Analysis of Models for Main Potato Nutrients Content Based on Near Infrared Spectroscopy

Abstract

Abstract:

Analysis of Models for Main Potato Nutrients Content Based on Near Infrared Spectroscopy ZHANG Xiao-yan, YANG Bing-nan*, LIU Wei, ZHAO Feng-min, YANG Yan-chen, XING Li (Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China) Abstract: With 44 different potato cultivars as the material, Principal component analysis (PCA) was used to select four indicators (water, reducing sugar, starch and protein) which can cover most information of potato samples. Predicted mathematic models for analysis were established and evaluated with partial least square (PLS) method. The accuracy of models was estimated by the determination coefficients (R2cal), relative predictive determination (RPD) and the root mean square errors of calibration (RMSEE), the determination coefficients (R2cv) and the root mean square errors of cross validation (RMSECV). For the calibration model of water, R2cal =98.37%, RMSEE=0.445, RPD=7.84; for the cross validation model of water, R2cv=93.05%, RMSECV=0.84, RPD=3.79. For the calibration model of reducing sugar, R2cal = 98.43%, RMSEE= 0.0236, RPD= 7.99; for the cross validation model of reducing sugar, R2cv= 86.42%, RMSECV= 0.0598, RPD= 2.71. For the calibration model of starch, R2cal = 97.13%, RMSEE= 0.577, RPD= 5.9; for the cross validation model of starch, R2cv= 95.370%, RMSECV= 0.7, RPD= 4.65. For the calibration model of protein, R2cal = 98.41%, RMSEE= 0.0334, RPD= 7.92; for the cross validation model of protein, R2cv= 89.49%, RMSECV= 0.0767, RPD= 3.08.

CLC Number:

O433.4

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