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Study on the Influence of Outlier Elimination in the Quantitative Near-Infrared Models of Paprika Quality

文献类型： 外文期刊

作者： Wang, Dong 1 ; Ma, Zhihong 2 ; He, Hongju 3 ; Pan, Ligang 2 ; Min, Shungeng 5 ; Wang, Jihua 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Beijing Res Ctr Agrifood Testing & Farmland Monit, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Vegetables, Beijing 100097, Peoples R China

4.China Agr Univ, Coll Food Sci & Nutr Engn, Beijing 100083, Peoples R China

5.China Agr Univ, Coll Sci, Beijing 100193, Peoples R China

关键词： Outlier;Near Infrared Spectroscopy;Quantitative Analysis Model;Capsaicin;Dihydrocapsaicin;Paprika

期刊名称：SENSOR LETTERS （ 影响因子：0.558； 五年影响因子：0.58 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Outlier elimination plays an important role in the procedures of NIR calibration model development. In this thesis, the calibration models of capsaicin, dihydrocapsaicin and total capsaicin were developed by partial least square combined with full cross validation algorithm before and after the outliers being eliminated. The result indicated that after the outliers being eliminated, the increasings of determination coefficients (R~2) of the models of capsaicin, dihydrocapsaicin and total capsaicin are 6.9%, 4.5% and 5.5% respectively; the decreasings of root mean square error of calibration (RMSEC) of the three models are 26.8%, 12.9% and 23.4% respectively; the decreasings of root mean square error of cross validation (RMSECV) of the three models are 25.8%, 15.1% and 23.3% respectively. The result indicated that the precision of the models has been increased remarkably after the outliers being eliminated. This research, to some extent, will provide some references to the development of robust NIR quantitative calibration models of agricultural products including paprika.

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