chromatographic fingerprinting with multivariate data analysis for detection and quantification of apricot kernel in almond powder

Adulteration of almond powder samples with apricot kernel was solved by gas chromatographic fatty acid fingerprinting combined with multivariate data analysis methods (principal component analysis (pca), pca-linear discriminant analysis (pca-lda), partial least squares (pls), and ls support vector machine (ls-svm). different almond and apricot kernel samples were mixed at concentrations ranging from 10 to 90% w/w. pca and pca-lda methods were applied for the classification of almonds, apricot kernels, and mixtures. pls and ls-svm were used for the quantification of adulteration ratios of almond. models were developed using a training data set and evaluated using a validation data set. the root mean square error of prediction (rmsep) and coefficient of determination (r 2) of validation data set obtained for pls and ls-svm were 5.01, 0.964 and 2.29, 0.995, respectively. the results showed that the methods can be applied as an effective and feasible method for testing almond adulteration.