基于电子舌的掺假羊奶快速定量预测模型

为实现对掺假羊奶的快速、客观辨别,模仿人体味觉感知机理研制了一套便携式电子舌检测系统,并建立了一种能够快速鉴别掺假羊奶的新方法。系统检测时,首先对样本溶液进行大幅脉冲扫描,用以获取掺假羊奶的"指纹"信息,然后利用离散小波变换(discrete wavelet transform,DWT)对"指纹"数据中的特征信息进行提取,最后在此基础上,采用主成分分析(principal component analysis,PCA)方法对不同掺假比例的羊奶进行定性辨别。采用粒子群优化极限学习机(Particle swarm optimization extreme learning machine,PSO-ELM)对不同掺假比例的羊奶进行了定量预测。通过试验数据得出,PCA对6种不同掺假比例的羊奶区分达到100%,区分效果好。PSO-ELM羊奶纯度预测模型拟合曲线非常接近实测值曲线,因此采用PSO-ELM方法建立掺假羊奶纯度定量预测模型具有较高的预测精度。

Rapid quantitative prediction model of adulterated goat milk based on electronic tongue

In order to discriminate adulterated goat milk quickly and objectively, a set of portable electronic tongue detection system was exploited, and a new method of fast identification is developed. When detected in the system, the sample solution was first scanned to obtain the "fingerprint" information of adulterated goat milk, and then the discrete wavelet transform (DWT) was used to obtain the characteristics of the "fingerprint" data. On this basis, the principal component analysis (PCA) was used to determine the quality of goat milk with different adulteration ratio. Particle swarm optimization extreme learning machine (PSO-ELM) was applied to quantitatively predict goat milk with different adulteration proportions. According to the experimental data, PCA could distinguish six kinds of goat milk with different adulteration ratios up to 100%, and it had a good effect on distinguishing adulterated goat milk. In order to realize the quantitative prediction of goat milk with different adulteration ratios, the fitting curve of PSO-ELM goat milk purity prediction model was very close to the measured curve, so the PSO-ELM method was used to establish the quantitative prediction model of goat milk purity with high prediction accuracy. This study might provide new ideas and technical support for qualitative identification and quantitative prediction of adulterated goat milk.