Nondestructive detection of reducing sugar of potato flours by near infrared spectroscopy and kernel partial least square algorithm
The feasibility of near infrared (NIR) spectroscopy combination with kernel partial least square (PLS) regression algorithms for quantitative determination of reducing sugar content in potato flours was investigated. The PLS, kernel-PLS, wide-kernel-PLS and least square support vector machine (LS-SVM) algorithms were performed comparatively to develop multivariate calibration models with the pretreatment spectral variables. Through comparing the performance of multivariate calibration models with new samples, the optimal models of reducing sugar content was obtained using wide-kernel-PLS algorithm with correlation coefficient (r) of 0.950 and root mean square error of prediction of 2.44 mg/g. Moreover, the predictive values for new potato flour samples obtained with wide-kernel-PLS model did not show significant difference with the reference values. These results suggested NIR spectroscopy coupled with wide-kernel-PLS algorithm was suitable for quantitative analysis of complicated chemical component of reducing sugar in potato flour.
KeywordsNear infrared spectroscopy Partial least square Kernel function Reducing sugar Potato
The authors gratefully acknowledge the financial support provided by Jiangxi Outstanding Youth Talent Program (20171BCB23060), Jiangxi Provincial Education Department Project (GJJ160478), China Scholarship (201808360317), Jiangxi Association for Science and Technology (JAST) and Doctor Start-up Program (368).
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