Bu Youhua, Jiang Xinna, Tian Jianping, Hu Xinjun, Han Lipeng, Huang Dan, Luo Huibo
College of Mechanical Engineering, Sichuan University of Science and Engineering, Yibin, China.
Key Laboratory of Brewing Biotechnology and Application of Sichuan Province, Yibin, China.
J Sci Food Agric. 2023 Jun;103(8):3970-3983. doi: 10.1002/jsfa.12344. Epub 2022 Nov 29.
The purity of sorghum varieties is an important indicator of the quality of raw materials used in the distillation of liquors. Different varieties of sorghum may be mixed during the acquisition process, which will affect the flavor and quality of liquor. To facilitate the rapid identification of sorghum varieties, this study proposes a sorghum variety identification model using hyperspectral imaging (HSI) technology combined with convolutional neural network (AlexNet).
First, the watershed algorithm, which was modified with the extended-maxim transform, was used to segment the hyperspectral images of a single sorghum grain. The isolated forest algorithm was used to eliminate abnormal spectral data from the complete spectral data. Secondly, the AlexNet model of sorghum variety identification was established based on the two-dimensional gray image data of sorghum grain in group 1. The effects of different preprocessing methods and different convolution kernel sizes on the performance of the AlexNet model were discussed. The eigenvalues of the last layer of the AlexNet model were visualized using the t-distributed random neighborhood embedding method, which is used to evaluate the separability of features extracted by the AlexNet model. The performance differences between the optimal AlexNet model and traditional machine learning models for sorghum variety identification were compared. Finally, the varieties of sorghum grains in groups 2 and 3 were identified based on the optimal AlexNet model, and the average accuracy values of the test set reached 95.62% and 95.91% respectively.
The results in this study demonstrated that HSI combined with the AlexNet model could provide a feasible technical approach for the detection of sorghum varieties. © 2022 Society of Chemical Industry.
高粱品种纯度是白酒酿造所用原料质量的重要指标。在收购过程中，不同品种的高粱可能会混合，这会影响白酒的风味和品质。为便于快速鉴定高粱品种，本研究提出一种利用高光谱成像（HSI）技术结合卷积神经网络（AlexNet）的高粱品种鉴定模型。
首先，采用经扩展最大变换修正的分水岭算法对单个高粱籽粒的高光谱图像进行分割。利用孤立森林算法从完整光谱数据中剔除异常光谱数据。其次，基于第1组高粱籽粒的二维灰度图像数据建立高粱品种鉴定的AlexNet模型。讨论了不同预处理方法和不同卷积核大小对AlexNet模型性能的影响。利用t分布随机邻域嵌入方法对AlexNet模型最后一层的特征值进行可视化，以评估AlexNet模型提取特征的可分离性。比较了最优AlexNet模型与传统机器学习模型在高粱品种鉴定方面的性能差异。最后，基于最优AlexNet模型对第2组和第3组高粱籽粒的品种进行鉴定，测试集的平均准确率分别达到95.62%和95.91%。
本研究结果表明，HSI结合AlexNet模型可为高粱品种检测提供一种可行的技术方法。© 2022化学工业协会。
