chemometric analysis (2)


In this study, a pocket-sized spectrometer and multivariate analysis were used for rapid authentication of coffee varieties (Arabica and Robusta) in three physical states (raw beans, roasted beans and powdered beans) to check mislabelling and fraud. Two main coffee varieties were collected from different locations in Africa, and the three physical states were scanned in the 740–1070 nm wavelength range. The spectral data were pre-treated with several  derivative-based methods followed by chemometric analysis to build the prediction models for coffee beans (raw, roasted and powdered).  The best results  obtained for raw coffee beans was an accuracy of 0.92 and efficiency of 0.82; for roasted coffee beans, an accuracy of 0.92 and efficiency of 0.87; while for roasted powdered coffee, an accuracy of 0.97 and efficiency of 0.97. The results reveal that for a more accurate differentiation of coffee beans, the roasted powder offers the best results. 

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Virgin coconut oil (VCO) is in high demand because of its uses in cooking, frying, as well as being used as an ingredient in food, pharmacy, and cosmetic goods. Given its high consumer demand, there is a need to establish a reliable method for the identification of its geographical origin especially if producers wish to protect regional speciality production. IAEA has collaborated with Indian researchers to develop a method based on multi-elemental analysis of VCO using ICP-MS (inductively coupled plasma mass spectrometry) to differentiate between VCO's from 5 major producing states of Southern India (Kerala, Karnataka, Andhra Pradesh, Tamil Nadu and Goa). Samples of coconuts were collected in each state and VCO prepared in the laboratory, and analysed by ICP-MS.  The concentration of 20 elements in a total of 21 samples were measured, and 17 of these elements (Na, Mg, Al, P, Ca, Cr, Mn, Fe, Ni, Cu, Zn, Se, Rb, Sr, Mo, Cs, Pb) were chosen for chemometric analysis. PCA (Principal Component Analysis), HCA (Hierachical Cluster Analysis), and LDA (Linear Discriminat Analysis) were able to differentiate and classify the VCO samples of different geographical origins.  Further, calibration models based on PCR (Principal Component Regression) and PLS-R (Partial Least Squares Regression) were developed on the calibration dataset of the elemental concentrations, and were able to distinguish between the different geographical origins. Therefore, ICP-MS combined with regression modelling can be used as an excellent tool for the identification of the geographical origin of the VCO samples of various Indian states.

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