Fusion of Ultraviolet and Infrared Spectra Using Support Vector Machine and Random Forest Models for the Discrimination of Wild and Cultivated Mushrooms

文献类型： 外文期刊

第一作者： Yao, Sen

作者： Yao, Sen;Li, Jie-Qing;Duan, Zhi-Li;Yao, Sen;Wang, Yuan-Zhong;Li, Tao

作者机构：

关键词： Discrimination; data fusion; Fourier transform infrared (FTIR) spectroscopy; P; portentosus; spectrophotometry; W; cocos

期刊名称：ANALYTICAL LETTERS （ 影响因子：2.329； 五年影响因子：1.738 ）

ISSN： 0003-2719

年卷期： 2020 年 53 卷 7 期

页码：

收录情况： SCI

摘要： Discrimination of wild and cultivated mushrooms is important for their quality assessment. In this study, data fusion of infrared and ultraviolet spectroscopies combined with support vector machine or random forest were applied for the discrimination of wild and cultivated W. cocos and P. portentosusis. For the low- and mid-level data fusion classification models, the accuracies of validation set are 90.91% and 98.70%, respectively. However, the parameter c is too large which means the models are unreliable. For the high-level data fusion, the results showed that the accuracy of validation set is 100% and the sensitivity and specificity increased significantly compared to the single technique. Therefore, high-level data fusion combined with random forest is an effective method for the discrimination of wild and cultivated grown W. cocos and P. portentosus, and may be applied for protecting the steady development of the edible mushroom market.

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