ResNet models for rapid identification of species and geographical origin of wild boletes from Yunnan, and MaxEnt model for delineation of potential distribution

文献类型： 外文期刊

第一作者： Chen, Xiong

作者： Chen, Xiong;Liu, Hong Gao;Li, Jie Qing;Chen, Xiong;Wang, Yuan Zhong;Liu, Hong Gao

作者机构：

关键词： food authentication; maximum entropy; mushroom; ResNet; two-dimensional correlation spectroscopy

期刊名称：JOURNAL OF CHEMOMETRICS （ 影响因子：2.5； 五年影响因子：2.301 ）

ISSN： 0886-9383

年卷期： 2022 年 36 卷 11 期

页码：

收录情况： SCI

摘要： Yunnan is known for its rich biodiversity and is known as the Wild Mushroom Kingdom. Boletes are a world-renowned wild edible mushroom, with unique sensory characteristics, nutritional value and medicinal value extraordinary. However, the species and geographical origin of boletes influence their price and quality. In this study, a method was developed to identify species and geographical origin simultaneously. Therefore, Fourier transform near-infrared (FT-NIR) data sets of boletes were collected and converted to two-dimensional correlation spectroscopy (2D-COS). On this basis, the species and geographic origins of boletes were identified using Residual neural network (ResNet) image analysis model. The results showed that FT-NIR could identify boletes species and geographical origins, 7000-4000 cm(-1) band was more suitable for species identification, 7000-5300 cm(-1) band was more suitable for geographical origins identification. In addition, the environmental factors with high contribution to the distribution of boletes were screened based on the maximum entropy (MaxEnt) model. This allows characterization of the potential geographic distribution of boletes. The results showed that precipitation factors played a vital role in its distribution and might even be responsible for the difference in chemical composition.

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