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中文摘要：
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4200-4800cm0和1400-1704cm^-1为建模区问，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14001704cm^-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。
英文摘要：
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.
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