Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China.
College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China.
J Food Sci. 2022 Jul;87(7):2908-2919. doi: 10.1111/1750-3841.16220. Epub 2022 Jun 23.
Boletes are recognized as a worldwide delicacy. Adulteration of the expired and low-value sliced boletes is a pressing problem in the supply chain of commercial sliced boletes. This study aimed at developing a rapid method to identify the storage duration and species of sliced boletes, using near-infrared (NIR) spectroscopy. In the study, 1376 fruiting bodies of wild-grown boletes were collected from 2017 to 2020 in Yunnan, containing four common species of edible boletes. A NIR spectroscopy-based strategy was proposed, that is, identify the storage duration of sliced boletes to ensure that they are within the shelf life firstly; then identify the species of sliced boletes within the shelf life to evaluate their economic value. Three supervised methods, partial least squares discriminant analysis (PLS-DA), extreme learning machine (ELM), and two-dimensional correlation spectroscopy (2DCOS) images with residual convolutional neural network (ResNet) model were applied to identify. The results showed that PLS-DA model cannot accurately identify the storage duration and species of sliced boletes, and the ELM model can identify the storage duration of boletes samples, but cannot accurately discriminate different species of samples. And ResNet model established by 2DCOS images showed superiority in classification performance, 100% accuracy was obtained for both the storage duration and species classification. Moreover, compared to traditional methods, the 2DCOS images with ResNet model was free of complicated data preprocessing. The results obtained in the present study indicated a promising way of combining 2DCOS images with ResNet methods, in tandem with NIR for the rapid identification of the storage duration and species of sliced boletes. PRACTICAL APPLICATION: In the boletes supply chain, the method can be considered as a reliable method for testing the authenticity of boletes slices. The current study can also provide a reference for quality control of other edible mushroom.
牛肝菌被公认为是一种全球性的美食。在商业切片牛肝菌供应链中，过期和低价值切片牛肝菌的掺假是一个紧迫的问题。本研究旨在开发一种快速识别切片牛肝菌储存时间和种类的方法，使用近红外（NIR）光谱。在这项研究中，从 2017 年到 2020 年，在云南共采集了 1376 个野生牛肝菌的子实体，其中包含四个常见的可食用牛肝菌品种。提出了一种基于近红外光谱的策略，即首先识别切片牛肝菌的储存时间，以确保其在保质期内；然后在保质期内识别切片牛肝菌的种类，以评估其经济价值。应用了三种有监督的方法，偏最小二乘判别分析（PLS-DA）、极限学习机（ELM）和二维相关光谱（2DCOS）图像与残差卷积神经网络（ResNet）模型来进行识别。结果表明，PLS-DA 模型不能准确识别切片牛肝菌的储存时间和种类，ELM 模型可以识别牛肝菌样本的储存时间，但不能准确区分不同种类的样本。而基于 2DCOS 图像建立的 ResNet 模型在分类性能方面表现出优越性，对储存时间和种类的分类都达到了 100%的准确率。此外，与传统方法相比，基于 2DCOS 图像与 ResNet 模型的方法无需复杂的数据预处理。本研究结果表明，结合 2DCOS 图像与 ResNet 方法，与 NIR 相结合，是一种快速识别切片牛肝菌储存时间和种类的有前途的方法。实际应用：在牛肝菌供应链中，该方法可以被视为测试牛肝菌切片真实性的可靠方法。本研究还可为其他食用蘑菇的质量控制提供参考。
