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

• Published in: Industrial crops and products 2022-11, Vol.187 (Part A), p.115349, Article 115349
• Main Authors: Coelho, Christian, Figueredo, Gilles, Lafarge, Céline, Bou-Maroun, Elias, Flahaut, Stéphanie
• Format: Article
• Language: English
• Subjects: Agricultural sciences; Artificial neural networking; Biochemistry, Molecular Biology; Bitter composition; Bitter content prediction; French mountains; Gentiana lutea; Growing conditions; Life Sciences; Mid-infrared spectra; Multivariate analysis; Sciences and technics of agriculture; Vegetal Biology
• ISSN: 0926-6690; 1872-633X
• DOI: 10.1016/j.indcrop.2022.115349

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. [Display omitted] •Gentiana lutea roots explored by FTIR spectroscopy and liquid chromatography.•55 wild and cultivated gentian roots analyzed from the four main French mountains.•Predictive models used to distinguish growing conditions and geographical origin.•Unprecedented bitter content classes determined by mid-infrared and chemometrics.