基于特征脂肪酸的掺伪芝麻油快速鉴别模型建立

Establishment of a Model for Rapid Identification of Adulterated Sesame Oil Based on Characteristic Fatty Acids

脂肪酸是植物油中的主要营养成分,不同种类的食用油中所含的脂肪酸含量也不相同,本研究据此对芝麻油掺入大豆油、花生油、棉籽油的油样应用近红外光谱技术建立测定4种脂肪酸含量的方法。以气相色谱法测定的脂肪酸含量作为化学值,校正集样品数为122,验证集样品数为38,结果表明:掺假芝麻油油样的亚麻酸(C18∶3)、花生酸(C20∶0)、木焦油酸(C24∶0)和肉豆蔻酸(C14∶0)对近红外有特异吸收。分别建立4种脂肪酸含量的模型,通过对模型进行优化,校正集样品的化学值与近红外的预测值的相关系数(R2)分别为R2(C18∶3)=0.989、R2(C20∶0)=0.995、R2(C24∶0)=0.993、R2(C14∶0)=0.996。验证集样品的化学值与近红外的预测值的R2分别为0.984、0.949、0.956、0.988。4种脂肪酸含量的预测平均相对误差依次为6.0%、5.6%、4.4%、4.8%。

Fatty acids are the major nutrients in vegetable oils in different amounts depending on the type of oil. A predictive method based on near infrared spectroscopy(NIR) was proposed to determine the contents of four fatty acids in adulterated sesame oil samples. The chemical values were analyzed by GC. The calibration set consisted of 122 samples and the number of samples in the validation set was 38. The results showed that linolenic acid(C18:3), arachidonic acid(C20:0), tetracosanoic acid(C24:0) and myristic acid(C14:0) were the characteristic NIR absorption peaks of the adulterated oils. The optimum conditions for mathematical modeling of the four components were studied with respect to sample set selection, chemical value analysis, detection methods and detection conditions. The correlation coefficients(R2) between the NIR-predicted data and the chemically measured data for the samples of the calibration set were R2(C14:0) = 0.996, R2(C18:3) = 0.989, R2(C20:0) = 0.995, and R2(C24:0) = 0.993, respectively, and 0.984, 0.949, 0.956, and 0.988 for the validation set samples, respectively. For the validation set, 9 unknown samples were selected to be analyzed by NIR. Our results demonstrated that the error between the predicted values and the chemical values was less than 6.0%.