Zheng Pengfei, Solomon Adade Selorm Yao-Say, Rong Yanna, Zhao Songguang, Han Zhang, Gong Yuting, Chen Xuanyu, Yu Jinghao, Huang Chunchi, Lin Hao
School of Food and Bioengineering, Jiangsu University, Zhenjiang 212013, China.
Chichun Machinery (Xiamen) Co., Ltd., Xiamen 361100, China.
Foods. 2024 May 29;13(11):1708. doi: 10.3390/foods13111708.
During the fermentation process of Oolong tea, significant changes occur in both its external characteristics and its internal components. This study aims to determine the fermentation degree of Oolong tea using visible-near-infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projection algorithm (SPA) feature selection. Subsequently, traditional machine learning and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highest prediction rates among traditional machine learning models and deep learning models with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing possesses the capability for rapid non-destructive online determination of the fermentation degree of Oolong tea. Additionally, the predictive rate of traditional machine learning models exceeds that of deep learning models in this study. This study provides a theoretical basis for the fermentation of Oolong tea.
在乌龙茶的发酵过程中，其外在特征和内部成分都会发生显著变化。本研究旨在利用可见-近红外光谱（vis-VIS-NIR）和图像处理技术来确定乌龙茶的发酵程度。经过预处理的vis-VIS-NIR光谱数据在采用连续投影算法（SPA）进行特征选择后，与图像特征进行融合。随后，对传统机器学习和深度学习分类模型进行比较，支持向量机（SVM）和卷积神经网络（CNN）模型在传统机器学习模型和深度学习模型中预测率最高，预测集中分别为97.14%和95.15%。结果表明，VIS-NIR与图像处理相结合具备快速无损在线测定乌龙茶发酵程度的能力。此外，在本研究中传统机器学习模型的预测率超过了深度学习模型。本研究为乌龙茶的发酵提供了理论依据。
