Cai Zeyi, Huang Zihong, He Mengyu, Li Cheng, Qi Hengnian, Peng Jiyu, Zhou Fei, Zhang Chu
School of Information Engineering, Huzhou University, Huzhou 313000, China.
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Food Chem. 2023 Oct 1;422:136169. doi: 10.1016/j.foodchem.2023.136169. Epub 2023 Apr 19.
The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with numerous clinical and nutritional benefits. Rapid and accurate identification of the geographical origins of Baishao is crucial for planters, traders and consumers. Hyperspectral imaging (HSI) was used in this study to acquire spectral images of Baishao samples from its two sides. Convolutional neural network (CNN) and attention mechanism was used to distinguish the origins of Baishao using spectra extracted from one side. The data-level and feature-level deep fusion models were proposed using information from both sides of the samples. CNN models outperformed the conventional machine learning methods in classifying Baishao origins. The generalized Gradient-weighted Class Activation Mapping (Grad-CAM++) was utilized to visualize and identify important wavelengths that significantly contribute to model performance. The overall results illustrated that HSI combined with deep learning strategies was effective in identifying the geographical origins of Baishao, having good prospects of real-world applications.
白芍是一种具有众多临床和营养益处的传统中药。快速准确地鉴定白芍的地理来源对种植者、贸易商和消费者来说至关重要。本研究采用高光谱成像（HSI）从白芍样本的两面获取光谱图像。利用卷积神经网络（CNN）和注意力机制，通过从一面提取的光谱来区分白芍的产地。利用样本两面的信息提出了数据级和特征级深度融合模型。在白芍产地分类方面，CNN模型优于传统机器学习方法。利用广义梯度加权类激活映射（Grad-CAM++）来可视化和识别对模型性能有显著贡献的重要波长。总体结果表明，HSI结合深度学习策略在鉴定白芍地理来源方面是有效的，具有良好的实际应用前景。
