Eiceman G A, Wang M, Prasad S, Schmidt H, Tadjimukhamedov F K, Lavine Barry K, Mirjankar Nikhil
Department of Chemistry and Biochemistry, New Mexico State University, Las Cruces, NM 88003, United States.
Anal Chim Acta. 2006 Oct 2;579(1):1-10. doi: 10.1016/j.aca.2006.07.013. Epub 2006 Jul 14.
Differential mobility spectra for alkanes, alcohols, ketones, cycloalkanes, substituted ketones, and substituted benzenes with carbon numbers between 3 and 10 were obtained from gas chromatography-differential mobility spectrometry (GC-DMS) analyses of mixtures in dilute solution. Spectra were produced in a supporting atmosphere of purified air with 0.6-0.8 ppm moisture, gas temperature of 120 degrees C, sample concentrations of approximately 0.2-5 ppm, and ion source of 5 mCi (185 MBq) 63Ni. Multiple spectra were extracted from chromatographic elution profiles for each chemical providing a library of 390 spectra from 39 chemicals. The spectra were analyzed for structural content by chemical family using two different approaches. In the one approach, the wavelet packet transform was used to denoise and deconvolute the DMS data by decomposing each spectrum into its wavelet coefficients, which represent the sample's constituent frequencies. The wavelet coefficients characteristic of the compound's structural class were identified using a genetic algorithm (GA) for pattern recognition analysis. The pattern recognition GA uses both supervised and unsupervised learning to identify coefficients which optimize clustering of the spectra in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected coefficients is about differences between chemical families in the data set. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to coefficients whose principal component plots show clustering on the basis of chemical family. In a second approach, a back propagation neural network was trained to categorize spectra by chemical families and the network was successfully tested using familiar and unfamiliar chemicals. Performance of the network was associated with a region of the spectrum associated with fragment ions which could be extracted from spectra and were class specific.
通过对稀溶液中混合物进行气相色谱 - 差分迁移谱法（GC - DMS）分析，获得了碳数在3至10之间的烷烃、醇类、酮类、环烷烃、取代酮类和取代苯类的差分迁移谱。在含有0.6 - 0.8 ppm水分的净化空气的支持气氛中产生谱图，气体温度为120摄氏度，样品浓度约为0.2 - 5 ppm，离子源为5 mCi（185 MBq）的63Ni。从每种化学物质的色谱洗脱曲线中提取多个谱图，形成了一个包含39种化学物质的390个谱图的库。使用两种不同方法按化学类别对谱图的结构内容进行分析。在一种方法中，小波包变换用于对DMS数据进行去噪和解卷积，通过将每个谱图分解为其小波系数来实现，这些系数代表样品的组成频率。使用遗传算法（GA）进行模式识别分析来识别化合物结构类别的特征小波系数。模式识别GA使用监督学习和无监督学习来识别系数，这些系数在数据的两个或三个最大主成分的图中优化谱图的聚类。由于主成分使方差最大化，所选系数编码的大部分信息是关于数据集中化学类别之间的差异。模式识别GA的适应度函数中嵌入的主成分分析程序充当信息过滤器，显著减小了搜索空间的大小，因为它将搜索限制在主成分图基于化学类别显示聚类的系数上。在第二种方法中，训练了一个反向传播神经网络按化学类别对谱图进行分类，并使用熟悉和不熟悉的化学物质对该网络进行了成功测试。网络的性能与谱图中与碎片离子相关的区域有关，这些碎片离子可以从谱图中提取出来并且是类别特异性的。
