文献类型: 外文期刊
作者: Luo, Qing 1 ; Tang, Ting 1 ; Duan, Yuxin 1 ; Li, Junlin 1 ; Ling, Caijin 2 ; Gao, Ting 3 ; Wu, Weibin 1 ;
作者机构: 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China
2.Guangdong Acad Agr Sci, Tea Res Inst, Guangzhou 510640, Peoples R China
3.South China Agr Univ, Coll Nat Resources & Environm, Guangzhou 510642, Peoples R China
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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