirley; Cassella, Ricardo Jorgensen

2018-08-05

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm -1 region. Copyright © 2018 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.

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.

Patterns of Circulating Inflammatory Biomarkers in Older Persons with Varying Levels of Physical Performance: A Partial Least Squares-Discriminant Analysis Approach

PubMed Central

Marzetti, Emanuele; Landi, Francesco; Marini, Federico; Cesari, Matteo; Buford, Thomas W.; Manini, Todd M.; Onder, Graziano; Pahor, Marco; Bernabei, Roberto; Leeuwenburgh, Christiaan; Calvani, Riccardo

2014-01-01

Background: Chronic, low-grade inflammation and declining physical function are hallmarks of the aging process. However, previous attempts to correlate individual inflammatory biomarkers with physical performance in older people have produced mixed results. Given the complexity of the inflammatory response, the simultaneous analysis of an array of inflammatory mediators may provide more insights into the relationship between inflammation and age-related physical function decline. This study was designed to explore the association between a panel of inflammatory markers and physical performance in older adults through a multivariate statistical approach. Methods: Community-dwelling older persons were categorized into “normal walkers” (NWs; n = 27) or “slow walkers” (SWs; n = 11) groups using 0.8 m s−1 as the 4-m gait speed cutoff. A panel of 14 circulating inflammatory biomarkers was assayed by multiplex analysis. Partial least squares-discriminant analysis (PLS-DA) was used to identify patterns of inflammatory mediators associated with gait speed categories. Results: The optimal complexity of the PLS-DA model was found to be five latent variables. The proportion of correct classification was 88.9% for NW subjects (74.1% in cross-validation) and 90.9% for SW individuals (81.8% in cross-validation). Discriminant biomarkers in the model were interleukin 8, myeloperoxidase, and tumor necrosis factor alpha (all higher in the SW group), and P-selectin, interferon gamma, and granulocyte–macrophage colony-stimulating factor (all higher in the NW group). Conclusion: Distinct profiles of circulating inflammatory biomarkers characterize older subjects with different levels of physical performance. The dissection of these patterns may provide novel insights into the role played by inflammation in the disabling cascade and possible new targets for interventions. PMID:25593902

Chemical Characterization and Determination of the Anti-Oxidant Capacity of Two Brown Algae with Respect to Sampling Season and Morphological Structures Using Infrared Spectroscopy and Multivariate Analyses.

PubMed

Beratto, Angelo; Agurto, Cristian; Freer, Juanita; Peña-Farfal, Carlos; Troncoso, Nicolás; Agurto, Andrés; Castillo, Rosario Del P

2017-10-01

Brown algae biomass has been shown to be a highly important industrial source for the production of alginates and different nutraceutical products. The characterization of this biomass is necessary in order to allocate its use to specific applications according to the chemical and biological characteristics of this highly variable resource. The methods commonly used for algae characterization require a long time for the analysis and rigorous pretreatments of samples. In this work, nondestructive and fast analyses of different morphological structures from Lessonia spicata and Macrocystis pyrifera, which were collected during different seasons, were performed using Fourier transform infrared (FT-IR) techniques in combination with chemometric methods. Mid-infrared (IR) and near-infrared (NIR) spectral ranges were tested to evaluate the spectral differences between the species, seasons, and morphological structures of algae using a principal component analysis (PCA). Quantitative analyses of the polyphenol and alginate contents and the anti-oxidant capacity of the samples were performed using partial least squares (PLS) with both spectral ranges in order to build a predictive model for the rapid quantification of these parameters with industrial purposes. The PCA mainly showed differences in the samples based on seasonal sampling, where changes were observed in the bands corresponding to polysaccharides, proteins, and lipids. The obtained PLS models had high correlation coefficients (r) for the polyphenol content and anti-oxidant capacity (r > 0.9) and lower values for the alginate determination (0.7 < r < 0.8). Fourier transform infrared-based techniques were suitable tools for the rapid characterization of algae biomass, in which high variability in the samples was incorporated for the qualitative and quantitative analyses, and have the potential to be used on an industrial scale.

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.

[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.

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.

A novel exploratory chemometric approach to environmental monitorring by combining block clustering with Partial Least Square (PLS) analysis

PubMed Central

2013-01-01

Background Given the serious threats posed to terrestrial ecosystems by industrial contamination, environmental monitoring is a standard procedure used for assessing the current status of an environment or trends in environmental parameters. Measurement of metal concentrations at different trophic levels followed by their statistical analysis using exploratory multivariate methods can provide meaningful information on the status of environmental quality. In this context, the present paper proposes a novel chemometric approach to standard statistical methods by combining the Block clustering with Partial least square (PLS) analysis to investigate the accumulation patterns of metals in anthropized terrestrial ecosystems. The present study focused on copper, zinc, manganese, iron, cobalt, cadmium, nickel, and lead transfer along a soil-plant-snai food chain, and the hepatopancreas of the Roman snail (Helix pomatia) was used as a biological end-point of metal accumulation. Results Block clustering deliniates between the areas exposed to industrial and vehicular contamination. The toxic metals have similar distributions in the nettle leaves and snail hepatopancreas. PLS analysis showed that (1) zinc and copper concentrations at the lower trophic levels are the most important latent factors that contribute to metal accumulation in land snails; (2) cadmium and lead are the main determinants of pollution pattern in areas exposed to industrial contamination; (3) at the sites located near roads lead is the most threatfull metal for terrestrial ecosystems. Conclusion There were three major benefits by applying block clustering with PLS for processing the obtained data: firstly, it helped in grouping sites depending on the type of contamination. Secondly, it was valuable for identifying the latent factors that contribute the most to metal accumulation in land snails. Finally, it optimized the number and type of data that are best for monitoring the status of metallic contamination in terrestrial ecosystems exposed to different kinds of anthropic polution. PMID:23987502

Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

PubMed

Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

2017-08-15

The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Monitoring method for macroporous resin column chromatography process of salvianolic acids based on near infrared spectroscopy].

PubMed

Hou, Xiang-Mei; Zhang, Lei; Yue, Hong-Shui; Ju, Ai-Chun; Ye, Zheng-Liang

2016-07-01

To study and establish a monitoring method for macroporous resin column chromatography process of salvianolic acids by using near infrared spectroscopy (NIR) as a process analytical technology (PAT).The multivariate statistical process control (MSPC) model was developed based on 7 normal operation batches, and 2 test batches (including one normal operation batch and one abnormal operation batch) were used to verify the monitoring performance of this model. The results showed that MSPC model had a good monitoring ability for the column chromatography process. Meanwhile, NIR quantitative calibration model was established for three key quality indexes (rosmarinic acid, lithospermic acid and salvianolic acid B) by using partial least squares (PLS) algorithm. The verification results demonstrated that this model had satisfactory prediction performance. The combined application of the above two models could effectively achieve real-time monitoring for macroporous resin column chromatography process of salvianolic acids, and can be used to conduct on-line analysis of key quality indexes. This established process monitoring method could provide reference for the development of process analytical technology for traditional Chinese medicines manufacturing. Copyright© by the Chinese Pharmaceutical Association.

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.

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

A Database Approach for Predicting and Monitoring Baked Anode Properties

NASA Astrophysics Data System (ADS)

Lauzon-Gauthier, Julien; Duchesne, Carl; Tessier, Jayson

2012-11-01

The baked anode quality control strategy currently used by most carbon plants based on testing anode core samples in the laboratory is inadequate for facing increased raw material variability. The low core sampling rate limited by lab capacity and the common practice of reporting averaged properties based on some anode population mask a significant amount of individual anode variability. In addition, lab results are typically available a few weeks after production and the anodes are often already set in the reduction cells preventing early remedial actions when necessary. A database approach is proposed in this work to develop a soft-sensor for predicting individual baked anode properties at the end of baking cycle. A large historical database including raw material properties, process operating parameters and anode core data was collected from a modern Alcoa plant. A multivariate latent variable PLS regression method was used for analyzing the large database and building the soft-sensor model. It is shown that the general low frequency trends in most anode physical and mechanical properties driven by raw material changes are very well captured by the model. Improvements in the data infrastructure (instrumentation, sampling frequency and location) will be necessary for predicting higher frequency variations in individual baked anode properties. This paper also demonstrates how multivariate latent variable models can be interpreted against process knowledge and used for real-time process monitoring of carbon plants, and detection of faults and abnormal operation.

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.

HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule

NASA Astrophysics Data System (ADS)

Hadad, Ghada M.; El-Gindy, Alaa; Mahmoud, Waleed M. M.

2008-08-01

High-performance liquid chromatography (HPLC) and multivariate spectrophotometric methods are described for the simultaneous determination of ambroxol hydrochloride (AM) and doxycycline (DX) in combined pharmaceutical capsules. The chromatographic separation was achieved on reversed-phase C 18 analytical column with a mobile phase consisting of a mixture of 20 mM potassium dihydrogen phosphate, pH 6-acetonitrile in ratio of (1:1, v/v) and UV detection at 245 nm. Also, the resolution has been accomplished by using numerical spectrophotometric methods as classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS-1) applied to the UV spectra of the mixture and graphical spectrophotometric method as first derivative of the ratio spectra ( 1DD) method. Analytical figures of merit (FOM), such as sensitivity, selectivity, analytical sensitivity, limit of quantitation and limit of detection were determined for CLS, PLS-1 and PCR methods. The proposed methods were validated and successfully applied for the analysis of pharmaceutical formulation and laboratory-prepared mixtures containing the two component combination.

HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule.

PubMed

Hadad, Ghada M; El-Gindy, Alaa; Mahmoud, Waleed M M

2008-08-01

High-performance liquid chromatography (HPLC) and multivariate spectrophotometric methods are described for the simultaneous determination of ambroxol hydrochloride (AM) and doxycycline (DX) in combined pharmaceutical capsules. The chromatographic separation was achieved on reversed-phase C(18) analytical column with a mobile phase consisting of a mixture of 20mM potassium dihydrogen phosphate, pH 6-acetonitrile in ratio of (1:1, v/v) and UV detection at 245 nm. Also, the resolution has been accomplished by using numerical spectrophotometric methods as classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS-1) applied to the UV spectra of the mixture and graphical spectrophotometric method as first derivative of the ratio spectra ((1)DD) method. Analytical figures of merit (FOM), such as sensitivity, selectivity, analytical sensitivity, limit of quantitation and limit of detection were determined for CLS, PLS-1 and PCR methods. The proposed methods were validated and successfully applied for the analysis of pharmaceutical formulation and laboratory-prepared mixtures containing the two component combination.

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.

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.

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.

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.

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.

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol.

PubMed

Yehia, Ali M; Mohamed, Heba M

2016-01-05

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference. Copyright © 2015 Elsevier B.V. All rights reserved.

Multivariate analysis of nystatin and metronidazole in a semi-solid matrix by means of diffuse reflectance NIR spectroscopy and PLS regression.

PubMed

Baratieri, Sabrina C; Barbosa, Juliana M; Freitas, Matheus P; Martins, José A

2006-01-23

A multivariate method of analysis of nystatin and metronidazole in a semi-solid matrix, based on diffuse reflectance NIR measurements and partial least squares regression, is reported. The product, a vaginal cream used in the antifungal and antibacterial treatment, is usually, quantitatively analyzed through microbiological tests (nystatin) and HPLC technique (metronidazole), according to pharmacopeial procedures. However, near infrared spectroscopy has demonstrated to be a valuable tool for content determination, given the rapidity and scope of the method. In the present study, it was successfully applied in the prediction of nystatin (even in low concentrations, ca. 0.3-0.4%, w/w, which is around 100,000 IU/5g) and metronidazole contents, as demonstrated by some figures of merit, namely linearity, precision (mean and repeatability) and accuracy.

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.

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.

Valorization of onion waste and by-products: MCR-ALS applied to reveal the compositional profiles of alcoholic fermentations of onion juice monitored by near-infrared spectroscopy.

PubMed

González-Sáiz, José-María; Esteban-Díez, Isabel; Rodríguez-Tecedor, Sofía; Pizarro, Consuelo

2008-11-01

The overall purpose of the project, of which this study is a part, was to examine the feasibility of onion waste as a support-substrate for the profitable production of food-grade products. This study focused on the efficient production of ethanol from worthless onions by transforming the onion juice into onion liquor via alcoholic fermentation with the yeast Saccharomyces cerevisiae. The onion bioethanol produced could be later used as a favorable substrate for acetic fermentation to finally obtain onion vinegar. Near-infrared spectroscopy (NIRS), coupled with the multivariate curve resolution-alternating least squares (MCR-ALS) method, has been used to reveal the compositional and spectral profiles for both substrates and products of alcoholic fermentation runs, that is, total sugars, ethanol, and biomass concentration. The ambiguity associated with the ALS calculation was resolved by applying suitable inequality and equality constraints. The quality of the results provided by the NIR-based MCR-ALS methodology adopted was evaluated by several performance indicators, including the variance explained by the model, the lack of fit and the agreement between the MCR-ALS achieved solution and the results computed by applying previously validated PLS reference models. An additional fermentation run was employed to test the actual predictive ability of the ALS model developed. For all the components resolved in the fermentation system studied (i.e., total sugars, ethanol, and biomass), the final model obtained showed a high predictive ability and suitable accuracy and precision, both in calibration and external validation, confirmed by the very good agreement between the ALS responses and the reference values (the coefficient of determination was, in all cases, very close to 1, and the statistics confirmed that no significant difference was found between PLS reference models and the MCR-ALS methodology applied). Thus, the proven reliability of the MCR-ALS model presented in this study, based only on NIR measurements, makes it suitable for monitoring of the key species involved in the alcoholic fermentation of onion juice, allowing the process to be modeled and controlled in real time.

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.

[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.

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.

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.

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).

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.

Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares.

PubMed

Zhou, Zhenyu; Liu, Wei; Cui, Jiali; Wang, Xunheng; Arias, Diana; Wen, Ying; Bansal, Ravi; Hao, Xuejun; Wang, Zhishun; Peterson, Bradley S; Xu, Dongrong

2011-02-01

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstrapping estimation) that use univariate or multivariate regression methods to fit MRI data to a pre-specified model. Copyright © 2011 Elsevier Inc. All rights reserved.

Impact of three radiographic methods in the outcome of nonsurgical endodontic treatment: a five-year follow-up.

PubMed

Fernández, Rafael; Cadavid, Diego; Zapata, Sandra M; Alvarez, Luis G; Restrepo, Felipe A

2013-09-01

The periapical film radiograph (PFR) and digital periapical radiograph (DPR) techniques have some limitations in the visualization of small periapical lesions (PLs) when compared with cone-beam computed tomography (CBCT). However, the evidence supporting their effectiveness is very limited. This retrospective longitudinal cohort study evaluated the outcome of endodontic treatments measured/monitored by PFR, DPR, and CBCT during a 5-year follow-up and also determined the prognostic factors that influenced treatment success. A total of 132 teeth (208 roots) with vital pulps received endodontic treatment. The periapical indexes with scores ≥2 for PFR and DPR and ≥1 for CBCT indicated the presence of PLs. Prognostic factors were determined by bivariate and multivariate analyses. Statistical significance was defined at a P level <.05. CBCT detected a higher number of PLs (18.7%, n = 39 roots), followed by DPR (7.7%, n = 16 roots) and PFR (5.7%, n = 12 roots). Likewise, CBCT was more sensitive than PFR and DPR in detecting deficiencies in extension and density of the root canal filling (P ≤ .001). Of the 17 prognostic factors evaluated, 4 were significantly associated with poor outcome to the treatment (P < .05): root canal curvature, disinfection of gutta-percha, presence of missed canals, and the quality of definitive coronal restoration. The success outcome of endodontic treatment after 5 years in teeth with vital pulps varied with each radiographic method: 94.3%/PFR, 92.3%/DPR, and 81.3%/CBCT. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

On-line near infrared spectroscopy as a Process Analytical Technology (PAT) tool to control an industrial seeded API crystallization.

PubMed

Schaefer, C; Lecomte, C; Clicq, D; Merschaert, A; Norrant, E; Fotiadu, F

2013-09-01

The final step of an active pharmaceutical ingredient (API) manufacturing synthesis process consists of a crystallization during which the API and residual solvent contents have to be quantified precisely in order to reach a predefined seeding point. A feasibility study was conducted to demonstrate the suitability of on-line NIR spectroscopy to control this step in line with new version of the European Medicines Agency (EMA) guideline [1]. A quantitative method was developed at laboratory scale using statistical design of experiments (DOE) and multivariate data analysis such as principal component analysis (PCA) and partial least squares (PLS) regression. NIR models were built to quantify the API in the range of 9-12% (w/w) and to quantify the residual methanol in the range of 0-3% (w/w). To improve the predictive ability of the models, the development procedure encompassed: outliers elimination, optimum model rank definition, spectral range and spectral pre-treatment selection. Conventional criteria such as, number of PLS factors, R(2), root mean square errors of calibration, cross-validation and prediction (RMSEC, RMSECV, RMSEP) enabled the selection of three model candidates. These models were tested in the industrial pilot plant during three technical campaigns. Results of the most suitable models were evaluated against to the chromatographic reference methods. Maximum relative bias of 2.88% was obtained about API target content. Absolute bias of 0.01 and 0.02% (w/w) respectively were achieved at methanol content levels of 0.10 and 0.13% (w/w). The repeatability was assessed as sufficient for the on-line monitoring of the 2 analytes. The present feasibility study confirmed the possibility to use on-line NIR spectroscopy as a PAT tool to monitor in real-time both the API and the residual methanol contents, in order to control the seeding of an API crystallization at industrial scale. Furthermore, the successful scale-up of the method proved its capability to be implemented in the manufacturing plant with the launch of the new API process. Copyright © 2013 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.

Maldi-tof fingerprinting of seminal plasma lipids in the study of human male infertility.

PubMed

Camargo, Mariana; Intasqui, Paula; de Lima, Camila Bruna; Montani, Daniela Antunes; Nichi, Marcílio; Pilau, Eduardo Jorge; Gozzo, Fabio Cesar; Lo Turco, Edson Guimarães; Bertolla, Ricardo Pimenta

2014-09-01

This study proposed lipid fingerprinting of human seminal plasma by mass spectrometry as an analytical method to differentiate biological conditions. For this purpose, we chose infertile men as a model to study specific conditions, namely: high and low seminal plasma lipid peroxidation levels (sub-study 1.1), high and low sperm nuclear DNA fragmentation (sub-study 1.2), and intervention status: before and after subinguinal microsurgical varicocelectomy (study 2). Study 1 included 133 patients, of which 113 were utilized for sub-study 1.1 and 89 for sub-study 1.2. Study 2 included 17 adult men submitted to subinguinal varicocelectomy, before and 90 days after varicocelectomy. Lipids were extracted from seminal plasma and submitted to Matrix-Assisted Laser Desorption Ionization Quadrupole-Time-of-Flight Mass Spectrometry in the positive ionization mode. Spectra were processed using Waters(®) MassLynx, and MetaboAnalyst online software was used for statistical analyses. For sub-studies 1.1 and 1.2, and study 2, univariate analysis revealed 8, 87 and 34 significant ions, respectively. Multivariate analysis was performed through PCA and PLS-DA. PCA generated 56, 32 and 34 components respectively for each study and these were submitted to logistic regression. A ROC curve was plotted and the area under the curve was equal to 97.4, 92.5 and 96.5%. PLS-DA generated a list of 19, 24 and 23 VIP ions for sub-studies 1.1 and 1.2, and study 2, respectively. Therefore, this study established the lipid profile and comparison of patterns altered in response to specific biological conditions.

Successive Projections Algorithm-Multivariable Linear Regression Classifier for the Detection of Contaminants on Chicken Carcasses in Hyperspectral Images

NASA Astrophysics Data System (ADS)

Wu, W.; Chen, G. Y.; Kang, R.; Xia, J. C.; Huang, Y. P.; Chen, K. J.

2017-07-01

During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.

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.

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.

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.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

Haq, Quazi M. I.; Mabood, Fazal; Naureen, Zakira; Al-Harrasi, Ahmed; Gilani, Sayed A.; Hussain, Javid; Jabeen, Farah; Khan, Ajmal; Al-Sabari, Ruqaya S. M.; Al-khanbashi, Fatema H. S.; Al-Fahdi, Amira A. M.; Al-Zaabi, Ahoud K. A.; Al-Shuraiqi, Fatma A. M.; Al-Bahaisi, Iman M.

2018-06-01

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2 days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.

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.

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.

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.

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

NASA Astrophysics Data System (ADS)

Zhang, Fangkun; Liu, Tao; Wang, Xue Z.; Liu, Jingxiang; Jiang, Xiaobin

2017-02-01

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

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.

Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra.

PubMed

Cernuda, Carlos; Lughofer, Edwin; Klein, Helmut; Forster, Clemens; Pawliczek, Marcin; Brandstetter, Markus

2017-01-01

During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.e., samples are taken from the process and analyzed in the laboratory. This typically requires significant lab technicians efforts for only a small fraction of samples to be analyzed, which leads to significant costs for beer breweries and companies. Fourier transform mid-infrared (FT-MIR) spectroscopy was used in combination with nonlinear multivariate calibration techniques to overcome (i) the time consuming off-line analyses in beer production and (ii) already known limitations of standard linear chemometric methods, like partial least squares (PLS), for important quality parameters Speers et al. (J I Brewing. 2003;109(3):229-235), Zhang et al. (J I Brewing. 2012;118(4):361-367) such as bitterness, citric acid, total acids, free amino nitrogen, final attenuation, or foam stability. The calibration models are established with enhanced nonlinear techniques based (i) on a new piece-wise linear version of PLS by employing fuzzy rules for local partitioning the latent variable space and (ii) on extensions of support vector regression variants (-PLSSVR and ν-PLSSVR), for overcoming high computation times in high-dimensional problems and time-intensive and inappropriate settings of the kernel parameters. Furthermore, we introduce a new model selection scheme based on bagged ensembles in order to improve robustness and thus predictive quality of the final models. The approaches are tested on real-world calibration data sets for wort and beer mix beverages, and successfully compared to linear methods, showing a clear out-performance in most cases and being able to meet the model quality requirements defined by the experts at the beer company. Figure Workflow for calibration of non-Linear model ensembles from FT-MIR spectra in beer production .

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils.

PubMed

Kharbach, Mourad; Kamal, Rabie; Mansouri, Mohammed Alaoui; Marmouzi, Ilias; Viaene, Johan; Cherrah, Yahia; Alaoui, Katim; Vercammen, Joeri; Bouklouze, Abdelaziz; Vander Heyden, Yvan

2018-10-15

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H 3 O + , NO + and O 2 + ). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

A novel multivariate approach using science-based calibration for direct coating thickness determination in real-time NIR process monitoring.

PubMed

Möltgen, C-V; Herdling, T; Reich, G

2013-11-01

This study demonstrates an approach, using science-based calibration (SBC), for direct coating thickness determination on heart-shaped tablets in real-time. Near-Infrared (NIR) spectra were collected during four full industrial pan coating operations. The tablets were coated with a thin hydroxypropyl methylcellulose (HPMC) film up to a film thickness of 28 μm. The application of SBC permits the calibration of the NIR spectral data without using costly determined reference values. This is due to the fact that SBC combines classical methods to estimate the coating signal and statistical methods for the noise estimation. The approach enabled the use of NIR for the measurement of the film thickness increase from around 8 to 28 μm of four independent batches in real-time. The developed model provided a spectroscopic limit of detection for the coating thickness of 0.64 ± 0.03 μm root-mean square (RMS). In the commonly used statistical methods for calibration, such as Partial Least Squares (PLS), sufficiently varying reference values are needed for calibration. For thin non-functional coatings this is a challenge because the quality of the model depends on the accuracy of the selected calibration standards. The obvious and simple approach of SBC eliminates many of the problems associated with the conventional statistical methods and offers an alternative for multivariate calibration. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

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.

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.

Partial Least Squares and Neural Networks for Quantitative Calibration of Laser-induced Breakdown Spectroscopy (LIBs) of Geologic Samples

NASA Technical Reports Server (NTRS)

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

2010-01-01

The ChemCam instrument [1] on the Mars Science Laboratory (MSL) rover will be used to obtain the chemical composition of surface targets within 7 m of the rover using Laser Induced Breakdown Spectroscopy (LIBS). ChemCam analyzes atomic emission spectra (240-800 nm) from a plasma created by a pulsed Nd:KGW 1067 nm laser. The LIBS spectra can be used in a semiquantitative way to rapidly classify targets (e.g., basalt, andesite, carbonate, sulfate, etc.) and in a quantitative way to estimate their major and minor element chemical compositions. Quantitative chemical analysis from LIBS spectra is complicated by a number of factors, including chemical matrix effects [2]. Recent work has shown promising results using multivariate techniques such as partial least squares (PLS) regression and artificial neural networks (ANN) to predict elemental abundances in samples [e.g. 2-6]. To develop, refine, and evaluate analysis schemes for LIBS spectra of geologic materials, we collected spectra of a diverse set of well-characterized natural geologic samples and are comparing the predictive abilities of PLS, cascade correlation ANN (CC-ANN) and multilayer perceptron ANN (MLP-ANN) analysis procedures.

Exploring the influence of encoding format on subsequent memory.

PubMed

Turney, Indira C; Dennis, Nancy A; Maillet, David; Rajah, M Natasha

2017-05-01

Distinctive encoding is greatly influenced by gist-based processes and has been shown to suffer when highly similar items are presented in close succession. Thus, elucidating the mechanisms underlying how presentation format affects gist processing is essential in determining the factors that influence these encoding processes. The current study utilised multivariate partial least squares (PLS) analysis to identify encoding networks directly associated with retrieval performance in a blocked and intermixed presentation condition. Subsequent memory analysis for successfully encoded items indicated no significant differences between reaction time and retrieval performance and presentation format. Despite no significant behavioural differences, behaviour PLS revealed differences in brain-behaviour correlations and mean condition activity in brain regions associated with gist-based vs. distinctive encoding. Specifically, the intermixed format encouraged more distinctive encoding, showing increased activation of regions associated with strategy use and visual processing (e.g., frontal and visual cortices, respectively). Alternatively, the blocked format exhibited increased gist-based processes, accompanied by increased activity in the right inferior frontal gyrus. Together, results suggest that the sequence that information is presented during encoding affects the degree to which distinctive encoding is engaged. These findings extend our understanding of the Fuzzy Trace Theory and the role of presentation format on encoding processes.

Raman spectroscopy-based screening of IgM positive and negative sera for dengue virus infection

NASA Astrophysics Data System (ADS)

Bilal, M.; Saleem, M.; Bilal, Maria; Ijaz, T.; Khan, Saranjam; Ullah, Rahat; Raza, A.; Khurram, M.; Akram, W.; Ahmed, M.

2016-11-01

A statistical method based on Raman spectroscopy for the screening of immunoglobulin M (IgM) in dengue virus (DENV) infected human sera is presented. In total, 108 sera samples were collected and their antibody indexes (AI) for IgM were determined through enzyme-linked immunosorbent assay (ELISA). Raman spectra of these samples were acquired using a 785 nm wavelength excitation laser. Seventy-eight Raman spectra were selected randomly and unbiasedly for the development of a statistical model using partial least square (PLS) regression, while the remaining 30 were used for testing the developed model. An R-square (r 2) value of 0.929 was determined using the leave-one-sample-out (LOO) cross validation method, showing the validity of this model. It considers all molecular changes related to IgM concentration, and describes their role in infection. A graphical user interface (GUI) platform has been developed to run a developed multivariate model for the prediction of AI of IgM for blindly tested samples, and an excellent agreement has been found between model predicted and clinically determined values. Parameters like sensitivity, specificity, accuracy, and area under receiver operator characteristic (ROC) curve for these tested samples are also reported to visualize model performance.

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.

Bivariate versus multivariate smart spectrophotometric calibration methods for the simultaneous determination of a quaternary mixture of mosapride, pantoprazole and their degradation products.

PubMed

Hegazy, M A; Yehia, A M; Moustafa, A A

2013-05-01

The ability of bivariate and multivariate spectrophotometric methods was demonstrated in the resolution of a quaternary mixture of mosapride, pantoprazole and their degradation products. The bivariate calibrations include bivariate spectrophotometric method (BSM) and H-point standard addition method (HPSAM), which were able to determine the two drugs, simultaneously, but not in the presence of their degradation products, the results showed that simultaneous determinations could be performed in the concentration ranges of 5.0-50.0 microg/ml for mosapride and 10.0-40.0 microg/ml for pantoprazole by bivariate spectrophotometric method and in the concentration ranges of 5.0-45.0 microg/ml for both drugs by H-point standard addition method. Moreover, the applied multivariate calibration methods were able for the determination of mosapride, pantoprazole and their degradation products using concentration residuals augmented classical least squares (CRACLS) and partial least squares (PLS). The proposed multivariate methods were applied to 17 synthetic samples in the concentration ranges of 3.0-12.0 microg/ml mosapride, 8.0-32.0 microg/ml pantoprazole, 1.5-6.0 microg/ml mosapride degradation products and 2.0-8.0 microg/ml pantoprazole degradation products. The proposed bivariate and multivariate calibration methods were successfully applied to the determination of mosapride and pantoprazole in their pharmaceutical preparations.

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.

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.

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.

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.

Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery.

PubMed

Catana, Cornel; Stouten, Pieter F W

2007-01-01

The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, "standard" scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning "customized" scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein-ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure-activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-07-15

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular-for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm-partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)-are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis. Copyright © 2011 Elsevier B.V. All rights reserved.

Proton Nuclear Magnetic Resonance-Spectroscopic Discrimination of Wines Reflects Genetic Homology of Several Different Grape (V. vinifera L.) Cultivars.

PubMed

Hu, Boran; Yue, Yaqing; Zhu, Yong; Wen, Wen; Zhang, Fengmin; Hardie, Jim W

2015-01-01

Proton nuclear magnetic resonance spectroscopy coupled multivariate analysis (1H NMR-PCA/PLS-DA) is an important tool for the discrimination of wine products. Although 1H NMR has been shown to discriminate wines of different cultivars, a grape genetic component of the discrimination has been inferred only from discrimination of cultivars of undefined genetic homology and in the presence of many confounding environmental factors. We aimed to confirm the influence of grape genotypes in the absence of those factors. We applied 1H NMR-PCA/PLS-DA and hierarchical cluster analysis (HCA) to wines from five, variously genetically-related grapevine (V. vinifera) cultivars; all grown similarly on the same site and vinified similarly. We also compared the semi-quantitative profiles of the discriminant metabolites of each cultivar with previously reported chemical analyses. The cultivars were clearly distinguishable and there was a general correlation between their grouping and their genetic homology as revealed by recent genomic studies. Between cultivars, the relative amounts of several of the cultivar-related discriminant metabolites conformed closely with reported chemical analyses. Differences in grape-derived metabolites associated with genetic differences alone are a major source of 1H NMR-based discrimination of wines and 1H NMR has the capacity to discriminate between very closely related cultivars. The study confirms that genetic variation among grape cultivars alone can account for the discrimination of wine by 1H NMR-PCA/PLS and indicates that 1H NMR spectra of wine of single grape cultivars may in future be used in tandem with hierarchical cluster analysis to elucidate genetic lineages and metabolomic relations of grapevine cultivars. In the absence of genetic information, for example, where predecessor varieties are no longer extant, this may be a particularly useful approach.

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

NASA Astrophysics Data System (ADS)

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-01

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

Multivariate Analysis and Prediction of Dioxin-Furan ...

EPA Pesticide Factsheets

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

PubMed

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-05

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Multivariate evaluation of Thyroid Imaging Reporting and Data System (TI-RADS) in diagnosis malignant thyroid nodule: application to PCA and PLS-DA analysis.

PubMed

Zhang, Tan; Li, Fangxuan; Mu, Jiali; Liu, Juntian; Zhang, Sheng

2017-06-01

To explore the significance of ultrasonic features in differential diagnosis of thyroid nodules via combining the thyroid imaging reporting and data system (TI-RADS) and multivariate statistical analysis. Patients who received surgical treatment and was diagnosed with single thyroid nodule by postoperative pathology and preoperative ultrasound were enrolled in this study. Multivariate analysis was applied to assess the significant ultrasonic features which correlated with identifying benign or malignance and grading the TI-RADS classification of thyroid nodule. There were significant differences in the nodule size, aspect ratio, internal, echogenicity, boundary, presence or absence of calcifications, calcification type and CDFI between benign and malignant thyroid nodules. Multivariate analysis showed clear-cut distinction both between benign and malignance and among different TI-RADS categories of malignancy nodules. The shape and calcification of the nodule were important factors for distinguish the benign and malignance. Height of the nodule, aspect and calcification was important factors for grading TI-RADS categories of malignancy thyroid nodules. Ill-defined boundary, irregular shape and presence of calcification related with highly malignant risk for thyroid nodule. The larger height and aspect and presence of calcification related with higher TI-RADS classification of malignancy thyroid nodule.

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

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.

Chemical data as markers of the geographical origins of sugarcane spirits.

PubMed

Serafim, F A T; Pereira-Filho, Edenir R; Franco, D W

2016-04-01

In an attempt to classify sugarcane spirits according to their geographic region of origin, chemical data for 24 analytes were evaluated in 50 cachaças produced using a similar procedure in selected regions of Brazil: São Paulo - SP (15), Minas Gerais - MG (11), Rio de Janeiro - RJ (11), Paraiba -PB (9), and Ceará - CE (4). Multivariate analysis was applied to the analytical results, and the predictive abilities of different classification methods were evaluated. Principal component analysis identified five groups, and chemical similarities were observed between MG and SP samples and between RJ and PB samples. CE samples presented a distinct chemical profile. Among the samples, partial linear square discriminant analysis (PLS-DA) classified 50.2% of the samples correctly, K-nearest neighbor (KNN) 86%, and soft independent modeling of class analogy (SIMCA) 56.2%. Therefore, in this proof of concept demonstration, the proposed approach based on chemical data satisfactorily predicted the cachaças' geographic origins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Use of laser-induced breakdown spectroscopy for the determination of polycarbonate (PC) and acrylonitrile-butadiene-styrene (ABS) concentrations in PC/ABS plastics from e-waste.

PubMed

Costa, Vinicius Câmara; Aquino, Francisco Wendel Batista; Paranhos, Caio Marcio; Pereira-Filho, Edenir Rodrigues

2017-12-01

Due to the continual increase in waste generated from electronic devices, the management of plastics, which represents between 10 and 30% by weight of waste electrical and electronic equipment (WEEE or e-waste), becomes indispensable in terms of environmental and economic impacts. Considering the importance of acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC), and their blends in the electronics and other industries, this study presents a new application of laser-induced breakdown spectroscopy (LIBS) for the fast and direct determination of PC and ABS concentrations in blends of these plastics obtained from samples of e-waste. From the LIBS spectra acquired for the PC/ABS blend, multivariate calibration models were built using partial least squares (PLS) regression. In general, it was possible to infer that the relative errors between the theoretical or reference and predicted values for the spiked samples were lower than 10%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment.

PubMed

Bagnasco, Lucia; Cosulich, M Elisabetta; Speranza, Giovanna; Medini, Luca; Oliveri, Paolo; Lanteri, Silvia

2014-08-15

The relationships between sensory attribute and analytical measurements, performed by electronic tongue (ET) and near-infrared spectroscopy (NIRS), were investigated in order to develop a rapid method for the assessment of umami taste. Commercially available umami products and some aminoacids were submitted to sensory analysis. Results were analysed in comparison with the outcomes of analytical measurements. Multivariate exploratory analysis was performed by principal component analysis (PCA). Calibration models for prediction of the umami taste on the basis of ET and NIR signals were obtained using partial least squares (PLS) regression. Different approaches for merging data from the two different analytical instruments were considered. Both of the techniques demonstrated to provide information related with umami taste. In particular, ET signals showed the higher correlation with umami attribute. Data fusion was found to be slightly beneficial - not so significantly as to justify the coupled use of the two analytical techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Authentication of animal origin of heparin and low molecular weight heparin including ovine, porcine and bovine species using 1D NMR spectroscopy and chemometric tools.

PubMed

Monakhova, Yulia B; Diehl, Bernd W K; Fareed, Jawed

2018-02-05

High resolution (600MHz) nuclear magnetic resonance (NMR) spectroscopy is used to distinguish heparin and low-molecular weight heparins (LMWHs) produced from porcine, bovine and ovine mucosal tissues as well as their blends. For multivariate analysis several statistical methods such as principal component analysis (PCA), factor discriminant analysis (FDA), partial least squares - discriminant analysis (PLS-DA), linear discriminant analysis (LDA) were utilized for the modeling of NMR data of more than 100 authentic samples. Heparin and LMWH samples from the independent test set (n=15) were 100% correctly classified according to its animal origin. Moreover, by using 1 H NMR coupled with chemometrics and several batches of bovine heparins from two producers were differentiated. Thus, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of animal origin and process based manufacturing difference in heparin products. Copyright © 2017 Elsevier B.V. All rights reserved.

External cavity-quantum cascade laser (EC-QCL) spectroscopy for protein analysis in bovine milk.

PubMed

Kuligowski, Julia; Schwaighofer, Andreas; Alcaráz, Mirta Raquel; Quintás, Guillermo; Mayer, Helmut; Vento, Máximo; Lendl, Bernhard

2017-04-22

The analytical determination of bovine milk proteins is important in food and non-food industrial applications and yet, rather labour-intensive wet-chemical, low-throughput methods have been employed since decades. This work proposes the use of external cavity-quantum cascade laser (EC-QCL) spectroscopy for the simultaneous quantification of the most abundant bovine milk proteins and the total protein content based on the chemical information contained in mid-infrared (IR) spectral features of the amide I band. Mid-IR spectra of protein standard mixtures were used for building partial least squares (PLS) regression models. Protein concentrations in commercial bovine milk samples were calculated after chemometric compensation of the matrix contribution employing science-based calibration (SBC) without sample pre-processing. The use of EC-QCL spectroscopy together with advanced multivariate data analysis allowed the determination of casein, α-lactalbumin, β-lactoglobulin and total protein content within several minutes. Copyright © 2017 Elsevier B.V. All rights reserved.

NMR metabolomics of ripened and developing oilseed rape (Brassica napus) and turnip rape (Brassica rapa).

PubMed

Kortesniemi, Maaria; Vuorinen, Anssi L; Sinkkonen, Jari; Yang, Baoru; Rajala, Ari; Kallio, Heikki

2015-04-01

The oilseeds of the commercially important oilseed rape (Brassica napus) and turnip rape (Brassica rapa) were investigated with (1)H NMR metabolomics. The compositions of ripened (cultivated in field trials) and developing seeds (cultivated in controlled conditions) were compared in multivariate models using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Differences in the major lipids and the minor metabolites between the two species were found. A higher content of polyunsaturated fatty acids and sucrose were observed in turnip rape, while the overall oil content and sinapine levels were higher in oilseed rape. The genotype traits were negligible compared to the effect of the growing site and concomitant conditions on the oilseed metabolome. This study demonstrates the applicability of NMR-based analysis in determining the species, geographical origin, developmental stage, and quality of oilseed Brassicas. Copyright © 2014 Elsevier Ltd. All rights reserved.

Short wavelength Raman spectroscopy applied to the discrimination and characterization of three cultivars of extra virgin olive oils in different maturation stages.

PubMed

Gouvinhas, Irene; Machado, Nelson; Carvalho, Teresa; de Almeida, José M M M; Barros, Ana I R N A

2015-01-01

Extra virgin olive oils produced from three cultivars on different maturation stages were characterized using Raman spectroscopy. Chemometric methods (principal component analysis, discriminant analysis, principal component regression and partial least squares regression) applied to Raman spectral data were utilized to evaluate and quantify the statistical differences between cultivars and their ripening process. The models for predicting the peroxide value and free acidity of olive oils showed good calibration and prediction values and presented high coefficients of determination (>0.933). Both the R(2), and the correlation equations between the measured chemical parameters, and the values predicted by each approach are presented; these comprehend both PCR and PLS, used to assess SNV normalized Raman data, as well as first and second derivative of the spectra. This study demonstrates that a combination of Raman spectroscopy with multivariate analysis methods can be useful to predict rapidly olive oil chemical characteristics during the maturation process. Copyright © 2014 Elsevier B.V. All rights reserved.

Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations.

PubMed

Cámara, María S; Ferroni, Félix M; De Zan, Mercedes; Goicoechea, Héctor C

2003-07-01

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods.

Pharmaceutical analysis in solids using front face fluorescence spectroscopy and multivariate calibration with matrix correction by piecewise direct standardization

NASA Astrophysics Data System (ADS)

Alves, Julio Cesar L.; Poppi, Ronei J.

2013-02-01

This paper reports the application of piecewise direct standardization (PDS) for matrix correction in front face fluorescence spectroscopy of solids when different excipients are used in a pharmaceutical preparation based on a mixture of acetylsalicylic acid (ASA), paracetamol (acetaminophen) and caffeine. As verified in earlier studies, the use of different excipients and their ratio can cause a displacement, change in fluorescence intensity or band profile. To overcome this important drawback, a standardization strategy was adopted to convert all the excitation-emission fluorescence spectra into those used for model development. An excitation-emission matrix (EEM) for which excitation and emission wavelengths ranging from 265 to 405 nm and 300 to 480 nm, respectively, was used. Excellent results were obtained using unfolded partial least squares (U-PLS), with RMSEP values of 8.2 mg/g, 10.9 mg/g and 2.7 mg/g for ASA, paracetamol and caffeine, respectively, and with relative errors lesser than 5% for the three analytes.

Simple transmission Raman measurements using a single multivariate model for analysis of pharmaceutical samples contained in capsules of different colors.

PubMed

Lee, Yeojin; Kim, Jaejin; Lee, Sanguk; Woo, Young-Ah; Chung, Hoeil

2012-01-30

Direct transmission Raman measurements for analysis of pharmaceuticals in capsules are advantageous since they can be used to determine active pharmaceutical ingredient (API) concentrations in a non-destructive manner and with much less fluorescence background interference from the capsules themselves compared to conventional back-scattering measurements. If a single calibration model such as developed from spectra simply collected in glass vials could be used to determine API concentrations of samples contained in capsules of different colors rather than constructing individual models for each capsule color, the utility of transmission measurements would be further enhanced. To evaluate the feasibility, transmission Raman spectra of binary mixtures of ambroxol and lactose were collected in a glass vial and a partial least squares (PLS) model for the determination of ambroxol concentration was developed. Then, the model was directly applied to determine ambroxol concentrations of samples contained in capsules of 4 different colors (blue, green, white and yellow). Although the prediction performance was slightly degraded when the samples were placed in blue or green capsules, due to the presence of weak fluorescence, accurate determination of ambroxol was generally achieved in all cases. The prediction accuracy was also investigated when the thickness of the capsule was varied. Copyright © 2011 Elsevier B.V. All rights reserved.

[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.

[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.

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.

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

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.

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

Multivariate Methods for Prediction of Geologic Sample Composition with Laser-Induced Breakdown Spectroscopy

NASA Technical Reports Server (NTRS)

Morris, Richard; Anderson, R.; Clegg, S. M.; Bell, J. F., III

2010-01-01

Laser-induced breakdown spectroscopy (LIBS) uses pulses of laser light to ablate a material from the surface of a sample and produce an expanding plasma. The optical emission from the plasma produces a spectrum which can be used to classify target materials and estimate their composition. The ChemCam instrument on the Mars Science Laboratory (MSL) mission will use LIBS to rapidly analyze targets remotely, allowing more resource- and time-intensive in-situ analyses to be reserved for targets of particular interest. ChemCam will also be used to analyze samples that are not reachable by the rover's in-situ instruments. Due to these tactical and scientific roles, it is important that ChemCam-derived sample compositions are as accurate as possible. We have compared the results of partial least squares (PLS), multilayer perceptron (MLP) artificial neural networks (ANNs), and cascade correlation (CC) ANNs to determine which technique yields better estimates of quantitative element abundances in rock and mineral samples. The number of hidden nodes in the MLP ANNs was optimized using a genetic algorithm. The influence of two data preprocessing techniques were also investigated: genetic algorithm feature selection and averaging the spectra for each training sample prior to training the PLS and ANN algorithms. We used a ChemCam-like laboratory stand-off LIBS system to collect spectra of 30 pressed powder geostandards and a diverse suite of 196 geologic slab samples of known bulk composition. We tested the performance of PLS and ANNs on a subset of these samples, choosing to focus on silicate rocks and minerals with a loss on ignition of less than 2 percent. This resulted in a set of 22 pressed powder geostandards and 80 geologic samples. Four of the geostandards were used as a validation set and 18 were used as the training set for the algorithms. We found that PLS typically resulted in the lowest average absolute error in its predictions, but that the optimized MLP ANN and the CC ANN often gave results comparable to PLS. Averaging the spectra for each training sample and/or using feature selection to choose a small subset of wavelengths to use for predictions gave mixed results, with degraded performance in some cases and similar or slightly improved performance in other cases. However, training time was significantly reduced for both PLS and ANN methods by implementing feature selection, making this a potentially appealing method for initial, rapid-turn-around analyses necessary for Chemcam's tactical role on MSL. Choice of training samples has a strong influence on the accuracy of predictions. We are currently investigating the use of clustering algorithms (e.g. k-means, neural gas, etc.) to identify training sets that are spectrally similar to the unknown samples that are being predicted, and therefore result in improved predictions

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.

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

Optical glucose monitoring using vertical cavity surface emitting lasers (VCSELs)

NASA Astrophysics Data System (ADS)

Talebi Fard, Sahba; Hofmann, Werner; Talebi Fard, Pouria; Kwok, Ezra; Amann, Markus-Christian; Chrostowski, Lukas

2009-08-01

Diabetes Mellitus is a common chronic disease that has become a public health issue. Continuous glucose monitoring improves patient health by stabilizing the glucose levels. Optical methods are one of the painless and promising methods that can be used for blood glucose predictions. However, having accuracies lower than what is acceptable clinically has been a major concern. Using lasers along with multivariate techniques such as Partial Least Square (PLS) can improve glucose predictions. This research involves investigations for developing a novel optical system for accurate glucose predictions, which leads to the development of a small, low power, implantable optical sensor for diabetes patients.

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.

Influence of Different Drying Treatments and Extraction Solvents on the Metabolite Profile and Nitric Oxide Inhibitory Activity of Ajwa Dates.

PubMed

Abdul-Hamid, Nur Ashikin; Abas, Faridah; Ismail, Intan Safinar; Shaari, Khozirah; Lajis, Nordin H

2015-11-01

This study aimed to examine the variation in the metabolite profiles and nitric oxide (NO) inhibitory activity of Ajwa dates that were subjected to 2 drying treatments and different extraction solvents. (1)H NMR coupled with multivariate data analysis was employed. A Griess assay was used to determine the inhibition of the production of NO in RAW 264.7 cells treated with LPS and interferon-γ. The oven dried (OD) samples demonstrated the absence of asparagine and ascorbic acid as compared to the freeze dried (FD) dates. The principal component analysis showed distinct clusters between the OD and FD dates by the second principal component. In respect of extraction solvents, chloroform extracts can be distinguished by the absence of arginine, glycine and asparagine compared to the methanol and 50% methanol extracts. The chloroform extracts can be clearly distinguished from the methanol and 50% methanol extracts by first principal component. Meanwhile, the loading score plot of partial least squares analysis suggested that beta glucose, alpha glucose, choline, ascorbic acid and glycine were among the metabolites that were contributing to higher biological activity displayed by FD and methanol extracts of Ajwa. The results highlight an alternative method of metabolomics approach for determination of the metabolites that contribute to NO inhibitory activity. The association between metabolite profiles and nitric oxide (NO) inhibitory activity of the various extracts of Ajwa dates was evaluated by utilizing partial least squares (PLS) model. The validated PLS model can be employed to predict the NO inhibitory activity of new samples of date fruits based on their NMR spectra which was important for assessing fruit quality. The information gained might be used as guidance for quality control, nutritional values and as a basis for the preparation of any food supplements for human health that employs date palm fruit as the raw material. © 2015 Institute of Food Technologists®

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.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

PubMed Central

ChariDingari, Narahara; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real world applications, e.g. quality assurance and process monitoring. Specifically, variability in sample, system and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a non-linear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data – highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples as well as in related areas of forensic and biological sample analysis. PMID:22292496

Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient.

PubMed

Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan

2016-08-25

Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

PubMed

Dingari, Narahara Chari; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P; Kumar Gundawar, Manoj

2012-03-20

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Unbiased plasma metabolomics reveal the correlation of metabolic pathways and Prakritis of humans.

PubMed

Shirolkar, Amey; Chakraborty, Sutapa; Mandal, Tusharkanti; Dabur, Rajesh

2017-11-25

Ayurveda, an ancient Indian medicinal system, has categorized human body constitutions in three broad constitutional types (prakritis) i.e. Vata, Pitta and Kapha. Analysis of plasma metabolites and related pathways to classify Prakriti specific dominant marker metabolites and metabolic pathways. 38 healthy male individuals were assessed for dominant Prakritis and their fasting blood samples were collected. The processed plasma samples were subjected to rapid resolution liquid chromatography-electrospray ionization-quadrupole time of flight mass spectrometry (RRLC-ESI-QTOFMS). Mass profiles were aligned and subjected to multivariate analysis. Partial least square discriminant analysis (PLS-DA) model showed 97.87% recognition capability. List of PLS-DA metabolites was subjected to permutative Benjamini-Hochberg false discovery rate (FDR) correction and final list of 76 metabolites with p < 0.05 and fold-change > 2.0 was identified. Pathway analysis using metascape and JEPETTO plugins in Cytoscape revealed that steroidal hormone biosynthesis, amino acid, and arachidonic acid metabolism are major pathways varying with different constitution. Biological Go processes analysis showed that aromatic amino acids, sphingolipids, and pyrimidine nucleotides metabolic processes were dominant in kapha type of body constitution. Fat soluble vitamins, cellular amino acid, and androgen biosynthesis process along with branched chain amino acid and glycerolipid catabolic processes were dominant in pitta type individuals. Vata Prakriti was found to have dominant catecholamine, arachidonic acid and hydrogen peroxide metabolomics processes. The neurotransmission and oxidative stress in vata, BCAA catabolic, androgen, xenobiotics metabolic processes in pitta, and aromatic amino acids, sphingolipid, and pyrimidine metabolic process in kaphaPrakriti were the dominant marker pathways. Copyright © 2017 Transdisciplinary University, Bangalore and World Ayurveda Foundation. Published by Elsevier B.V. All rights reserved.

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.

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.

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

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

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.

Linear model correction: A method for transferring a near-infrared multivariate calibration model without standard samples.

PubMed

Liu, Yan; Cai, Wensheng; Shao, Xueguang

2016-12-05

Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.

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.

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.

Validated univariate and multivariate spectrophotometric methods for the determination of pharmaceuticals mixture in complex wastewater

NASA Astrophysics Data System (ADS)

Riad, Safaa M.; Salem, Hesham; Elbalkiny, Heba T.; Khattab, Fatma I.

2015-04-01

Five, accurate, precise, and sensitive univariate and multivariate spectrophotometric methods were developed for the simultaneous determination of a ternary mixture containing Trimethoprim (TMP), Sulphamethoxazole (SMZ) and Oxytetracycline (OTC) in waste water samples collected from different cites either production wastewater or livestock wastewater after their solid phase extraction using OASIS HLB cartridges. In univariate methods OTC was determined at its λmax 355.7 nm (0D), while (TMP) and (SMZ) were determined by three different univariate methods. Method (A) is based on successive spectrophotometric resolution technique (SSRT). The technique starts with the ratio subtraction method followed by ratio difference method for determination of TMP and SMZ. Method (B) is successive derivative ratio technique (SDR). Method (C) is mean centering of the ratio spectra (MCR). The developed multivariate methods are principle component regression (PCR) and partial least squares (PLS). The specificity of the developed methods is investigated by analyzing laboratory prepared mixtures containing different ratios of the three drugs. The obtained results are statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p = 0.05.

Validated univariate and multivariate spectrophotometric methods for the determination of pharmaceuticals mixture in complex wastewater.

PubMed

Riad, Safaa M; Salem, Hesham; Elbalkiny, Heba T; Khattab, Fatma I

2015-04-05

Five, accurate, precise, and sensitive univariate and multivariate spectrophotometric methods were developed for the simultaneous determination of a ternary mixture containing Trimethoprim (TMP), Sulphamethoxazole (SMZ) and Oxytetracycline (OTC) in waste water samples collected from different cites either production wastewater or livestock wastewater after their solid phase extraction using OASIS HLB cartridges. In univariate methods OTC was determined at its λmax 355.7 nm (0D), while (TMP) and (SMZ) were determined by three different univariate methods. Method (A) is based on successive spectrophotometric resolution technique (SSRT). The technique starts with the ratio subtraction method followed by ratio difference method for determination of TMP and SMZ. Method (B) is successive derivative ratio technique (SDR). Method (C) is mean centering of the ratio spectra (MCR). The developed multivariate methods are principle component regression (PCR) and partial least squares (PLS). The specificity of the developed methods is investigated by analyzing laboratory prepared mixtures containing different ratios of the three drugs. The obtained results are statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p=0.05. Copyright © 2015 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

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.