1. Rice Research Institute, Fujian Academy of Agricultural Sciences/Key Laboratory of Germplasm Innovation and Molecular Breeding of Hybrid Rice for South China, Ministry of Agriculture/Fuzhou Branch, National Rice Improvement Center of China/Fujian Engineering Laboratory of Crop Molecular Breeding/ Fujian Key Laboratory of Rice Molecular Breeding, Fuzhou 350003，China 2. Incubator of National Key Laboratory of Fujian Crop Germplasm Innovation and Molecular Breeding between Fujian and Ministry of Sciences and Technology，Fuzhou 350003，China 3. South-China Base of National Key Laboratory of Hybrid Rice of China, Fuzhou 350003，China 4. National Engineering Laboratory of Rice, Fuzhou 350003，China
Abstract：A new method based on near-infrared reflectance spectroscopy (NIRS) 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 (R2) 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 (R2) 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 (R2) 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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