摘要
潲水油回流餐桌等食品安全问题越来越受到社会关注,探寻准确、快速、高效的潲水油鉴别新方法成为食用油安全性检测的新要求。用傅里叶变换中红外光谱技术(Fourier transform mid-infrared spectroscopy, FT-MIR)对精炼潲水油(refining hogwash oils, RHOs)和4种不同正常食用植物油(菜籽油、大豆油、花生油和玉米油)进行快速检测,结合偏最小二乘判别法(PLS.DA)建立了RHOs和4种不同正常食用植物油的判别模型。结果表明,在全光谱范围(4000-450cm^-1)内,经二阶求导(Savitzky—Golay,5点)后,RHOs和4种不同正常食用植物油FT—MIR有显著差异。PLS-DA模型对22个未知样品预测发现,判别模型的整体正确判别率均为100%。此结果表明FT-MIR结合化学计量学方法可以作为RHOs和4种不同正常食用植物油(菜籽油、大豆油、花生油和玉米油)区分的一种有效技术手段。
In this study, Fourier transform-infrared spectroscopy (FT-MIR) was applied to rapidly distinguish refined hogwash oils (RHOs) from four different edible vegetable oils, rapeseed oil, soybean oil, peanut oil and corn oil. A multivariate statistical procedure based on cluster analysis (CA) coupled to partial least squares-discriminant analysis (PSL- DA), was elaborated, providing an effective classification method. It was shown that there were significant differences between RHOs and different edible vegetable oils based on FT-MIR spectra after second derivative (Savitzky-Golay, 5 point) transformation in the whole wavelength range (4 000-450 cm-1). The PLS-DA procedure was then applied to classify twenty-two unknown oil samples with a correction rate of 100%. These results demonstrate that FF-MIR combined with chemometric analysis can be used as an effective method to discriminate RHOs from these four different edible vegetable oils.
出处
《食品科学》
EI
CAS
CSCD
北大核心
2014年第6期121-124,共4页
Food Science
基金
重庆市自然科学基金项目(cstc2012jjA00022)