Classification of bolete species and drying temperature using LC–MS and infrared spectroscopy and simultaneous prediction of their major compounds using chemometrics

Abstract

Postharvest processing of wild edible mushrooms is an important subject, and drying increases the difficulty of species identification and affects their nutritional value, biological activity, and sensory properties. In this study, liquid chromatography-mass spectrometry (LC-MS), Fourier-transform near-infrared (FT-NIR) and Fourier-transform infrared (FTIR) spectroscopy combined with chemometrics were used for the identification of species and quality assessment of dried boletes, a common bolete from Yunnan, China. The chemometric results revealed that LC-MS, FT-NIR, and FT-IR spectroscopy could identify boletes' species and drying temperature with an accuracy of 100.00% and 92.31%, respectively. FT-NIR combined with partial least squares regression (PLSR) was used to develop the calibration model. The coefficients of determination (R²c) of the calibration model for L-malic acid in different species and drying temperatures were 0.91 and 0.97, respectively. The predictive model (R²p) values ranged from 0.86 (drying temperature) to 0.97 (species). This study was based on infrared spectroscopy and organic acid data combined with chemometrics for effective qualitative identification and quantitative prediction of boletes. The method can be used for quality evaluation of edible mushrooms and other food products.