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Apparent Amylose Content of Rice by Near Infrared Reflectance Analysis of Ground Milled Samples

SHU Qing-yao1, WU Dian-xing1, XlA Ying-wu1, GAo Ming-wei1, Anna McCLUNG 2(1Institute of Nuclear Agricultural Sciences, Zhejiang University, Hangzhou 310029, China;2 Agricultural Research Strvice,Department ofAgriculture, the United States of America, RT7,  
Using about 3 g ground milled rice testing samples, the effectiveness of apparent amylose content (AAC) analyzed by near infrared reflectance spectroscopy (NIRS) as well as the influencing factors for calibration were studied, whichwas indicated by the standard errors [[SEC for calibration, SECV for cross validation, and SEP(C) for validation ] and determination coefficients (R2 for calibration and RSQ for validation). Three different regression techniques, e. g. modified PartialLeast Square (MPLS), Partial Least Square (PLS), and Principal Component Regression (PCR), were compared for their effectiveness in calibration.The SEC, SECV and SEP(C) were o. 83, l. 75 and 1. o9 for MPLS; l. 73, l. 98 and l. 74 for PLS;and 2.29, 2. 56 and 1. 72 for PCR; While R2 and RSQ were o. 983 and o. 9l for MPLSI ;o. 927 and o. 84 for PLSl ;and o. 87 ando. 84 for PCR. These results indicated that MPLS is the best statistic method for calibration of AAC by NIRS. Two differentsample populations were compared for their efficiency of AAC calibration. one was selected by a computer program SELECTbased on NIRS characteristics, another was established by proportionally selection of samp1es with various AAC which wasdetermined by chemical analysis, these two populations were designated with P I and P Ⅱ, respectively. The SEC, SECV andSEP(C) for P I were smaller than that for PⅡ, while the R2 and RSQ were similar, which indicated that sample selection byspectroscopic character was more suitable for NIRS calibration.
【CateGory Index】: TS201.2
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