Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS-DA
文献类型： 外文期刊
作者： Li Wang 1 ; Liu, Honggao 4 ; Li, Tao 5 ; Li, Jieqing 2 ; Wang, Yuanzhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Yunnan, Peoples R China
2.Yunnan Agr Univ, Coll Resources & Environm, Kunming 650201, Yunnan, Peoples R China
3.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming, Yunnan, Peoples R China
4.Zhaotong Univ, Coll Agron & Life Sci, Zhaotong, Peoples R China
5.Yuxi Normal Univ, Coll Resources & Environm, Yuxi, Peoples R China
关键词： porcini; food safety; deep learning; FT-NIR; Cd
期刊名称：JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE （ 影响因子：3.639； 五年影响因子：3.803 ）
ISSN： 0022-5142
年卷期：
页码：
收录情况： SCI
摘要： BACKGROUND How to quickly identify poisonous mushrooms is a worldwide problem, because poisonous mushrooms and edible mushrooms have very similar appearances. Even some edible mushrooms must be processed further before they can be eaten. In addition, mushrooms from different geographical origins contain different levels of heavy metals. Eating frequent mushrooms with excessive heavy metal content can also cause food poisoning. This information is very important and needs to be informed to consumers in advance. Through the demand for the safety of porcini mushrooms in the Yunnan area we propose a hierarchical identification system based on Fourier-transform near-infrared (FT-NIR) spectroscopy to evaluate the edible safety of porcini species. RESULTS We found that deep learning is the most effective means to identify the edible safety of porcini, and the recognition accuracy was 100%, by comparing two pattern recognition tools, deep learning and partial least square discriminant analysis (PLS-DA). Although the accuracy of the PLS-DA test set is 96.10%, the poisonous porcini is not allowed to be wrongly judged. In addition, the cadmium (Cd) content of Leccinum rugosiceps in the Midu area exceeded the standard. Deep learning can trace Le. rugosiceps geographic origin with an accuracy of 100%. CONCLUSION The overall results show that deep learning methods based on FT-NIR can identify porcini that is at risk of being eaten. This has useful application prospects in food safety. (c) 2021 Society of Chemical Industry.
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