College of Chemical Engineering, Sichuan University, Chengdu 610065, PR China.
College of Chemical Engineering, Sichuan University, Chengdu 610065, PR China.
Food Chem. 2015 Oct 15;185:326-32. doi: 10.1016/j.foodchem.2015.04.005. Epub 2015 Apr 9.
UV-Vis spectroscopy coupled with chemometrics was used effectively to study the impact of heating on edible oils (corn oil, sunflower oil, rapeseed oil, peanut oil, soybean oil and sesame oil) and determine their acid value. Analysis of their first derivative spectra showed that the peak at 370 nm was a common indicator of the heated oils. Partial least squares regression (PLS) and principle component regression (PCR) were applied to building individual quantitative models of acid value for each kind of oil, respectively. The PLS models had a better performance than PCR models, with determination coefficients (R(2)) of 0.9904-0.9977 and root mean square errors (RMSE) of 0.0230-0.0794 for the prediction sets of each kind of oil, respectively. An integrate quantitative model built by support vector regression for all the six kinds of oils was also developed and gave a satisfactory prediction with a R(2) of 0.9932 and a RMSE of 0.0656.
利用紫外可见光谱结合化学计量学方法，有效地研究了加热对食用油（玉米油、葵花籽油、菜籽油、花生油、大豆油和芝麻油）的影响，并测定了它们的酸值。分析其一阶导数光谱表明，370nm 处的峰是加热油的共同指标。分别采用偏最小二乘回归（PLS）和主成分回归（PCR）建立了每种油的酸值的单独定量模型。PLS 模型的性能优于 PCR 模型，对于每种油的预测集，其决定系数（R²）分别为 0.9904-0.9977，均方根误差（RMSE）分别为 0.0230-0.0794。还建立了一个支持向量回归的综合定量模型，用于所有六种油，预测结果令人满意，R²为 0.9932，RMSE 为 0.0656。
