Improving the detection accuracy of the nitrogen content of fresh tea leaves by combining FT-NIR with moisture removal method
文献类型： 外文期刊
作者： Guo, Jiaming 1 ; Huang, Han 1 ; He, Xiaolong 1 ; Cai, Jinwei 1 ; Zeng, Zhixiong 1 ; Ma, Chengying 3 ; Lue, Enli 1 ; Shen, Qunyu 4 ; Liu, Yanhua 1 ;
作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China
2.Guangdong Lab Lingnan Modern Agr, Maoming Branch, Guangdong 525000, Peoples R China
3.Guangdong Acad Agr Sci, Tea Res Inst, Guangdong Prov Key Lab Tea Plant Resources Innovat, Guangzhou 510640, Peoples R China
4.Univ Illinois, Grainger Coll Engn, Urbana, IL 61801 USA
关键词： Yinghong NO; 9 black tea; NIR spectroscopy; External parameter orthogonalization; Variables selection
期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）
ISSN： 0308-8146
年卷期： 2023 年 405 卷
页码：
收录情况： SCI
摘要： The nitrogen content (NC) is one of the critical indicators of tea quality, and many studies have been conducted using NIR spectroscopy to determine tea constituents. However, this method has been found to have limited accuracy for component estimation because the spectra are affected by moisture in the samples. In this study, external parameter orthogonalization (EPO) was introduced to filter out the effect of moisture in fresh tea leaves on NIR spectra. Then, a feature selection algorithm was applied to determine the optimal NC wavelength to improve the prediction precision. Finally, a partial least squares (PLS) prediction model was established. The PLS model based on EPO and VCPA-IRIV achieved satisfactory prediction results, with an increase in Rp2 to 0.9371 from 0.5846 for the full spectral PLS model without treatment. Overall, this study found that eliminating the effect of moisture on spectra could improve detection accuracy of the model significantly.
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