Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS-DA.

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Abstract

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.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. 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 Cd content of Le. rugosiceps in the Midu area exceeded the standard. Deep learning can trace Le. rugosiceps geographic origin with an accuracy of 100%.The overall results show that deep learning methods based on FT-NIR can identify porcini that is at risk of eating. This has useful application prospects in food safety. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.