Multivariate calibration applied to the quantitative analysis of infrared spectra
NASA Astrophysics Data System (ADS)
Haaland, David M.
1992-03-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in- situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mid- or near-infrared spectra of the blood. Progress toward the noninvasive determination of glucose levels in diabetics is an ultimate goal of this research.
Multivariate calibration applied to the quantitative analysis of infrared spectra
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
Multivariate Regression with Calibration*
Liu, Han; Wang, Lie; Zhao, Tuo
2014-01-01
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861
Primer on multivariate calibration
Thomas, E.V. )
1994-08-01
In analytical chemistry, calibration is the procedure that relates instrumental measurements to an analyte of interest. Typically, instrumental measurements are obtained from specimens in which the amount (or level) of the analyte has been determined by some independent and inherently accurate assay (e.g., wet chemistry). Together, the instrumental measurements and results from the independent assays are used to construct a model that relates the analyte level to the instrumental measurements. The advent of high-speed digital computers has greatly increased data acquisition and analysis capabilities and has provided the analytical chemist with opportunities to use many measurements - perhaps hundreds - for calibrating an instrument (e.g., absorbances at multiple wave-lengths). To take advantage of this technology, however, new methods (i.e., multivariate calibration methods) were needed for analyzing and modeling the experimental data. The purpose of this report is to introduce several evolving multivariate calibration methods and to present some important issues regarding their use. 30 refs., 7 figs.
Savescu, Roxana Florenta; Laba, Marian
2016-06-01
This paper highlights the statistical methodology used in a dissection experiment carried out in Romania to calibrate and standardize two classification devices, OptiGrade PRO (OGP) and Fat-o-Meat'er (FOM). One hundred forty-five carcasses were measured using the two probes and dissected according to the European reference method. To derive prediction formulas for each device, multiple linear regression analysis was performed on the relationship between the reference lean meat percentage and the back fat and muscle thicknesses, using the ordinary least squares technique. The root mean squared error of prediction calculated using the leave-one-out cross validation met European Commission (EC) requirements. The application of the new prediction equations reduced the gap between the lean meat percentage measured with the OGP and FOM from 2.43% (average for the period Q3/2006-Q2/2008) to 0.10% (average for the period Q3/2008-Q4/2014), providing the basis for a fair payment system for the pig producers. PMID:26835835
NASA Astrophysics Data System (ADS)
Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.
2013-09-01
Four simple, accurate and specific methods were developed and validated for the simultaneous estimation of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in commercial tablets. The derivative spectrophotometric methods include Derivative Ratio Zero Crossing (DRZC) and Double Divisor Ratio Spectra-Derivative Spectrophotometry (DDRS-DS) methods, while the multivariate calibrations used are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods were applied successfully in the determination of the drugs in laboratory-prepared mixtures and in commercial pharmaceutical preparations. The validity of the proposed methods was assessed using the standard addition technique. The linearity of the proposed methods is investigated in the range of 2-32, 4-44 and 2-20 μg/mL for AML, VAL and HCT, respectively.
Darwish, Hany W; Hassan, Said A; Salem, Maissa Y; El-Zeany, Badr A
2013-09-01
Four simple, accurate and specific methods were developed and validated for the simultaneous estimation of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in commercial tablets. The derivative spectrophotometric methods include Derivative Ratio Zero Crossing (DRZC) and Double Divisor Ratio Spectra-Derivative Spectrophotometry (DDRS-DS) methods, while the multivariate calibrations used are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods were applied successfully in the determination of the drugs in laboratory-prepared mixtures and in commercial pharmaceutical preparations. The validity of the proposed methods was assessed using the standard addition technique. The linearity of the proposed methods is investigated in the range of 2-32, 4-44 and 2-20 μg/mL for AML, VAL and HCT, respectively. PMID:23727675
Adaptable Multivariate Calibration Models for Spectral Applications
THOMAS,EDWARD V.
1999-12-20
Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.
Exploration of new multivariate spectral calibration algorithms.
Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J.; Guenard, Robert D.
2004-03-01
A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.
Different approaches to multivariate calibration of nonlinear sensor data.
Dieterle, Frank; Busche, Stefan; Gauglitz, Günter
2004-10-01
In this study, different approaches to the multivariate calibration of the vapors of two refrigerants are reported. As the relationships between the time-resolved sensor signals and the concentrations of the analytes are nonlinear, the widely used partial least-squares regression (PLS) fails. Therefore, different methods are used, which are known to be able to deal with nonlinearities present in data. First, the Box-Cox transformation, which transforms the dependent variables nonlinearly, was applied. The second approach, the implicit nonlinear PLS regression, tries to account for nonlinearities by introducing squared terms of the independent variables to the original independent variables. The third approach, quadratic PLS (QPLS), uses a nonlinear quadratic inner relationship for the model instead of a linear relationship such as PLS. Tree algorithms are also used, which split a nonlinear problem into smaller subproblems, which are modeled using linear methods or discrete values. Finally, neural networks are applied, which are able to model any relationship. Different special implementations, like genetic algorithms with neural networks and growing neural networks, are also used to prevent an overfitting. Among the fast and simpler algorithms, QPLS shows good results. Different implementations of neural networks show excellent results. Among the different implementations, the most sophisticated and computing-intensive algorithms (growing neural networks) show the best results. Thus, the optimal method for the data set presented is a compromise between quality of calibration and complexity of the algorithm. PMID:15156303
McGuire, J.A.; Adhihetty, I.S.; Niemczyk, T.M. . Dept. of Chemistry); Haaland, D.M.; Taylor, D.F.; Blankenship, D.M. )
1991-01-01
Partial least squares multivariate calibration methods were applied to the infrared spectra of a new set of borophosphosilicate glass (BPSG) thin films on silicon wafers. The calibration samples were prepared by a low pressure chemical vapor deposition (LPCVD) process. The statistically designed calibration set included data from nearly 400 coated Si wafers. Calibrations were attempted for properties such as dopant concentrations, thickness, etch rate, film stress, and electrical parameters. It was found that annealed films were predicted more precisely than unannealed films. B, P, and thickness measurements yielded the most precise results by these techniques. Multivariate calibration methods applied to etch rate for annealed films and unannealed film stress provided some limited predictive ability. The detection and removal of outliers greatly improved the analysis precisions. Finally, within wafer and between wafer dopant uniformity may be responsible for degrading the precision of these analytical methods. 7 refs., 3 figs., 2 tabs.
Transfer of multivariate calibration models between spectrometers: A progress report
Haaland, D.; Jones, H.; Rohrback, B.
1994-12-31
Multivariate calibration methods are extremely powerful for quantitative spectral analyses and have myriad uses in quality control and process monitoring. However, when analyses are to be completed at multiple sites or when spectrometers drift, recalibration is required. Often a full recalibration of an instrument can be impractical: the problem is particularly acute when the number of calibration standards is large or the standards chemically unstable. Furthermore, simply using Instrument A`s calibration model to predict unknowns on Instrument B can lead to enormous errors. Therefore, a mathematical procedure that would allow for the efficient transfer of a multivariate calibration model from one instrument to others using a small number of transfer standards is highly desirable. In this study, near-infrared spectral data have been collected from two sets of statistically designed round-robin samples on multiple FT-IR and grating spectrometers. One set of samples encompasses a series of dilute aqueous solutions of urea, creatinine, and NaCl while the second set is derived from mixtures of heptane, monochlorobenzene, and toluene. A systematic approach has been used to compare the results from four published transfer algorithms in order to determine parameters that affect the quality of the transfer for each class of sample and each type of spectrometer.
Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration.
Chang, Haitao; Zhu, Lianqing; Lou, Xiaoping; Meng, Xiaochen; Guo, Yangkuan; Wang, Zhongyu
2016-01-01
One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20-200 µg/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity. PMID:27271636
Local Strategy Combined with a Wavelength Selection Method for Multivariate Calibration
Chang, Haitao; Zhu, Lianqing; Lou, Xiaoping; Meng, Xiaochen; Guo, Yangkuan; Wang, Zhongyu
2016-01-01
One of the essential factors influencing the prediction accuracy of multivariate calibration models is the quality of the calibration data. A local regression strategy, together with a wavelength selection approach, is proposed to build the multivariate calibration models based on partial least squares regression. The local algorithm is applied to create a calibration set of spectra similar to the spectrum of an unknown sample; the synthetic degree of grey relation coefficient is used to evaluate the similarity. A wavelength selection method based on simple-to-use interactive self-modeling mixture analysis minimizes the influence of noisy variables, and the most informative variables of the most similar samples are selected to build the multivariate calibration model based on partial least squares regression. To validate the performance of the proposed method, ultraviolet-visible absorbance spectra of mixed solutions of food coloring analytes in a concentration range of 20–200 µg/mL is measured. Experimental results show that the proposed method can not only enhance the prediction accuracy of the calibration model, but also greatly reduce its complexity. PMID:27271636
CHAMBERS,WILLIAM B.; HAALAND,DAVID M.; KEENAN,MICHAEL R.; MELGAARD,DAVID K.
1999-10-01
The advent of inductively coupled plasma-atomic emission spectrometers (ICP-AES) equipped with charge-coupled-device (CCD) detector arrays allows the application of multivariate calibration methods to the quantitative analysis of spectral data. We have applied classical least squares (CLS) methods to the analysis of a variety of samples containing up to 12 elements plus an internal standard. The elements included in the calibration models were Ag, Al, As, Au, Cd, Cr, Cu, Fe, Ni, Pb, Pd, and Se. By performing the CLS analysis separately in each of 46 spectral windows and by pooling the CLS concentration results for each element in all windows in a statistically efficient manner, we have been able to significantly improve the accuracy and precision of the ICP-AES analyses relative to the univariate and single-window multivariate methods supplied with the spectrometer. This new multi-window CLS (MWCLS) approach simplifies the analyses by providing a single concentration determination for each element from all spectral windows. Thus, the analyst does not have to perform the tedious task of reviewing the results from each window in an attempt to decide the correct value among discrepant analyses in one or more windows for each element. Furthermore, it is not necessary to construct a spectral correction model for each window prior to calibration and analysis: When one or more interfering elements was present, the new MWCLS method was able to reduce prediction errors for a selected analyte by more than 2 orders of magnitude compared to the worst case single-window multivariate and univariate predictions. The MWCLS detection limits in the presence of multiple interferences are 15 rig/g (i.e., 15 ppb) or better for each element. In addition, errors with the new method are only slightly inflated when only a single target element is included in the calibration (i.e., knowledge of all other elements is excluded during calibration). The MWCLS method is found to be vastly
NASA Astrophysics Data System (ADS)
Yang, Haiqing; Wu, Di; He, Yong
2007-11-01
Near-infrared spectroscopy (NIRS) with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc. is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for variety discrimination of brown sugars using short-wave NIR spectroscopy (800-1050nm) was developed in this work. The relationship between the absorbance spectra and brown sugar varieties was established. The spectral data were compressed by the principal component analysis (PCA). The resulting features can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectral samples. It appears to provide a reasonable variety clustering of brown sugars. The 2-D PCs plot obtained using the first two PCs can be used for the pattern recognition. Least-squares support vector machines (LS-SVM) was applied to solve the multivariate calibration problems in a relatively fast way. The work has shown that short-wave NIR spectroscopy technique is available for the brand identification of brown sugar, and LS-SVM has the better identification ability than PLS when the calibration set is small.
Improved Quantitative Analysis of Ion Mobility Spectrometry by Chemometric Multivariate Calibration
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 the 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.
Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy
Wolfrum, E.; Payne, C.; Stefaniak, T.; Rooney, W.; Dighe, N.; Bean, B.; Dahlberg, J.
2013-03-01
NREL developed calibration models based on near-infrared (NIR) spectroscopy coupled with multivariate statistics to predict compositional properties relevant to cellulosic biofuels production for a variety of sorghum cultivars. A robust calibration population was developed in an iterative fashion. The quality of models developed using the same sample geometry on two different types of NIR spectrometers and two different sample geometries on the same spectrometer did not vary greatly.
Ferreira, Vicente; Herrero, Paula; Zapata, Julián; Escudero, Ana
2015-08-14
SPME is extremely sensitive to experimental parameters affecting liquid-gas and gas-solid distribution coefficients. Our aims were to measure the weights of these factors and to design a multivariate strategy based on the addition of a pool of internal standards, to minimize matrix effects. Synthetic but real-like wines containing selected analytes and variable amounts of ethanol, non-volatile constituents and major volatile compounds were prepared following a factorial design. The ANOVA study revealed that even using a strong matrix dilution, matrix effects are important and additive with non-significant interaction effects and that it is the presence of major volatile constituents the most dominant factor. A single internal standard provided a robust calibration for 15 out of 47 analytes. Then, two different multivariate calibration strategies based on Partial Least Square Regression were run in order to build calibration functions based on 13 different internal standards able to cope with matrix effects. The first one is based in the calculation of Multivariate Internal Standards (MIS), linear combinations of the normalized signals of the 13 internal standards, which provide the expected area of a given unit of analyte present in each sample. The second strategy is a direct calibration relating concentration to the 13 relative areas measured in each sample for each analyte. Overall, 47 different compounds can be reliably quantified in a single fully automated method with overall uncertainties better than 15%. PMID:26166296
Vershinin, Viacheslav I; Petrov, Sergey V
2016-02-01
Alkanes, cycloalkanes and arenes have rather different sensitivities to IR-spectrometric determination, leading to high relative uncertainty (δc) for the total petroleum hydrocarbon index (TPH) in natural and waste waters. Another source of TPH uncertainty is the mismatch of group composition of the hydrocarbon mixture in the sample and in the standard substance used for one-dimensional calibration. Increasing the number of wavelengths and using of multivariate calibrations permit the reduction of δc to <10% rel. These calibrations may be constructed from IR-spectra and findings of extracts from aqueous solutions with known content of hydrocarbons. The method takes into account the losses of hydrocarbons during sample preparation. The accuracy of TPH estimations for this method is much better than for standard methods based on one-dimensional calibration with Simard mixture. This new method is useful in produced waste water analysis. PMID:26653437
de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.
2014-01-01
Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625
Kay, D; McDonald, A
1983-01-01
This paper reports on the calibration and use of a multiple regression model designed to predict concentrations of Escherichia coli and total coliforms in two upland British impoundments. The multivariate approach has improved predictive capability over previous univariate linear models because it includes predictor variables for the timing and magnitude of hydrological input to the reservoirs and physiochemical parameters of water quality. The significance of these results for catchment management research is considered. PMID:6639016
Wolfrum, E. J.; Sluiter, A. D.
2009-01-01
We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.
Hernandez, Silvia R; Kergaravat, Silvina V; Pividori, Maria Isabel
2013-03-15
An approach based on the electrochemical detection of the horseradish peroxidase enzymatic reaction by means of square wave voltammetry was developed for the determination of phenolic compounds in environmental samples. First, a systematic optimization procedure of three factors involved in the enzymatic reaction was carried out using response surface methodology through a central composite design. Second, the enzymatic electrochemical detection coupled with a multivariate calibration method based in the partial least-squares technique was optimized for the determination of a mixture of five phenolic compounds, i.e. phenol, p-aminophenol, p-chlorophenol, hydroquinone and pyrocatechol. The calibration and validation sets were built and assessed. In the calibration model, the LODs for phenolic compounds oscillated from 0.6 to 1.4 × 10(-6) mol L(-1). Recoveries for prediction samples were higher than 85%. These compounds were analyzed simultaneously in spiked samples and in water samples collected close to tanneries and landfills. PMID:23598144
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
Darwish, Hany W; Backeit, Ahmed H
2013-01-01
Olmesartan medoxamil (OLM, an angiotensin II receptor blocker) and amlodipine besylate (AML, a dihydropyridine calcium channel blocker), are co-formulated in a single-dose combination for the treatment of hypertensive patients whose blood pressure is not adequately controlled on either component monotherapy. In this work, four multivariate and two univariate calibration methods were applied for simultaneous spectrofluorimetric determination of OLM and AML in their combined pharmaceutical tablets in all ratios approved by FDA. The four multivariate methods are partial least squares (PLS), genetic algorithm PLS (GA-PLS), principal component ANN (PC-ANN) and GA-ANN. The two proposed univariate calibration methods are, direct spectrofluorimetric method for OLM and isoabsorpitive method for determination of total concentration of OLM and AML and hence AML by subtraction. The results showed the superiority of multivariate calibration methods over univariate ones for the analysis of the binary mixture. The optimum assay conditions were established and the proposed multivariate calibration methods were successfully applied for the assay of the two drugs in validation set and combined pharmaceutical tablets with excellent recoveries. No interference was observed from common pharmaceutical additives. The results were favorably compared with those obtained by a reference spectrophotometric method. PMID:22895851
Li, Hongdong; Liang, Yizeng; Xu, Qingsong; Cao, Dongsheng
2009-08-19
By employing the simple but effective principle 'survival of the fittest' on which Darwin's Evolution Theory is based, a novel strategy for selecting an optimal combination of key wavelengths of multi-component spectral data, named competitive adaptive reweighted sampling (CARS), is developed. Key wavelengths are defined as the wavelengths with large absolute coefficients in a multivariate linear regression model, such as partial least squares (PLS). In the present work, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each wavelength. Then, based on the importance level of each wavelength, CARS sequentially selects N subsets of wavelengths from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a calibration model. Next, based on the regression coefficients, a two-step procedure including exponentially decreasing function (EDF) based enforced wavelength selection and adaptive reweighted sampling (ARS) based competitive wavelength selection is adopted to select the key wavelengths. Finally, cross validation (CV) is applied to choose the subset with the lowest root mean square error of CV (RMSECV). The performance of the proposed procedure is evaluated using one simulated dataset together with one near infrared dataset of two properties. The results reveal an outstanding characteristic of CARS that it can usually locate an optimal combination of some key wavelengths which are interpretable to the chemical property of interest. Additionally, our study shows that better prediction is obtained by CARS when compared to full spectrum PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE) and moving window partial least squares regression (MWPLSR). PMID:19616692
Applying Multivariate Discrete Distributions to Genetically Informative Count Data.
Kirkpatrick, Robert M; Neale, Michael C
2016-03-01
We present a novel method of conducting biometric analysis of twin data when the phenotypes are integer-valued counts, which often show an L-shaped distribution. Monte Carlo simulation is used to compare five likelihood-based approaches to modeling: our multivariate discrete method, when its distributional assumptions are correct, when they are incorrect, and three other methods in common use. With data simulated from a skewed discrete distribution, recovery of twin correlations and proportions of additive genetic and common environment variance was generally poor for the Normal, Lognormal and Ordinal models, but good for the two discrete models. Sex-separate applications to substance-use data from twins in the Minnesota Twin Family Study showed superior performance of two discrete models. The new methods are implemented using R and OpenMx and are freely available. PMID:26497008
Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration.
Godinho, Robson B; Santos, Mauricio C; Poppi, Ronei J
2016-03-15
An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies. PMID:26771246
Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration
NASA Astrophysics Data System (ADS)
Godinho, Robson B.; Santos, Mauricio C.; Poppi, Ronei J.
2016-03-01
An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies.
Coelho, Clarimar José; Galvão, Roberto K H; de Araújo, Mário César U; Pimentel, Maria Fernanda; da Silva, Edvan Cirino
2003-01-01
A novel strategy for the optimization of wavelet transforms with respect to the statistics of the data set in multivariate calibration problems is proposed. The optimization follows a linear semi-infinite programming formulation, which does not display local maxima problems and can be reproducibly solved with modest computational effort. After the optimization, a variable selection algorithm is employed to choose a subset of wavelet coefficients with minimal collinearity. The selection allows the building of a calibration model by direct multiple linear regression on the wavelet coefficients. In an illustrative application involving the simultaneous determination of Mn, Mo, Cr, Ni, and Fe in steel samples by ICP-AES, the proposed strategy yielded more accurate predictions than PCR, PLS, and nonoptimized wavelet regression. PMID:12767151
NASA Technical Reports Server (NTRS)
Liberty, S. R.; Mielke, R. R.; Tung, L. J.
1981-01-01
Applied research in the area of spectral assignment in multivariable systems is reported. A frequency domain technique for determining the set of all stabilizing controllers for a single feedback loop multivariable system is described. It is shown that decoupling and tracking are achievable using this procedure. The technique is illustrated with a simple example.
Kalivas, John H; Héberger, Károly; Andries, Erik
2015-04-15
Most multivariate calibration methods require selection of tuning parameters, such as partial least squares (PLS) or the Tikhonov regularization variant ridge regression (RR). Tuning parameter values determine the direction and magnitude of respective model vectors thereby setting the resultant predication abilities of the model vectors. Simultaneously, tuning parameter values establish the corresponding bias/variance and the underlying selectivity/sensitivity tradeoffs. Selection of the final tuning parameter is often accomplished through some form of cross-validation and the resultant root mean square error of cross-validation (RMSECV) values are evaluated. However, selection of a "good" tuning parameter with this one model evaluation merit is almost impossible. Including additional model merits assists tuning parameter selection to provide better balanced models as well as allowing for a reasonable comparison between calibration methods. Using multiple merits requires decisions to be made on how to combine and weight the merits into an information criterion. An abundance of options are possible. Presented in this paper is the sum of ranking differences (SRD) to ensemble a collection of model evaluation merits varying across tuning parameters. It is shown that the SRD consensus ranking of model tuning parameters allows automatic selection of the final model, or a collection of models if so desired. Essentially, the user's preference for the degree of balance between bias and variance ultimately decides the merits used in SRD and hence, the tuning parameter values ranked lowest by SRD for automatic selection. The SRD process is also shown to allow simultaneous comparison of different calibration methods for a particular data set in conjunction with tuning parameter selection. Because SRD evaluates consistency across multiple merits, decisions on how to combine and weight merits are avoided. To demonstrate the utility of SRD, a near infrared spectral data set and a
Rapid detection of whey in milk powder samples by spectrophotometric and multivariate calibration.
de Carvalho, Bruna Mara Aparecida; de Carvalho, Lorendane Millena; dos Reis Coimbra, Jane Sélia; Minim, Luis Antônio; de Souza Barcellos, Edilton; da Silva Júnior, Willer Ferreira; Detmann, Edenio; de Carvalho, Gleidson Giordano Pinto
2015-05-01
A rapid method for the detection and quantification of the adulteration of milk powder by the addition of whey was assessed by measuring glycomacropeptide protein using mid-infrared spectroscopy (MIR). Fluid milk samples were dried and then spiked with different concentrations of GMP and whey. Calibration models were developed using multivariate techniques, from spectral data. For the principal component analysis and discriminant analysis, excellent percentages of correct classification were achieved in accordance with the increase in the proportion of whey samples. For partial least squares regression analysis, the correlation coefficient (r) and root mean square error of prediction (RMSEP) in the best model were 0.9885 and 1.17, respectively. The rapid analysis, low cost monitoring and high throughput number of samples tested per unit time indicate that MIR spectroscopy may hold potential as a rapid and reliable method for detecting milk powder frauds using cheese whey. PMID:25529644
Tortajada-Genaro, L A; Campíns-Falcó, P
2007-05-15
Multivariate standardisation is proposed for the successful chemiluminescence determination of chromium based on luminol-hydrogen peroxide reaction. In an extended concentration range, non-linear calibration model is needed. The studied instrumental situations were different detection cells, instruments, assemblies, time and their possible combinations. Chemiluminescence kinetic registers have been transferred using piecewise direct standardisation (PDS) method. The optimisation of transfer parameters has been carried out based on the prediction residual error criteria. Non-linear principal component regression (NL-PCR) and non-linear partial least square regression (NL-PLS) were chosen for modelling the relationship signal-concentration of transferred registers. Good accuracy and precision were obtained for water samples. The concentrations of chromium were statistically in agreement with reference method values and with recovery studies. Therefore, it is possible to transfer chemiluminescence curves without loosing ability of prediction, even the presence of a non-linear behaviour. PMID:19071716
da Silva, Gilmare A; Maretto, Danilo A; Bolini, Helena Maria A; Teófilo, Reinaldo F; Augusto, Fabio; Poppi, Ronei J
2012-10-01
In this study, two important sensorial parameters of beer quality - bitterness and grain taste - were correlated with data obtained after headspace solid phase microextraction - gas chromatography with mass spectrometric detection (HS-SPME-GC-MS) analysis. Sensorial descriptors of 32 samples of Pilsner beers from different brands were previously estimated by conventional quantitative descriptive analyses (QDA). Areas of 54 compounds systematically found in the HS-SPME-GC-MS chromatograms were used as input data. Multivariate calibration models were established between the chromatographic areas and the sensorial parameters. The peaks (compounds) relevant to build each multivariate calibration model were determined by genetic algorithm (GA) and ordered predictors selection (OPS), tools for variable selection. GA selected 11 and 15 chromatographic peak areas, for bitterness and grain taste, respectively; while OPS selected 17 and 16 compounds for the same parameters. It could be noticed that seven variables were commonly pointed out by both variable selection methods to bitterness parameter and 10 variables were commonly selected to grain taste attribute. The peak areas most significant to the evaluation of the parameters found by both variable selection methods fed to the PLS algorithm to find the proper models. The obtained models estimated the sensorial descriptors with good accuracy and precision, showing that the utilised approaches were efficient in finding the evaluated correlations. Certainly, the combination of proper chemometric methodologies and instrumental data can be used as a potential tool for sensorial evaluation of foods and beverages, allowing for fast and secure replication of parameters usually measured by trained panellists. PMID:25005998
NASA Astrophysics Data System (ADS)
Tan, Chao; Wang, Jinyue; Wu, Tong; Qin, Xin; Li, Menglong
2010-12-01
Based on the combination of uninformative variable elimination (UVE), bootstrap and mutual information (MI), a simple ensemble algorithm, named ESPLS, is proposed for spectral multivariate calibration (MVC). In ESPLS, those uninformative variables are first removed; and then a preparatory training set is produced by bootstrap, on which a MI spectrum of retained variables is calculated. The variables that exhibit higher MI than a defined threshold form a subspace on which a candidate partial least-squares (PLS) model is constructed. This process is repeated. After a number of candidate models are obtained, a small part of models is picked out to construct an ensemble model by simple/weighted average. Four near/mid-infrared (NIR/MIR) spectral datasets concerning the determination of six components are used to verify the proposed ESPLS. The results indicate that ESPLS is superior to UVEPLS and its combination with MI-based variable selection (SPLS) in terms of both the accuracy and robustness. Besides, from the perspective of end-users, ESPLS does not increase the complexity of a calibration when enhancing its performance.
Tonello, Natalia; Moressi, Marcela Beatriz; Robledo, Sebastián Noel; D'Eramo, Fabiana; Marioli, Juan Miguel
2016-09-01
The simultaneous determination of eugenol (EU), thymol (Ty) and carvacrol (CA) in honey samples, employing square wave voltammetry (SWV) and chemometrics tools, is informed for the first time. For this purpose, a glassy carbon electrode (GCE) was used as working electrode. The operating conditions and influencing parameters (involving several chemical and instrumental parameters) were first optimized by cyclic voltammetry (CV). Thus, the effects of the scan rate, pH and analyte concentration on the electrochemical response of the above mentioned molecules were studied. The results show that the electrochemical responses of the three compounds are very similar and that the voltammetric traces present a high degree of overlap under all the experimental conditions used in this study. Therefore, two chemometric tools were tested to obtain the multivariate calibration model. One method was the partial least squares regression (PLS-1), which assumes a linear behaviour. The other nonlinear method was an artificial neural network (ANN). In this last case we used a supervised, feed-forward network with Levenberg-Marquardt back propagation training. From the accuracies and precisions analysis between nominal and estimated concentrations calculated by using both methods, it was inferred that the ANN method was a good model to quantify EU, Ty and CA in honey samples. Recovery percentages were between 87% and 104%, except for two samples whose values were 136% and 72%. The analytical methodology was simple, fast and accurate. PMID:27343610
Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM
ERIC Educational Resources Information Center
Warner, Rebecca M.
2007-01-01
This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…
Allegrini, Franco; Olivieri, Alejandro C
2013-10-15
A new optimization strategy for multivariate partial-least-squares (PLS) regression analysis is described. It was achieved by integrating three efficient strategies to improve PLS calibration models: (1) variable selection based on ant colony optimization, (2) mathematical pre-processing selection by a genetic algorithm, and (3) sample selection through a distance-based procedure. Outlier detection has also been included as part of the model optimization. All the above procedures have been combined into a single algorithm, whose aim is to find the best PLS calibration model within a Monte Carlo-type philosophy. Simulated and experimental examples are employed to illustrate the success of the proposed approach. PMID:24054659
Multivariate calibration modeling of liver oxygen saturation using near-infrared spectroscopy
NASA Astrophysics Data System (ADS)
Cingo, Ndumiso A.; Soller, Babs R.; Puyana, Juan C.
2000-05-01
The liver has been identified as an ideal site to spectroscopically monitor for changes in oxygen saturation during liver transplantation and shock because it is susceptible to reduced blood flow and oxygen transport. Near-IR spectroscopy, combined with multivariate calibration techniques, has been shown to be a viable technique for monitoring oxygen saturation changes in various organs in a minimally invasive manner. The liver has a dual system circulation. Blood enters the liver through the portal vein and hepatic artery, and leaves through the hepatic vein. Therefore, it is of utmost importance to determine how the liver NIR spectroscopic information correlates with the different regions of the hepatic lobule as the dual circulation flows from the presinusoidal space into the post sinusoidal region of the central vein. For NIR spectroscopic information to reliably represent the status of liver oxygenation, the NIR oxygen saturation should best correlate with the post-sinusoidal region. In a series of six pigs undergoing induced hemorrhagic chock, NIR spectra collected from the liver were used together with oxygen saturation reference data from the hepatic and portal veins, and an average of the two to build partial least-squares regression models. Results obtained from these models show that the hepatic vein and an average of the hepatic and portal veins provide information that is best correlate with NIR spectral information, while the portal vein reference measurement provides poorer correlation and accuracy. These results indicate that NIR determination of oxygen saturation in the liver can provide an assessment of liver oxygen utilization.
NASA Astrophysics Data System (ADS)
Samadi-Maybodi, Abdolraouf; Hassani Nejad-Darzi, Seyed Karim
2010-04-01
Resolution of binary mixtures of paracetamol, phenylephrine hydrochloride and chlorpheniramine maleate with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable, principal component regression and hybrid linear analysis. Data of analysis were obtained from UV-vis spectra of the above compounds. The method of central composite design was used in the ranges of 1-15 mg L -1 for both calibration and validation sets. The models refinement procedure and their validation were performed by cross-validation. Figures of merit such as selectivity, sensitivity, analytical sensitivity and limit of detection were determined for all three compounds. The procedure was successfully applied to simultaneous determination of the above compounds in pharmaceutical tablets.
Balss, Karin M; Long, Frederick H; Veselov, Vladimir; Orana, Argjenta; Akerman-Revis, Eugena; Papandreou, George; Maryanoff, Cynthia A
2008-07-01
Multivariate data analysis was applied to confocal Raman measurements on stents coated with the polymers and drug used in the CYPHER Sirolimus-eluting Coronary Stents. Partial least-squares (PLS) regression was used to establish three independent calibration curves for the coating constituents: sirolimus, poly(n-butyl methacrylate) [PBMA], and poly(ethylene-co-vinyl acetate) [PEVA]. The PLS calibrations were based on average spectra generated from each spatial location profiled. The PLS models were tested on six unknown stent samples to assess accuracy and precision. The wt % difference between PLS predictions and laboratory assay values for sirolimus was less than 1 wt % for the composite of the six unknowns, while the polymer models were estimated to be less than 0.5 wt % difference for the combined samples. The linearity and specificity of the three PLS models were also demonstrated with the three PLS models. In contrast to earlier univariate models, the PLS models achieved mass balance with better accuracy. This analysis was extended to evaluate the spatial distribution of the three constituents. Quantitative bitmap images of drug-eluting stent coatings are presented for the first time to assess the local distribution of components. PMID:18510342
Sorouraddin, Mohammad-Hossein; Khani, Mohammad-Yaser; Amini, Kaveh; Naseri, Abdolhossein; Asgari, Davoud; Rashidi, Mohammad-Reza
2011-01-01
Introduction 6-Mercaptopurine (6MP) is an important chemotherapeutic drug in the conventional treatment of childhood acute lymphoblastic leukemia (ALL). It is catabolized to 6-thiouric acid (6TUA) through 8-hydroxo-6-mercaptopurine (8OH6MP) or 6-thioxanthine (6TX) intermediates. Methods High-performance liquid chromatography (HPLC) is usually used to determine the contents of therapeutic drugs, metabolites and other important biomedical analytes in biological samples. In the present study, the multivariate calibration methods, partial least squares (PLS-1) and principle component regression (PCR) have been developed and validated for the simultaneous determination of 6MP and its oxidative metabolites (6TUA, 8OH6MP and 6TX) without analyte separation in spiked human plasma. Mixtures of 6MP, 8-8OH6MP, 6TX and 6TUA have been resolved by PLS-1 and PCR to their UV spectra. Results Recoveries (%) obtained for 6MP, 8-8OH6MP, 6TX and 6TUA were 94.5-97.5, 96.6-103.3, 95.1-96.9 and 93.4-95.8, respectively, using PLS-1 and 96.7-101.3, 96.2-98.8, 95.8-103.3 and 94.3-106.1, respectively, using PCR. The NAS (Net analyte signal) concept was used to calculate multivariate analytical figures of merit such as limit of detection (LOD), selectivity and sensitivity. The limit of detections for 6MP, 8-8OH6MP, 6TX and 6TUA were calculated to be 0.734, 0.439, 0.797 and 0.482 μmol L-1, respectively, using PLS and 0.724, 0.418, 0783 and 0.535 μmol L-1, respectively, using PCR. HPLC was also applied as a validation method for simultaneous determination of these thiopurines in the synthetic solutions and human plasma. Conclusion Combination of spectroscopic techniques and chemometric methods (PLS and PCR) has provided a simple but powerful method for simultaneous analysis of multicomponent mixtures PMID:23678408
Applying the multivariate time-rescaling theorem to neural population models
Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon
2011-01-01
Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436
Applying Isotopic Effect in ITS-90 SPRT Calibrations
NASA Astrophysics Data System (ADS)
Pavese, F.
2014-07-01
The International Temperature Scale of 1990 (ITS-90) defines exact values for all fixed-point temperatures. For example, for the standard platinum resistance thermometers (SPRT), at each fixed point, the measured SPRT resistance and the temperature defined in the ITS-90 are used as input data into the correction equations of the ITS-90. Starting from 2006, formal equations were added to the Technical Annex for the ITS-90 for computing the fixed-point temperatures of the substances of different isotopic compositions, presently the triple and vapor-pressure points, Ne triple point, and triple point. This paper addresses the issue of the method required to apply the procedure defined in the ITS-90 for the calibration of a SPRT, according to the new requirements. The required procedure does not involve a "correction" of the fixed-point temperatures, since they are defined exactly by the ITS-90, but requires instead, the re-computing of the measured resistances at the relevant fixed points. In those cases where resistance ratios with respect to the triple point of water are required, the re-computation must be first applied separately to the specific fixed points and to the triple point of water. In case the re-computation is not possible because of insufficient information on the isotopic composition of the sample used, an additional component must be added to the total uncertainty budget.
Chen, Tianbing; Huang, Lin; Yao, Mingyin; Hu, Huiqin; Wang, Caihong; Liu, Muhua
2015-09-01
Laser-induced breakdown spectroscopy (LIBS) coupled with the linear multivariate regression method was utilized to analyze chromium (Cr) quantitatively in potatoes. The plasma was generated using a Nd:YAG laser, and the spectra were acquired by an Andor spectrometer integrated with an ICCD detector. The models between intensity of LIBS characteristic line(s) and concentration of Cr were constructed to predict quantitatively the content of target. The unary, binary, ternary, and quaternary variables were chosen for verifying the accuracy of linear regression calibration curves. The intensity of characteristic lines Cr (CrI: 425.43, 427.48, 428.97 nm) and Ca (CaI: 422.67, 428.30, 430.25, 430.77, 431.86 nm) were used as input data for the multivariate calculations. According to the results of linear regression, the model of quaternary linear regression was established better in comparing with the other three models. A good agreement was observed between the actual content provided by atomic absorption spectrometry and the predicted value obtained by the quaternary linear regression model. And the relative error was below 5.5% for validation samples S1 and S2. The result showed that the multivariate approach can obtain better predicted accuracy than the univariate ones. The result also suggested that the LIBS technique coupled with the linear multivariate calibration method could be a great tool to predict heavy metals in farm products in a rapid manner even though samples have similar elemental compositions. PMID:26368908
A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil
NASA Technical Reports Server (NTRS)
Negri, A. J.; Xu, L.; Adler, R. F.; Einaudi, Franco (Technical Monitor)
2000-01-01
The development of a satellite infrared technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall in Amazonia are presented. The Convective-Stratiform. Technique, calibrated by coincident, physically retrieved rain rates from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI), is applied during January to April 1999 over northern South America. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall is presented. Results compare well (a one-hour lag) with the diurnal cycle derived from Tropical Ocean-Global Atmosphere (TOGA) radar-estimated rainfall in Rondonia. The satellite estimates reveal that the convective rain constitutes, in the mean, 24% of the rain area while accounting for 67% of the rain volume. The effects of geography (rivers, lakes, coasts) and topography on the diurnal cycle of convection are examined. In particular, the Amazon River, downstream of Manaus, is shown to both enhance early morning rainfall and inhibit afternoon convection. Monthly estimates from this technique, dubbed CST/TMI, are verified over a dense rain gage network in the state of Ceara, in northeast Brazil. The CST/TMI showed a high bias equal to +33% of the gage mean, indicating that possibly the TMI estimates alone are also high. The root mean square difference (after removal of the bias) equaled 36.6% of the gage mean. The correlation coefficient was 0.77 based on 72 station-months.
A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil
NASA Technical Reports Server (NTRS)
Negri, Andrew J.; Xu, L.; Adler, R. F.; Einaudi, Franco (Technical Monitor)
2001-01-01
A satellite infrared (IR) technique for estimating rainfall over northern South America is presented. The objectives are to examine the diurnal variability of rainfall and to investigate the relative contributions from the convective and stratiform components. In this study, we apply the Convective-Stratiform Technique (CST) of Adler and Negri (1988). The parameters of the original technique were re-calibrated using coincident rainfall estimates (Olson et W., 2000) derived from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI) and GOES IR (11 micrometer) observations. Local circulations were found to play a major role in modulating the rainfall and its diurnal cycle. These included land/sea circulations (notably along the northeast Brazilian coast and in the Gulf of Panama), mountain/valley circulations (along the Andes Mountains), and circulations associated with the presence of rivers. This last category was examined in detail along the Amazon R. east of Manaus. There we found an early morning rainfall maximum along the river (5 LT at 58W, 3 LT at 56W). Rainfall avoids the river in the afternoon (12 LT and later), notably at 56 W. The width of the river seems to be generating a land/river circulation which enhances early morning rainfall but inhibits afternoon rainfall. Results are compared to ground-based radar data collected during the Large-Scale Biosphere-Atmosphere (LBA) experiment in southwest Brazil, to monthly raingages in northeastern Brazil, and to data from the TRMM Precipitation Radar.
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.
Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen
2014-10-15
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. PMID:24830631
Zhang, Jie; Stonnington, Cynthia; Li, Qingyang; Shi, Jie; Bauer, Robert J.; Gutman, Boris A.; Chen, Kewei; Reiman, Eric M.; Thompson, Paul M.; Ye, Jieping; Wang, Yalin
2016-01-01
Alzheimer’s disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD risk presymptomatically. Here, we applied a recently developed multivariate tensor-based morphometry (mTBM) method to extract features from hippocampal surfaces, derived from anatomical brain MRI. For such surface-based features, the feature dimension is usually much larger than the number of subjects. We used dictionary learning and sparse coding to effectively reduce the feature dimensions. With the new features, an Adaboost classifier was employed for binary group classification. In tests on publicly available data from the Alzheimers Disease Neuroimaging Initiative, the new framework outperformed several standard imaging measures in classifying different stages of AD. The new approach combines the efficiency of sparse coding with the sensitivity of surface mTBM, and boosts classification performance. PMID:27499829
Gallagher, Neal B.; Blake, Thomas A.; Gassman, Paul L.; Shaver, Jeremy M.; Windig, Willem
2006-07-01
Multivariate curve resolution (MCR) is a powerful technique for extracting chemical information from measured spectra on complex mixtures. The difficulty with applying MCR to soil reflectance measurements is that light scattering artifacts can contribute much more variance to the measurements than the analyte(s) of interest. Two methods were integrated into a MCR decomposition to account for light scattering effects. Firstly, an extended mixture model using pure analyte spectra augmented with scattering ‘spectra’ was used for the measured spectra. And secondly, second derivative preprocessed spectra, which have higher selectivity than the unprocessed spectra, were included in a second block as a part of the decomposition. The conventional alternating least squares (ALS) algorithm was modified to simultaneously decompose the measured and second derivative spectra in a two-block decomposition. Equality constraints were also included to incorporate information about sampling conditions. The result was an MCR decomposition that provided interpretable spectra from soil reflectance measurements.
NASA Astrophysics Data System (ADS)
Tawakkol, Shereen M.; Farouk, M.; Elaziz, Omar Abd; Hemdan, A.; Shehata, Mostafa A.
2014-12-01
Three simple, accurate, reproducible, and selective methods have been developed and subsequently validated for the simultaneous determination of Moexipril (MOX) and Hydrochlorothiazide (HCTZ) in pharmaceutical dosage form. The first method is the new extended ratio subtraction method (EXRSM) coupled to ratio subtraction method (RSM) for determination of both drugs in commercial dosage form. The second and third methods are multivariate calibration which include Principal Component Regression (PCR) and Partial Least Squares (PLSs). A detailed validation of the methods was performed following the ICH guidelines and the standard curves were found to be linear in the range of 10-60 and 2-30 for MOX and HCTZ in EXRSM method, respectively, with well accepted mean correlation coefficient for each analyte. The intra-day and inter-day precision and accuracy results were well within the acceptable limits.
Liu, Xianhua; Wang, Lili
2015-01-01
A series of ultraviolet-visible (UV-Vis) spectra from seawater samples collected from sites along the coastline of Tianjin Bohai Bay in China were subjected to multivariate partial least squares (PLS) regression analysis. Calibration models were developed for monitoring chemical oxygen demand (COD) and concentrations of total organic carbon (TOC). Three different PLS models were developed using the spectra from raw samples (Model-1), diluted samples (Model-2), and diluted and raw samples combined (Model-3). Experimental results showed that: (i) possible nonlinearities in the signal concentration relationships were well accounted for by the multivariate PLS model; (ii) the predicted values of COD and TOC fit the analytical values well; the high correlation coefficients and small root mean squared error of cross-validation (RMSECV) showed that this method can be used for seawater quality monitoring; and (iii) compared with Model-1 and Model-2, Model-3 had the highest coefficient of determination (R2) and the lowest number of latent variables. This latter finding suggests that only large data sets that include data representing different combinations of conditions (i.e., various seawater matrices) will produce stable site-specific regressions. The results of this study illustrate the effectiveness of the proposed method and its potential for use as a seawater quality monitoring technique. PMID:26442484
Santa-Cruz, Pablo; García-Reiriz, Alejandro
2014-10-01
In the present work a new application of third-order multivariate calibration algorithms is presented, in order to quantify carbaryl, naphthol and propoxur using kinetic spectroscopic data. The time evolution of fluorescence data matrices was measured, in order to follow the alkaline hydrolysis of the pesticides mentioned above. This experimental system has the additional complexity that one of the analytes is the reaction product of another analyte, and this fact generates linear dependency problems between concentration profiles. The data were analyzed by three different methods: parallel factor analysis (PARAFAC), unfolded partial least-squares (U-PLS) and multi-dimensional partial least-squares (N-PLS); these last two methods were assisted with residual trilinearization (RTL) to model the presence of unexpected signals not included in the calibration step. The ability of the different algorithms to predict analyte concentrations was checked with validation samples. Samples with unexpected components, tiabendazole and carbendazim, were prepared and spiked water samples of a natural stream were used to check the recovered concentrations. The best results were obtained with U-PLS/RTL and N-PLS/RTL with an average of the limits of detection of 0.035 for carbaryl, 0.025 for naphthol and 0.090 for propoxur (mg L(-1)), because these two methods are more flexible regarding the structure of the data. PMID:25059185
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. PMID:21238758
Multivariable control theory applied to hierarchial attitude control for planetary spacecraft
NASA Technical Reports Server (NTRS)
Boland, J. S., III; Russell, D. W.
1972-01-01
Multivariable control theory is applied to the design of a hierarchial attitude control system for the CARD space vehicle. The system selected uses reaction control jets (RCJ) and control moment gyros (CMG). The RCJ system uses linear signal mixing and a no-fire region similar to that used on the Skylab program; the y-axis and z-axis systems which are coupled use a sum and difference feedback scheme. The CMG system uses the optimum steering law and the same feedback signals as the RCJ system. When both systems are active the design is such that the torques from each system are never in opposition. A state-space analysis was made of the CMG system to determine the general structure of the input matrices (steering law) and feedback matrices that will decouple the axes. It is shown that the optimum steering law and proportional-plus-rate feedback are special cases. A derivation of the disturbing torques on the space vehicle due to the motion of the on-board television camera is presented. A procedure for computing an upper bound on these torques (given the system parameters) is included.
Multivariate Curve Resolution Applied to Hyperspectral Imaging Analysis of Chocolate Samples.
Zhang, Xin; de Juan, Anna; Tauler, Romà
2015-08-01
This paper shows the application of Raman and infrared hyperspectral imaging combined with multivariate curve resolution (MCR) to the analysis of the constituents of commercial chocolate samples. The combination of different spectral data pretreatment methods allowed decreasing the high fluorescent Raman signal contribution of whey in the investigated chocolate samples. Using equality constraints during MCR analysis, estimations of the pure spectra of the chocolate sample constituents were improved, as well as their relative contributions and their spatial distribution on the analyzed samples. In addition, unknown constituents could be also resolved. White chocolate constituents resolved from Raman hyperspectral image indicate that, at macro scale, sucrose, lactose, fat, and whey constituents were intermixed in particles. Infrared hyperspectral imaging did not suffer from fluorescence and could be applied for white and milk chocolate. As a conclusion of this study, micro-hyperspectral imaging coupled to the MCR method is confirmed to be an appropriate tool for the direct analysis of the constituents of chocolate samples, and by extension, it is proposed for the analysis of other mixture constituents in commercial food samples. PMID:26162693
Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders
Levman, Jacob; Takahashi, Emi
2015-01-01
Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us. PMID:26640765
Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI
Levman, Jacob; Takahashi, Emi
2016-01-01
Multivariate analysis (MVA) is a class of statistical and pattern recognition techniques that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neurological medical imaging related challenges including the evaluation of healthy brain development, the automated analysis of brain tissues and structures through image segmentation, evaluating the effects of genetic and environmental factors on brain development, evaluating sensory stimulation's relationship with functional brain activity and much more. Compared to adult imaging, pediatric, neonatal and fetal imaging have attracted less attention from MVA researchers, however, recent years have seen remarkable MVA research growth in pre-adult populations. This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in brain MRI, the field is still young and significant research growth will continue into the future. PMID:26834576
Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders.
Levman, Jacob; Takahashi, Emi
2015-01-01
Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us. PMID:26640765
Applying a multivariate statistical analysis model to evaluate the water quality of a watershed.
Wu, Edward Ming-Yang; Kuo, Shu-Lung
2012-12-01
Multivariate statistics have been applied to evaluate the water quality data collected at six monitoring stations in the Feitsui Reservoir watershed of Taipei, Taiwan. The objective is to evaluate the mutual correlations among the various water quality parameters to reveal the primary factors that affect reservoir water quality, and the differences among the various water quality parameters in the watershed. In this study, using water quality samples collected over a period of two and a half years will effectively raise the efficacy and reliability of the factor analysis results. This will be a valuable reference for managing water pollution in the watershed. Additionally, results obtained using the proposed theory and method to analyze and interpret statistical data must be examined to verify their similarity to field data collected on the stream geographical and geological characteristics, the physical and chemical phenomena of stream self-purification, and the stream hydrological phenomena. In this research, the water quality data has been collected over two and a half years so that sufficient sets of water quality data are available to increase the stability, effectiveness, and reliability of the final factor analysis results. These data sets can be valuable references for managing, regulating, and remediating water pollution in a reservoir watershed. PMID:23342938
Differential Evolution algorithm applied to FSW model calibration
NASA Astrophysics Data System (ADS)
Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.
2014-03-01
Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.
Zhou, Chengfeng; Jiang, Wei; Cheng, Qingzheng; Via, Brian K
2015-01-01
This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15 cm(-1) from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry. PMID:26576321
Variable selection in multivariate calibration based on clustering of variable concept.
Farrokhnia, Maryam; Karimi, Sadegh
2016-01-01
Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached. PMID:26703255
Zhou, Chengfeng; Jiang, Wei; Cheng, Qingzheng; Via, Brian K.
2015-01-01
This research addressed a rapid method to monitor hardwood chemical composition by applying Fourier transform infrared (FT-IR) spectroscopy, with particular interest in model performance for interpretation and prediction. Partial least squares (PLS) and principal components regression (PCR) were chosen as the primary models for comparison. Standard laboratory chemistry methods were employed on a mixed genus/species hardwood sample set to collect the original data. PLS was found to provide better predictive capability while PCR exhibited a more precise estimate of loading peaks and suggests that PCR is better for model interpretation of key underlying functional groups. Specifically, when PCR was utilized, an error in peak loading of ±15 cm−1 from the true mean was quantified. Application of the first derivative appeared to assist in improving both PCR and PLS loading precision. Research results identified the wavenumbers important in the prediction of extractives, lignin, cellulose, and hemicellulose and further demonstrated the utility in FT-IR for rapid monitoring of wood chemistry. PMID:26576321
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.
Masoum, Saeed; Mehran, Mehdi; Ghaheri, Salehe
2015-02-01
Thyme species are used in traditional medicine throughout the world and are known for their antiseptic, antispasmodic, and antitussive properties. Also, antioxidant activity is one of the interesting properties of thyme essential oil. In this research, we aim to identify peaks potentially responsible for the antioxidant activity of thyme oil from chromatographic fingerprints. Therefore, the chemical compositions of hydrodistilled essential oil of thyme species from different regions were analyzed by gas chromatography with mass spectrometry and antioxidant activities of essential oils were measured by a 1,1-diphenyl-2-picrylhydrazyl radical scavenging test. Several linear multivariate calibration techniques with different preprocessing methods were applied to the chromatograms of thyme essential oils to indicate the peaks responsible for the antioxidant activity. These techniques were applied on data both before and after alignment of chromatograms with correlation optimized warping. In this study, orthogonal projection to latent structures model was found to be a good technique to indicate the potential antioxidant active compounds in the thyme oil due to its simplicity and repeatability. PMID:25403421
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. PMID:26519913
NASA Astrophysics Data System (ADS)
Liu, Fei; He, Yong; Wang, Li
2008-02-01
The feasibility of visible and near infrared (Vis/NIR) spectroscopy, in combination with a hybrid multivariate methods of partial least squares (PLS) analysis and BP neural network (BPNN), was investigated to identify the variety of rice vinegars with different internal qualities. Five varieties of rice vinegars were prepared and 225 samples (45 for each variety) were selected randomly for the calibration set, while 75 samples (15 for each variety) for the validation set. After some pretreatments with moving average and standard normal variate (SNV), partial least squares (PLS) analysis was implemented for the extraction of principal components (PCs), which would be used as the inputs of BP neural network (BPNN) according to their accumulative reliabilities. Finally, a PLS-BPNN model with sigmoid transfer function was achieved. The performance was validated by the 75 unknown samples in validation set. The threshold error of prediction was set as +/-0.1 and an excellent precision and recognition ratio of 100% was achieved. Simultaneously, certain effective wavelengths for the identification of varieties were proposed by x-loading weights and regression coefficients. The prediction results indicated that Vis/NIR spectroscopy could be used as a rapid and high precision method for the identification of different varieties of rice vinegars.
Rasouli, Zolaikha; Ghavami, Raouf
2016-08-01
Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD=0.12], 0.67-23.19 [LOD=0.13] and 0.73-25.12 [LOD=0.15] μgmL(-1) for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples. PMID:27176001
NASA Astrophysics Data System (ADS)
Rasouli, Zolaikha; Ghavami, Raouf
2016-08-01
Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD = 0.12], 0.67-23.19 [LOD = 0.13] and 0.73-25.12 [LOD = 0.15] μg mL- 1 for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.
Tencate, Alister J; Kalivas, John H; White, Alexander J
2016-05-19
New multivariate calibration methods and other processes are being developed that require selection of multiple tuning parameter (penalty) values to form the final model. With one or more tuning parameters, using only one measure of model quality to select final tuning parameter values is not sufficient. Optimization of several model quality measures is challenging. Thus, three fusion ranking methods are investigated for simultaneous assessment of multiple measures of model quality for selecting tuning parameter values. One is a supervised learning fusion rule named sum of ranking differences (SRD). The other two are non-supervised learning processes based on the sum and median operations. The effect of the number of models evaluated on the three fusion rules are also evaluated using three procedures. One procedure uses all models from all possible combinations of the tuning parameters. To reduce the number of models evaluated, an iterative process (only applicable to SRD) is applied and thresholding a model quality measure before applying the fusion rules is also used. A near infrared pharmaceutical data set requiring model updating is used to evaluate the three fusion rules. In this case, calibration of the primary conditions is for the active pharmaceutical ingredient (API) of tablets produced in a laboratory. The secondary conditions for calibration updating is for tablets produced in the full batch setting. Two model updating processes requiring selection of two unique tuning parameter values are studied. One is based on Tikhonov regularization (TR) and the other is a variation of partial least squares (PLS). The three fusion methods are shown to provide equivalent and acceptable results allowing automatic selection of the tuning parameter values. Best tuning parameter values are selected when model quality measures used with the fusion rules are for the small secondary sample set used to form the updated models. In this model updating situation, evaluation of
Wang, Lei; Cao, Peng; Li, Wei; Tong, Peijin; Zhang, Xiaofang; Du, Yiping
2016-04-15
Solid Phase Extraction Spectroscopy (SPES) developed in this paper is a technique to measure spectrum directly on the solid phase material where the analytes are concentrated in SPE process. Membrane enrichment and UV-Visible spectroscopy were utilized to fulfill SPES, and multivariate calibration method of partial least squares (PLS) was used to simultaneously detect the concentrations of trace cobalt (II) and zinc (II) in water samples. The proposed method is simple, sensitive and selective. The complexes of analyte ions were collected on the cellulose acetate membranes via membrane filtration after the complexation reaction with 1-2-pyridylazo 2-naphthol (PAN). The spectra of the membranes which contained the complexes of metal ions and PAN were measured directly without eluting. The analytical conditions including pH, reaction time, sample volume, the amount of PAN, and flow rates were optimized. Nonionic surfactant Brij-30 was absorbed on the membranes prior to SPES to modify the membranes for improving the enrichment and spectrum measurement. The interference from other ions to the determination was investigated. Under the optimal condition, the absorbance was linearly related to the concentration at the range of 0.1-3.0 μg/L and 0.1-2.0 μg/L, with the correlation coefficients (R(2)) of 0.9977 and 0.9951 for Co (II) and Zn (II), respectively. The limits of detection were 0.066 μg/L for cobalt (II) and 0.104 μg/L for zinc (II). PLS regression with leave-one-out cross-validation was utilized to build models to detect cobalt (II) and zinc (II) in drinking water samples simultaneously. The correlation coefficient between ion concentration and spectrum of calibration set and independent prediction set were 1.0000 and 0.9974 for cobalt (II) and 1.0000 and 0.9956 for zinc (II). For cobalt (II) and zinc (II), the errors of the prediction set were in the range 0.0406-0.1353 μg/L and 0.0025-0.1884 μg/L. PMID:26845581
NASA Astrophysics Data System (ADS)
Wang, Lei; Cao, Peng; Li, Wei; Tong, Peijin; Zhang, Xiaofang; Du, Yiping
2016-04-01
Solid Phase Extraction Spectroscopy (SPES) developed in this paper is a technique to measure spectrum directly on the solid phase material where the analytes are concentrated in SPE process. Membrane enrichment and UV-Visible spectroscopy were utilized to fulfill SPES, and multivariate calibration method of partial least squares (PLS) was used to simultaneously detect the concentrations of trace cobalt (II) and zinc (II) in water samples. The proposed method is simple, sensitive and selective. The complexes of analyte ions were collected on the cellulose acetate membranes via membrane filtration after the complexation reaction with 1-2-pyridylazo 2-naphthol (PAN). The spectra of the membranes which contained the complexes of metal ions and PAN were measured directly without eluting. The analytical conditions including pH, reaction time, sample volume, the amount of PAN, and flow rates were optimized. Nonionic surfactant Brij-30 was absorbed on the membranes prior to SPES to modify the membranes for improving the enrichment and spectrum measurement. The interference from other ions to the determination was investigated. Under the optimal condition, the absorbance was linearly related to the concentration at the range of 0.1-3.0 μg/L and 0.1-2.0 μg/L, with the correlation coefficients (R2) of 0.9977 and 0.9951 for Co (II) and Zn (II), respectively. The limits of detection were 0.066 μg/L for cobalt (II) and 0.104 μg/L for zinc (II). PLS regression with leave-one-out cross-validation was utilized to build models to detect cobalt (II) and zinc (II) in drinking water samples simultaneously. The correlation coefficient between ion concentration and spectrum of calibration set and independent prediction set were 1.0000 and 0.9974 for cobalt (II) and 1.0000 and 0.9956 for zinc (II). For cobalt (II) and zinc (II), the errors of the prediction set were in the range 0.0406-0.1353 μg/L and 0.0025-0.1884 μg/L.
Goicoechea, H C; Olivieri, A C
1999-07-12
The mucolitic bromhexine [N-(2-amino-3,5-dibromobenzyl)-N-methylcyclohexylamine] has been determined in cough suppressant syrups by multivariate spectrophotometric calibration, together with partial least-squares (PLS-1) and hybrid linear analysis (HLA). Notwithstanding the spectral overlapping between bromhexine and syrup excipients, as well as the intrinsic variability of the latter in unknown samples, the recoveries are excellent. A novel method of wavelength selection was also applied, based on the concept of net analyte signal regression, as adapted to the HLA methodology. This method allows one to improve the performance of both PLS-1 and HLA in samples containing nonmodeled interferences. PMID:18967655
A PID de-tuned method for multivariable systems, applied for HVAC plant
NASA Astrophysics Data System (ADS)
Ghazali, A. B.
2015-09-01
A simple yet effective de-tuning of PID parameters for multivariable applications has been described. Although the method is felt to have wider application it is simulated in a 3-input/ 2-output building energy management system (BEMS) with known plant dynamics. The controller performances such as the sum output squared error and total energy consumption when the system is at steady state conditions are studied. This tuning methodology can also be extended to reduce the number of PID controllers as well as the control inputs for specified output references that are necessary for effective results, i.e. with good regulation performances being maintained.
Pedersen, Kristine B; Lejon, Tore; Jensen, Pernille E; Ottosen, Lisbeth M
2016-05-01
Multivariate methodology was employed for finding optimum remediation conditions for electrodialytic remediation of harbour sediment from an Arctic location in Norway. The parts of the experimental domain in which both sediment- and technology-specific remediation objectives were met were identified. Objectives targeted were removal of the sediment-specific pollutants Cu and Pb, while minimising the effect on the sediment matrix by limiting the removal of naturally occurring metals while maintaining low energy consumption. Two different cell designs for electrochemical remediation were tested and final concentrations of Cu and Pb were below background levels in large parts of the experimental domain when operating at low current densities (<0.12 mA/cm(2)). However, energy consumption, remediation times and the effect on naturally occurring metals were different for the 2- and 3-compartment cells. PMID:26928331
Mabood, Fazal; Al-Harrasi, Ahmed; Boqué, Ricard; Jabeen, Farah; Hussain, Javid; Hafidh, A; Hind, K; Ahmed, M A G; Manzoor, A; Hussain, Hidayat; Ur Rehman, Najeeb; Iman, S H; Said, Jahina J; Hamood, Sara A
2015-11-01
A Near Infrared (NIR) spectroscopic method combined with multivariate calibration was developed for the determination of the amount of sucrose in date fruits growing in the Sultanate of Oman. In this study two groups of samples were used: one group of 48 sucrose standard solutions in the concentration range from 0.01% to 50% (w/v) and another group of 54 date fruit samples of 18 different varieties. The sucrose standard samples were split in two sets, i.e. one training set of 31 samples and one test set of 17 samples. All samples were measured with a NIR spectrophotometer in the wavelength range from 700 to 2500 nm. The spectra collected were preprocessed using baseline correction and Savitzky-Golay 1st derivative. Partial least-squares regression (PLSR) was used to build the regression model with the training set of 31 samples. This model was then validated by using random leave-one-out cross-validation. Later, the PLS regression model was externally validated by using the test set of 17 samples of known sucrose concentration. The root mean squared error of prediction (RMSEP) was found to be of 1.5%, which shows a good prediction ability of the model. Finally, the PLS model was applied to the spectra of 54 date fruit samples to quantify their sucrose amount. It was found that the Khalas, Barnia Nizwi, Ajwa Almadina, Maan, and Khunizi varieties contain high amounts of sucrose, i.e. ranging from 36% to 60%, while Naghal, Fardh, Nashu and Qash Tabaq varieties contain the least amount of sucrose, ranging from 3.5% to 8.1%. PMID:26048559
Kaplan, Jonas T.; Man, Kingson; Greening, Steven G.
2015-01-01
Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application. PMID:25859202
Kaplan, Jonas T; Man, Kingson; Greening, Steven G
2015-01-01
Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application. PMID:25859202
NASA Astrophysics Data System (ADS)
Bauer, M.; Baumbach, D.; Buder, M.; Börner, A.; Grießbach, D.; Peter, G.; Santier, E.; Säuberlich, T.; Schischmanow, A.; Schrader, S.; Walter, I.
2015-09-01
Geometrical sensor calibration is essential for space applications based on high accuracy optical measurements, in this case for the thermal infrared push-broom imaging spectrometer MERTIS. The goal is the determination of the interior sensor orientation. A conventional method is to measure the line of sight for a subset of pixels by single pixel illumination with collimated light. To adjust angles, which define the line of sight of a pixel, a manipulator construction is used. A new method for geometrical sensor calibration is using Diffractive Optical Elements (DOE) in connection with laser beam equipment. Diffractive optical elements (DOE) are optical microstructures, which are used to split an incoming laser beam with a dedicated wavelength into a number of beams with well-known propagation directions. As the virtual sources of the diffracted beams are points at infinity, the resulting image is invariant against translation. This particular characteristic allows a complete geometrical sensor calibration with only one taken image avoiding complex adjustment procedures, resulting in a significant reduction of calibration effort. We present a new method for geometrical calibration of a thermal infrared optical system, including an thermal infrared test optics and the MERTIS spectrometer bolometer detector. The fundamentals of this new approach for geometrical infrared optical systems calibration by applying diffractive optical elements and the test equipment are shown.
Villar, Alberto; Gorritxategi, Eneko; Otaduy, Deitze; Ciria, Jose I; Fernandez, Luis A
2011-10-31
This paper describes the calibration process of a Visible-Near Infrared sensor for the condition monitoring of a gas engine's lubricating oil correlating transmittance oil spectra with the degradation of a gas engine's oil via a regression model. Chemometric techniques were applied to determine different parameters: Base Number (BN), Acid Number (AN), insolubles in pentane and viscosity at 40 °C. A Visible-Near Infrared (400-1100 nm) sensor developed in Tekniker research center was used to obtain the spectra of artificial and real gas engine oils. In order to improve sensor's data, different preprocessing methods such as smoothing by Saviztky-Golay, moving average with Multivariate Scatter Correction or Standard Normal Variate to eliminate the scatter effect were applied. A combination of these preprocessing methods was applied to each parameter. The regression models were developed by Partial Least Squares Regression (PLSR). In the end, it was shown that only some models were valid, fulfilling a set of quality requirements. The paper shows which models achieved the established validation requirements and which preprocessing methods perform better. A discussion follows regarding the potential improvement in the robustness of the models. PMID:21962360
Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio
2016-09-01
Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). PMID:27041319
A Multivariate Randomization Text of Association Applied to Cognitive Test Results
NASA Technical Reports Server (NTRS)
Ahumada, Albert; Beard, Bettina
2009-01-01
Randomization tests provide a conceptually simple, distribution-free way to implement significance testing. We have applied this method to the problem of evaluating the significance of the association among a number (k) of variables. The randomization method was the random re-ordering of k-1 of the variables. The criterion variable was the value of the largest eigenvalue of the correlation matrix.
Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed
2017-01-01
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. PMID:27423110
NASA Astrophysics Data System (ADS)
Chen, Quan; Kissel, Catherine; Govin, Aline; Liu, Zhifei; Xie, Xin
2016-05-01
Fast and nondestructive X-ray fluorescence (XRF) core scanning provides high-resolution element data that are widely used in paleoclimate studies. However, various matrix and specimen effects prevent the use of semiquantitative raw XRF core-scanning intensities for robust paleoenvironmental interpretations. We present here a case study of a 50.8 m-long piston Core MD12-3432 retrieved from the northern South China Sea. The absorption effect of interstitial water is identified as the major source of deviations between XRF core-scanning intensities and measured element concentrations. The existing two calibration methods, i.e., normalized median-scaled calibration (NMS) and multivariate log-ratio calibration (MLC), are tested with this sequence after the application of water absorption correction. The results indicate that an improvement is still required to appropriately correct the influence of downcore changes in interstitial water content in the long sediment core. Consequently, we implement a new polynomial water content correction in NMS and MLC methods, referred as NPS and P_MLC calibrations. Results calibrated by these two improved methods indicate that the influence of downcore water content changes is now appropriately corrected. We therefore recommend either of the two methods to be applied for robust paleoenvironmental interpretations of major elements measured by XRF-scanning in long sediment sequences with significant downcore interstitial water content changes.
Multivariate analysis applied to agglomerated macrobenthic data from an unpolluted estuary.
Conde, Anxo; Novais, Júlio M; Domínguez, Jorge
2013-01-01
We agglomerated species into higher taxonomic aggregations and functional groups to analyse environmental gradients in an unpolluted estuary. We then applied non-metric Multidimensional Scaling and Redundancy Analysis (RDA) for ordination of the agglomerated data matrices. The correlation between the ordinations produced by both methods was generally high. However, the performance of the RDA models depended on the data matrix used to fit the model. As a result, salinity and total nitrogen were only found significant when aggregated data matrices were used rather than species data matrix. We used the results to select a RDA model that explained a higher percentage of variance in the species data set than the parsimonious model. We conclude that the use of aggregated matrices may be considered complementary to the use of species data to obtain a broader insight into the distribution of macrobenthic assemblages in relation to environmental gradients. PMID:23684322
NASA Technical Reports Server (NTRS)
Bosworth, John T.; Burken, John J.
1997-01-01
Safety and productivity of the initial flight test phase of a new vehicle have been enhanced by developing the ability to measure the stability margins of the combined control system and vehicle in flight. One shortcoming of performing this analysis is the long duration of the excitation signal required to provide results over a wide frequency range. For flight regimes such as high angle of attack or hypersonic flight, the ability to maintain flight condition for this time duration is difficult. Significantly reducing the required duration of the excitation input is possible by tailoring the input to excite only the frequency range where the lowest stability margin is expected. For a multiple-input/multiple-output system, the inputs can be simultaneously applied to the control effectors by creating each excitation input with a unique set of frequency components. Chirp-Z transformation algorithms can be used to match the analysis of the results to the specific frequencies used in the excitation input. This report discusses the application of a tailored excitation input to a high-fidelity X-31A linear model and nonlinear simulation. Depending on the frequency range, the results indicate the potential to significantly reduce the time required for stability measurement.
Liu, Yan; Cai, Wensheng; Shao, Xueguang
2016-12-01
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. PMID:27380302
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. PMID:15848006
Calibration methodology for proportional counters applied to yield measurements of a neutron burst
Tarifeño-Saldivia, Ariel E-mail: atarisal@gmail.com; Pavez, Cristian; Soto, Leopoldo; Mayer, Roberto E.
2014-01-15
This paper introduces a methodology for the yield measurement of a neutron burst using neutron proportional counters. This methodology is to be applied when single neutron events cannot be resolved in time by nuclear standard electronics, or when a continuous current cannot be measured at the output of the counter. The methodology is based on the calibration of the counter in pulse mode, and the use of a statistical model to estimate the number of detected events from the accumulated charge resulting from the detection of the burst of neutrons. The model is developed and presented in full detail. For the measurement of fast neutron yields generated from plasma focus experiments using a moderated proportional counter, the implementation of the methodology is herein discussed. An experimental verification of the accuracy of the methodology is presented. An improvement of more than one order of magnitude in the accuracy of the detection system is obtained by using this methodology with respect to previous calibration methods.
NASA Astrophysics Data System (ADS)
Kunze, Hans-Joachim
Commercial spectrographic systems are usually supplied with some wave-length calibration, but it is essential that the experimenter performs his own calibration for reliable measurements. A number of sources emitting well-known emission lines are available, and the best values of their wavelengths may be taken from data banks accessible on the internet. Data have been critically evaluated for many decades by the National Institute of Standards and Technology (NIST) of the USA [13], see also p. 3. Special data bases have been established by the astronomy and fusion communities (Appendix B).
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. PMID:26003699
Pereira, Claudete Fernandes; Pasquini, Celio
2010-05-01
A flow system is proposed to produce a concentration perturbation in liquid samples, aiming at the generation of two-dimensional correlation near-infrared spectra. The system presents advantages in relation to batch systems employed for the same purpose: the experiments are accomplished in a closed system; application of perturbation is rapid and easy; and the experiments can be carried out with micro-scale volumes. The perturbation system has been evaluated in the investigation and selection of relevant variables for multivariate calibration models for the determination of quality parameters of gasoline, including ethanol content, MON (motor octane number), and RON (research octane number). The main advantage of this variable selection approach is the direct association between spectral features and chemical composition, allowing easy interpretation of the regression models. PMID:20482969
NASA Astrophysics Data System (ADS)
Barbeira, Paulo J. S.; Paganotti, Rosilene S. N.; Ássimos, Ariane A.
2013-10-01
This study had the objective of determining the content of dry extract of commercial alcoholic extracts of bee propolis through Partial Least Squares (PLS) multivariate calibration and electronic spectroscopy. The PLS model provided a good prediction of dry extract content in commercial alcoholic extracts of bee propolis in the range of 2.7 a 16.8% (m/v), presenting the advantage of being less laborious and faster than the traditional gravimetric methodology. The PLS model was optimized with outlier detection tests according to the ASTM E 1655-05. In this study it was possible to verify that a centrifugation stage is extremely important in order to avoid the presence of waxes, resulting in a more accurate model. Around 50% of the analyzed samples presented content of dry extract lower than the value established by Brazilian legislation, in most cases, the values found were different from the values claimed in the product's label.
NASA Astrophysics Data System (ADS)
Batistela, Vagner Roberto; Pellosi, Diogo Silva; de Souza, Franciane Dutra; da Costa, Willian Ferreira; de Oliveira Santin, Silvana Maria; de Souza, Vagner Roberto; Caetano, Wilker; de Oliveira, Hueder Paulo Moisés; Scarminio, Ieda Spacino; Hioka, Noboru
2011-09-01
Xanthenes form to an important class of dyes which are widely used. Most of them present three acid-base groups: two phenolic sites and one carboxylic site. Therefore, the p Ka determination and the attribution of each group to the corresponding p Ka value is a very important feature. Attempts to obtain reliable p Ka through the potentiometry titration and the electronic absorption spectrophotometry using the first and second orders derivative failed. Due to the close p Ka values allied to strong UV-Vis spectral overlap, multivariate analysis, a powerful chemometric method, is applied in this work. The determination was performed for eosin Y, erythrosin B, and bengal rose B, and also for other synthesized derivatives such as 2-(3,6-dihydroxy-9-acridinyl) benzoic acid, 2,4,5,7-tetranitrofluorescein, eosin methyl ester, and erythrosin methyl ester in water. These last two compounds (esters) permitted to attribute the p Ka of the phenolic group, which is not easily recognizable for some investigated dyes. Besides the p Ka determination, the chemometry allowed for estimating the electronic spectrum of some prevalent protolytic species and the substituents effects evaluation.
NASA Astrophysics Data System (ADS)
Land, Walker H., Jr.; Anderson, Frances; Smith, Tom; Fahlbusch, Stephen; Choma, Robert; Wong, Lut
2005-04-01
Achieving consistent and correct database cases is crucial to the correct evaluation of any computer-assisted diagnostic (CAD) paradigm. This paper describes the application of artificial intelligence (AI), knowledge engineering (KE) and knowledge representation (KR) to a data set of ~2500 cases from six separate hospitals, with the objective of removing/reducing inconsistent outlier data. Several support vector machine (SVM) kernels were used to measure diagnostic performance of the original and a "cleaned" data set. Specifically, KE and ER principles were applied to the two data sets which were re-examined with respect to the environment and agents. One data set was found to contain 25 non-characterizable sets. The other data set contained 180 non-characterizable sets. CAD system performance was measured with both the original and "cleaned" data sets using two SVM kernels as well as a multivariate probabilistic neural network (PNN). Results demonstrated: (i) a 10% average improvement in overall Az and (ii) approximately a 50% average improvement in partial Az.
NASA Astrophysics Data System (ADS)
Zhang, Xiaoyu; Li, Qingbo; Zhang, Guangjun
2013-11-01
In this paper, a modified single-index signal regression (mSISR) method is proposed to construct a nonlinear and practical model with high-accuracy. The mSISR method defines the optimal penalty tuning parameter in P-spline signal regression (PSR) as initial tuning parameter and chooses the number of cycles based on minimizing root mean squared error of cross-validation (RMSECV). mSISR is superior to single-index signal regression (SISR) in terms of accuracy, computation time and convergency. And it can provide the character of the non-linearity between spectra and responses in a more precise manner than SISR. Two spectra data sets from basic research experiments, including plant chlorophyll nondestructive measurement and human blood glucose noninvasive measurement, are employed to illustrate the advantages of mSISR. The results indicate that the mSISR method (i) obtains the smooth and helpful regression coefficient vector, (ii) explicitly exhibits the type and amount of the non-linearity, (iii) can take advantage of nonlinear features of the signals to improve prediction performance and (iv) has distinct adaptability for the complex spectra model by comparing with other calibration methods. It is validated that mSISR is a promising nonlinear modeling strategy for multivariate calibration.
De Almeida Brehm, Franciane; de Azevedo, Julio Cesar R; da Costa Pereira, Jorge; Burrows, Hugh D
2015-11-01
Dissolved organic carbon (DOC) is frequently used as a diagnostic parameter for the identification of environmental contamination in aqueous systems. Since this organic matter is evolving and decaying over time. If samples are collected under environmental conditions, some sample stabilization process is needed until the corresponding analysis can be made. This may affect the analysis results. This problem can be avoided using the direct determination of DOC. We report a study using in situ synchronous fluorescence spectra, with independent component analysis to retrieve relevant major spectral contributions and their respective component contributions, for the direct determination of DOC. Fluorescence spectroscopy is a very powerful and sensitive technique to evaluate vestigial organic matter dissolved in water and is thus suited for the analytical task of direct monitoring of dissolved organic matter in water, thus avoiding the need for the stabilization step. We also report the development of an accurate calibration model for dissolved organic carbon determinations using environmental samples of humic and fulvic acids. The method described opens the opportunity for a fast, in locus, DOC estimation in environmental or other field studies using a portable fluorescence spectrometer. This combines the benefits of the use of fresh samples, without the need of stabilizers, and also allows the interpretation of various additional spectral contributions based on their respective estimated properties. We show how independent component analysis may be used to describe tyrosine, tryptophan, humic acid and fulvic acid spectra and, thus, to retrieve the respective individual component contribution to the DOC. PMID:26497563
Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia
2016-01-01
Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration. PMID:26901203
Liu, Bailing; Zhang, Fumin; Qu, Xinghua; Shi, Xiaojia
2016-01-01
Coordinate transformation plays an indispensable role in industrial measurements, including photogrammetry, geodesy, laser 3-D measurement and robotics. The widely applied methods of coordinate transformation are generally based on solving the equations of point clouds. Despite the high accuracy, this might result in no solution due to the use of ill conditioned matrices. In this paper, a novel coordinate transformation method is proposed, not based on the equation solution but based on the geometric transformation. We construct characteristic lines to represent the coordinate systems. According to the space geometry relation, the characteristic line scan is made to coincide by a series of rotations and translations. The transformation matrix can be obtained using matrix transformation theory. Experiments are designed to compare the proposed method with other methods. The results show that the proposed method has the same high accuracy, but the operation is more convenient and flexible. A multi-sensor combined measurement system is also presented to improve the position accuracy of a robot with the calibration of the robot kinematic parameters. Experimental verification shows that the position accuracy of robot manipulator is improved by 45.8% with the proposed method and robot calibration. PMID:26901203
Heise, H Michael; Damm, Uwe; Lampen, Peter; Davies, Antony N; McIntyre, Peter S
2005-10-01
The limits of quantitative multivariate assays for the analysis of extra virgin olive oil samples from various Greek sites adulterated by sunflower oil have been evaluated based on their Fourier transform (FT) Raman spectra. Different strategies for wavelength selection were tested for calculating optimal partial least squares (PLS) models. Compared to the full spectrum methods previously applied, the optimum standard error of prediction (SEP) for the sunflower oil concentrations in spiked olive oil samples could be significantly reduced. One efficient approach (PMMS, pair-wise minima and maxima selection) used a special variable selection strategy based on a pair-wise consideration of significant respective minima and maxima of PLS regression vectors, calculated for broad spectral intervals and a low number of PLS factors. PMMS provided robust calibration models with a small number of variables. On the other hand, the Tabu search strategy recently published (search process guided by restrictions leading to Tabu list) achieved lower SEP values but at the cost of extensive computing time when searching for a global minimum and less robust calibration models. Robustness was tested by using packages of ten and twenty randomly selected samples within cross-validation for calculating independent prediction values. The best SEP values for a one year's harvest with a total number of 66 Cretian samples were obtained by such spectral variable optimized PLS calibration models using leave-20-out cross-validation (values between 0.5 and 0.7% by weight). For the more complex population of olive oil samples from all over Greece (total number of 92 samples), results were between 0.7 and 0.9% by weight with a cross-validation sample package size of 20. Notably, the calibration method with Tabu variable selection has been shown to be a valid chemometric approach by which a single model can be applied with a low SEP of 1.4% for olive oil samples across three different harvest years
Towards an effective calibration theory for a broadly applied land surface model (VIC)
NASA Astrophysics Data System (ADS)
Melsen, Lieke; Teuling, Adriaan; Torfs, Paul; Zappa, Massimiliano
2014-05-01
The Variable Infiltration Capacity (VIC, Liang et al., 1994) model has been used for a broad range of applications, in hydrology as well as in the fields of climate and global change. Despite the attention for the model and its output, calibration is often not performed. To improve the calibration procedures for VIC applied at grid resolutions varying from meso-scale catchments to the 1 km 'hyper'resolution now used in several global modeling studies, the parameters of the model are studied in more detail. An earlier sensitivity analysis study on a selection of parameters of the VIC model by Demaria et al (2007) showed that the model is not or hardly sensitive to many of its parameters. With improved sensitivity analysis methods and computational power, this study focuses on a broader spectrum of parameters and with state of the art methods: both the DELSA sensitivity analysis method (Rakovec et al., 2013) and the ABC-method (Vrugt et al., 2013) will be employed parallel to a single cell VIC model of the Rietholzbach in Switzerland (representative of the 1 km hyperresolution), and a single and multiple-cell VIC model of the meso-scale Thur basin in Switzerland. In the latter case, also routing plays an important role. With critically screening the parameters of the model, it is possible to define a frame work for calibration of the model at multiple scales. References Demaria, E., B. Nijssen, and T. Wagener (2007), Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model, J. Geophys. Res., 112, D11,113. Liang, X., D. Lettenmaier, E. Wood, and S. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99 (D7),14,415-14,458. Rakovec, O., M. Hill, M. Clark, A. Weerts, A. Teuling, and R. Uijlenhoet (2013), A new computationally frugal method for sensitivity analysis of environmental models, Water Resour. Res., in press Vrugt, J.A. and M
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.
Augmented classical least squares multivariate spectral analysis
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
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.
Augmented Classical Least Squares Multivariate Spectral Analysis
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.
Farouk, M; Elaziz, Omar Abd; Tawakkol, Shereen M; Hemdan, A; Shehata, Mostafa A
2014-04-01
Four simple, accurate, reproducible, and selective methods have been developed and subsequently validated for the determination of Benazepril (BENZ) alone and in combination with Amlodipine (AML) in pharmaceutical dosage form. The first method is pH induced difference spectrophotometry, where BENZ can be measured in presence of AML as it showed maximum absorption at 237nm and 241nm in 0.1N HCl and 0.1N NaOH, respectively, while AML has no wavelength shift in both solvents. The second method is the new Extended Ratio Subtraction Method (EXRSM) coupled to Ratio Subtraction Method (RSM) for determination of both drugs in commercial dosage form. The third and fourth methods are multivariate calibration which include Principal Component Regression (PCR) and Partial Least Squares (PLSs). A detailed validation of the methods was performed following the ICH guidelines and the standard curves were found to be linear in the range of 2-30μg/mL for BENZ in difference and extended ratio subtraction spectrophotometric method, and 5-30 for AML in EXRSM method, with well accepted mean correlation coefficient for each analyte. The intra-day and inter-day precision and accuracy results were well within the acceptable limits. PMID:24424258
Applying transport-distance specific SOC distribution to calibrate soil erosion model WaTEM
NASA Astrophysics Data System (ADS)
Hu, Yaxian; Heckrath, Goswin J.; Kuhn, Nikolaus J.
2016-04-01
Slope-scale soil erosion, transport and deposition fundamentally decide the spatial redistribution of eroded sediments in terrestrial and aquatic systems, which further affect the burial and decomposition of eroded SOC. However, comparisons of SOC contents between upper eroding slope and lower depositional site cannot fully reflect the movement of eroded SOC in-transit along hillslopes. The actual transport distance of eroded SOC is decided by its settling velocity. So far, the settling velocity distribution of eroded SOC is mostly calculated from mineral particle specific SOC distribution. Yet, soil is mostly eroded in form of aggregates, and the movement of aggregates differs significantly from individual mineral particles. This urges a SOC erodibility parameter based on actual transport distance distribution of eroded fractions to better calibrate soil erosion models. Previous field investigation on a freshly seeded cropland in Denmark has shown immediate deposition of fast settling soil fractions and the associated SOC at footslopes, followed by a fining trend at the slope tail. To further quantify the long-term effects of topography on erosional redistribution of eroded SOC, the actual transport-distance specific SOC distribution observed on the field was applied to a soil erosion model WaTEM (based on USLE). After integrating with local DEM, our calibrated model succeeded in locating the hotspots of enrichment/depletion of eroded SOC on different topographic positions, much better corresponding to the real-world field observation. By extrapolating into repeated erosion events, our projected results on the spatial distribution of eroded SOC are also adequately consistent with the SOC properties in the consecutive sample profiles along the slope.
Calibration and uncertainty issues of a hydrological model (SWAT) applied to West Africa
NASA Astrophysics Data System (ADS)
Schuol, J.; Abbaspour, K. C.
2006-09-01
Distributed hydrological models like SWAT (Soil and Water Assessment Tool) are often highly over-parameterized, making parameter specification and parameter estimation inevitable steps in model calibration. Manual calibration is almost infeasible due to the complexity of large-scale models with many objectives. Therefore we used a multi-site semi-automated inverse modelling routine (SUFI-2) for calibration and uncertainty analysis. Nevertheless, the question of when a model is sufficiently calibrated remains open, and requires a project dependent definition. Due to the non-uniqueness of effective parameter sets, parameter calibration and prediction uncertainty of a model are intimately related. We address some calibration and uncertainty issues using SWAT to model a four million km2 area in West Africa, including mainly the basins of the river Niger, Volta and Senegal. This model is a case study in a larger project with the goal of quantifying the amount of global country-based available freshwater. Annual and monthly simulations with the "calibrated" model for West Africa show promising results in respect of the freshwater quantification but also point out the importance of evaluating the conceptual model uncertainty as well as the parameter uncertainty.
Lefèvre, Thomas; Rondet, Claire; Parizot, Isabelle; Chauvin, Pierre
2014-01-01
Background Cost containment policies and the need to satisfy patients’ health needs and care expectations provide major challenges to healthcare systems. Identification of homogeneous groups in terms of healthcare utilisation could lead to a better understanding of how to adjust healthcare provision to society and patient needs. Methods This study used data from the third wave of the SIRS cohort study, a representative, population-based, socio-epidemiological study set up in 2005 in the Paris metropolitan area, France. The data were analysed using a cross-sectional design. In 2010, 3000 individuals were interviewed in their homes. Non-conventional multivariate clustering techniques were used to determine homogeneous user groups in data. Multinomial models assessed a wide range of potential associations between user characteristics and their pattern of healthcare utilisation. Results We identified four distinct patterns of healthcare use. Patterns of consumption and the socio-demographic characteristics of users differed qualitatively and quantitatively between these four profiles. Extensive and intensive use by older, wealthier and unhealthier people contrasted with narrow and parsimonious use by younger, socially deprived people and immigrants. Rare, intermittent use by young healthy men contrasted with regular targeted use by healthy and wealthy women. Conclusion The use of an original technique of massive multivariate analysis allowed us to characterise different types of healthcare users, both in terms of resource utilisation and socio-demographic variables. This method would merit replication in different populations and healthcare systems. PMID:25506916
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. PMID:23194509
NASA Astrophysics Data System (ADS)
Chu, Ning; Fan, Shihua
2009-12-01
A new analytical method was developed for the simultaneous kinetic spectrophotometric determination of a quaternary carbamate pesticide mixture consisting of carbofuran, propoxur, metolcarb and fenobucarb using sequential injection analysis (SIA). The procedure was based upon the different kinetic properties between the analytes reacted with reagent in flow system in the non-stopped-flow mode, in which their hydrolysis products coupled with diazotized p-nitroaniline in an alkaline medium to form the corresponding colored complexes. The absorbance data from SIA peak time profile were recorded at 510 nm and resolved by the use of back-propagation-artificial neural network (BP-ANN) algorithms for multivariate quantitative analysis. The experimental variables and main network parameters were optimized and each of the pesticides could be determined in the concentration range of 0.5-10.0 μg mL -1, at a sampling frequency of 18 h -1. The proposed method was compared to other spectrophotometric methods for simultaneous determination of mixtures of carbamate pesticides, and it was proved to be adequately reliable and was successfully applied to the simultaneous determination of the four pesticide residues in water and fruit samples, obtaining the satisfactory results based on recovery studies (84.7-116.0%).
NASA Astrophysics Data System (ADS)
Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur
2014-05-01
Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos
Geist, David R. ); Brown, Richard S.; Lepla, Ken; Chandler, James P.
2001-12-01
One of the practical problems with quantifying the amount of energy used by fish implanted with electromyogram (EMG) radio transmitters is that the signals emitted by the transmitter provide only a relative index of activity unless they are calibrated to the swimming speed of the fish. Ideally calibration would be conducted for each fish before it is released, but this is often not possible and calibration curves derived from more than one fish are used to interpret EMG signals from individuals which have not been calibrated. We tested the validity of this approach by comparing EMG data within three groups of three wild juvenile white sturgeon Acipenser transmontanus implanted with the same EMG radio transmitter. We also tested an additional six fish which were implanted with separate EMG transmitters. Within each group, a single EMG radio transmitter usually did not produce similar results in different fish. Grouping EMG signals among fish produced less accurate results than having individual EMG-swim speed relationships for each fish. It is unknown whether these differences were a result of different swimming performances among individual fish or inconsistencies in the placement or function of the EMG transmitters. In either case, our results suggest that caution should be used when applying calibration curves from one group of fish to another group of uncalibrated fish.
NASA Astrophysics Data System (ADS)
Abdel-Aziz, Omar; El Kosasy, A. M.; El-Sayed Okeil, S. M.
2014-12-01
A modified dispersive liquid-liquid extraction (DLLE) procedure coupled with spectrophotometric techniques was adopted for simultaneous determination of naphthalene, anthracene, benzo(a)pyrene, alpha-naphthol and beta-naphthol in water samples. Two different methods were used, partial least-squares (PLS) method and a new derivative ratio method, namely extended derivative ratio (EDR). A PLS-2 model was established for simultaneous determination of the studied pollutants in methanol, by using twenty mixtures as calibration set and five mixtures as validation set. Also, in methanol a novel (EDR) method was developed for determination of the studied pollutants, where each component in the mixture of the five PAHs was determined by using a mixture of the other four components as divisor. Chemometric and EDR methods could be also adopted for determination of the studied PAH in water samples after transferring them from aqueous medium to the organic one by utilizing dispersive liquid-liquid extraction technique, where different parameters were investigated using a full factorial design. Both methods were compared and the proposed method was validated according to ICH guidelines and successfully applied to determine these PAHs simultaneously in spiked water samples, where satisfactory results were obtained. All the results obtained agreed with those of published methods, where no significant difference was observed.
NASA Astrophysics Data System (ADS)
Xie, Yunfei; Song, Yan; Zhang, Yong; Zhao, Bing
2010-05-01
Pefloxacin mesylate, a broad-spectrum antibacterial fluoroquinolone, has been widely used in clinical practice. Therefore, it is very important to detect the concentration of Pefloxacin mesylate. In this research, the near-infrared spectroscopy (NIRS) has been applied to quantitatively analyze on 108 injection samples, which was divided into a calibration set containing 89 samples and a prediction set containing 19 samples randomly. In order to get a satisfying result, partial least square (PLS) regression and principal components regression (PCR) have been utilized to establish quantitative models. Also, the process of establishing the models, parameters of the models, and prediction results were discussed in detail. In the PLS regression, the values of the coefficient of determination ( R2) and root mean square error of cross-validation (RMSECV) of PLS regression are 0.9263 and 0.00119, respectively. For comparison, though applying PCR method to get the values of R2 and RMSECV we obtained are 0.9685 and 0.00108, respectively. And the values of the standard error of prediction set (SEP) of PLS and PCR models are 0.001480 and 0.001140. The result of the prediction set suggests that these two quantitative analysis models have excellent generalization ability and prediction precision. However, for this PFLX injection samples, the PCR quantitative analysis model achieved more accurate results than the PLS model. The experimental results showed that NIRS together with PCR method provide rapid and accurate quantitative analysis of PFLX injection samples. Moreover, this study supplied technical support for the further analysis of other injection samples in pharmaceuticals.
Dead-blow hammer design applied to a calibration target mechanism to dampen excessive rebound
NASA Technical Reports Server (NTRS)
Lim, Brian Y.
1991-01-01
An existing rotary electromagnetic driver was specified to be used to deploy and restow a blackbody calibration target inside of a spacecraft infrared science instrument. However, this target was much more massive than any other previously inherited design applications. The target experienced unacceptable bounce when reaching its stops. Without any design modification, the momentum generated by the driver caused the target to bounce back to its starting position. Initially, elastomeric dampers were used between the driver and the target. However, this design could not prevent the bounce, and it compromised the positional accuracy of the calibration target. A design that successfully met all the requirements incorporated a sealed pocket 85 percent full of 0.75 mm diameter stainless steel balls in the back of the target to provide the effect of a dead-blow hammer. The energy dissipation resulting from the collision of balls in the pocket successfully dampened the excess momentum generated during the target deployment. The disastrous effects of new requirements on a design with a successful flight history, the modifications that were necessary to make the device work, and the tests performed to verify its functionality are described.
Fan, Shou-Zen; Shieh, Jiann-Shing
2014-01-01
We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability. PMID:25587533
Mujica Ascencio, Saul; Choe, ChunSik; Meinke, Martina C; Müller, Rainer H; Maksimov, George V; Wigger-Alberti, Walter; Lademann, Juergen; Darvin, Maxim E
2016-07-01
Propylene glycol is one of the known substances added in cosmetic formulations as a penetration enhancer. Recently, nanocrystals have been employed also to increase the skin penetration of active components. Caffeine is a component with many applications and its penetration into the epidermis is controversially discussed in the literature. In the present study, the penetration ability of two components - caffeine nanocrystals and propylene glycol, applied topically on porcine ear skin in the form of a gel, was investigated ex vivo using two confocal Raman microscopes operated at different excitation wavelengths (785nm and 633nm). Several depth profiles were acquired in the fingerprint region and different spectral ranges, i.e., 526-600cm(-1) and 810-880cm(-1) were chosen for independent analysis of caffeine and propylene glycol penetration into the skin, respectively. Multivariate statistical methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) combined with Student's t-test were employed to calculate the maximum penetration depths of each substance (caffeine and propylene glycol). The results show that propylene glycol penetrates significantly deeper than caffeine (20.7-22.0μm versus 12.3-13.0μm) without any penetration enhancement effect on caffeine. The results confirm that different substances, even if applied onto the skin as a mixture, can penetrate differently. The penetration depths of caffeine and propylene glycol obtained using two different confocal Raman microscopes are comparable showing that both types of microscopes are well suited for such investigations and that multivariate statistical PCA-LDA methods combined with Student's t-test are very useful for analyzing the penetration of different substances into the skin. PMID:27108784
NASA Astrophysics Data System (ADS)
Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik
2016-04-01
Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.
NASA Technical Reports Server (NTRS)
Crutcher, H. L.; Falls, L. W.
1976-01-01
Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.
NASA Astrophysics Data System (ADS)
Giachetti, A.; Daffara, C.; Reghelin, C.; Gobbetti, E.; Pintus, R.
2015-06-01
In this paper we analyze some problems related to the acquisition of multiple illumination images for Polynomial Texture Maps (PTM) or generic Reflectance Transform Imaging (RTI). We show that intensity and directionality nonuniformity can be a relevant issue when acquiring manual sets of images with the standard highlight-based setup both using a flash lamp and a LED light. To maintain a cheap and flexible acquisition setup that can be used on field and by non-experienced users we propose to use a dynamic calibration and correction of the lights based on multiple intensity and direction estimation around the imaged object during the acquisition. Preliminary tests on the results obtained have been performed by acquiring a specifically designed 3D printed pattern in order to see the accuracy of the acquisition obtained both for spatial discrimination of small structures and normal estimation, and on samples of different types of paper in order to evaluate material discrimination. We plan to design and build from our analysis and from the tools developed and under development a set of novel procedures and guidelines that can be used to turn the cheap and common RTI acquisition setup from a simple way to enrich object visualization into a powerful method for extracting quantitative characterization both of surface geometry and of reflective properties of different materials. These results could have relevant applications in the Cultural Heritage domain, in order to recognize different materials used in paintings or investigate the ageing status of artifacts' surface.
Voronov, Alexey; Urakawa, Atsushi; van Beek, Wouter; Tsakoumis, Nikolaos E; Emerich, Hermann; Rønning, Magnus
2014-08-20
Large datasets containing many spectra commonly associated with in situ or operando experiments call for new data treatment strategies as conventional scan by scan data analysis methods have become a time-consuming bottleneck. Several convenient automated data processing procedures like least square fitting of reference spectra exist but are based on assumptions. Here we present the application of multivariate curve resolution (MCR) as a blind-source separation method to efficiently process a large data set of an in situ X-ray absorption spectroscopy experiment where the sample undergoes a periodic concentration perturbation. MCR was applied to data from a reversible reduction-oxidation reaction of a rhenium promoted cobalt Fischer-Tropsch synthesis catalyst. The MCR algorithm was capable of extracting in a highly automated manner the component spectra with a different kinetic evolution together with their respective concentration profiles without the use of reference spectra. The modulative nature of our experiments allows for averaging of a number of identical periods and hence an increase in the signal to noise ratio (S/N) which is efficiently exploited by MCR. The practical and added value of the approach in extracting information from large and complex datasets, typical for in situ and operando studies, is highlighted. PMID:25086889
Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir
2014-01-01
Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. PMID:24532460
NASA Astrophysics Data System (ADS)
Froes, Roberta Eliane Santos; Neto, Waldomiro Borges; Silva, Nilton Oliveira Couto e.; Naveira, Rita Lopes Pereira; Nascentes, Clésia Cristina; da Silva, José Bento Borba
2009-06-01
A method for the direct determination (without sample pre-digestion) of microelements in fruit juice by inductively coupled plasma optical emission spectrometry has been developed. The method has been optimized by a 2 3 factorial design, which evaluated the plasma conditions (nebulization gas flow rate, applied power, and sample flow rate). A 1:1 diluted juice sample with 2% HNO 3 (Tetra Packed, peach flavor) and spiked with 0.5 mg L - 1 of Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, Sn, and Zn was employed in the optimization. The results of the factorial design were evaluated by exploratory analysis (Hierarchical Cluster Analysis, HCA, and Principal Component Analysis, PCA) to determine the optimum analytical conditions for all elements. Central point condition differentiation (0.75 L min - 1 , 1.3 kW, and 1.25 mL min - 1 ) was observed for both methods, Principal Component Analysis and Hierarchical Cluster Analysis, with higher analytical signal values, suggesting that these are the optimal analytical conditions. F and t-student tests were used to compare the slopes of the calibration curves for aqueous and matrix-matched standards. No significant differences were observed at 95% confidence level. The correlation coefficient was higher than 0.99 for all the elements evaluated. The limits of quantification were: Al 253, Cu 3.6, Fe 84, Mn 0.4, Zn 71, Ni 67, Cd 69, Pb 129, Sn 206, Cr 79, Co 24, and Ba 2.1 µg L - 1 . The spiking experiments with fruit juice samples resulted in recoveries between 80 and 120%, except for Co and Sn. Al, Cd, Pb, Sn and Cr could not be quantified in any of the samples investigated. The method was applied to the determination of several elements in fruit juice samples commercialized in Brazil.
Al-Degs, Yahya S; El-Sheikh, Amjad H; Al-Ghouti, Mohammad A; Hemmateenejad, Bahram; Walker, Gavin M
2008-05-30
A simple and rapid analytical method for the determination of trace levels of five sulphonated and azo sulphonated reactive dyes: Cibacron Reactive Blue 2 (C-Blue, trisulphonated dye), Cibacron Reactive Red 4 (C-Red, tetrasulphonated azo dye), Cibacron Reactive Yellow 2 (C-Yellow, trisulphonated azo dye), Levafix Brilliant Red E-4BA (L-Red, trisulphonated dye), and Levafix Brilliant Blue E-4BA (L-Blue, disulphonated dye) in water is presented. Initially, the dyes were preconcentrated from 250 ml of water samples with solid-phase extraction using natural zeolite sample previously modified with a microemulsion. The modified zeolite exhibited an excellent extraction for the dyes from solution. The parameters that influence quantitative recovery of reactive dyes like amount of extractant, volume of dye solution, pH, ionic strength, and extraction-elution flow rate were varied and optimized. After elution of the adsorbed dyes, the concentration of dyes was determined spectrophotometrically with the aid of principle component regression (PCR) method without separation of dyes. The results obtained from PCR method were comparable to those obtained from HPLC method confirming the effectiveness of the proposed method. With the aid of SPE by M-zeolite, the concentration of dyes could be reproducibly detected over the range 25-200 ppb for C-Yellow and L-Blue and from 50 to 250 ppb for C-Blue, C-Red, and L-Red. The multivariate detection limits of dyes were found to be 15 ppb for C-Yellow and L-Blue and 25 ppb for C-Blue, C-Red, and L-Red dyes. The proposed chemometric method gave recoveries from 85.4 to 115.3% and R.S.D. from 1.0 to 14.5% for determination of the five dyes without any prior separation for solutes. PMID:18585163
Prokeš, Lubomír; Amato, Filippo; Pivetta, Tiziana; Hampl, Aleš; Havel, Josef; Vaňhara, Petr
2016-01-01
Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general. PMID:26821236
Valletta, Elisa; Kučera, Lukáš; Prokeš, Lubomír; Amato, Filippo; Pivetta, Tiziana; Hampl, Aleš; Havel, Josef; Vaňhara, Petr
2016-01-01
Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general. PMID:26821236
Darwish, Hany W; Elzanfaly, Eman S; Saad, Ahmed S; Abdelaleem, Abdelaziz El-Bayoumi
2016-12-01
Five different chemometric methods were developed for the simultaneous determination of betamethasone dipropionate (BMD), clotrimazole (CT) and benzyl alcohol (BA) in their combined dosage form (Lotriderm® cream). The applied methods included three full spectrum based chemometric techniques; namely principal component regression (PCR), Partial Least Squares (PLS) and Artificial Neural Networks (ANN), while the other two methods were PLS and ANN preceded by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. A multilevel multifactor experimental design was adopted for proper construction of the models. A validation set composed of 12 mixtures containing different ratios of the three analytes was used to evaluate the predictive power of the suggested models. All the proposed methods except ANN, were successfully applied for the analysis of their pharmaceutical formulation (Lotriderm® cream). Results demonstrated the efficiency of the four methods as quantitative tool for analysis of the three analytes without prior separation procedures and without any interference from the co-formulated excipient. Additionally, the work highlighted the effect of GA on increasing the predictive power of PLS and ANN models. PMID:27327260
NASA Technical Reports Server (NTRS)
Lerch, F. J.; Nerem, R. S.; Chinn, D. S.; Chan, J. C.; Patel, G. B.; Klosko, S. M.
1993-01-01
A new method has been developed to provide a direct test of the error calibrations of gravity models based on actual satellite observations. The basic approach projects the error estimates of the gravity model parameters onto satellite observations, and the results of these projections are then compared with data residual computed from the orbital fits. To allow specific testing of the gravity error calibrations, subset solutions are computed based on the data set and data weighting of the gravity model. The approach is demonstrated using GEM-T3 to show that the gravity error estimates are well calibrated and that reliable predictions of orbit accuracies can be achieved for independent orbits.
Ebrahimabadi, Ebrahim H; Ghoreishi, Sayed Mehdi; Masoum, Saeed; Ebrahimabadi, Abdolrasoul H
2016-01-01
Myrtus communis L. is an aromatic evergreen shrub and its essential oil possesses known powerful antimicrobial activity. However, the contribution of each component of the plant essential oil in observed antimicrobial ability is unclear. In this study, chemical components of the essential oil samples of the plant were identified qualitatively and quantitatively using GC/FID/Mass spectrometry system, antimicrobial activity of these samples against three microbial strains were evaluated and, these two set of data were correlated using chemometrics methods. Three chemometric methods including principal component regression (PCR), partial least squares (PLS) and orthogonal projections to latent structures (OPLS) were applied for the study. These methods showed similar results, but, OPLS was selected as preferred method due to its predictive and interpretational ability, facility, repeatability and low time-consuming. The results showed that α-pinene, 1,8 cineole, β-pinene and limonene are the highest contributors in antimicrobial properties of M. communis essential oil. Other researches have reported high antimicrobial activities for the plant essential oils rich in these compounds confirming our findings. PMID:26625337
Samadi, Naser; Masoum, Saeed; Mehrara, Bahare; Hosseini, Hossein
2015-09-15
Satureja hortensis L. and Oliveria decumbens Vent. are known for their diverse effects in drug therapy and traditional medicine. One of the most interesting properties of their essential oils is good antioxidant activity. In this paper, essential oils of aerial parts of S. hortensis L. and O. decumbens Vent. from different regions were obtained by hydrodistillation and were analyzed by gas chromatography-mass spectrometry (GC-MS). Essential oils were tested for their free radical scavenging activity using 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay to identify the peaks potentially responsible for the antioxidant activity from chromatographic fingerprints by numerous linear multivariate calibration techniques. Because of its simplicity and high repeatability, orthogonal projection to latent structures (OPLS) model had the best performance in indicating the potential antioxidant compounds in S. hortensis L. and O. decumbens Vent. essential oils. In this study, P-cymene, carvacrol and β-bisabolene for S. hortensis L. and P-cymene, Ç-terpinen, thymol, carvacrol, and 1,3-benzodioxole, 4-methoxy-6-(2-propenyl) for O. decumbens Vent. are suggested as the potentially antioxidant compounds. PMID:26262598
Pérez, Rocío L; Escandar, Graciela M
2016-02-01
A green method is reported based on non-sophisticated instrumental for the quantification of seven natural and synthetic estrogens, three progestagens and one androgen in the presence of real interferences. The method takes advantage of: (1) chromatography, allowing total or partial resolution of a large number of compounds, (2) dual detection, permitting selection of the most appropriate signal for each analyte and, (3) second-order calibration, enabling mathematical resolution of incompletely resolved chromatographic bands and analyte determination in the presence of interferents. Consumption of organic solvents for cleaning, extraction and separation are markedly decreased because of the coupling with MCR-ALS (multivariate curve resolution/alternating least-squares) which allows the successful resolution in the presence of other co-eluting matrix constituents. Rigorous IUPAC detection limits were obtained: 6-24 ng L(-1) in water, and 0.1-0.9 ng g(-1) in sediments. Relative prediction errors were 2-10% (water) and 1-8% (sediments). PMID:26650083
Marengo, Emilio; Robotti, Elisa; Bobba, Marco; Liparota, Maria Cristina; Rustichelli, Chiara; Zamò, Alberto; Chilosi, Marco; Righetti, Pier Giorgio
2006-02-01
Mantle cell lymphoma (MCL) cell lines have been difficult to generate, since only few have been described so far and even fewer have been thoroughly characterized. Among them, there is only one cell line, called GRANTA-519, which is well established and universally adopted for most lymphoma studies. We succeeded in establishing a new MCL cell line, called MAVER-1, from a leukemic MCL, and performed a thorough phenotypical, cytogenetical and molecular characterization of the cell line. In the present report, the phenotypic expression of GRANTA-519 and MAVER-1 cell lines has been compared and evaluated by a proteomic approach, exploiting 2-D map analysis. By univariate statistical analysis (Student's t-test, as commonly used in most commercial software packages), most of the protein spots were found to be identical between the two cell lines. Thirty spots were found to be unique for the GRANTA-519, whereas another 11 polypeptides appeared to be expressed only by the MAVER-1 cell line. A number of these spots could be identified by MS. These data were confirmed and expanded by multivariate statistical tools (principal component analysis and soft-independent model of class analogy) that allowed identification of a larger number of differently expressed spots. Multivariate statistical tools have the advantage of reducing the risk of false positives and of identifying spots that are significantly altered in terms of correlated expression rather than absolute expression values. It is thus suggested that, in future work in differential proteomic profiling, both univariate and multivariate statistical tools should be adopted. PMID:16372308
Multivariate postprocessing techniques for probabilistic hydrological forecasting
NASA Astrophysics Data System (ADS)
Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian
2016-04-01
Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power
Almeida, Luciano F; Vale, Maria G R; Dessuy, Morgana B; Silva, Márcia M; Lima, Renato S; Santos, Vagner B; Diniz, Paulo H D; Araújo, Mário C U
2007-10-31
The increasing development of miniaturized flow systems and the continuous monitoring of chemical processes require dramatically simplified and cheap flow schemes and instrumentation with large potential for miniaturization and consequent portability. For these purposes, the development of systems based on flow and batch technologies may be a good alternative. Flow-batch analyzers (FBA) have been successfully applied to implement analytical procedures, such as: titrations, sample pre-treatment, analyte addition and screening analysis. In spite of its favourable characteristics, the previously proposed FBA uses peristaltic pumps to propel the fluids and this kind of propulsion presents high cost and large dimension, making unfeasible its miniaturization and portability. To overcome these drawbacks, a low cost, robust, compact and non-propelled by peristaltic pump FBA is proposed. It makes use of a lab-made piston coupled to a mixing chamber and a step motor controlled by a microcomputer. The piston-propelled FBA (PFBA) was applied for automatic preparation of calibration solutions for manganese determination in mineral waters by electrothermal atomic-absorption spectrometry (ET AAS). Comparing the results obtained with two sets of calibration curves (five by manual and five by PFBA preparations), no significant statistical differences at a 95% confidence level were observed by applying the paired t-test. The standard deviation of manual and PFBA procedures were always smaller than 0.2 and 0.1mugL(-1), respectively. By using PFBA it was possible to prepare about 80 calibration solutions per hour. PMID:19073119
Uncertainty Analysis of Instrument Calibration and Application
NASA Technical Reports Server (NTRS)
Tripp, John S.; Tcheng, Ping
1999-01-01
Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.
NASA Technical Reports Server (NTRS)
Tripp, John S.; Tcheng, Ping
1999-01-01
Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.
Fragoso, Wallace; Allegrini, Franco; Olivieri, Alejandro C
2016-08-24
Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different calibration methodologies, and helps to solve literature inconsistencies on the relationship between classical sensitivity and prediction error. In contrast to the classical plain sensitivity, γ incorporates the noise properties in its definition, and its inverse is well correlated with root mean square errors of prediction in the presence of general noise structures. The proposal is supported by studying simulated and experimental first-order multivariate calibration systems with various models, namely multiple linear regression, principal component regression (PCR) and maximum likelihood PCR (MLPCR). The simulations included instrumental noise of different types: independently and identically distributed (iid), correlated (pink) and proportional noise, while the experimental data carried noise which is clearly non-iid. PMID:27496995
Li, D.Y.; Chen, L.Q.
1998-01-05
Coherent precipitation of multi-variant Ti{sub 11}Ni{sup 14} precipitates in TiNi alloys was investigated by employing a continuum field kinetic model. The structural difference between the precipitate phase and the matrix as well as the orientational differences between precipitate variants are distinguished by nonconserved structural field variables, whereas the compositional difference between the precipitate and matrix is described by a conserved field variable. The temporal evolution of the spatially dependent field variables is determined by numerically solving the time-dependent Ginzburg-Landau (TDGL) equations for the structural variables and the Cahn-Hilliard diffusion equation for the composition. In particular, the interaction between precipitates, and the growth morphology of Ti{sub 11}Ni{sub 14} precipitates under strain-constraints were studied, without a priori assumptions on the precipitate shape and distribution. The predicted morphology and distribution of Ti{sub 11}Ni{sub 14} variants were compared with experimental observations. Excellent agreement between the simulation and experimental observations was found.
NASA Astrophysics Data System (ADS)
Cerqueira, J. G.; Fernandez, J. H.; Hoelzemann, J. J.; Leme, N. M. P.; Sousa, C. T.
2014-10-01
Due to the high costs of commercial monitoring instruments, a portable sun photometer was developed at INPE/CRN laboratories, operating in four bands, with two bands in the visible spectrum and two in near infrared. The instrument calibration process is performed by applying the classical Langley method. Application of the Langley’s methodology requires a site with high optical stability during the measurements, which is usually found in high altitudes. However, far from being an ideal site, Harrison et al. (1994) report success with applying the Langley method to some data for a site in Boulder, Colorado. Recently, Liu et al. (2011) show that low elevation sites, far away from urban and industrial centers can provide a stable optical depth, similar to high altitudes. In this study we investigated the feasibility of applying the methodology in the semiarid region of northeastern Brazil, far away from pollution areas with low altitudes, for sun photometer calibration. We investigated optical depth stability using two periods of measurements in the year during dry season in austral summer. The first one was in December when the native vegetation naturally dries, losing all its leaves and the second one was in September in the middle of the dry season when the vegetation is still with leaves. The data were distributed during four days in December 2012 and four days in September 2013 totaling eleven half days of collections between mornings and afternoons and by means of fitted line to the data V0 values were found. Despite the high correlation between the collected data and the fitted line, the study showed a variation between the values of V0 greater than allowed for sun photometer calibration. The lowest V0 variation reached in this experiment with values lower than 3% for the bands 500, 670 and 870 nm are displayed in tables. The results indicate that the site needs to be better characterized with studies in more favorable periods, soon after the rainy season.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M.
2004-03-23
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Hybrid least squares multivariate spectral analysis methods
Haaland, David M.
2002-01-01
A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Haaland, David M.
1999-07-14
The analysis precision of any multivariate calibration method will be severely degraded if unmodeled sources of spectral variation are present in the unknown sample spectra. This paper describes a synthetic method for correcting for the errors generated by the presence of unmodeled components or other sources of unmodeled spectral variation. If the spectral shape of the unmodeled component can be obtained and mathematically added to the original calibration spectra, then a new synthetic multivariate calibration model can be generated to accommodate the presence of the unmodeled source of spectral variation. This new method is demonstrated for the presence of unmodeled temperature variations in the unknown sample spectra of dilute aqueous solutions of urea, creatinine, and NaCl. When constant-temperature PLS models are applied to spectra of samples of variable temperature, the standard errors of prediction (SEP) are approximately an order of magnitude higher than that of the original cross-validated SEPs of the constant-temperature partial least squares models. Synthetic models using the classical least squares estimates of temperature from pure water or variable-temperature mixture sample spectra reduce the errors significantly for the variable temperature samples. Spectrometer drift adds additional error to the analyte determinations, but a method is demonstrated that can minimize the effect of drift on prediction errors through the measurement of the spectra of a small subset of samples during both calibration and prediction. In addition, sample temperature can be predicted with high precision with this new synthetic model without the need to recalibrate using actual variable-temperature sample data. The synthetic methods eliminate the need for expensive generation of new calibration samples and collection of their spectra. The methods are quite general and can be applied using any known source of spectral variation and can be used with any multivariate
NASA Technical Reports Server (NTRS)
Schiller, Stephen; Luvall, Jeffrey C.; Rickman, Doug L.; Arnold, James E. (Technical Monitor)
2000-01-01
Detecting changes in the Earth's environment using satellite images of ocean and land surfaces must take into account atmospheric effects. As a result, major programs are underway to develop algorithms for image retrieval of atmospheric aerosol properties and atmospheric correction. However, because of the temporal and spatial variability of atmospheric transmittance it is very difficult to model atmospheric effects and implement models in an operational mode. For this reason, simultaneous in situ ground measurements of atmospheric optical properties are vital to the development of accurate atmospheric correction techniques. Presented in this paper is a spectroradiometer system that provides an optimized set of surface measurements for the calibration and validation of atmospheric correction algorithms. The Portable Ground-based Atmospheric Monitoring System (PGAMS) obtains a comprehensive series of in situ irradiance, radiance, and reflectance measurements for the calibration of atmospheric correction algorithms applied to multispectral. and hyperspectral images. The observations include: total downwelling irradiance, diffuse sky irradiance, direct solar irradiance, path radiance in the direction of the north celestial pole, path radiance in the direction of the overflying satellite, almucantar scans of path radiance, full sky radiance maps, and surface reflectance. Each of these parameters are recorded over a wavelength range from 350 to 1050 nm in 512 channels. The system is fast, with the potential to acquire the complete set of observations in only 8 to 10 minutes depending on the selected spatial resolution of the sky path radiance measurements
Classical least squares multivariate spectral analysis
Haaland, David M.
2002-01-01
An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.
Implicit Spacecraft Gyro Calibration
NASA Technical Reports Server (NTRS)
Harman, Richard; Bar-Itzhack, Itzhack Y.
2003-01-01
This paper presents an implicit algorithm for spacecraft onboard instrument calibration, particularly to onboard gyro calibration. This work is an extension of previous work that was done where an explicit gyro calibration algorithm was applied to the AQUA spacecraft gyros. The algorithm presented in this paper was tested using simulated data and real data that were downloaded from the Microwave Anisotropy Probe (MAP) spacecraft. The calibration tests gave very good results. A comparison between the use of the implicit calibration algorithm used here with the explicit algorithm used for AQUA spacecraft indicates that both provide an excellent estimation of the gyro calibration parameters with similar accuracies.
NASA Astrophysics Data System (ADS)
Divya, O.; Shinde, Mandakini
2013-07-01
A multivariate calibration model for the simultaneous estimation of propranolol (PRO) and amiloride (AMI) using synchronous fluorescence spectroscopic data has been presented in this paper. Two multivariate techniques, PCR (Principal Component Regression) and PLSR (Partial Least Square Regression), have been successfully applied for the simultaneous determination of AMI and PRO in synthetic binary mixtures and pharmaceutical dosage forms. The SF spectra of AMI and PRO (calibration mixtures) were recorded at several concentrations within their linear range between wavelengths of 310 and 500 nm at an interval of 1 nm. Calibration models were constructed using 32 samples and validated by varying the concentrations of AMI and PRO in the calibration range. The results indicated that the model developed was very robust and able to efficiently analyze the mixtures with low RMSEP values.
Multivariate Padé Approximations For Solving Nonlinear Diffusion Equations
NASA Astrophysics Data System (ADS)
Turut, V.
2015-11-01
In this paper, multivariate Padé approximation is applied to power series solutions of nonlinear diffusion equations. As it is seen from tables, multivariate Padé approximation (MPA) gives reliable solutions and numerical results.
NASA Astrophysics Data System (ADS)
Hoffman, Ross N.; Ardizzone, Joseph V.; Leidner, S. Mark; Smith, Deborah K.; Atlas, Robert M.
2013-04-01
The cross-calibrated, multi-platform (CCMP) ocean surface wind project [Atlas et al., 2011] generates high-quality, high-resolution, vector winds over the world's oceans beginning with the 1987 launch of the SSM/I F08, using Remote Sensing Systems (RSS) microwave satellite wind retrievals, as well as in situ observations from ships and buoys. The variational analysis method [VAM, Hoffman et al., 2003] is at the center of the CCMP project's analysis procedures for combining observations of the wind. The VAM was developed as a smoothing spline and so implicitly defines the background error covariance by means of several constraints with adjustable weights, and does not provide an explicit estimate of the analysis error. Here we report on our research to develop uncertainty estimates for wind speed for the VAM inputs and outputs, i.e., for the background (B), the observations (O) and the analysis (A) wind speed, based on the Desroziers et al. [2005] diagnostics (DD hereafter). The DD are applied to the CCMP ocean surface wind data sets to estimate wind speed errors of the ECMWF background, the microwave satellite observations and the resulting CCMP analysis. The DD confirm that the ECMWF operational surface wind speed error standard deviations vary with latitude in the range 0.7-1.5 m/s and that the cross-calibrated Remote Sensing Systems (RSS) wind speed retrievals standard deviations are in the range 0.5-0.8 m/s. Further the estimated CCMP analysis wind speed standard deviations are in the range 0.2-0.4 m/s. The results suggests the need to revise the parameterization of the errors due to the FGAT (first guess at the appropriate time) procedure. Errors for wind speeds < 16 m/s are homogeneous, but for the relatively rare, but critical higher wind speed situations, errors are much larger. Atlas, R., R. N. Hoffman, J. Ardizzone, S. M. Leidner, J. C. Jusem, D. K. Smith, and D. Gombos, A cross-calibrated, multi-platform ocean surface wind velocity product for
NASA Astrophysics Data System (ADS)
Enßlin, Torsten A.; Junklewitz, Henrik; Winderling, Lars; Greiner, Maksim; Selig, Marco
2014-10-01
Response calibration is the process of inferring how much the measured data depend on the signal one is interested in. It is essential for any quantitative signal estimation on the basis of the data. Here, we investigate self-calibration methods for linear signal measurements and linear dependence of the response on the calibration parameters. The common practice is to augment an external calibration solution using a known reference signal with an internal calibration on the unknown measurement signal itself. Contemporary self-calibration schemes try to find a self-consistent solution for signal and calibration by exploiting redundancies in the measurements. This can be understood in terms of maximizing the joint probability of signal and calibration. However, the full uncertainty structure of this joint probability around its maximum is thereby not taken into account by these schemes. Therefore, better schemes, in sense of minimal square error, can be designed by accounting for asymmetries in the uncertainty of signal and calibration. We argue that at least a systematic correction of the common self-calibration scheme should be applied in many measurement situations in order to properly treat uncertainties of the signal on which one calibrates. Otherwise, the calibration solutions suffer from a systematic bias, which consequently distorts the signal reconstruction. Furthermore, we argue that nonparametric, signal-to-noise filtered calibration should provide more accurate reconstructions than the common bin averages and provide a new, improved self-calibration scheme. We illustrate our findings with a simplistic numerical example.
A multivariate CAR model for mismatched lattices.
Porter, Aaron T; Oleson, Jacob J
2014-10-01
In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey. PMID:25457598
Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?
ERIC Educational Resources Information Center
Thompson, Bruce
Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…
Haaland, D.M.; Jones, H.D.T.
1997-09-01
Multivariate calibration methods have been applied extensively to the quantitative analysis of Fourier transform infrared (FT-IR) spectral data. Partial least squares (PLS) methods have become the most widely used multivariate method for quantitative spectroscopic analyses. Most often these methods are limited by model error or the accuracy or precision of the reference methods. However, in some cases, the precision of the quantitative analysis is limited by the noise in the spectroscopic signal. In these situations, the precision of the PLS calibrations and predictions can be improved by the incorporation of weighting in the PLS algorithm. If the spectral noise of the system is known (e.g., in the case of detector-noise-limited cases), then appropriate weighting can be incorporated into the multivariate spectral calibrations and predictions. A weighted PLS (WPLS) algorithm was developed to improve the precision of the analysis in the case of spectral-noise-limited data. This new PLS algorithm was then tested with real and simulated data, and the results compared with the unweighted PLS algorithm. Using near-infrared (NIR) calibration precision when the WPLS algorithm was applied. The best WPLS method improved prediction precision for the analysis of one of the minor components by a factor of nearly 9 relative to the unweighted PLS algorithm.
Multivariate Data EXplorer (MDX)
Steed, Chad Allen
2012-08-01
The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.
NASA Astrophysics Data System (ADS)
Mader, D.; Westfeld, P.; Maas, H.-G.
2014-06-01
The paper presents a flexible approach for the geometric calibration of a 2D infrared laser scanning range finder. It does not require spatial object data, thus avoiding the time-consuming determination of reference distances or coordinates with superior accuracy. The core contribution is the development of an integrated bundle adjustment, based on the flexible principle of a self-calibration. This method facilitates the precise definition of the geometry of the scanning device, including the estimation of range-measurement-specific correction parameters. The integrated calibration routine jointly adjusts distance and angular data from the laser scanning range finder as well as image data from a supporting DSLR camera, and automatically estimates optimum observation weights. The validation process carried out using a Hokuyo UTM-30LX-EW confirms the correctness of the proposed functional and stochastic contexts and allows detailed accuracy analyses. The level of accuracy of the observations is computed by variance component estimation. For the Hokuyo scanner, we obtained 0.2% of the measured distance in range measurement and 0.2 deg for the angle precision. The RMS error of a 3D coordinate after the calibration becomes 5 mm in lateral and 9 mm in depth direction. Particular challenges have arisen due to a very large elliptical laser beam cross-section of the scanning device used.
NASA Astrophysics Data System (ADS)
Houchin, J. S.
2014-09-01
A common problem for the off-line validation of the calibration algorithms and algorithm coefficients is being able to run science data through the exact same software used for on-line calibration of that data. The Joint Polar Satellite System (JPSS) program solved part of this problem by making the Algorithm Development Library (ADL) available, which allows the operational algorithm code to be compiled and run on a desktop Linux workstation using flat file input and output. However, this solved only part of the problem, as the toolkit and methods to initiate the processing of data through the algorithms were geared specifically toward the algorithm developer, not the calibration analyst. In algorithm development mode, a limited number of sets of test data are staged for the algorithm once, and then run through the algorithm over and over as the software is developed and debugged. In calibration analyst mode, we are continually running new data sets through the algorithm, which requires significant effort to stage each of those data sets for the algorithm without additional tools. AeroADL solves this second problem by providing a set of scripts that wrap the ADL tools, providing both efficient means to stage and process an input data set, to override static calibration coefficient look-up-tables (LUT) with experimental versions of those tables, and to manage a library containing multiple versions of each of the static LUT files in such a way that the correct set of LUTs required for each algorithm are automatically provided to the algorithm without analyst effort. Using AeroADL, The Aerospace Corporation's analyst team has demonstrated the ability to quickly and efficiently perform analysis tasks for both the VIIRS and OMPS sensors with minimal training on the software tools.
Multivariate Intraclass Correlation.
ERIC Educational Resources Information Center
Wiley, David E.; Hawkes, Thomas H.
This paper is an explication of a statistical model which will permit an interpretable intraclass correlation coefficient that is negative, and a generalized extension of that model to cover a multivariate problem. The methodological problem has its practical roots in an attempt to find a statistic which could indicate the degree of similarity or…
Multivariate postprocessing techniques for probabilistic hydrological forecasting
NASA Astrophysics Data System (ADS)
Hemri, S.; Lisniak, D.; Klein, B.
2015-09-01
Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both location and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well-calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. The domain of this study covers three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. In this study, the two approaches to model the temporal dependence structure are ensemble copula coupling (ECC), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA), which estimates the temporal correlations from training observations. The results indicate that both methods are suitable for modeling the temporal dependencies of probabilistic hydrologic forecasts.
Energy calibration via correlation
NASA Astrophysics Data System (ADS)
Maier, Daniel; Limousin, Olivier
2016-03-01
The main task of an energy calibration is to find a relation between pulse-height values and the corresponding energies. Doing this for each pulse-height channel individually requires an elaborated input spectrum with an excellent counting statistics and a sophisticated data analysis. This work presents an easy to handle energy calibration process which can operate reliably on calibration measurements with low counting statistics. The method uses a parameter based model for the energy calibration and concludes on the optimal parameters of the model by finding the best correlation between the measured pulse-height spectrum and multiple synthetic pulse-height spectra which are constructed with different sets of calibration parameters. A CdTe-based semiconductor detector and the line emissions of an 241Am source were used to test the performance of the correlation method in terms of systematic calibration errors for different counting statistics. Up to energies of 60 keV systematic errors were measured to be less than ~ 0.1 keV. Energy calibration via correlation can be applied to any kind of calibration spectra and shows a robust behavior at low counting statistics. It enables a fast and accurate calibration that can be used to monitor the spectroscopic properties of a detector system in near realtime.
NASA Astrophysics Data System (ADS)
Wan, Boyong
2007-12-01
Airborne passive Fourier transform infrared spectrometry is gaining increased attention in environmental applications because of its great flexibility. Usually, pattern recognition techniques are used for automatic analysis of large amount of collected data. However, challenging problems are the constantly changing background and high calibration cost. As aircraft is flying, background is always changing. Also, considering the great variety of backgrounds and high expense of data collection from aircraft, cost of collecting representative training data is formidable. Instead of using airborne data, data generated from simulation strategies can be used for training purposes. Training data collected under controlled conditions on the ground or synthesized from real backgrounds can be both options. With both strategies, classifiers may be developed with much lower cost. For both strategies, signal processing techniques need to be used to extract analyte features. In this dissertation, signal processing methods are applied either in interferogram or spectral domain for features extraction. Then, pattern recognition methods are applied to develop binary classifiers for automated detection of air-collected methanol and ethanol vapors. The results demonstrate, with optimized signal processing methods and training set composition, classifiers trained from ground-collected or synthetic data can give good classification on real air-collected data. Near-infrared (NIR) spectrometry is emerging as a promising tool for noninvasive blood glucose detection. In combination with multivariate calibration techniques, NIR spectroscopy can give quick quantitative determinations of many species with minimal sample preparation. However, one main problem with NIR calibrations is degradation of calibration model over time. The varying background information will worsen the prediction precision and complicate the multivariate models. To mitigate the needs for frequent recalibration and
Calibration of Germanium Resistance Thermometers
NASA Technical Reports Server (NTRS)
Ladner, D.; Urban, E.; Mason, F. C.
1987-01-01
Largely completed thermometer-calibration cryostat and probe allows six germanium resistance thermometers to be calibrated at one time at superfluid-helium temperatures. In experiments involving several such thermometers, use of this calibration apparatus results in substantial cost savings. Cryostat maintains temperature less than 2.17 K through controlled evaporation and removal of liquid helium from Dewar. Probe holds thermometers to be calibrated and applies small amount of heat as needed to maintain precise temperature below 2.17 K.
Multivariate Data EXplorer (MDX)
Energy Science and Technology Software Center (ESTSC)
2012-08-01
The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views wherebymore » selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.« less
Multivariate Analysis in Metabolomics
Worley, Bradley; Powers, Robert
2015-01-01
Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916
COSIMA data analysis using multivariate techniques
NASA Astrophysics Data System (ADS)
Silén, J.; Cottin, H.; Hilchenbach, M.; Kissel, J.; Lehto, H.; Siljeström, S.; Varmuza, K.
2015-02-01
We describe how to use multivariate analysis of complex TOF-SIMS (time-of-flight secondary ion mass spectrometry) spectra by introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a cross-validation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative similarity of measurements.
Sparse Multivariate Regression With Covariance Estimation
Rothman, Adam J.; Levina, Elizaveta; Zhu, Ji
2014-01-01
We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online. PMID:24963268
Optimal and multivariable control of a turbogenerator
NASA Astrophysics Data System (ADS)
Lahoud, M. A.; Harley, R. G.; Secker, A.
The use of modern control methods to design multivariable controllers which improve the performance of a turbogenerator was investigated. The turbogenerator nonlinear mathematical model from which a linearized model is deduced is presented. The inverse Nyquist Array method and the theory of optimal control are both applied to the linearized model to generate two alternative control schemes. The schemes are implemented on the nonlinear simulation model to assess their dynamic performance. Results from modern multivariable control schemes are compared with the classical automatic voltage regulator and speed governor system.
Method of multivariate spectral analysis
Keenan, Michael R.; Kotula, Paul G.
2004-01-06
A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).
System identification for multivariable control
NASA Astrophysics Data System (ADS)
Vanzee, G. A.
1981-05-01
System identification methods and modern control theory are applied to industrial processes. These processes must often be controlled in order to meet certain requirements with respect to the product quality, safety, energy consumption, and environmental load. Modern control system design methods which take the occurring interaction phenomena and stochastic disturbances into account are used. An accurate dynamic mathematical model of the process, by theoretical modelling and/or by system identification is obtained. The computational aspects of two important types of identifications methods, i.e., stochastic realization and prediction error based parameter estimation are studied. The studied computational aspects are the robustness, the accuracy, and the computational costs of the methods. Theoretical analyses and applications to a multivariable pilot scale process, operating under closed loop conditions are investigated.
Multivariate respiratory motion prediction
NASA Astrophysics Data System (ADS)
Dürichen, R.; Wissel, T.; Ernst, F.; Schlaefer, A.; Schweikard, A.
2014-10-01
In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs—normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)—and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.
Introduction to multivariate discrimination
NASA Astrophysics Data System (ADS)
Kégl, Balázs
2013-07-01
Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
Crawfis, R.A.
1996-03-01
This paper presents a new technique for representing multivalued data sets defined on an integer lattice. It extends the state-of-the-art in volume rendering to include nonhomogeneous volume representations. That is, volume rendering of materials with very fine detail (e.g. translucent granite) within a voxel. Multivariate volume rendering is achieved by introducing controlled amounts of noise within the volume representation. Varying the local amount of noise within the volume is used to represent a separate scalar variable. The technique can also be used in image synthesis to create more realistic clouds and fog.
Herman, J.R.; Hudson, R.; McPeters, R.; Stolarski, R. ); Ahmad, Z.; Gu, X.Y., Taylor, S.; Wellemeyer, C. )
1991-04-20
The currently archived (1989) total ozone mapping spectrometer (TOMS) and solar backscattered ultraviolet (SBUV) total ozone data (version 5) show a global average decrease of about 9.0% from November 1978 to November 1988. This large decrease disagrees with an approximate 3.5% decrease estimated from the ground-based Dobson network. The primary source of disagreement was found to arise from an overestimate of reflectivity change and its incorrect wavelengths dependence for the diffuser plate used when measuring solar irradiance. For total ozone measured by TOMS, a means has been found to use the measured radiance-irradiance ratio from several wavelengths pairs to construct an internally self consistent calibration. The method uses the wavelength dependence of the sensitivity to calibration errors and the requirement that albedo ratios for each wavelength pair yield the same total ozone amounts. Smaller errors in determining spacecraft attitude, synchronization problems with the photon counting electronics, and sea glint contamination of boundary reflectivity data have been corrected or minimized. New climatological low-ozone profiles have been incorporated into the TOMS algorithm that are appropriate for Antarctic ozone hole conditions and other low ozone cases. The combined corrections have led to a new determination of the global average total ozone trend (version 6) as a 2.9 {plus minus} 1.3% decrease over 11 years. Version 6 data are shown to be in agreement within error limits with the average of 39 ground-based Dobson stations and with the world standard Dobson spectrometer 83 at Mauna Loa, Hawaii.
A complete procedure for multivariate index-flood model application
NASA Astrophysics Data System (ADS)
Requena, Ana Isabel; Chebana, Fateh; Mediero, Luis
2016-04-01
Multivariate frequency analyses are needed to study floods due to dependence existing among representative variables of the flood hydrograph. Particularly, multivariate analyses are essential when flood-routing processes significantly attenuate flood peaks, such as in dams and flood management in flood-prone areas. Besides, regional analyses improve at-site quantile estimates obtained at gauged sites, especially when short flow series exist, and provide estimates at ungauged sites where flow records are unavailable. However, very few studies deal simultaneously with both multivariate and regional aspects. This study seeks to introduce a complete procedure to conduct a multivariate regional hydrological frequency analysis (HFA), providing guidelines. The methodology joins recent developments achieved in multivariate and regional HFA, such as copulas, multivariate quantiles and the multivariate index-flood model. The proposed multivariate methodology, focused on the bivariate case, is applied to a case study located in Spain by using hydrograph volume and flood peak observed series. As a result, a set of volume-peak events under a bivariate quantile curve can be obtained for a given return period at a target site, providing flexibility to practitioners to check and decide what the design event for a given purpose should be. In addition, the multivariate regional approach can also be used for obtaining the multivariate distribution of the hydrological variables when the aim is to assess the structure failure for a given return period.
Mossavar-Rahmani, Yasmin; Shaw, Pamela A; Wong, William W; Sotres-Alvarez, Daniela; Gellman, Marc D; Van Horn, Linda; Stoutenberg, Mark; Daviglus, Martha L; Wylie-Rosett, Judith; Siega-Riz, Anna Maria; Ou, Fang-Shu; Prentice, Ross L
2015-06-15
We investigated measurement error in the self-reported diets of US Hispanics/Latinos, who are prone to obesity and related comorbidities, by background (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) in 2010-2012. In 477 participants aged 18-74 years, doubly labeled water and urinary nitrogen were used as objective recovery biomarkers of energy and protein intakes. Self-report was captured from two 24-hour dietary recalls. All measures were repeated in a subsample of 98 individuals. We examined the bias of dietary recalls and their associations with participant characteristics using generalized estimating equations. Energy intake was underestimated by 25.3% (men, 21.8%; women, 27.3%), and protein intake was underestimated by 18.5% (men, 14.7%; women, 20.7%). Protein density was overestimated by 10.7% (men, 11.3%; women, 10.1%). Higher body mass index and Hispanic/Latino background were associated with underestimation of energy (P<0.05). For protein intake, higher body mass index, older age, nonsmoking, Spanish speaking, and Hispanic/Latino background were associated with underestimation (P<0.05). Systematic underreporting of energy and protein intakes and overreporting of protein density were found to vary significantly by Hispanic/Latino background. We developed calibration equations that correct for subject-specific error in reporting that can be used to reduce bias in diet-disease association studies. PMID:25995289
NASA Technical Reports Server (NTRS)
Lessard, Wendy B.
1999-01-01
The objective of this study is to calibrate a Navier-Stokes code for the TCA (30/10) baseline configuration (partial span leading edge flaps were deflected at 30 degs. and all the trailing edge flaps were deflected at 10 degs). The computational results for several angles of attack are compared with experimental force, moments, and surface pressures. The code used in this study is CFL3D; mesh sequencing and multi-grid were used to full advantage to accelerate convergence. A multi-grid approach was used similar to that used for the Reference H configuration allowing point-to-point matching across all the trailingedge block interfaces. From past experiences with the Reference H (ie, good force, moment, and pressure comparisons were obtained), it was assumed that the mounting system would produce small effects; hence, it was not initially modeled. However, comparisons of lower surface pressures indicated the post mount significantly influenced the lower surface pressures, so the post geometry was inserted into the existing grid using Chimera (overset grids).
Mossavar-Rahmani, Yasmin; Shaw, Pamela A.; Wong, William W.; Sotres-Alvarez, Daniela; Gellman, Marc D.; Van Horn, Linda; Stoutenberg, Mark; Daviglus, Martha L.; Wylie-Rosett, Judith; Siega-Riz, Anna Maria; Ou, Fang-Shu; Prentice, Ross L.
2015-01-01
We investigated measurement error in the self-reported diets of US Hispanics/Latinos, who are prone to obesity and related comorbidities, by background (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) in 2010–2012. In 477 participants aged 18–74 years, doubly labeled water and urinary nitrogen were used as objective recovery biomarkers of energy and protein intakes. Self-report was captured from two 24-hour dietary recalls. All measures were repeated in a subsample of 98 individuals. We examined the bias of dietary recalls and their associations with participant characteristics using generalized estimating equations. Energy intake was underestimated by 25.3% (men, 21.8%; women, 27.3%), and protein intake was underestimated by 18.5% (men, 14.7%; women, 20.7%). Protein density was overestimated by 10.7% (men, 11.3%; women, 10.1%). Higher body mass index and Hispanic/Latino background were associated with underestimation of energy (P < 0.05). For protein intake, higher body mass index, older age, nonsmoking, Spanish speaking, and Hispanic/Latino background were associated with underestimation (P < 0.05). Systematic underreporting of energy and protein intakes and overreporting of protein density were found to vary significantly by Hispanic/Latino background. We developed calibration equations that correct for subject-specific error in reporting that can be used to reduce bias in diet-disease association studies. PMID:25995289
A variable acceleration calibration system
NASA Astrophysics Data System (ADS)
Johnson, Thomas H.
2011-12-01
A variable acceleration calibration system that applies loads using gravitational and centripetal acceleration serves as an alternative, efficient and cost effective method for calibrating internal wind tunnel force balances. Two proof-of-concept variable acceleration calibration systems are designed, fabricated and tested. The NASA UT-36 force balance served as the test balance for the calibration experiments. The variable acceleration calibration systems are shown to be capable of performing three component calibration experiments with an approximate applied load error on the order of 1% of the full scale calibration loads. Sources of error are indentified using experimental design methods and a propagation of uncertainty analysis. Three types of uncertainty are indentified for the systems and are attributed to prediction error, calibration error and pure error. Angular velocity uncertainty is shown to be the largest indentified source of prediction error. The calibration uncertainties using a production variable acceleration based system are shown to be potentially equivalent to current methods. The production quality system can be realized using lighter materials and a more precise instrumentation. Further research is needed to account for balance deflection, forcing effects due to vibration, and large tare loads. A gyroscope measurement technique is shown to be capable of resolving the balance deflection angle calculation. Long term research objectives include a demonstration of a six degree of freedom calibration, and a large capacity balance calibration.
Multivariate Hypergeometric Similarity Measure
Kaddi, Chanchala D.; Parry, R. Mitchell; Wang, May D.
2016-01-01
We propose a similarity measure based on the multivariate hypergeometric distribution for the pairwise comparison of images and data vectors. The formulation and performance of the proposed measure are compared with other similarity measures using synthetic data. A method of piecewise approximation is also implemented to facilitate application of the proposed measure to large samples. Example applications of the proposed similarity measure are presented using mass spectrometry imaging data and gene expression microarray data. Results from synthetic and biological data indicate that the proposed measure is capable of providing meaningful discrimination between samples, and that it can be a useful tool for identifying potentially related samples in large-scale biological data sets. PMID:24407308
NASA Astrophysics Data System (ADS)
Burrows, Chris
2004-03-01
This document contains a listing of all WFPC2 reference files, grouped by type, that are presently available in the Calibration Data Base (CDB) System, and a summary of how they are used in the calibration of WFPC2 data. A summary memo is kept on STEIS and kept up to date as the reference files change. That memo is intended to inform observers as to the quality of the calibration applied to their data by the PODPS pipeline processing and to provide an aid in selecting appropriate reference files for the re-calibration of WFPC2 observations. The datafiles may be requested by name from the STScI in the same fashion as any other nonproprietary data products.
NASA Technical Reports Server (NTRS)
Bate, T.; Calkins, D. E.; Price, P.; Veikins, O.
1971-01-01
Calibrator generates accurate flow velocities over wide range of gas pressure, temperature, and composition. Both pressure and flow velocity can be maintained within 0.25 percent. Instrument is essentially closed loop hydraulic system containing positive displacement drive.
Heavy flavor identification using multivariate analysis at H1
Pandurovic, Mila; Bozovic-Jelisavcic, Ivanka; Mudrinic, Mihajlo
2010-01-21
We discuss b quark identification in deep inelastic scattering of electron on proton at H1 by applying multivariate analysis method. Separation between heavy and light flavors can be further used to extract proton quark content.
NASA Astrophysics Data System (ADS)
Manz, B.; Buytaert, W.; Tobón, C.; Villacis, M.; García, F.
2014-12-01
With the imminent release of GPM it is essential for the hydrological user community to improve the spatial resolution of satellite precipitation products (SPPs), also retrospectively of historical time-series. Despite the growing number of applications, to date SPPs have two major weaknesses. Firstly, geosynchronous infrared (IR) SPPs, relying exclusively on cloud elevation/ IR temperature, fail to replicate ground rainfall rates especially for convective rainfall. Secondly, composite SPPs like TRMM include microwave and active radar to overcome this, but the coarse spatial resolution (0.25°) from infrequent orbital sampling often fails to: a) characterize precipitation patterns (especially extremes) in complex topography regions, and b) allow for gauge comparisons with adequate spatial support. This is problematic for satellite-gauge merging and subsequent hydrological modelling applications. We therefore present a new re-calibration and downscaling routine that is applicable to 0.25°/ 3-hrly TRMM 3B42 and Level 3 GPM time-series to generate 1 km estimates. 16 years of instantaneous TRMM radar (TPR) images were evaluated against a unique dataset of over 100 10-min rain gauges from the tropical Andes (Colombia & Ecuador) to develop a spatially distributed error surface. Long-term statistics on occurrence frequency, convective/ stratiform fraction and extreme precipitation probability (Gamma & Generalized Pareto distributions) were computed from TPR at the 1 km scale as well as from TPR and 3B42 at the 0.25° scale. To downscale from 0.25° to 1 km a stochastic generator was used to restrict precipitation occurrence to a fraction of the 1 km pixels within the 0.25° gridcell at every time-step. Regression modelling established a relationship between probability distributions at the 0.25° scale and rainfall amounts were assigned to the retained 1 km pixels by quantile-matching to the gridcell. The approach inherently provides mass conservation of the downscaled
NASA Astrophysics Data System (ADS)
Hulbert, S.; Hodge, P.; Lindler, D.; Shaw, R.; Goudfrooij, P.; Katsanis, R.; Keener, S.; McGrath, M.; Bohlin, R.; Baum, S.
1997-05-01
Routine calibration of STIS observations in the HST data pipeline is performed by the CALSTIS task. CALSTIS can: subtract the over-scan region and a bias image from CCD observations; remove cosmic ray features from CCD observations; correct global nonlinearities for MAMA observations; subtract a dark image; and, apply flat field corrections. In the case of spectral data, CALSTIS can also: assign a wavelength to each pixel; apply a heliocentric correction to the wavelengths; convert counts to absolute flux; process the automatically generated spectral calibration lamp observations to improve the wavelength solution; rectify two-dimensional (longslit) spectra; subtract interorder and sky background; and, extract one-dimensional spectra. CALSTIS differs in significant ways from the current HST calibration tasks. The new code is written in ANSI C and makes use of a new C interface to IRAF. The input data, reference data, and output calibrated data are all in FITS format, using IMAGE or BINTABLE extensions. Error estimates are computed and include contributions from the reference images. The entire calibration can be performed by one task, but many steps can also be performed individually.
Simultaneous calibration of ensemble river flow predictions over an entire range of lead times
NASA Astrophysics Data System (ADS)
Hemri, S.; Fundel, F.; Zappa, M.
2013-10-01
Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.
NASA Technical Reports Server (NTRS)
Peay, Christopher S.; Palacios, David M.
2011-01-01
Calibrate_Image calibrates images obtained from focal plane arrays so that the output image more accurately represents the observed scene. The function takes as input a degraded image along with a flat field image and a dark frame image produced by the focal plane array and outputs a corrected image. The three most prominent sources of image degradation are corrected for: dark current accumulation, gain non-uniformity across the focal plane array, and hot and/or dead pixels in the array. In the corrected output image the dark current is subtracted, the gain variation is equalized, and values for hot and dead pixels are estimated, using bicubic interpolation techniques.
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. PMID:20645279
Analytical advantages of multivariate data processing. One, two, three, infinity?
Olivieri, Alejandro C
2008-08-01
Multidimensional data are being abundantly produced by modern analytical instrumentation, calling for new and powerful data-processing techniques. Research in the last two decades has resulted in the development of a multitude of different processing algorithms, each equipped with its own sophisticated artillery. Analysts have slowly discovered that this body of knowledge can be appropriately classified, and that common aspects pervade all these seemingly different ways of analyzing data. As a result, going from univariate data (a single datum per sample, employed in the well-known classical univariate calibration) to multivariate data (data arrays per sample of increasingly complex structure and number of dimensions) is known to provide a gain in sensitivity and selectivity, combined with analytical advantages which cannot be overestimated. The first-order advantage, achieved using vector sample data, allows analysts to flag new samples which cannot be adequately modeled with the current calibration set. The second-order advantage, achieved with second- (or higher-) order sample data, allows one not only to mark new samples containing components which do not occur in the calibration phase but also to model their contribution to the overall signal, and most importantly, to accurately quantitate the calibrated analyte(s). No additional analytical advantages appear to be known for third-order data processing. Future research may permit, among other interesting issues, to assess if this "1, 2, 3, infinity" situation of multivariate calibration is really true. PMID:18613646
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert M.
2013-01-01
A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.
Calibration of a visible polarimeter
NASA Astrophysics Data System (ADS)
Gibney, Mark
2012-06-01
The calibration of a visible polarimeter is discussed. Calibration coefficients that provide a complete linear characterization of a polarimeter are represented in this paper by the analyzer vector, where sensor response in counts is given by the dot product of the analyzer vector and the incoming Stokes vector. Using the analyzer vector to represent the effect of the sensor on the incoming Stokes vector, we can include elements of the calibration Stokes vector in the fit used to estimate the analyzer vectors/calibration coefficients. This technique allows us to alleviate some of the strict requirements usually levied on the source used to generate the calibration Stokes vectors, such as source temporal stability. Data will be shown that validate the resultant analyzer vectors/calibration coefficients, using a novel technique with a tilted glass plate. A discussion of how these techniques are applied to IR sensors will also be touched on.
Advancing emotion theory with multivariate pattern classification
Kragel, Philip A.; LaBar, Kevin S.
2016-01-01
Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system.
NASA Astrophysics Data System (ADS)
Libera, D.; Arumugam, S.
2015-12-01
Water quality observations are usually not available on a continuous basis because of the expensive cost and labor requirements so calibrating and validating a mechanistic model is often difficult. Further, any model predictions inherently have bias (i.e., under/over estimation) and require techniques that preserve the long-term mean monthly attributes. This study suggests and compares two multivariate bias-correction techniques to improve the performance of the SWAT model in predicting daily streamflow, TN Loads across the southeast based on split-sample validation. The first approach is a dimension reduction technique, canonical correlation analysis that regresses the observed multivariate attributes with the SWAT model simulated values. The second approach is from signal processing, importance weighting, that applies a weight based off the ratio of the observed and model densities to the model data to shift the mean, variance, and cross-correlation towards the observed values. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are also compared with independent estimates from the USGS LOADEST model. Uncertainties in the bias-corrected estimates due to limited water quality observations are also discussed.
Giacomo, Della Riccia; Stefania, Del Zotto
2013-12-15
Fumonisins are mycotoxins produced by Fusarium species that commonly live in maize. Whereas fungi damage plants, fumonisins cause disease both to cattle breedings and human beings. Law limits set fumonisins tolerable daily intake with respect to several maize based feed and food. Chemical techniques assure the most reliable and accurate measurements, but they are expensive and time consuming. A method based on Near Infrared spectroscopy and multivariate statistical regression is described as a simpler, cheaper and faster alternative. We apply Partial Least Squares with full cross validation. Two models are described, having high correlation of calibration (0.995, 0.998) and of validation (0.908, 0.909), respectively. Description of observed phenomenon is accurate and overfitting is avoided. Screening of contaminated maize with respect to European legal limit of 4 mg kg(-1) should be assured. PMID:23993617
Pattern recognition used to investigate multivariate data in analytical chemistry
Jurs, P.C.
1986-06-06
Pattern recognition and allied multivariate methods provide an approach to the interpretation of the multivariate data often encountered in analytical chemistry. Widely used methods include mapping and display, discriminant development, clustering, and modeling. Each has been applied to a variety of chemical problems, and examples are given. The results of two recent studies are shown, a classification of subjects as normal or cystic fibrosis heterozygotes and simulation of chemical shifts of carbon-13 nuclear magnetic resonance spectra by linear model equations.
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.
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 2-12microgml(-1) of NIF and 2-8microgml(-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. PMID:17631041
Damage detection using multivariate recurrence quantification analysis
NASA Astrophysics Data System (ADS)
Nichols, J. M.; Trickey, S. T.; Seaver, M.
2006-02-01
Recurrence-quantification analysis (RQA) has emerged as a useful tool for detecting subtle non-stationarities and/or changes in time-series data. Here, we extend the RQA analysis methods to multivariate observations and present a method by which the "length scale" parameter ɛ (the only parameter required for RQA) may be selected. We then apply the technique to the difficult engineering problem of damage detection. The structure considered is a finite element model of a rectangular steel plate where damage is represented as a cut in the plate, starting at one edge and extending from 0% to 25% of the plate width in 5% increments. Time series, recorded at nine separate locations on the structure, are used to reconstruct the phase space of the system's dynamics and subsequently generate the multivariate recurrence (and cross-recurrence) plots. Multivariate RQA is then used to detect damage-induced changes to the structural dynamics. These results are then compared with shifts in the plate's natural frequencies. Two of the RQA-based features are found to be more sensitive to damage than are the plate's frequencies.
Regional dissociated heterochrony in multivariate analysis.
Mitteroecker, P; Gunz, P; Weber, G W; Bookstein, F L
2004-12-01
Heterochrony, the classic framework to study ontogeny and phylogeny, in essence relies on a univariate concept of shape. Though principal component plots of multivariate shape data seem to resemble classical bivariate allometric plots, the language of heterochrony cannot be translated directly into general multivariate methodology. We simulate idealized multivariate ontogenetic trajectories and demonstrate their behavior in principal component plots in shape space and in size-shape space. The concept of "dissociation", which is conventionally regarded as a change in the relationship between shape change and size change, appears to be algebraically the same as regional dissociation - the variation of apparent heterochrony by region. Only if the trajectories of two related species lie along exactly the same path in shape space can the classic terminology of heterochrony apply so that pure dissociation of size change against shape change can be detected. We demonstrate a geometric morphometric approach to these issues using adult and subadult crania of 48 Pan paniscus and 47 P. troglodytes. On each specimen we digitized 47 landmarks and 144 semilandmarks on ridge curves and the external neurocranial surface. The relation between these two species' growth trajectories is too complex for a simple summary in terms of global heterochrony. PMID:15646279
Multivariate streamflow forecasting using independent component analysis
NASA Astrophysics Data System (ADS)
Westra, Seth; Sharma, Ashish; Brown, Casey; Lall, Upmanu
2008-02-01
Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advance based on current or a projected climate state. We present an approach for forecasting multivariate time series using independent component analysis (ICA) to transform the multivariate data to a set of univariate time series that are mutually independent, thereby allowing for the much broader class of univariate models to provide seasonal forecasts for each transformed series. Uncertainty is incorporated by bootstrapping the error component of each univariate model so that the probability distribution of the errors is maintained. Although all analyses are performed on univariate time series, the spatial dependence of the streamflow is captured by applying the inverse ICA transform to the predicted univariate series. We demonstrate the technique on a multivariate streamflow data set in Colombia, South America, by comparing the results to a range of other commonly used forecasting methods. The results show that the ICA-based technique is significantly better at representing spatial dependence, while not resulting in any loss of ability in capturing temporal dependence. As such, the ICA-based technique would be expected to yield considerable advantages when used in a probabilistic setting to manage large reservoir systems with multiple inflows or data collection points.
NASA Astrophysics Data System (ADS)
Zaconte, V.; Altea Team
The ALTEA project is aimed at studying the possible functional damages to the Central Nervous System (CNS) due to particle radiation in space environment. The project is an international and multi-disciplinary collaboration. The ALTEA facility is an helmet-shaped device that will study concurrently the passage of cosmic radiation through the brain, the functional status of the visual system and the electrophysiological dynamics of the cortical activity. The basic instrumentation is composed by six active particle telescopes, one ElectroEncephaloGraph (EEG), a visual stimulator and a pushbutton. The telescopes are able to detect the passage of each particle measuring its energy, trajectory and released energy into the brain and identifying nuclear species. The EEG and the Visual Stimulator are able to measure the functional status of the visual system, the cortical electrophysiological activity, and to look for a correlation between incident particles, brain activity and Light Flash perceptions. These basic instruments can be used separately or in any combination, permitting several different experiments. ALTEA is scheduled to fly in the International Space Station (ISS) in November, 15th 2004. In this paper the calibration of the Flight Model of the silicon telescopes (Silicon Detector Units - SDUs) will be shown. These measures have been taken at the GSI heavy ion accelerator in Darmstadt. First calibration has been taken out in November 2003 on the SDU-FM1 using C nuclei at different energies: 100, 150, 400 and 600 Mev/n. We performed a complete beam scan of the SDU-FM1 to check functionality and homogeneity of all strips of silicon detector planes, for each beam energy we collected data to achieve good statistics and finally we put two different thickness of Aluminium and Plexiglas in front of the detector in order to study fragmentations. This test has been carried out with a Test Equipment to simulate the Digital Acquisition Unit (DAU). We are scheduled to
Local hadron calibration with ATLAS
NASA Astrophysics Data System (ADS)
Giovannini, Paola; ATLAS Liquid Argon Calorimeter Group
2011-04-01
The method of Local Hadron Calibration is used in ATLAS as one of the two major calibration schemes for the reconstruction of jets and missing transverse energy. The method starts from noise suppressed clusters and corrects them for non-compensation effects and for losses due to noise threshold and dead material. Jets are reconstructed using the calibrated clusters and are then corrected for out of cone effects. The performance of the corrections applied to the calorimeter clusters is tested with detailed GEANT4 information. Results obtained with this procedure are discussed both for single pion simulations and for di-jet simulations. The calibration scheme is validated on data, by comparing the calibrated cluster energy in data with Mote Carlo simulations. Preliminary results obtained with GeV collision data are presented. The agreement between data and Monte Carlo is within 5% for the final cluster scale.
Nested Taylor decomposition in multivariate function decomposition
NASA Astrophysics Data System (ADS)
Baykara, N. A.; Gürvit, Ercan
2014-12-01
Fluctuationlessness approximation applied to the remainder term of a Taylor decomposition expressed in integral form is already used in many articles. Some forms of multi-point Taylor expansion also are considered in some articles. This work is somehow a combination these where the Taylor decomposition of a function is taken where the remainder is expressed in integral form. Then the integrand is decomposed to Taylor again, not necessarily around the same point as the first decomposition and a second remainder is obtained. After taking into consideration the necessary change of variables and converting the integration limits to the universal [0;1] interval a multiple integration system formed by a multivariate function is formed. Then it is intended to apply the Fluctuationlessness approximation to each of these integrals one by one and get better results as compared with the single node Taylor decomposition on which the Fluctuationlessness is applied.
Banquet-Terán, Julio; Johnson-Restrepo, Boris; Hernández-Morelo, Alveiro; Ropero, Jorge; Fontalvo-Gomez, Miriam; Romañach, Rodolfo J
2016-07-01
A nondestructive and faster methodology to quantify mechanical properties of polypropylene (PP) pellets, obtained from an industrial plant, was developed with Raman spectroscopy. Raman spectra data were obtained from several types of samples such as homopolymer PP, random ethylene-propylene copolymer, and impact ethylene-propylene copolymer. Multivariate calibration models were developed by relating the changes in the Raman spectra to mechanical properties determined by ASTM tests (Young's traction modulus, tensile strength at yield, elongation at yield on traction, and flexural modulus at 1% secant). Several strategies were evaluated to build robust models including the use of preprocessing methods (baseline correction, vector normalization, de-trending, and standard normal variate), selecting the best subset of wavelengths to model property response and discarding irrelevant variables by applying genetic algorithm (GA). Linear multivariable models were investigated such as partial least square regression (PLS) and PLS with genetic algorithm (GA-PLS) while nonlinear models were implemented with artificial neural network (ANN) preceded by GA (GA-ANN). The best multivariate calibration models were obtained when a combination of genetic algorithms and artificial neural network were used on Raman spectral data with relative standard errors (%RSE) from 0.17 to 0.41 for training and 0.42 to 0.88% validation data sets. PMID:27287847
NASA Astrophysics Data System (ADS)
Badocco, Denis; Lavagnini, Irma; Mondin, Andrea; Favaro, Gabriella; Pastore, Paolo
2015-12-01
The limit of quantification (LOQ) in the presence of instrumental and non-instrumental errors was proposed. It was theoretically defined combining the two-component variance regression and LOQ schemas already present in the literature and applied to the calibration of zinc by the ICP-MS technique. At low concentration levels, the two-component variance LOQ definition should be always used above all when a clean room is not available. Three LOQ definitions were accounted for. One of them in the concentration and two in the signal domain. The LOQ computed in the concentration domain, proposed by Currie, was completed by adding the third order terms in the Taylor expansion because they are of the same order of magnitude of the second ones so that they cannot be neglected. In this context, the error propagation was simplified by eliminating the correlation contributions by using independent random variables. Among the signal domain definitions, a particular attention was devoted to the recently proposed approach based on at least one significant digit in the measurement. The relative LOQ values resulted very large in preventing the quantitative analysis. It was found that the Currie schemas in the signal and concentration domains gave similar LOQ values but the former formulation is to be preferred as more easily computable.
TIME CALIBRATED OSCILLOSCOPE SWEEP
Owren, H.M.; Johnson, B.M.; Smith, V.L.
1958-04-22
The time calibrator of an electric signal displayed on an oscilloscope is described. In contrast to the conventional technique of using time-calibrated divisions on the face of the oscilloscope, this invention provides means for directly superimposing equal time spaced markers upon a signal displayed upon an oscilloscope. More explicitly, the present invention includes generally a generator for developing a linear saw-tooth voltage and a circuit for combining a high-frequency sinusoidal voltage of a suitable amplitude and frequency with the saw-tooth voltage to produce a resultant sweep deflection voltage having a wave shape which is substantially linear with respect to time between equal time spaced incremental plateau regions occurring once each cycle of the sinusoidal voltage. The foregoing sweep voltage when applied to the horizontal deflection plates in combination with a signal to be observed applied to the vertical deflection plates of a cathode ray oscilloscope produces an image on the viewing screen which is essentially a display of the signal to be observed with respect to time. Intensified spots, or certain other conspicuous indications corresponding to the equal time spaced plateau regions of said sweep voltage, appear superimposed upon said displayed signal, which indications are therefore suitable for direct time calibration purposes.
A "Model" Multivariable Calculus Course.
ERIC Educational Resources Information Center
Beckmann, Charlene E.; Schlicker, Steven J.
1999-01-01
Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…
Multivariate Model of Infant Competence.
ERIC Educational Resources Information Center
Kierscht, Marcia Selland; Vietze, Peter M.
This paper describes a multivariate model of early infant competence formulated from variables representing infant-environment transaction including: birthweight, habituation index, personality ratings of infant social orientation and task orientation, ratings of maternal responsiveness to infant distress and social signals, and observational…
Parameter Sensitivity in Multivariate Methods
ERIC Educational Resources Information Center
Green, Bert F., Jr.
1977-01-01
Interpretation of multivariate models requires knowing how much the fit of the model is impaired by changes in the parameters. The relation of parameter change to loss of goodness of fit can be called parameter sensitivity. Formulas are presented for assessing the sensitivity of multiple regression and principal component weights. (Author/JKS)
Multichannel hierarchical image classification using multivariate copulas
NASA Astrophysics Data System (ADS)
Voisin, Aurélie; Krylov, Vladimir A.; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane
2012-03-01
This paper focuses on the classification of multichannel images. The proposed supervised Bayesian classification method applied to histological (medical) optical images and to remote sensing (optical and synthetic aperture radar) imagery consists of two steps. The first step introduces the joint statistical modeling of the coregistered input images. For each class and each input channel, the class-conditional marginal probability density functions are estimated by finite mixtures of well-chosen parametric families. For optical imagery, the normal distribution is a well-known model. For radar imagery, we have selected generalized gamma, log-normal, Nakagami and Weibull distributions. Next, the multivariate d-dimensional Clayton copula, where d can be interpreted as the number of input channels, is applied to estimate multivariate joint class-conditional statistics. As a second step, we plug the estimated joint probability density functions into a hierarchical Markovian model based on a quadtree structure. Multiscale features are extracted by discrete wavelet transforms, or by using input multiresolution data. To obtain the classification map, we integrate an exact estimator of the marginal posterior mode.
NASA Astrophysics Data System (ADS)
Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.
2014-12-01
Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.
Extracting the MESA SR4000 calibrations
NASA Astrophysics Data System (ADS)
Charleston, Sean A.; Dorrington, Adrian A.; Streeter, Lee; Cree, Michael J.
2015-05-01
Time-of-flight range imaging cameras are capable of acquiring depth images of a scene. Some algorithms require these cameras to be run in `raw mode', where any calibrations from the off-the-shelf manufacturers are lost. The calibration of the MESA SR4000 is herein investigated, with an attempt to reconstruct the full calibration. Possession of the factory calibration enables calibrated data to be acquired and manipulated even in "raw mode." This work is motivated by the problem of motion correction, in which the calibration must be separated into component parts to be applied at different stages in the algorithm. There are also other applications, in which multiple frequencies are required, such as multipath interference correction. The other frequencies can be calibrated in a similar way, using the factory calibration as a base. A novel technique for capturing the calibration data is described; a retro-reflector is used on a moving platform, which acts as a point source at a distance, resulting in planar waves on the sensor. A number of calibrations are retrieved from the camera, and are then modelled and compared to the factory calibration. When comparing the factory calibration to both the "raw mode" data, and the calibration described herein, a root mean squared error improvement of 51:3mm was seen, with a standard deviation improvement of 34:9mm.
An uncertain journey around the tails of multivariate hydrological distributions
NASA Astrophysics Data System (ADS)
Serinaldi, Francesco
2013-10-01
Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.
Multivariable PID control by decoupling
NASA Astrophysics Data System (ADS)
Garrido, Juan; Vázquez, Francisco; Morilla, Fernando
2016-04-01
This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.
Information extraction from multivariate images
NASA Technical Reports Server (NTRS)
Park, S. K.; Kegley, K. A.; Schiess, J. R.
1986-01-01
An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.
Muon Energy Calibration of the MINOS Detectors
Miyagawa, Paul S.
2004-09-01
MINOS is a long-baseline neutrino oscillation experiment designed to search for conclusive evidence of neutrino oscillations and to measure the oscillation parameters precisely. MINOS comprises two iron tracking calorimeters located at Fermilab and Soudan. The Calibration Detector at CERN is a third MINOS detector used as part of the detector response calibration programme. A correct energy calibration between these detectors is crucial for the accurate measurement of oscillation parameters. This thesis presents a calibration developed to produce a uniform response within a detector using cosmic muons. Reconstruction of tracks in cosmic ray data is discussed. This data is utilized to calculate calibration constants for each readout channel of the Calibration Detector. These constants have an average statistical error of 1.8%. The consistency of the constants is demonstrated both within a single run and between runs separated by a few days. Results are presented from applying the calibration to test beam particles measured by the Calibration Detector. The responses are calibrated to within 1.8% systematic error. The potential impact of the calibration on the measurement of oscillation parameters by MINOS is also investigated. Applying the calibration reduces the errors in the measured parameters by {approx} 10%, which is equivalent to increasing the amount of data by 20%.
Multivariate-normality goodness-of-fit tests
NASA Technical Reports Server (NTRS)
Falls, L. W.; Crutcher, H. L.
1977-01-01
Computer program applies chi-square Pearson test to multivariate statistics for application in any field in which data of two or more variables (dimensions) are sampled for statistical purposes. Program handles dimensions two through five, with up to thousand data sets.
Multivariate classification of infrared spectra of cell and tissue samples
Haaland, David M.; Jones, Howland D. T.; Thomas, Edward V.
1997-01-01
Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.
Univariate Analysis of Multivariate Outcomes in Educational Psychology.
ERIC Educational Resources Information Center
Hubble, L. M.
1984-01-01
The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…
Calibration of sound calibrators: an overview
NASA Astrophysics Data System (ADS)
Milhomem, T. A. B.; Soares, Z. M. D.
2016-07-01
This paper presents an overview of calibration of sound calibrators. Initially, traditional calibration methods are presented. Following, the international standard IEC 60942 is discussed emphasizing parameters, target measurement uncertainty and criteria for conformance to the requirements of the standard. Last, Regional Metrology Organizations comparisons are summarized.
Calibration age and quartet divergence date estimation.
Brochu, Christopher A
2004-06-01
The date of a single divergence point--between living alligators and crocodiles--was estimated with quartet dating using calibrations of widely divergent ages. For five mitochondrial sequence datasets, there is a clear relationship between calibration age and quartet estimate--quartets based on two relatively recent calibrations support younger divergence estimates than do quartets based on two older calibrations. Some of the estimates supported by young quartets are impossibly young and exclude the first appearance of the group in the fossil record as too old. The older estimates--those based on two relatively old calibrations--may be overestimates, and those based on one old and one recent calibration support divergence estimates very close to fossil data. This suggests that quartet dating methods may be most effective when calibrations are applied from different parts of a clade's history. PMID:15266985
Multivariate Strategies in Functional Magnetic Resonance Imaging
ERIC Educational Resources Information Center
Hansen, Lars Kai
2007-01-01
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.
Multivariable quadratic synthesis of an advanced turbofan engine controller
NASA Technical Reports Server (NTRS)
Dehoff, R. L.; Hall, W. E., Jr.
1978-01-01
A digital controller for an advanced turbofan engine utilizing multivariate feedback is described. The theoretical background of locally linearized control synthesis is reviewed briefly. The application of linear quadratic regulator techniques to the practical control problem is presented. The design procedure has been applied to the F100 turbofan engine, and details of the structure of this system are explained. Selected results from simulations of the engine and controller are utilized to illustrate the operation of the system. It is shown that the general multivariable design procedure will produce practical and implementable controllers for modern, high-performance turbine engines.
Steady-state decoupling and design of linear multivariable systems
NASA Technical Reports Server (NTRS)
Thaler, G. J.
1974-01-01
A constructive criterion for decoupling the steady states of a linear time-invariant multivariable system is presented. This criterion consists of a set of inequalities which, when satisfied, will cause the steady states of a system to be decoupled. Stability analysis and a new design technique for such systems are given. A new and simple connection between single-loop and multivariable cases is found. These results are then applied to the compensation design for NASA STOL C-8A aircraft. Both steady-state decoupling and stability are justified through computer simulations.
Design of feedforward controllers for multivariable plants
NASA Technical Reports Server (NTRS)
Seraji, H.
1987-01-01
Simple methods for the design of feedforward controllers to achieve steady-state disturbance rejection and command tracking in stable multivariable plants are developed in this paper. The controllers are represented by simple and low-order transfer functions and are not based on reconstruction of the states of the commands and disturbances. For unstable plants, it is shown that the present method can be applied directly when an additional feedback controller is employed to stabilize the plant. The feedback and feedforward controllers do not affect each other and can be designed independently based on the open-loop plant to achieve stability, disturbance rejection and command tracking, respectivley. Numerical examples are given for illustration.
Bayesian Local Contamination Models for Multivariate Outliers
Page, Garritt L.; Dunson, David B.
2013-01-01
In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development. PMID:24363465
Software For Multivariate Bayesian Classification
NASA Technical Reports Server (NTRS)
Saul, Ronald; Laird, Philip; Shelton, Robert
1996-01-01
PHD general-purpose classifier computer program. Uses Bayesian methods to classify vectors of real numbers, based on combination of statistical techniques that include multivariate density estimation, Parzen density kernels, and EM (Expectation Maximization) algorithm. By means of simple graphical interface, user trains classifier to recognize two or more classes of data and then use it to identify new data. Written in ANSI C for Unix systems and optimized for online classification applications. Embedded in another program, or runs by itself using simple graphical-user-interface. Online help files makes program easy to use.
Neuro-sliding mode multivariable control of a powered wheelchair.
Nguyen, Nghia; Nguyen, Hung T; Su, Steven
2008-01-01
This paper proposes a neuro-sliding mode multivariable control approach for the control of a powered wheelchair system. In the first stage, a systematic decoupling technique is applied to the wheelchair system in order to reduce the multivariable control problem into two independent scalar control problems. Then two Neuro-Sliding Mode Controllers (NSMCs) are designed for these independent subsystems to guarantee system robustness under model uncertainties and unknown external disturbances. Both off-line and on-line trainings are involved in the second stage. Real-time experimental results confirm that robust performance for this multivariable wheelchair control system under model uncertainties and unknown external disturbances can indeed be achieved. PMID:19163456
Scalable Software for Multivariate Integration on Hybrid Platforms
NASA Astrophysics Data System (ADS)
de Doncker, E.; Yuasa, F.; Kapenga, J.; Olagbemi, O.
2015-09-01
The paper describes the software infrastructure of the PARINT package for multivariate numerical integration, layered over a hybrid parallel environment with distributed memory computations (on MPI). The parallel problem distribution is typically performed on the region level in the adaptive partitioning procedure. Our objective has been to provide the end-user with state of the art problem solving power packaged as portable software. We will give test results of the multivariate ParInt engine, with significant speedups for a set of 3-loop Feynman integrals. An extrapolation with respect to the dimensional regularization parameter (ε) is applied to sequences of multivariate ParInt results Q(ε) to obtain the leading asymptotic expansion coefficients as ε → 0. This paper further introduces a novel method for a parallel computation of the Q(ε) sequence as the components of the integral of a vector function.
Bayesian Calibration of Microsimulation Models.
Rutter, Carolyn M; Miglioretti, Diana L; Savarino, James E
2009-12-01
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models. PMID:20076767
Bayesian Calibration of Microsimulation Models
Rutter, Carolyn M.; Miglioretti, Diana L.; Savarino, James E.
2009-01-01
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models. PMID:20076767
Multivariate image processing technique for noninvasive glucose sensing
NASA Astrophysics Data System (ADS)
Webb, Anthony J.; Cameron, Brent D.
2010-02-01
A potential noninvasive glucose sensing technique was investigated for application towards in vivo glucose monitoring for individuals afflicted with diabetes mellitus. Three dimensional ray tracing simulations using a realistic iris pattern integrated into an advanced human eye model are reported for physiological glucose concentrations ranging between 0 to 500 mg/dL. The anterior chamber of the human eye contains a clear fluid known as the aqueous humor. The optical refractive index of the aqueous humor varies on the order of 1.5x10-4 for a change in glucose concentration of 100 mg/dL. The simulation data was analyzed with a developed multivariate chemometrics procedure that utilizes iris-based images to form a calibration model. Results from these simulations show considerable potential for use of the developed method in the prediction of glucose. For further demonstration, an in vitro eye model was developed to validate the computer based modeling technique. In these experiments, a realistic iris pattern was placed in an analog eye model in which the glucose concentration within the fluid representing the aqueous humor was varied. A series of high resolution digital images were acquired using an optical imaging system. These images were then used to form an in vitro calibration model utilizing the same multivariate chemometric technique demonstrated in the 3-D optical simulations. In general, the developed method exhibits considerable applicability towards its use as an in vivo platform for the noninvasive monitoring of physiological glucose concentration.
Kong, Wenwen; Zhang, Chu; Liu, Fei; Nie, Pengcheng; He, Yong
2013-01-01
A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique. PMID:23857260
Image based autodocking without calibration
Sutanto, H.; Sharma, R.; Varma, V.
1997-03-01
The calibration requirements for visual servoing can make it difficult to apply in many real-world situations. One approach to image-based visual servoing without calibration is to dynamically estimate the image Jacobian and use it as the basis for control. However, with the normal motion of a robot toward the goal, the estimation of the image Jacobian deteriorates over time. The authors propose the use of additional exploratory motion to considerably improve the estimation of the image Jacobian. They study the role of such exploratory motion in a visual servoing task. Simulations and experiments with a 6-DOF robot are used to verify the practical feasibility of the approach.
Multivariable Burchnall-Chaundy theory.
Previato, Emma
2008-03-28
Burchnall & Chaundy (Burchnall & Chaundy 1928 Proc. R. Soc. A 118, 557-583) classified the (rank 1) commutative subalgebras of the algebra of ordinary differential operators. To date, there is no such result for several variables. This paper presents the problem and the current state of the knowledge, together with an interpretation in differential Galois theory. It is known that the spectral variety of a multivariable commutative ring will not be associated to a KP-type hierarchy of deformations, but examples of related integrable equations were produced and are reviewed. Moreover, such an algebro-geometric interpretation is made to fit into A.N. Parshin's newer theory of commuting rings of partial pseudodifferential operators and KP-type hierarchies which uses higher local fields. PMID:17588865
Assessing calibration of prognostic risk scores.
Crowson, Cynthia S; Atkinson, Elizabeth J; Therneau, Terry M
2016-08-01
Current methods used to assess calibration are limited, particularly in the assessment of prognostic models. Methods for testing and visualizing calibration (e.g. the Hosmer-Lemeshow test and calibration slope) have been well thought out in the binary regression setting. However, extension of these methods to Cox models is less well known and could be improved. We describe a model-based framework for the assessment of calibration in the binary setting that provides natural extensions to the survival data setting. We show that Poisson regression models can be used to easily assess calibration in prognostic models. In addition, we show that a calibration test suggested for use in survival data has poor performance. Finally, we apply these methods to the problem of external validation of a risk score developed for the general population when assessed in a special patient population (i.e. patients with particular comorbidities, such as rheumatoid arthritis). PMID:23907781
Staeheli, Sandra N; Poetzsch, Michael; Kraemer, Thomas; Steuer, Andrea E
2015-11-01
Postmortem redistribution (PMR) is one of numerous problems in postmortem toxicology making correct interpretation of measured drug concentrations difficult or even impossible. Time-dependent PMR in peripheral blood and especially in tissue samples is still under-explored. For further investigation, an easy applicable method for the simultaneous quantitation of over 80 forensically relevant compounds in 11 different postmortem matrices should be developed and validated overcoming the challenges of high inter-matrix and intra-matrix concentration variances. Biopsy samples (20 mg) or body fluids (20 μL) were spiked with an analyte mix and deuterated internal standards, extracted by liquid-liquid extraction, and analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). For highest applicability, an easy solvent calibration was used. Furthermore, time-consuming dilution of high concentration samples showing detector saturation was circumvented by two overlapping calibration curves using (12)C isotope monitoring for low concentrations and (13)C isotopes for high concentration, respectively. The method was validated according to international guidelines with modifications. Matrix effects and extraction efficiency were strongly matrix and analyte dependent. In general, brain and adipose tissue produced the highest matrix effects, whereas cerebrospinal fluid showed the least matrix effects. Accuracy and precision results were rather matrix independent with some exceptions. Despite using an external solvent calibration, the accuracy requirements were fulfilled for 66 to 81 % of the 83 analytes. Depending on the matrix, 75-93 % of the analytes showed intra-day precisions at <20 %. (12)C and (13)C calibrations gave comparable results and proved to be a useful tool in expanding the dynamic range. PMID:26396081
Hydraulic Calibrator for Strain-Gauge Balances
NASA Technical Reports Server (NTRS)
Skelly, Kenneth; Ballard, John
1987-01-01
Instrument for calibrating strain-gauge balances uses hydraulic actuators and load cells. Eliminates effects of nonparallelism, nonperpendicularity, and changes of cable directions upon vector sums of applied forces. Errors due to cable stretching, pulley friction, and weight inaccuracy also eliminated. New instrument rugged and transportable. Set up quickly. Developed to apply known loads to wind-tunnel models with encapsulated strain-gauge balances, also adapted for use in calibrating dynamometers, load sensors on machinery and laboratory instruments.
Slab coupled optical fiber sensor calibration
NASA Astrophysics Data System (ADS)
Whitaker, B.; Noren, J.; Chadderdon, S.; Wang, W.; Forber, R.; Selfridge, R.; Schultz, S.
2013-02-01
This paper presents a method for calibrating slab coupled optical fiber sensors (SCOS). An automated system is presented for selecting the optimal laser wavelength for use in SCOS interrogation. The wavelength calibration technique uses a computer sound card for both the creation of the applied electric field and the signal detection. The method used to determine the ratio between the measured SCOS signal and the applied electric field is also described along with a demonstration of the calibrated SCOS involving measuring the dielectric breakdown of air.
Tripathi, Markandey M.; Krishnan, Sundar R.; Srinivasan, Kalyan K.; Yueh, Fang-Yu; Singh, Jagdish P.
2011-09-07
Chemiluminescence emissions from OH*, CH*, C2, and CO2 formed within the reaction zone of premixed flames depend upon the fuel-air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH*/CH* intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane-air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using the leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of CO2 emission that is required for typical OH*/CH* intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH*/CH* intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions ( > 1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed.
Multivariable control altitude demonstration on the F100 turbofan engine
NASA Technical Reports Server (NTRS)
Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.
1979-01-01
The control system designed under the Multivariable Control Synthesis (MVCS) program for the F100 turbofan engine is described. The MVCS program, applied the linear quadratic regulator (LQR) synthesis methods in the design of a multivariable engine control system to obtain enhanced performance from cross-coupled controls, maximum use of engine variable geometry, and a systematic design procedure that can be applied efficiently to new engine systems. Basic components of the control system, a reference value generator for deriving a desired equilibrium state and an approximate control vector, a transition model to produce compatible reference point trajectories during gross transients, gain schedules for producing feedback terms appropriate to the flight condition, and integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance are described and the details of the F100 implementation presented. The engine altitude test phase of the MVCS program, and engine responses in a variety of test operating points and power transitions are presented.
NASA Technical Reports Server (NTRS)
Chen, Siqi; Cheng, Yang; Willson, Reg
2006-01-01
Automated Camera Calibration (ACAL) is a computer program that automates the generation of calibration data for camera models used in machine vision systems. Machine vision camera models describe the mapping between points in three-dimensional (3D) space in front of the camera and the corresponding points in two-dimensional (2D) space in the camera s image. Calibrating a camera model requires a set of calibration data containing known 3D-to-2D point correspondences for the given camera system. Generating calibration data typically involves taking images of a calibration target where the 3D locations of the target s fiducial marks are known, and then measuring the 2D locations of the fiducial marks in the images. ACAL automates the analysis of calibration target images and greatly speeds the overall calibration process.
NASA Astrophysics Data System (ADS)
Cornic, Philippe; Illoul, Cédric; Cheminet, Adam; Le Besnerais, Guy; Champagnat, Frédéric; Le Sant, Yves; Leclaire, Benjamin
2016-09-01
We address calibration and self-calibration of tomographic PIV experiments within a pinhole model of cameras. A complete and explicit pinhole model of a camera equipped with a 2-tilt angles Scheimpflug adapter is presented. It is then used in a calibration procedure based on a freely moving calibration plate. While the resulting calibrations are accurate enough for Tomo-PIV, we confirm, through a simple experiment, that they are not stable in time, and illustrate how the pinhole framework can be used to provide a quantitative evaluation of geometrical drifts in the setup. We propose an original self-calibration method based on global optimization of the extrinsic parameters of the pinhole model. These methods are successfully applied to the tomographic PIV of an air jet experiment. An unexpected by-product of our work is to show that volume self-calibration induces a change in the world frame coordinates. Provided the calibration drift is small, as generally observed in PIV, the bias on the estimated velocity field is negligible but the absolute location cannot be accurately recovered using standard calibration data.
Multivariate Time Series Similarity Searching
Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng
2014-01-01
Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665
Multivariate time series similarity searching.
Wang, Jimin; Zhu, Yuelong; Li, Shijin; Wan, Dingsheng; Zhang, Pengcheng
2014-01-01
Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the single-dimension similarity based on weighted BORDA voting method. The dimension-combination method could use the existing similarity searching method. Several experiments, which used the classification accuracy as a measure, were performed on six datasets from the UCI KDD Archive to validate the method. The results show the advantage of the approach compared to the traditional similarity measures, such as Euclidean distance (ED), cynamic time warping (DTW), point distribution (PD), PCA similarity factor (SPCA), and extended Frobenius norm (Eros), for MTS datasets in some ways. Our experiments also demonstrate that no measure can fit all datasets, and the proposed measure is a choice for similarity searches. PMID:24895665
Analytical multicollimator camera calibration
Tayman, W.P.
1978-01-01
Calibration with the U.S. Geological survey multicollimator determines the calibrated focal length, the point of symmetry, the radial distortion referred to the point of symmetry, and the asymmetric characteristiecs of the camera lens. For this project, two cameras were calibrated, a Zeiss RMK A 15/23 and a Wild RC 8. Four test exposures were made with each camera. Results are tabulated for each exposure and averaged for each set. Copies of the standard USGS calibration reports are included. ?? 1978.
Output feedback for linear multivariable systems with parameter uncertainty.
NASA Technical Reports Server (NTRS)
Basuthakur, S.; Knapp, C. H.
1973-01-01
A minimax design method is applied to the problem of obtaining an acceptable output feedback matrix for linear multivariable systems with parameter uncertainty. The result is a set of nonlinear matrix equations (similar to those obtained by Levine and Athans (1970)), which must be solved for the feedback matrix. An example illustrates the technique and the fact that better results are achieved for large parameter variation than with a purely nominal design.
Estimating the decomposition of predictive information in multivariate systems.
Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele
2015-03-01
In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep. PMID:25871169