Wild edible mushrooms are distributed all over the world and are delicious seasonal foods, rich in polysaccharides, amino acids, vitamins, and other components. At the same time, they contain many essential trace elements and are highly enriched in heavy metals (compared to green plants and cultivated edible mushrooms). Consumers may be exposed to health risks due to excessive heavy metals in the process of consumption. This is also one of the important factors affecting the import and export of edible mushrooms, which is of great concern to consumers and entry and exit inspection and quarantine departments. In this paper, the contents of four essential trace elements of iron, manganese, zinc, and copper and four harmful heavy metals of cadmium, lead, mercury, and arsenic in nearly 400 species of wild edible mushrooms from 10 countries are reviewed. It was found that the factors affecting the elemental content of edible mushrooms are mainly divided into internal and external factors. Internal is mainly the difference in species element-enrichment ability, and external is mainly environmental pollution and geochemical factors. The aim is to provide a reference for the risk assessment of edible mushrooms and their elemental distribution characteristics.
Wild Gastrodia elata resources are in short supply and the market is dominated by cultivated Gastrodia elata resources and the medicinal value of both is very different. It is significant to find a highly accurate and stable technique to identify wild and cultivated Gastrodia elata. It could prevent market fraud and protect the rights of consumers. In this context, this study is the first to combine three-dimensional correlated spectral (3DCOS) images with deep learning to identify wild and cultivated Gastrodia elata. Also, partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) models are compared with this model. The PLS-DA and SVM models are built based on Fourier transform mid-infrared (FT-MIR) spectral data after nine different preprocessing. The PLS-DA model with second-order derivatives (2D) gives the best results when comparing the effects of the models with different preprocessing. the SVM model with parameters c, g in a reasonable range also gives satisfactory model results. The advantage of the deep learning model over them is that no processing of the original spectral data is required. With only 46 iterations, the accuracy of the model is stable at 100% for the training set, test set and external validation set. The excellent performance of the model allows it to be used as a technical reference to solve studies on the qualitative aspects of Gastrodia elata.
To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three‐dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three‐dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS‐DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.
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