近红外光谱技术在食用油种类鉴别及 脂肪酸含量检测中的应用

应用近红外光谱分析技术分别建立了快速鉴别食用植物油种类的定性分析模型以及测定食用植物油主要脂肪酸含量的定量分析模型.实验根据19份食用植物油样品的近红外光谱,结合系统聚类方法建立了纯橄榄-芝麻-花生油定性识别模型,识别率和预测率可达100%.根据59个食用植物油样品的近红外光谱,结合模型优化方法建立了食用植物油中棕榈酸、硬脂酸、油酸3种主要脂肪酸含量的近红外定量分析模型,且模型指标较好.实验表明近红外光谱分析技术在食用植物油品质快速检测领域有很好的应用前景.

APPLICATION OF NIR TECHNOLOGY ON IDENTIFYING TYPES AND DETERMINING MAIN FATTY ACID CONTENT OF EDIBLE VEGETABLE OIL

Near infrared spectroscopy analysis technology was applied on edible vegetable oil quality detection in this paper.The qualitative model of identifying pure olive–sesame-peanut oil was established based on 19 edible vegetable oil samples of near infrared spectra,combining with the system clustering method.The recognition and prediction rate reach 100%.The quantitative analysis model of determining palmitic,stearic,and oleic acid content of edible vegetable oil was established according to 59 edible vegetable oil samples of near infrared spectra,combining optimization method.Correlation coefficients(R) of the three fatty acid are 0.987,0.992,and 9.986,respectively,cross validation calibration standard deviation(RMSECV) are 0.409,0.127,and 1.71,and root mean square error of prediction(RMSEP) are 0.596,0.128,1.71.The results indicated that near infrared spectral analysis technology may have good application prospects in edible vegetable oil quality testing field.