Zhang Yuying, Liao Binhui, Gouda Mostafa, Luo Xuelun, Song Xinbei, Guo Yihang, Qi Yingjie, Zeng Hui, Zhou Chuangchuang, Wang Yujie, Zhang Jingfei, Li Xiaoli
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Liandu Agriculture and Rural Bureau, Lishui 323000, China.
Foods. 2025 Apr 28;14(9):1551. doi: 10.3390/foods14091551.
The distribution of moisture content in stacked tea leaves during processing significantly influences tea quality. Visualizing this moisture distribution is crucial for optimizing processing parameters. In this study, we utilized hyperspectral imaging (HSI) technology combined with machine learning algorithms to evaluate the moisture content and its distribution in the stacked tea leaves in West Lake Longjing and Tencha green tea products during the processing flow line. A spectral quantitative determination model was developed, achieving high accuracy (Rp2 > 0.940) The model demonstrated strong generalization ability, allowing it to predict moisture content in both types of tea. Through hyperspectral imaging, we visualized moisture distribution in seven key processing steps and observed that moisture content was non-uniform, with the leaf tips and petioles having higher moisture levels than the leaf surface. This study offers a novel solution for real-time moisture monitoring of stacked tea leaves in tea production, ensuring consistent product quality. Future research could focus on refining model transfer techniques and exploring additional tea varieties to further enhance the generalization of the approach.
加工过程中堆叠茶叶的水分含量分布对茶叶品质有显著影响。可视化这种水分分布对于优化加工参数至关重要。在本研究中，我们利用高光谱成像（HSI）技术结合机器学习算法，对西湖龙井和抹茶绿茶产品加工流水线中堆叠茶叶的水分含量及其分布进行评估。开发了一种光谱定量测定模型，具有较高的准确性（Rp2 > 0.940）。该模型具有很强的泛化能力，能够预测两种茶叶的水分含量。通过高光谱成像，我们可视化了七个关键加工步骤中的水分分布，观察到水分含量不均匀，叶尖和叶柄的水分含量高于叶片表面。本研究为茶叶生产中堆叠茶叶的实时水分监测提供了一种新的解决方案，确保产品质量的一致性。未来的研究可以集中在改进模型转移技术和探索更多茶叶品种，以进一步提高该方法的泛化能力。
