基于PLS-模式识别近红外光谱技术快速检测鲜乳和掺假乳

Study on Rapid Determination of Raw Milk and Adulterated Milk Using Near Infrared Reflectance Spectroscopy Based on Partial Least Squares-Pattern Recognition Technique

本实验采用PLS-马氏距离法建立了鲜乳和掺假植物奶油牛乳的判别分析模型,用PLS法将原始数据压缩成3个主成分,在原始光谱的全波数段范围内,无需任何预处理方式,判别准确率达100%。同时对10个未知样品进行预测,预测准确率均为100%。其次建立了植物奶油掺假量的定量检测PLS模型,并采用交互校验和外部检验考察模型的可靠性,模型的校正相关系数为0.9963,均方估计残差(RMSEC)为0.110;交互校验均方残差(RMSECV)为0.142;应用所建PLS模型对样品中植物奶油添加量进行预测,并对预测值与真值进行配对t-检验,结果表明两者差异均不显著。

The model of discrimination analysis was established by PLS-Mahalanobis distance for differentiating raw milk and adulterated milk which was added with vegetable cream. Three principal components were compressed from original data by PLS. The whole wave numbers regions were selected without any pretreatment methods, and all the accuracy rates of this model are 100%. Meanwhile, 10 unknown samples were used to predict the results by the model, and all the prediction accuracy rates are 100%. PLS model for detecting the content of vegetable cream added with raw milk was set up with good veracity. Reliability of the model was verified by cross-validation and external-validation. Predictive correlation coefficient of the content of vegetable cream by PLS model is 0.9963. Root mean square error of calibration （RMSEC） is 0.11, while root mean square error of cross validation （RMSECV） is 0.142. By paired samples test, the results of prediction are compared with real values with no significant difference.