Determination of Protein and Fat in Liquid Milk by NIR Combined with CARS Variables Screening Method
Determination of Protein and Fat in Liquid Milk by NIR Combined with CARS Variables Screening Method
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发表时间：
2010
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通讯作者：
Tang Yu-lian
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作者：
Tang Yu-lian
近红外（NIR）光谱法以其快速、对样品无损等优点，已成为一种广泛应用于工业领域的分析技术。为了提高液态奶中蛋白质和脂肪近红外模型的预测精度，本文采用竞争自适应重加权采样方法。采用蒙特卡罗采样法检测离群点，然后采用定心法和卡尔诺里斯导数法对光谱进行预处理。采用竞争自适应重加权抽样方法，选取与样本性质密切相关的关键变量。在最佳条件下建立了预测蛋白质和脂肪含量的偏最小二乘（PLS）校准模型，并与未使用CARS的结果进行了比较。结果表明，与全谱PLS模型、蒙特卡罗无信息变量消除（MC-UVE）和移动窗口偏最小二乘回归（MWPLSR）相比，CARS模型的预测效果更好。采用相关系数（r2）、交叉验证均方根误差（RMSECV）和预测均方根误差（RMSEP）评价模型的质量。通过r2、RMSECV和RMSEP值测量，最佳模型的预测结果令人满意：蛋白质分别为0.975 0、0.194 8和0.113 3，脂肪分别为0.995 1、0.136 3和0.140 1。结果表明，采用CARS等变量选择方法可以有效地简化模型，解决实际问题。该方法适用于液体奶中蛋白质和脂肪的快速、可靠测定。
Near-infrared(NIR) spectroscopy has become an extensively used analytical technique in many industrial applications because of its rapidness and the fact that it is non-destructive to the samples.In this paper,competitive adaptive reweighted sampling method was employed to improve the prediction accuracy of the NIR model of protein and fat in liquid milk.Monte-Carlo sampling method was employed to detect the outlier before preprocessing the spectroscopy by means of centering and Karl Norris derivative method.The key variables which closely related to the nature of the samples were selected by competitive adaptive reweighted sampling method.The partial least squares(PLS) calibration models were established in the optimal conditions to predict the content of protein and fat,and compared with the results not using CARS.The results showed that better prediction was obtained by CARS when compared to full spectrum PLS modeling,Monte-Carlo uninformative variable elimination(MC-UVE) and moving window partial least squares regression(MWPLSR).Correlation coefficient(r2),root-mean-square error of cross-validation(RMSECV) and root-mean-square error of prediction(RMSEP) were used to evaluate the quality of the model.The best models showed satisfactory predictions as measured by r2,RMSECV and RMSEP values: protein,0.975 0,0.194 8 and 0.113 3;fat,0.995 1,0.136 3 and 0.140 1,respectively.The result showed that using variable selection method such as CARS could effectively simplify the model and solve practical problems.The proposed method was suitable for the fast and reliable determination of protein and fat in liquid milk.