Research on Resistant Starch Content of Rice Grain Based on NIR Spectroscopy Model

文献类型： 外文期刊

第一作者： Luo Xi

作者： Luo Xi;Wu Fang-xi;Xie Hong-guang;Zhu Yong-sheng;Zhang Jian-fu;Xie Hua-an;Luo Xi;Wu Fang-xi;Xie Hong-guang;Zhu Yong-sheng;Zhang Jian-fu;Xie Hua-an;Luo Xi;Wu Fang-xi;Xie Hong-guang;Zhu Yong-sheng;Zhang Jian-fu;Xie Hua-an;Luo Xi;Wu Fang-xi;Xie Hong-guang;Zhu Yong-sheng;Zhang Jian-fu;Xie Hua-an

作者机构：

关键词： Rice (Oryzae Sativa L.);Resistant starch;Near-Infrared reflectance spectroscopy;Calibration model;Content detection

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2016 年 36 卷 3 期

页码：

收录情况： SCI

摘要： A new method based on near-infrared reflectance spectroscopy (MRS) analysis was explored to determine the content of rice-resistant starch instead of common chemical method which took long time was high-cost. First of all, we collected 62 spectral data which have big differences in terms of resistant starch content of rice, and then the spectral data and detected chemical values are imported chemometrics software. After that a near-infrared spectroscopy calibration model for rice-resistant starch content was constructed with partial least squares (PLS) method. Results are as follows: In respect of internal cross validation, the coefficient of determination (R-2) of untreated, pretreatment with MSC+1thD, pretreatment with 1thD+SNV were 0. 920 2, 0. 967 0 and 0. 976 7 respectively. Root mean square error of prediction(RMSEP)were 1. 533 7,1. 011 2 and 0. 837 1 respectively. In respect of external validation, the coefficient of determination (R-2) of untreated, pretreatment with MSC+1thD, pretreatment with 1thD+SNV were 0. 805, 0. 976 and 0. 992 respectively. The average absolute error was 1.456, 0. 818, 0.515 respectively. There was no significant difference between chemical and predicted values (Turkey multiple comparison), so we think near infrared spectrum analysis is more feasible than chemical measurement. Among the different pretreatment, the first derivation and standard normal variate (1thD+SNV) have higher coefficient of determination (R-2) and lower error value whether in internal validation and external validation. In other words, the calibration model has higher precision and less error by pretreatment with 1thD+SNV.

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