Xie Li-Hong, Tang Shao-Qing, Wei Xing-Jin, Sheng Zhong-Hua, Shao Gao-Neng, Jiao Gui-Ai, Hu Shi-Kai, Hu Pei-Song
State Key Laboratory of Rice Biology/Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou 310006, PR China.
State Key Laboratory of Rice Biology/Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou 310006, PR China.
Food Chem. 2022 Sep 15;388:132944. doi: 10.1016/j.foodchem.2022.132944. Epub 2022 Apr 11.
Rice starch properties of apparent amylose content (AAC), amylose content (AC), and amylopectin content (AP) are considered as the most important factors influencing grain quality as they are highly correlated with eating quality. This report is the first effort of predicting AC and AP values in rice flours, and recognizing waxy rice from non-waxy rice using NIRS technique. Calibration models generated by different mathematical, preprocessing treatments and combinations of wavelengths and signals were compared and optimized. The model established by modified partial least squares (MPLS) with "2, 8, 8, 2"/ Inverse MSC and ∼138 wavelengths signals yielded high RSQ of 0.977, 0.928, and 0.912 for AAC, AC and AP, respectively, as simultaneous measurement. MPLS-DA (discriminant analysis) could classify waxy and non-waxy rice with 100% accuracy. This high-throughput technology is valuable for breeding programs, and for the purposes of quality control in the food industry.
水稻淀粉的表观直链淀粉含量(AAC)、直链淀粉含量(AC)和支链淀粉含量(AP)特性被认为是影响稻米品质的最重要因素,因为它们与食用品质高度相关。本报告首次尝试利用近红外光谱(NIRS)技术预测米粉中的AC和AP值,并区分糯稻和非糯稻。对不同数学方法、预处理、波长和信号组合生成的校准模型进行了比较和优化。采用“2, 8, 8, 2”/逆多元散射校正(Inverse MSC)和~138个波长信号的改进偏最小二乘法(MPLS)建立的模型,在同时测量时,AAC、AC和AP的决定系数(RSQ)分别高达0.977、0.928和0.912。MPLS判别分析(MPLS-DA)能够100%准确地区分糯稻和非糯稻。这种高通量技术对育种计划以及食品工业的质量控制具有重要价值。