【摘要】 采用偏最小二乘法建立芝麻油中菜籽油含量的近红外光谱定量检测模型。配制不同比例的菜籽油和芝麻油混合样品,采集样品在4200～10000cm-1范围内的近红外漫反射光谱,模型采用交互验证和外部检验来考察所建立模型的可靠性。建模参数为:最佳波长范围为4500～8745cm-1;最佳光谱预处理方法为:多元散射校正(MSC)/一阶微分/Norris Derivative(3,5)滤波。所建立定标模型的校正相关系数为0.99839;均方估计残差为0.976。应用建立的模型对未知样品进行预测,并对预测值和真实值进行比较,在含量为10%～70%之间范围准确可靠,研究结果表明,采用近红外光谱技术可以实现芝麻油中菜籽油的快速检测。更多还原

【Abstract】 Near-infrared spectroscopy (NIRS) quantitative detection model of sesame oil adulterated with rapeseed oil was established by partial least squares (PLS). Mixed samples of sesame and rapeseed oil with different proportions were scanned using a Thermo-Nicolet Protege-460 FT-IR spectrometer and their near infrared diffuse reflectance spectra were collected in 4200-10000 cm-1 region. The reliability of the model established was verified by cross-validation and external test. The results showed that the optimum wavelength range was 4500-8745 cm-1 and the optimum spectra pre-treatment way was multiplicative signal correction (MSC)/first derivative/Norris derivative filter (3, 5). The predictive correlation coefficient of the PLS model was 0.9983, and the root mean square error (RMSEC) was 0.976. Meanwhile, the model was applied to predict the unknown samples, and it was found that in the addition range of 10%-70% of rapeseed oil, the predicted values were accurate and reliable. Therefore, this PLS model based on NIRS can be used for rapid detection of rapeseed oil adulterated in sesame oil.更多还原