基于激光近红外的稻米油掺伪定性-定量分析

该文主要研究激光近红外光谱分析技术结合化学计量学方法对稻米油掺伪进行定性-定量分析。分别将大豆油、玉米油、菜籽油、餐饮废弃油掺入稻米油中，按照不同质量比配置189个掺伪油样，利用激光近红外光谱仪采集光谱;对采集的稻米油掺伪图谱数据进行多元散射校正(MSC)、正交信号校正 (OSC)、标准正态变量变换和去趋势技术联用算法(SNV_DT)三种不同预处理并与原始数据进行比较。采用连续投影算法(SPA)对经过预处理的光谱数据进行特征波长提取，应用支持向量机分类(SVC)方法建立稻米油掺伪样品的定性分类校正模型，选择网格搜索算法对模型参数组合(C，g)进行寻优，确定最优参数组合。另采用后向间隔偏最小二乘法(BiPLS)和SPA对预处理后的光谱数据进行特征波长提取，分别应用偏最小二乘法(PLS)和支持向量机回归(SVR)建立掺伪油含量的定量校正模型，并选用网格搜索算法对SVR模型参数组合(C，g)进行寻优，建立最优参数模型。研究表明，建立的SVC模型预测集和校正集的准确率分别达到了95%和100%;对比SVR和PLS方法建立的数学模型对稻米油中掺杂油脂的含量的预测，两种方法均能够实现含量预测，SVR模型的预测能力更好，相关系数R高于0.99，均方根误差(MSE)低于5.55×10-4，预测精度高。结果表明，采用激光近红外光谱分析技术可以实现稻米油掺伪的定性-定量分析，同时为其他油脂的掺伪分析提供了方法。

Qualitative-Quantitative Analysis of Rice Bran Oil Adulteration Based on Laser Near Inf rared Spectroscopy

The purpose of this study is mainly to have qualitative-quantitative analysis on the adulteration in rice bran oil by near-infrared spectroscopy analytical technology combined with chemo metrics methods. The author configured 189 adulterated oil samples according to the different mass ratios by selecting rice bran oil as base oil and choosing soybean oil, corn oil, colza oil, and waste oil of catering industry as adulterated oil. Then, the spectral data of samples was collected by using near-infrared spectrometer, and it was pre-processed through the following methods, including without processing, Multiplicative Scatter Correction(MSC), Orthogonal Signal Correction(OSC), Standard Normal Variate and Standard Normal Variate transformation De-Trending(SNV_DT). Furthermore, this article extracted characteristic wavelengths of the spectral datum from the pre-processed date by Successive Projections Algorithm(SPA), established qualitatively classified calibration methods of adulterated oil through classification method of Support Vector Machine(SVM), optimized model parameters(C, g) by Mesh Search Algorithm and determined the optimal process condition. In extracting characteristic wavelengths of the spectral datum from pretreatment by Backward interval Partial Least Squares(BiPLS) and SPA, quantitatively classified calibration models of adulterated oil through Partial Least Squares(PLS) and Support Vector Machine Regression(SVR) was established respectively. In the end, the author optimized the combination of model parameters(C, g) by Mesh Search Algorithm and determined the optimal parameter model. According to the analysis, the accuracy of prediction set and calibration set for SVC model reached 95% and 100% respectively. Compared with the prediction of the adulteration oil content of rice bran oil which was established by the PLS model, the SVR model is the better one, although both of them could implement the content prediction. Furthermore, the correlation coefficient R is above 0.99 and the Root Mean Square Error (MSE) is below 5.55×10-4. The results show that the near-infrared spectroscopy technology is effective in qualitative-quantitative analysis on the adulteration of rice bran oil. And the method is applicable to analyze adulteration in other oils.