近红外光谱技术快速鉴别地沟油与食用植物油的研究

地沟油检测是我国食品安全最为关注的话题之一，它给人们的生活健康带来了极大的危害。国内现有的检测手段也仅停留在定性检测水平上，只能确定地沟油的有无，还难以进行定量检测。本实验利用近红外光谱技术与光纤传感技术相结合的新方法对勾兑混合油中地沟油的含量进行了定量分析。将煎炸老油与九三大豆油按照一定的体积比进行勾兑，共计50个样本，采集其近红外透射光谱，分别采用偏最小二乘法(PLS)和BP人工神经网络建立了煎炸老油含量的定量分析模型，校正集决定系数分别为0.908和0.934，验证集决定系数分别为0.961和0.952，均方估计残差(RMSEC)为0.184和0.136，预测均方根误差(RMSEP)都为0.111 6，符合应用要求，同时还结合主成分分析法(PCA)对煎炸老油与食用植物油进行了鉴别，识别准确率为100%。实验研究证明近红外光谱技术不仅可以准确快速的定性分析地沟油, 还能定量的检测地沟油的含量，在油脂的检测方面具有很大的应用前景。

Rapid Discriminating Hogwash Oil and Edible Vegetable Oil Using Near Infrared Optical Fiber Spectrometer Technique

In the present study, a new method using near infrared spectroscopy combined with optical fiber sensing technology was applied to the analysis of hogwash oil in blended oil. The 50 samples were a blend of frying oil and “nine three” soybean oil according to a certain volume ratio. The near infrared transmission spectroscopies were collected and the quantitative analysis model of frying oil was established by partial least squares (PLS) and BP artificial neural network. The coefficients of determination of calibration sets were 0.908 and 0.934 respectively. The coefficients of determination of validation sets were 0.961 and 0.952, the root mean square error of calibrations (RMSEC) was 0.184 and 0.136, and the root mean square error of predictions (RMSEP) was all 0.111 6. They conform to the model application requirement. At the same time, frying oil and qualified edible oil were identified with the principal component analysis (PCA), and the accurate rate was 100%. The experiment proved that near infrared spectral technology not only can quickly and accurately identify hogwash oil, but also can quantitatively detect hogwash oil. This method has a wide application prospect in the detection of oil.