基于特征融合的减肥药太赫兹时域光谱模式识别

Terahertz Time-Domain Spectral Pattern Recognition for Weight Loss Drugs Based on Feature Fusion

为打击不法分子利用寄递渠道运输掺杂有毒有害等非食品原料的减肥药的现象,提出一种基于太赫兹时域光谱技术的减肥药模式识别方法。与传统方法相比,太赫兹光谱的时域频谱信噪比高,具有快速、省时和无损等特点。选用7种减肥药作为实验样品,采集样品的太赫兹时域光谱,用自动寻峰器找到0~0.19 THz、1.75~2.14 THz、2.23~2.5 THz三个特征频率区间;采用希尔伯特变换、巴特沃思低通滤波器、快速傅里叶变换低通滤波器、标准正态变换后的一阶导数处理特征频率区间,并对处理结果与原始光谱进行特征数据融合;采用粒子群优化最小二乘支持向量机和随机森林模型对原始数据和四种方法融合的数据进行分类识别。实验结果表明,粒子群优化最小二乘支持向量机模型对经过希尔伯特变换的光谱特征融合数据具有最佳的识别效果,准确率可达到100%,对法庭科学中减肥药的鉴别有一定借鉴意义。

To prevent criminals from using various delivery channels to transport weight loss drugs doped with toxic and harmful nonfood raw materials,a pattern recognition method for weight loss drugs based on terahertz timedomain spectroscopy is proposed in this study.Compared with traditional methods,terahertz spectrum has a high signaltonoise ratio in timedomain,which is fast,timesaving,and lossless.In this study,seven weight loss drug types were selected as experimental samples.The terahertz timedomain spectra of the samples were collected;accordingly,three characteristic frequency intervals of 0-0.19 THz,1.75-2.14 THz,and 2.23-2.5 THz were detected by the automatic peak finder.The characteristic frequency intervals were processed using the Hilbert transform,Butterworth lowpass filter,fast Fourier transform lowpass filter,and first derivative after standard normal transform.Subsequently,the obtained feature data was fused with the original spectrum.The original data and the data fused by the four methods were classified and recognized using particle swarm optimization least squares support vector machine and random forest models.The experimental results demonstrate that the particle swarm optimization least squares support vector machine model has the best recognition effect on the spectral feature fusion data after Hilbert transform,whose accuracy can reach 100%.This approach can be used as a reference for the identification of weight loss drugs in forensic science.