Multivariate Authentication of Herbs and Spices through UV-Vis and FT-IR Fingerprint

The aim of this study was to investigate the applicability of UV-Vis and FT-IR fingerprints combined with multivariate statistical tools to classify and authenticate Iranian standard herbs and spices, their mislabeled and adulterated samples in single and fusion model. The proposed strategy is as an alternative, rapid, easy, and economical approach for of the herbs and spices authentication. Sixty three samples of different herbs and spices were collected across several cities of Iran. The potency of Savitzky-Golay(SG) smoothing in combination with autoscaling for improving accuracy of clustering well studied and principal component analysis (PCA), PCA – linear discriminant analysis (PCA-LDA) and partial least squares - discriminant analysis (PLS-DA) were applied for classification. Additionally, data mining of spectral sets was performed using Kohonen self-organization maps (SOMs) of smoothed and unsmoothed individual data sets and classification results were compared. Also, the discriminant models using fusion matrix was built by concatenation of SG smoothed-atoscaled SOMs clusters of FTIR and UV-Vis (SG- autoscaled- SOMs) spectra. The results of different models showed that accuracy of single SG- autoscaled- SOMs-FTIR data was better than SG- autoscaled- UV-Vis data and the accuracy of SG- autoscaled- SOMs -fusion technique was better the other models. This method predicted class of samples more accurately (more than 95 %). The authentication and quality of fraud samples were identified more correctly with respect to raw data.