This study describes a newly developed method for the fast and straightforward differentiation of two turmeric species using Direct Analysis in Real Time mass spectrometry and miniaturized Near Infrared spectroscopy. Multivariate analyses (PCA and LDA) were performed on the mass spectrometric data, thus creating a powerful model for the discrimination of Curcumalonga and Curcumaxanthorrhiza. Cross-validation of the model revealed correctness-scores of 100% with 20-fold as well as leave-one-out validation techniques. To further estimate the models prediction power, seven retail samples of turmeric powder were analyzed and assorted to a species. Looking for a fast, non-invasive, cost-efficient and laboratory independent method, miniaturized NIR spectrometers offer an alternative for quality control of turmeric species. However, different technologies implemented to compensate for their small size, lead to different applicability of these spectrometers. Therefore, we investigated the three handheld spectrometers microPHAZIR, MicroNIR 2200 and MicroNIR 1700ES for their application in spice analysis in hyphenation to PCA, LDA and ANN methods used for the discriminant analysis. While microPHAZIR proved to be the most valuable device for differentiating C.longa and C.xanthorrhiza, MicroNIR 1700ES offered the worst results. These findings are interpreted on the basis of a quantum chemical simulation of the NIR spectrum of curcumin as the representative constituent. It was found that the information accessible to MicroNIR 1700ES that is relevant to the analyzed constituents is located in the spectral region prone to interferences with the matrix, likely limiting the performance of this spectrometer in this analytical scenario.
Keywords: Artificial neural network; Curcuma longa; Curcuma xanthorrhiza; Linear discriminant analysis; Pattern recognition algorithms; Principal component analysis.
Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.