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### 基于不同颜色模型定量预测永川秀芽在制品含水率

1. （重庆市农业科学院茶叶研究所，重庆市茶叶工程技术研究中心，重庆 402160）
• 出版日期:2022-05-25 发布日期:2022-05-27
• 基金资助:
重庆市技术创新与应用发展专项重点项目（cstc2019jscx-gksbX0092）； 重庆市自然科学基金面上项目（cstc2019jcyj-msxmX0621；cstc2021jcyj-msxmX0997）； 重庆市农业科学院农业发展资金项目（NKY-2020AB005）；重庆市农业科学院绩效激励引导专项（cqaas2021jxjl14）

### Quantitative Prediction of the Moisture Content in Work-In-Process Yongchuan Xiuya Tea Based on Different Color Models

WANG Jie, ZHANG Ying, CHANG Rui, CHEN Shanmin, YUAN Linying, ZHONG Yingfu, WU Xiuhong, XU Ze

1. (Tea Research Institute, Chongqing Engineering Technology Research Center for Tea, Chongqing Academy of Agricultural Science, Chongqing 402160, China)
• Online:2022-05-25 Published:2022-05-27

Abstract: A quantitative prediction model for the moisture content of work-in-process Yongchuan Xiuya tea was established using partial least squares regression (PLSR) based on its color changes as evaluated using different color models. The results showed that during the initial production process, the red-green and mean blue channel value increased, while the moisture content and 15 other color model components such as lightness, yellow-blue, mean red channel value, mean green channel value and mean hue value decreased, suggesting that the color became darker and yellower. Through heatmap and cluster analysis, the samples were divided into two categories and four sub-categories, and the carding process had the most significant impact on the moisture and color of the products. Based on the 17 color model components, the predictive model was established, and its performance was evaluated by considering correlation coefficient of calibration set (Rc), root-mean-square error of cross-validation (RMSECV), correlation coefficient of predication set (Rp), root-mean-square error of prediction (RMSEP) and relative percent deviation (RPD). The values of Rc, Rp, RMSECV and RMSEP were 0.979, 0.980, 0.044 7, and 0.044 3, respectively. The difference between RMSECV and RMSEP was merely 0.000 4, and the RPD value was 5.04, indicating that the model had excellent prediction capacity and generalization capacity and could provide a new method for the online monitoring of the moisture content in work-in-process Yongchuan Xiuya tea.