乳粉中蛋白质和脂肪的近红外定量模型优化研究
发布时间：2019-06-19 13:47
【摘要】：对乳粉中蛋白质和脂肪近红外定量模型的优化进行了研究。结果表明:利用全波段光谱建立的模型,蛋白质和脂肪的模型预测评价参数分别为:校正集预测偏差(RMSECV)为2.837、2.984,内部交互验证标准偏差(RMSEP)为3.406、3.751,交互检验相对偏差(RPD)为2.6、2.5。经过波长优选后,蛋白质和脂肪优选的波数范围分别为(9403.5~7498.1)cm-1、(9403.5~6098)cm-1,蛋白质和脂肪的模型预测评价参数有一定的提高,分别为RMSECV 1.963、2.317,RMSEP 2.396、3.035,RPD 2.9、2.6。通过正交试验对定标方法和光谱预处理方法进行了优化:蛋白质的最佳参数组合为:定标方法为改进偏最小二乘法,多元离散校正,一阶导数处理,导数处理间隔点为8,平滑处理间隔点数为4,二次平滑处理间隔点数为1,目标函数达到98.25%;脂肪的最佳参数组合为:定标方法为偏最小二乘法,散射校正为多元离散校正,导数处理为二阶,导数处理间隔点为1,平滑处理间隔点数为4,二次平滑处理间隔点数为4,目标函数达到95.26%。
[Abstract]:The optimization of the quantitative model of protein and fat in milk powder was studied. The results showed that the model, protein and fat model of the model, protein and fat were determined as follows: the corrected set prediction deviation (RMSECV) is 2.837, 2.984, the internal interaction verification standard deviation (RMSEP) is 3.406, 3.751, and the relative deviation (RPD) of the interaction test is 2.6, 2.5. After the optimized wavelength, the wavenumber range of protein and fat is (9403.5-749.1) cm-1, (9403.5-6098) cm-1, and the model of protein and fat has a certain improvement, which are RMSECV 1.963, 2.317, RMSEP 2.396, 3.035 and RPD 2.9, 2.6, respectively. The calibration method and the spectrum pretreatment method are optimized by the orthogonal test: the optimal parameters of the protein are as follows: the calibration method is to improve the partial least square method, the multivariate discrete correction, the first derivative processing, the derivative processing interval point is 8, the number of the smoothing processing intervals is 4, the number of the secondary smoothing processing intervals is 1, and the target function reaches 98.25 percent; the optimal parameter combination of the fat is that the scaling method is a partial least square method, the scattering correction is a multivariate discrete correction, the derivative processing is a second order, the derivative processing interval point is 1, the number of the smoothing processing intervals is 4, The number of secondary smoothing processing intervals is 4, and the target function reaches 95.26%.
【作者单位】： 江苏食品药品职业技术学院;
【基金】：淮安市应用研究与科技攻关计划项目(HAS2014024-4)
【分类号】：O657.33;TS252.7
，
本文编号：2502387
[Abstract]:The optimization of the quantitative model of protein and fat in milk powder was studied. The results showed that the model, protein and fat model of the model, protein and fat were determined as follows: the corrected set prediction deviation (RMSECV) is 2.837, 2.984, the internal interaction verification standard deviation (RMSEP) is 3.406, 3.751, and the relative deviation (RPD) of the interaction test is 2.6, 2.5. After the optimized wavelength, the wavenumber range of protein and fat is (9403.5-749.1) cm-1, (9403.5-6098) cm-1, and the model of protein and fat has a certain improvement, which are RMSECV 1.963, 2.317, RMSEP 2.396, 3.035 and RPD 2.9, 2.6, respectively. The calibration method and the spectrum pretreatment method are optimized by the orthogonal test: the optimal parameters of the protein are as follows: the calibration method is to improve the partial least square method, the multivariate discrete correction, the first derivative processing, the derivative processing interval point is 8, the number of the smoothing processing intervals is 4, the number of the secondary smoothing processing intervals is 1, and the target function reaches 98.25 percent; the optimal parameter combination of the fat is that the scaling method is a partial least square method, the scattering correction is a multivariate discrete correction, the derivative processing is a second order, the derivative processing interval point is 1, the number of the smoothing processing intervals is 4, The number of secondary smoothing processing intervals is 4, and the target function reaches 95.26%.
【作者单位】： 江苏食品药品职业技术学院;
【基金】：淮安市应用研究与科技攻关计划项目(HAS2014024-4)
【分类号】：O657.33;TS252.7
，
本文编号：2502387
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