Fingerprinting of Boletus bainiugan: FT-NIR spectroscopy combined with machine learning a new workflow for storage period identification

文献类型： 外文期刊

第一作者： Deng, Guangmei

作者： Deng, Guangmei;Li, Jieqing;Deng, Guangmei;Wang, Yuanzhong;Liu, Honggao

作者机构：

关键词： Boletus bainiugan; Fourier transform near infrared spectroscopy; Machine learning; Storage period identification

期刊名称：FOOD MICROBIOLOGY （ 影响因子：4.6； 五年影响因子：5.1 ）

ISSN： 0740-0020

年卷期： 2025 年 129 卷

页码：

收录情况： SCI

摘要： Food authenticity and food safety issues have threatened the prosperity of the entire community. The phenomenon of selling porcini mushrooms as old mixed with new jeopardizes consumer safety. Herein, nucleoside contents and spectra of 831 Boletus bainiugan stored for 0, 1 and 2 years are comprehensively analyzed by high performance liquid chromatography (HPLC) coupled with Fourier transform near infrared (FT-NIR) spectroscopy. Guanosine and adenosine increased with storage time, and uridine has a decreasing trend. Multiconventional machine learning and deep learning models are employed to identify the storage time of Boletus bainiugan, in which convolutional neural network (CNN) and back propagation neural network (BPNN) models have superior identification performance for distinct storage periods. The Data-driven soft independent modelling of class analogy (DD-SIMCA) model can completely differentiate between new and old samples, and partial least squares regression (PLSR) can accurately predict the three nucleoside compounds with an optimal R2 of 0.918 and an excellent residual predictive deviation (RPD) value of 3.492. This study provides a low-cost and user-friendly solution for the market to determine, in real time, storage period of Boletus bainiugan in the supply chain.

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