Study on the Influence of Outlier Elimination in the Quantitative Near-Infrared Models of Paprika Quality
Abstract: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.
Document Type: Research Article
Publication date: January 1, 2012
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