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Outlier Detection for Measurement of Protein Content in Maize Kernels Based on Near-infrared Reflectance Spectroscopy
Pages: 38-45
Year: Issue:  1
Journal: Acta Laser Biology Sinica

Keyword:  protein content in maize kerneloutlier screening methodsthe least squares support vector machine (LS-SVM)niche ant colony algorithm (NACA);
Abstract: As the classical chemical analysis of protein content in maize kernel was slow and destructive,and the exist-
ence of the outliers in the near infrared (NIR)spectra would affect the accuracy and stability of the NIR models,we ap-plied outlier detection methods for measuring protein content in maize kernel based on near infrared spectroscopy.3 out-lier screening methods,leverage method,resampling by half-mean method (RHM),leverage method,and monte-carlo sampling method (MCS),were compared to detect outliers in the protein spectra and the least squares support vector machine (LS-SVM)models were built with using partial least squares regression (PLSR)method to extract the optimal component scores and using niche ant colony algorithm (NACA)to optimize the parameters (γand σ2 )of the LS-SVM model.The results showed that the performances of the LS-SVM models with those samples removed the outliers were better than the LS-SVM model with all samples.The prediction results of the validation set also showed that the MCS method was optimal for detecting outliers in the spectra of the protein of the whole maize kernel based on NIRS.

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