Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species
文献类型： 外文期刊
作者： Li, Jie-Qing 1 ; Wang, Yuan-Zhong 2 ; Liu, Hong-Gao 1 ;
作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming, Peoples R China
2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming, Peoples R China
3.Zhaotong Univ, Zhaotong, Peoples R China
关键词： boletes species; 2DCOS images; 3DCOS images; Alexnet; Resnet; large-screen visualization
期刊名称：FRONTIERS IN MICROBIOLOGY （ 影响因子：5.2； 五年影响因子：6.2 ）
ISSN：
年卷期： 2023 年 13 卷
页码：
收录情况： SCI
摘要： Boletes are favored by consumers because of their unique flavor, rich nutrition and delicious taste. However, the different nutritional values of each species lead to obvious price differences, so shoddy products appear on the market, which affects food safety. The aim of this study was to find a rapid and effective method for boletes species identification. In this paper, 1,707 samples of eight boletes species were selected as the research objects. The original Mid-Infrared (MIR) spectroscopy data were adopted for support vector machine (SVM) modeling. The 11,949 spectral images belong to seven data sets such as two-dimensional correlation spectroscopy (2DCOS) and three-dimensional correlation spectroscopy (3DCOS) were used to carry out Alexnet and Residual network (Resnet) modeling, thus we established 15 models for the identification of boletes species. The results show that the SVM method needs to process complex feature data, the time cost is more than 11 times of other models, and the accuracy is not high enough, so it is not recommended to be used in data processing with large sample size. From the perspective of datasets, synchronous 2DCOS and synchronous 3DCOS have the best modeling results, while one-dimensional (1D) MIR Spectrum dataset has the worst modeling results. After comprehensive analysis, the modeling effect of Resnet on the synchronous 2DCOS dataset is the best. Moreover, we use large-screen visualization technology to visually display the sample information of this research and obtain their distribution rules in terms of species and geographical location. This research shows that deep learning combined with 2DCOS and 3DCOS spectral images can effectively and accurately identify boletes species, which provides a reference for the identification of other fields, such as food and Chinese herbal medicine.
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