Li Jie-Qing, Wang Yuan-Zhong, Liu Hong-Gao
College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China.
Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China.
Front Microbiol. 2023 Jan 12;13:1036527. doi: 10.3389/fmicb.2022.1036527. eCollection 2022.
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.
牛肝菌因其独特的风味、丰富的营养和鲜美的口感而受到消费者的青睐。然而，不同种类的牛肝菌营养价值差异明显，导致价格差异显著，因此市场上出现了以次充好的产品，这影响了食品安全。本研究的目的是找到一种快速有效的牛肝菌种类鉴定方法。本文选取了8种牛肝菌的1707个样本作为研究对象。采用原始的中红外（MIR）光谱数据进行支持向量机（SVM）建模。利用属于二维相关光谱（2DCOS）和三维相关光谱（3DCOS）等7个数据集的11949幅光谱图像进行Alexnet和残差网络（Resnet）建模，从而建立了15个牛肝菌种类鉴定模型。结果表明，SVM方法需要处理复杂的特征数据，时间成本是其他模型的11倍以上，且准确率不够高，因此不建议用于大样本量的数据处理。从数据集的角度来看，同步2DCOS和同步3DCOS的建模效果最好，而一维（1D）MIR光谱数据集的建模效果最差。综合分析后，Resnet在同步2DCOS数据集上的建模效果最佳。此外，我们使用大屏幕可视化技术直观地展示了本研究的样本信息，并获得了它们在种类和地理位置方面的分布规律。本研究表明，深度学习结合2DCOS和3DCOS光谱图像能够有效、准确地鉴定牛肝菌种类，为食品和中草药等其他领域的鉴定提供了参考。
