Mid-infrared spectroscopy combined with multivariate analysis and machine-learning: A powerful tool to simultaneously assess geographical origin, growing conditions and bitter content in Gentiana lutea roots

Abstract

Mid-infrared spectroscopy was explored in order to evaluate its ability to classify geographical origin and bitter content of Gentiana lutea roots sourced from wild and cultivated growing conditions. Wild and cultivated Gentiana lutea roots from the four French mountains Massif Central, Jura, Alpes and Pyrénées were analyzed by Infrared spectroscopy and liquid chromatography. Unsupervised analyses assessed heterogeneity of Gentiana lutea roots in the different sampling sites due to their evolutive composition along plant growth. Predictive models using partial least squares discriminant analysis and probabilistic artificial neural network were discussed according to FTIR spectral regions and gave 100 % accuracy in authentication of geographical origin and growing conditions. Nevertheless, classification of gentian roots according to their bitter content, based on FTIR spectral signatures, was challenging due to their biological heterogeneity. We propose an unprecedented classification of Gentiana lutea roots according to their analyzed bitter content: Low, Medium and High, comprised in the range [6–8] %, [8–10] % and [10–12] % in dry weight, respectively. FTIR coupled with chemometrics applied directly on gentian roots allowed for a decent level of predictability by PLS-DA (Q2Cum = 0.41) and Artificial Neural Network (89.1 % accuracy), when using the (650–1800 cm−1) infrared region.