Two dimensional correlation spectroscopy combined with ResNet: Efficient method to identify bolete species compared to traditional machine learning

文献类型： 外文期刊

第一作者： Yan, Ziyun

作者： Yan, Ziyun;Li, Jieqing;Liu, Honggao;Liu, Honggao;Li, Tao;Yan, Ziyun;Wang, Yuanzhong

作者机构：

关键词： Bolete; FT-NIR; Chemometrics; 2DCOS; ResNet

期刊名称：LWT-FOOD SCIENCE AND TECHNOLOGY （ 影响因子：6.056； 五年影响因子：6.295 ）

ISSN： 0023-6438

年卷期： 2022 年 162 卷

页码：

收录情况： SCI

摘要： Different species of bolete have different nutritional and medicinal value, which leads to the phenomenon of shoddy in the market from time to time. Therefore, consumers need a fast and effective detection method to identify their species. In this paper, different data pretreatment was carried out for the Fourier transform near infrared (FT-NIR) spectra, and the modeling results of partial least squares discrimination analysis (PLS-DA), support vector machines (SVM) and residual neural network (ResNet) were compared. The results show that PLS-DA and SVM models need a suitable combination of pretreatment for spectral data. The purpose is to improve the accuracy of the model and avoid over fitting. After spectral pretreatment, the accuracy of PLS-DA model were improved to 99.63% and 97.38% respectively. In order to ensure that the SVM model does not have the risk of over fitting, the accuracy of the SVM model after pretreatment were reduced to 98.5% and 93.63%. The ResNet model was established based on the original spectrum. The accuracy of the model was 100%, and there is no over fitting phenomenon, which is one of the advantages of the model. Comparing the above three models, ResNet is the best model for bolete species identification.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序