针对海洛因混合品、甲基苯丙胺混合品提出了一种基于支持向量机-多层感知器(SVM-MLP)融合模型的毒品混合品光谱鉴别方法。实验分别获取了海洛因、甲基苯丙胺与其他物质的90组毒品混合物光谱数据,采用基线自动校正和峰面积归一化除噪后借助主成分分析提取特征波数数据,建立基于SVM与MLP神经网络的融合分类模型。结果表明,基于高斯核函数、线性核函数、多项式核函数的SVM模型能够实现对不同质量分数海洛因混合品样本97.8%、97.8%、95.6%的准确分类,MLP模型能够对甲基苯丙胺混合品样本实现96.5%的准确分类。SVM-MLP融合模型无损、便捷、高效,有助于缉毒案件中毒品物证的鉴定和涉案人的司法量刑,具有一定的普适性和参考意义。

Aiming at the physical evidence of heroin mixtures and methamphetamine mixtures, a spectral identification method for drug mixtures based on the support vector machine-multilayer perceptron(SVM-MLP) fusion model is proposed. In the experiment, 90 sets of spectral data of the mixture of heroin, methamphetamine and other substances were obtained, and the baseline automatic correction and peak area normalization were used to eliminate noise and use principal component analysis extract characteristic wavenumber data spectral data fusion classification model based on SVM and MLP. The result shows that the SVM model based on Gaussian kernel function, linear kernel function, and polynomial kernel function can achieve accurate classification of 97.8%, 97.8%, and 95.6% of heroin mixture samples, respectively. The MLP model can achieve 96.5% for methamphetamine mixture samples accurate classification. The SVM-MLP fusion model is non-destructive, convenient and efficient is helpful for the identification of drug evidence in anti-drug cases and the judicial sentencing of the person involved has a certain universality and reference significance.