Nitrogen-phosphorus responses and Vis/NIR prediction in fresh tea leaves

文献类型： 外文期刊

第一作者： Luo, Qing

作者： Luo, Qing;Tang, Ting;Duan, Yuxin;Li, Junlin;Wu, Weibin;Ling, Caijin;Gao, Ting

作者机构：

关键词： Fresh tea leaves; Vis/NIR; VMDSG algorithm; Quantitative regression

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 476 卷

页码：

收录情况： SCI

摘要： Nitrogen and phosphorus are essential nutrients for the growth and development of tea plants.However, the nitrogen content (NC) and phosphorus content (PC) in different parts of fresh tea has not been paid attention. In this study, the NC and PC responses different nitrogen stress were analyzed, and a quantitative regression model for predicting NC and PC was established by using Vis/NIR spectroscopy and a variety of intelligent algorithms. Among them, NC and PC of different parts had significant difference. The selection of preprocessing algorithms has a significant impact on the predictive performance of the model. The VMDSG-D1-VCPA-IRIV-SVR prediction model for NC and the VMDSG-CARS-Stacking prediction model for PC have better prediction effects, and the correlation coefficients of the test set are more than 0.85, and the RPD is greater than 1.8. In conclusion, this study is helpful to guide the precise fertilization and in-situ detection of fresh tea leaves.

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