二维相关光谱结合偏最小二乘法测定牛奶中的掺杂尿素

为了检验牛奶中是否掺杂尿素并将其量化测定,配置含有尿素质量浓度范围为1～20g/L之间40个牛奶样品,以掺杂物尿素浓度为外扰,分别研究了掺杂尿素牛奶的二维相关(近红外-近红外,中红外-中红外,近红外-中红外)光谱特性,在此基础上,分别选择随浓度变化大的4200～4800cm-1和1400～1704cm-1为建模区间,采用偏最小二乘方法建立定量分析模型。研究结果表明:4200～4800cm-1建模分析效果优于1400～1704cm-1建模结果,其交叉验证均方根误差为0.266g/L,对未知样品集预测相关系数达到0.999,预测均方根误差为0.219g/L,这表明所建模型具有较好的预测效果。该方法无需样品处理,成本低,为快速判别牛奶是否掺杂提供了一种新的可能的方法。

Detection of urea in milk using two-dimensional correlation spectroscopy and partial least square method

For the detection and quantification of urea in milk, pure milk samples and 40 adulterated milk samples added different contents of urea were prepared. Then 2D correlation (NIR-NIR, IR-IR, NIR-IR) spectroscopy under the perturbation of adulteration concentration was calculated and the spectra in the range of 4 200-4 800 cm-1 and 1 400-1 704 cm-1 were selected to construct the partial least square (PLS) calibration model, respectively. The PLS calibration model showed 4 200-4 800 cm-1 was the better range for calibration performance and the root mean square errors of cross validation (RMSECV) of the model was 0.266 g/L. When using this model for predicting the urea contents in prediction set, the root mean square errors of prediction (RMSEP) was 0.219 g/L and the coefficient correlation of actual values and predicted values was 0.999, which means the model has good prediction ability. The method can be used for a correct discrimination on whether the milk is adulterated and provides a new and cost-effective alternative to test the adulteration of milk.