Determination of rice type by ¹H NMR spectroscopy in combination with different chemometric tools

Abstract

A 400-MHz 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non-Basmati long-grain rice, and round-grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy, and ICA), PLS-DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS-DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples

Improved classification of fused data: Synergetic effect of partial least squares discriminant analysis (PLS-DA) and common components and specific weights analysis (CCSWA) combination as applied to tomato profiles (NMR, IR and IRMS)

“…The range of applications of multivariate methods spreads from pharmaceutical products and foodstuff to complex environmental samples and clinical diagnostics [2][3][4][5].…”

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

“…However, it has recently been shown that an alternative approach, namely independent components analysis (ICA) can outperform standard PCA for data reduction and can even be applied for solving classification problems [25][26][27][28]. The goal of ICA is to recover the "pure" source signals from a data set of mixed signals by finding a transformation that minimizes the dependencies between "pure" source signals (called ICs) [29][30][31][32][33][34][35][36].…”

“…Based on our experience, the "pure" source signals extracted by ICA represent chemically significant sources in comparison with "abstract" PCA loadings [25][26][27][28][29][30]. The proportions of each "pure" source signal in the mixed signal of each sample can be compared to PCA scores for the visualization of the groups of objects [25][26][27][28].…”

“…The goal of ICA is to recover the "pure" source signals from a data set of mixed signals by finding a transformation that minimizes the dependencies between "pure" source signals (called ICs) [29][30][31][32][33][34][35][36]. Based on our experience, the "pure" source signals extracted by ICA represent chemically significant sources in comparison with "abstract" PCA loadings [25][26][27][28][29][30]. The proportions of each "pure" source signal in the mixed signal of each sample can be compared to PCA scores for the visualization of the groups of objects [25][26][27][28].…”

Independent components analysis as an alternative to principal component analysis and discriminant analysis algorithms in the processing of spectrometric data

“…In another recent study, ICA in conjunction with conventional chemometric methods, such as PCA, linear discriminant analysis (LDA), factorial discrim inant analysis (FDA), projections to latent structures discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), has been used for discriminating samples of rice regarding their varieties and geographical origin on the basis of 1 H NMR spec troscopy [18]. ICA modeling enabled a complete sep aration Basmati rice from other varieties of long grain rice, which is impossible with conventional PCA.…”