近红外光谱技术检测液态奶中微量三聚氰胺的可行性研究

为了快速检测液态奶中违法添加的三聚氰胺量,利用近红外光谱技术进行可行性研究分析。对采集的54组添加不同三聚氰胺含量的液态奶样本的近红外漫反射光谱进行小波变换(wavelet transform,WT)结合主成分分析(principal component analysis,PCA)后,提取得到特征信号,利用广义回归神经网络(general regression neural network,GRNN)建模进行三聚氰胺的定量分析,结果显示当三聚氰胺浓度低于0.05%时,模型预测能力较差,预测集模型R2仅为0.654。进一步利用支持向量机(support vector machine,SVM)建模对液态奶中是否添加三聚氰胺进行定性判别,结果显示测试集准确率为94.44%。因此,通过近红外光谱结合适当的化学计量学方法,对液态奶中添加三聚氰胺虽无法精确定量,但可准确对其进行定性判别。

Analysis on feasibility of detection of melamine in liquid milk based on near-infrared spectroscopy

The reasonability using near-infrared spectra (NIRS) for detection of melamine in liquid milk were studied. NIRS of samples were obtained by Fourier Transform Infrared Spectroscopy(FTIR) spectrometer, samples of liquid milk with adding melamine were analyzed while characteristic signals were extracted by wavelet transform and principal component analysis. Quantitative analysis was taken by General the Regression Neural Network(GRNN) for the diffuse reflectance spectroscopy of liquid milk adding melamine first. The result is not satisfactory enough that when concentration of melamine is lower than 0.05%, the R² of predict model is only 0.567. Qualitative discrimination by support vector machine was carried out later. The results showed that the model has a very good discriminate ability of the spectrum. The accuracy of the test set was 94.44%. Overall, NIRS combined with appropriate chemometrics methods is a feasible way to take qualitative discrimination of liquid milk whether or not adding melamine.