China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou 310006, PR China.
Food Chem. 2014 Jan 1;142:92-100. doi: 10.1016/j.foodchem.2013.07.030. Epub 2013 Jul 14.
Near-infrared reflectance spectroscopy (NIRS) has been used to predict the cooking quality parameters of rice, such as the protein (PC) and amylose content (AC). Using brown and milled flours from 519 rice samples representing a wide range of grain qualities, this study was to compare the calibration models generated by different mathematical, preprocessing treatments, and combinations of different regression algorithm. A modified partial least squares model (MPLS) with the mathematic treatment "2, 8, 8, 2" (2nd order derivative computed based on 8 data points, and 8 and 2 data points in the 1st and 2nd smoothing, respectively) and inverse multiplicative scattering correction preprocessing treatment was identified as the best model for simultaneously measurement of PC and AC in brown flours. MPLS/"2, 8, 8, 2"/detrend preprocessing was identified as the best model for milled flours. The results indicated that NIRS could be useful in estimation of PC and AC of breeding lines in early generations of the breeding programs, and for the purposes of quality control in the food industry.
近红外反射光谱（NIRS）已被用于预测大米的烹饪品质参数，如蛋白质（PC）和直链淀粉含量（AC）。本研究使用来自 519 个代表广泛谷物品质的大米样本的糙米和精米面粉，比较了不同数学、预处理处理以及不同回归算法组合生成的校准模型。具有数学处理“2,8,8,2”（基于 8 个数据点计算二阶导数，以及第一和第二平滑中分别有 8 和 2 个数据点）和逆乘性散射校正预处理的改进偏最小二乘模型（MPLS）被确定为同时测量糙米中 PC 和 AC 的最佳模型。MPLS/"2,8,8,2"/去趋势预处理被确定为精米面粉的最佳模型。结果表明，NIRS 可用于在育种计划的早期世代中估算育种系的 PC 和 AC，以及用于食品工业中的质量控制。
