以大豆油、 花生油和玉米油三组分食用调和油为研究对象， 采集样品在10 000～4 200 cm-1范围内的近红外透射光谱， 对光谱进行不同预处理后结合偏最小二乘法分别建立调和油中三组分的定量分析模型， 并检验模型预测的准确度和精密度。 结果显示， 一阶导数结合多元散射校正(FD+MSC)， 一阶导数结合减去一条直线（FD+SLS）以及一阶导数（FD）进行光谱预处理， 可以得到大豆油、 花生油以及玉米油含量的最优定标模型， 分别是在5 450.1～4 597.7 cm-1， 7 521.3～6 098.1 cm-1和9　993.7～7　498.2 cm-1谱区范围内获得的。 各预测模型的相关系数R2和预测均方根RMSEP分别为99.89%， 1.09%； 99.88%， 1.17%； 99.76%， 1.48%； 配对t检验值在0.371 9～0.007 9之间； 预测相对标准偏差RSD均小于1.50%。 表明傅里叶变换近红外透射光谱分析技术可以快速准确可靠地检测三组分食用调和油中大豆油、 花生油、 玉米油的含量。

The FT-NIR transmission spectra of ternary blended edible oil samples were collected over 10 000-4 200 cm-1. After being pretreated with different methods, the calibration models of quantitative analysis of soybean oil, peanut oil and corn oil contents in ternary blended edible oil were established using partial least square (PLS) regression. The accuracy and precision of the models for the predicted sample set were examined to make sure of the practicability of the models. After being pretreated with first derivative and multiplicative signal correction (FD+MSC), the optimal soybean oil NIR model was built over 5 450.1-4 597.7 cm-1. The best prediction model for peanut oil was established between 7 521.3 and 6 098.1 cm-1 after using first derivative with straight line subtraction (FD+SLS) preprocess method. The best pretreated method and the best spectrum range for corn oil content model were first derivative (FD) and 9 993.7-7 498.2 cm-1, respectively. The best correlation coefficients (R2) of the three prediction models were 99.89%, 99.88% and 99.76%, respectively. The RMSEP of the soybean oil content model was 1.09%, while the peanut oil prediction model’s RMSEP was 1.17%, and 1.48% for the corn oil prediction model. The values of the t-test were between 0.007 9 and 0.371 9, and all values of the relative standard deviation (RSD) were less than 1.50%. The results showed that NIR could be an ideal tool for fast determination of the soybean oil, peanut oil and corn oil contents in ternary blended edible oil.