电子鼻结合统计学分析对牛肉中猪肉掺假的识别

利用电子鼻结合统计学分析对掺入猪肉的掺假牛肉进行定性和定量研究。采用平均值法和K均值聚类分析法提取特征值;通过主成分分析、判别分析进行分析并使用多层感知神经网络进行模式识别;通过偏最小二乘、多元线性回归和BP神经网络建立定量模型来预测掺假物含量。结果表明:K均值聚类分析法提取的特征值能更全面地反映电子鼻的响应信号,同时判别分析能更好地对掺假牛肉进行定性检测。多层感知神经网络分析中训练集正确分类率达98.8%,验证集正确分类率达97.4%,说明分类结果较好。BP神经网络的决定系数R2(0.999 3、0.993 0)和均方根误差(0.90%、2.50%)明显优于其他两种方法,故BP神经网络建模分析能更好地预测掺假牛肉中猪肉的含量。说明应用电子鼻技术检测掺入猪肉的掺假牛肉具有一定的可行性。

Recognition of Beef Adulterated with Pork Using Electronic Nose Combined with Statistical Analysis

Adulterated beef mixed with pork was qualitatively and quantitatively studied by using an electronic nose combined with statistical analysis. The feature values were extracted by cluster analysis and mean value method. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used for qualitative analysis. Quantitative models were established by partial least squares (PLS), multivariate linear regression (MLR) and BP neural network (BPNN) to quantitatively predict pork adulteration in beef. The results showed that the characteristic value extracted by cluster analysis could reflect the response signal of the electronic nose more comprehensively, while LDA was more suited for qualitatively detecting adulterated beef. The correct classification rates for the training and verification sets were 98.8% and 97.4%, respectively in the multi-layer perceptron neural network analysis, indicating that the classification results are good. The coefficient of determination (0.999 3 and 0.993 0) and root mean square error (0.90% and 2.50%) of the BPNN model were significantly better than those of the other models. Thus, the BPNN model allowed better prediction of the content of pork in adulterated beef. These results conclusively show that an electronic nose is an feasible approach for the detection of adulterated beef mixed with pork.