Extended Canonical Variates Analysis Research Articles

A comprehensive study of protein–mesoporous–macroporous silica interactions by extended canonical variate analysis of Raman spectra

AbstractUnderstanding the protein–support interactions is of major importance when manufacturing bionanomaterials to a certain application. These interactions can be the cause for enhanced properties or denaturation phenomena in the target protein. Raman spectroscopy was applied to a bionanomaterial comprehending the protein β‐galactosidase immobilized by physical adsorption into a mesoporous–macroporous silica material, with a nanoporous network consisting of 9‐nm mesopores and 200‐nm macropores. Raman spectra of the bionanomaterial evidenced a complex amount of differences related to the Raman shifts, intensities, band enlargement, appearance of new bands, and overlapping, in comparison with the silica support and the protein spectra. To help in the analysis of the Raman spectra and in the inspection of possible protein–support interactions, ECVA (extended canonical variate analysis) was used as a chemometric complementary tool, dividing the spectra into four segments: 1 (3,100 to 2,800 cm−1), 2 (1,800 to 1,500 cm−1), 3 (1,500 to 1,200 cm−1), and 4 (1,200 to 900 cm−1). Major alterations in the Amide I band (1,800 to 1,500 cm−1) and the amino acid band regions demonstrated possible structure alterations to a non‐native form of the protein β‐galactosidase. Also, other minor alterations were observed in other spectral regions (3,100 to 2,800 cm−1,1,500 to 1,200 cm−1, and 1,200 to 900 cm−1) also representative of protein structure alteration due to protein–support interactions.

Simultaneous classification of multiple classes in NMR metabolomics and vibrational spectroscopy using interval-based classification methods: iECVA vs iPLS-DA

Interval based chemometric algorithms have proven to be very powerful for spectral alignments, spectral regressions and spectral classifications. The interval-based methods may not only improve the performance, but also reduce model complexity and enhance the spectral interpretation. Extended Canonical Variate Analysis (ECVA) is a powerful method for multiple group classifications of multivariate data and can easily be extended to an interval approach, iECVA. This study outlines the iECVA method and compares its performance to interval Partial Least Squares Discriminant Analysis (iPLS-DA) on three spectroscopic datasets from Nuclear Magnetic Resonance (NMR), Near Infrared (NIR) and Infrared (IR) spectroscopy, respectively. The results invariantly show that the interval-based classification methods greatly enhance the interpretability of the models by identifying important spectral regions, which facilitate interpretation and biomarker discovery. Although the results for the two methods are similar regarding the number of misclassifications and identified important regions, the model complexity of the PLS-DA proved to consistently lower than the ECVA.The Matlab source codes for both iECVA and iPLS-DA are made freely available at www.models.life.ku.dk.

Classification of methamphetamine seized in different regions of Iran using GC–MS and chemometrics

According to the intensive physical and mental risk of methamphetamine (crystal) on human, it is important to focus on the prevention of distribution and decrease of the usage of methamphetamine. In the current study, attempts was on the application of GC–MS analysis combined with chemometrics to present a classification model for methamphetamine samples seized in different regions of Iran. In this work, principal component analysis was not able to discriminate samples from different geographic regions. For the discrimination goal, partial least squares discriminant analysis (PLS-DA) and extended canonical variate analysis (ECVA) were utilized and a classification model was constructed to differentiate methamphetamine samples seized in three regions of Iran, i.e., south, west and central. PLS-DA showed good performance in calibration step; however, ECVA indicated better prediction ability. The difference of the classified samples can be because of difference in the synthetic root used in each of three investigated regions. Class sensitivity and selectivity for all three regions were excellent in ECVA model with nonsignificant misclassifications. Cross-validation and external validation using a test set confirmed the obtained classification model. Statistical results indicated a regional production/distribution pattern in the country.

Classification of Edible Oils Based on ATR-FTIR Spectral Information During a Long Heating Treatment.

Identification of oil type and its QC are important concerns in food control laboratories. Classifying edible oils that have not been used (i.e., unheated) with the aid of vibrational spectroscopy has previously been reported. However, the classification of used (i.e., heat-treated) oils needs special attention. The effect of long heating times on the classification of four kinds of edible oils (canola, corn, frying, and sunflower) based on attenuated total reflectance (ATR)-FTIR spectra was surveyed. The sampling was done on the oils during a 36 h heating process (at 170°C). The ATR-FTIR spectra of the samples were collected in the range of 4000-550 cm-1. Interval extended canonical variates analysis (ECVA), as a variable selection and classification tool, was used to determine the best intervals during the heating procedure for classification. Principal component analysis discriminate analysis, partial least-squares discriminate analysis, and ECVA were performed on the selected intervals and on the total heating time. The effect of autoscaling and mean-centering, as data preprocessing methods, was also investigated. The ECVA method resulted in the best performances for classification, with a 94% cross-validated nonerror rate (one misclassification) for the heating process times of 24-27 and 33-36 h.

Extended Canonical Variates Analysis for Wine Origin Discrimination by Using Infrared Spectroscopy

An extended canonical variates analysis (ECVA) method dealing with multicollinear data of infrared spectrum and a singular within-group covariance matrix is proposed for wine origin discrimination....

Identification of the Source of Geographical Origin of Iranian Crude Oil by Chemometrics Analysis of Fourier Transform Infrared Spectra

In this paper, we analyzed the crude oils of five different regions of Iran to identify their sources of geographical origin. The Fourier transform infrared (FTIR) spectra of samples were analyzed by chemometrics methods to make discrimination between the different crude oil sources. Infrared (IR) spectroscopy in conjunction with chemometrics techniques allows for online monitoring in real time, which can be of considerable use in the petroleum industry. Principal component analysis (PCA) and extended canonical variates analysis (ECVA), as unsupervised and supervised classification methods, respectively, were employed. The PCA scores made a relative discrimination between the different crude oil sources; however, the degree of classification was not satisfactory. Instead, more accurate classification results were achieved by ECVA. The results show that the spectral region 1350–1490 cm–1 possessed much better performances for classification by ECVA. This spectral region, which is attributed to the S═O, aro...

Identification of discriminatory variables in proteomics data analysis by clustering of variables

This article presents a data analysis method for biomarker discovery in proteomics data analysis. In factor analysis-based discriminate models, the latent variables (LV's) are calculated from the response data measured at all employed instrument channels. Since some channels are irrelevant and their responses do not possess useful information, the extracted LV's possess mixed information from both useful and irrelevant channels. In this work, clustering of variables (CLoVA) based on unsupervised pattern recognition is suggested as an efficient method to identify the most informative spectral region and then it is used to construct a more predictive multivariate classification model. In the suggested method, the instrument channels (m/z value) are clustered into different clusters via self-organization map. Subsequently, the spectral data of each cluster are separately used as the input variables of classification methods such as partial least square-discriminate analysis (PLS-DA) and extended canonical variate analysis (ECVA). The proposed method is evaluated by the analysis of two experimental data sets (ovarian and prostate cancer data set). It is found that our proposed method is able to detect cancerous from healthy samples with much higher sensitivity and selectivity than conventional PLS-DA and ECVA methods.

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

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

Confirmation of brand identity of a Trappist beer by mid-infrared spectroscopy coupled with multivariate data analysis

Authentication of foods is of importance both to consumers and producers for e.g. confidence in label descriptions and brand protection, respectively. The authentication of beers has received limited attention and in most cases only small data sets were analysed. In this study, Fourier-transform infrared attenuated total reflectance (FT-IR ATR) spectroscopy was applied to a set of 267 beers (53 different brands) to confirm claimed identity for samples of a single beer brand based on their spectral profiles. Skewness-adjusted robust principal component analysis (ROBPCA) was deployed to detect outliers in the data. Subsequently, extended canonical variates analysis (ECVA) was used to reduce the dimensionality of the data while simultaneously achieving maximum class separation. Finally, the reduced data were used as inputs to various linear and non-linear classifiers. Work focused on the specific identification of Rochefort 8° (a Trappist beer) and both direct and indirect (using an hierarchical approach) identification strategies were studied. For the classification problems Rochefort vs. non-Rochefort, Rochefort 8° vs. non-Rochefort 8° and Rochefort 8° vs. Rochefort 6° and 10°, correct prediction abilities of 93.8%, 93.3% and 97.3%, respectively were achieved.

Comparative Study of Multivariate Methods to Identify Paper Finishes Using Infrared Spectroscopy

Recycled paper is extensively used worldwide. In the last decades, its market has expanded considerably. The increasing use of recycled paper in papermaking has led to the production of paper containing several types of impurities. Consequently, wastepaper mills are forced to implement quality control schemes for evaluating the incoming wastepaper stock, thus guarantying the specifications of the final product. The main objective of this work is to present a fast and reliable system for identifying different paper types. Therefore, undesirable paper types can be refused, improving the performance of the paper machine and the final quality of the paper manufactured. For this purpose, two fast techniques, i.e., Fourier transform midinfrared (FTIR) and reflectance near infrared (NIR), were applied to acquire the infrared spectra of the paper samples. Next, four processing multivariate methods, i.e., principal component analysis, canonical variate analysis (CVA), extended CVA (ECVA), and support vector machines (SVMs), were employed in the feature-extraction or dimension-reduction stage. Afterward, the k nearest neighbor (k NN) algorithm was used in the classification phase. Experimental results show the usefulness of the proposed methodology and the potential of both FTIR and NIR spectroscopic methods. Using the FTIR spectrum in association with SVM and kNN, the system achieved a maximum classification accuracy of 100%, whereas using the NIR spectrum in association with ECVA or SVM and kNN, the system achieved a maximum classification accuracy of 96.4%.

Optimal Sample Size for Predicting Viability of Cabbage and Radish Seeds Based on near Infrared Spectra of Single Seeds

The effects of the number of seeds in a training sample set on the ability to predict the viability of cabbage or radish seeds are presented and discussed. The supervised classification method extended canonical variates analysis (ECVA) was used to develop a classification model. Calibration sub-sets of different sizes were chosen randomly with several iterations and using the spectral-based sample selection algorithms DUPLEX and CADEX. An independent test set was used to validate the developed classification models. The results showed that 200 seeds were optimal in a calibration set for both cabbage and radish data. The misclassification rates at optimal sample size were 8%, 6% and 7% for cabbage and 3%, 3% and 2% for radish respectively for random method (averaged for 10 iterations), DUPLEX and CADEX algorithms. This was similar to the misclassification rate of 6% and 2% for cabbage and radish obtained using all 600 seeds in the calibration set. Thus, the number of seeds in the calibration set can be reduced by up to 67% without significant loss of classification accuracy, which will effectively enhance the cost-effectiveness of NIR spectral analysis. Wavelength regions important for the discrimination between viable and non-viable seeds were identified using interval ECVA (iECVA) models, ECVA weight plots and the mean difference spectrum for viable and non-viable seeds.

Classification of Viable and Non-Viable Spinach (Spinacia Oleracea L.) Seeds by Single Seed near Infrared Spectroscopy and Extended Canonical Variates Analysis

Near-infrared (NIR) reflectance spectroscopy is a common non-destructive method for predicting seed quality parameters, such as moisture, oil, carbohydrates and protein content. Furthermore, variations in absorbance between germinating and non-germinating seeds have been shown in single seed studies. Spinach ( Spinacia oleracea L.) is the major crop in vegetable seed production in Denmark and two seed lots with viability percentages of 90% and 97% were chosen for examination by single seed NIR spectroscopy. Lipids play a major role in both ageing and germination. During accelerated ageing, lipid peroxidation leads to deterioration of cell membranes and contributes in that way to reducing seed viability of the seed sample. These biochemical changes may be the reason for a clear grouping between aged and non-aged seeds when performing the extended canonical variates analysis (ECVA). Assigning the difference of scatter corrected absorbance spectra from aged and non-aged seeds also lead to CH2, CH3 and HC=CH structures, which are some of the functional groups in lipids. In the ECVA plot, there was a clear difference between seeds with and without a pericarp. Evaluating the spectra, the pattern of peaks was almost similar, but the intensity was different in the absorption band at 1350 nm. The number of misclassified seeds ranged from 1.7% to 10.5% and it was lowest in seeds with a pericarp. This indicates the influence of the pericarp during germination, which is in accordance with earlier studies of spinach seeds. Single seed NIR and ECVA classification are potential methods for the prediction of seed viability.

Potential of infrared spectroscopy in combination with extended canonical variate analysis for identifying different paper types

The increasing use of secondary fiber in papermaking has led to the production of paper containing a wide range of contaminants. Wastepaper mills need to develop quality control methods for evaluating the incoming wastepaper stock as well as testing the specifications of the final product. The goal of this work is to present a fast and successful methodology for identifying different paper types. In this way, undesirable paper types can be refused, thus improving the runnability of the paper machine and the quality of the paper manufactured. In this work we examine various types of paper using information obtained by an appropriate chemometric treatment of infrared spectral data. For this purpose, we studied a large number of paper sheets of three different types (namely coated, offset and cast-coated) supplied by several paper manufacturers. We recorded Fourier transform infrared (FTIR) spectra with the aid of an attenuated total reflectance (ATR) module and near-infrared (NIR) reflectance spectra by means of fiber optics. Both techniques proved expeditious and required no sample pretreatment. The primary objective of this work was to develop a methodology for the accurate identification of samples of different paper types. For this purpose, we used the chemometric discrimination technique extended canonical variate analysis (ECVA) in combination with the k nearest neighbor (kNN) method to classify samples in the prediction set. Use of the NIR and FTIR techniques under these conditions allowed paper types to be identified with 100% success in prediction samples.

A physiochemical theory on the applicability of soft mathematical models—experimentally interpreted

AbstractAn extension of chemometric theory was experimentally explored to explain the physiochemical basis of the very high efficiency of soft modelling of data from nature. Soft modelling in self‐organisation was interpreted by studying the unique chemical patterns of mutants in an isogenic barley model on endosperm development. Extremely reproducible, differential Near Infrared (NIR) spectral patterns specifically overviewed the effect on cell composition of each mutant cause. Extended Canonical Variates Analysis (ECVA) classified spectra in wild type, starch and protein mutants. The spectra were interpreted by chemometric data analysis and by pattern inspection to morphological, genetic, molecular and chemical information. Deterministic chemical reactions were defined in the glucan pathway. A drastic mutation in a gene controlling the starch/ß‐glucan composition changed water activity that introduced a diffusive, stochastic effect on the catalysis of all active enzymes. ‘Decision making’ in self‐organisation is autonomous and performed by the soft modelling of the chemical deterministic and stochastic reactions in the endosperm cell as a whole. Uncertainty in the analysis of endosperm emergence was experimentally delimited as the ‘indeterminacy’ in local molecular path modelling ‘bottom up’ and the ‘irreducibility’ of the phenomenological NIR spectra ‘top down’. The experiment confirmed Ilya Prigogine's interpretation of self‐organisation by his dynamic computer model programmed with a self‐modeled non‐local extension of quantum mechanics (QM). The significance of self‐ organisation explained by Prigogine here interpreted as physiochemical soft modelling introduces a paradigm shift in macroscopic science that forwards a major argument for soft mathematical modelling and chemometrics to obtain full scientific legitimacy. Copyright © 2010 John Wiley & Sons, Ltd.

A chemometric approach to the environmental problem of predicting toxicity in contaminated sediments

AbstractSediments can act as sinks of multiple chemicals that accumulate over time and could represent a potentially significant hazard to the ecosystem and human health. The assessment of sediment quality, which is a complex task, usually involves, in its initial steps, the measurement of sediment contamination, followed by tests for assessing toxicity. This work presents a chemometric approach to tackle the challenging issue of linking concentrations of chemicals to the potential for observing toxicity in sediments. Various methods were applied to large databases of field‐collected chemical and biological effects data, including linear and quadratic discriminant analysis (LDA and QDA), partial least squares‐discriminant analysis (PLS‐DA), extended canonical variates analysis (ECVA), classification and regression trees (CART) and counter‐propagation artificial neural networks (CP‐ANN). LDA, QDA, PLS‐DA and ECVA showed very similar and satisfactory performances, but the best results were obtained with CP‐ANNs and CART. In any case, the developed models for predicting toxicity improved the classification performance compared to previous approaches, giving non‐error rates (NERs) in the range of 76.0–97.4. Moreover, the exploration of the internal structure of the models, jointly with the application of variable selection techniques, allowed the study of the importance of the 16 chemical contaminants considered, emphasizing Cu, followed by Zn, as the most discriminating variables for predicting toxicity in the developed models. Copyright © 2009 John Wiley & Sons, Ltd.

An exploratory NMR nutri-metabonomic investigation reveals dimethyl sulfone as a dietary biomarker for onion intake

The metabolome following intake of onion by-products is evaluated. Thirty-two rats were fed a diet containing an onion by-product or one of the two derived onion by-product fractions: an ethanol extract and the residue. A 24 hour urine sample was analyzed using (1)H NMR spectroscopy in order to investigate the effects of onion intake on the rat metabolism. Application of interval extended canonical variates analysis (ECVA) proved to be able to distinguish between the metabolomic profiles from rats consuming normal feed and rats fed with an onion diet. Two dietary biomarkers for onion intake were identified as dimethyl sulfone and 3-hydroxyphenylacetic acid. The same two dietary biomarkers were subsequently revealed by interval partial least squares regression (PLS) to be perfect quantitative markers for onion intake. The best PLS calibration model yielded a root mean square error of cross-validation (RMSECV) of 0.97% (w/w) with only 1 latent variable and a squared correlation coefficient of 0.94. This indicates that urine from rats on the by-product diet, the extract diet, and the residue diet all contain the same dietary biomarkers and it is concluded that dimethyl sulfone and 3-hydroxyphenylacetic acid are dietary biomarkers for onion intake. Being able to detect specific dietary biomarkers is highly beneficial in the control of nutritionally enhanced functional foods.

Fluorescence spectroscopy and chemometrics for classification of breast cancer samples—a feasibility study using extended canonical variates analysis

AbstractThe objective of this phase I feasibility study is to investigate whether fluorescence spectroscopy of serum samples in combination with multivariate data analysis can be used to discriminate healthy females from breast cancer patients with solitary and multiple metastases, respectively. Serum samples were obtained from 39 females: 13 healthy females (controls) and 26 clinically diagnosed patients with either solitary metastases (11 patients) or multiple metastases (15 patients). Fluorescence spectra were measured on undiluted samples and samples diluted 20 times and 500 times. Extended Canonical Variates Analysis (ECVA) was applied to develop classification models on the data. Three‐group ECVA based on all spectroscopic data (5221 variables) gave five misclassifications in total, while sequential ECVA models on selected excitation wavelengths yielded two errors. The fluorescence spectroscopic results were compared with results based on the three tumor markers cancer antigen 15‐3 (CA 15‐3), carcinoembryonic antigen (CEA), and tissue polypeptide antigen (TPA). The lowest number of errors obtained using ECVA on the biomarkers was seven. Furthermore, fluorescence spectroscopy made it possible to discover sample subgroupings: females with solitary and multiple metastases could be divided into two subgroups according to the spectral patterns of the samples. Copyright © 2007 John Wiley & Sons, Ltd.