Fasasi Ayuba, Mirjankar Nikhil, Stoian Razvan-Ionut, White Collin, Allen Matthew, Sandercock Mark P, Lavine Barry K
Department of Chemistry, Oklahoma State University, Stillwater, OK 74078 USA.
Appl Spectrosc. 2015 Jan;69(1):84-94. doi: 10.1366/14-07578. Epub 2014 Dec 1.
Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum.
模式识别技术已被开发用于搜索油漆数据查询（PDQ）数据库的红外（IR）光谱库，以区分相似但不完全相同的IR清漆光谱。库搜索系统由两个独立但相互关联的组件组成：搜索预过滤器，用于将IR库的大小缩减至与未知油漆样本对应的特定装配厂或多个装配厂；以及互相关搜索算法，用于在预过滤器识别出的光谱子集中识别与未知光谱最相似的IR光谱。为了开发具有必要精度的搜索预过滤器，使用小波对来自PDQ数据库的IR光谱进行预处理，以增强数据中细微但显著的特征。使用遗传算法进行模式识别和特征选择，识别出车辆装配厂特有的小波系数。然后使用搜索算法将未知光谱与搜索预过滤器识别出的库光谱子集中的每个IR光谱进行互相关。使用跨越互相关和自相关数据中点不同区域的几个光谱窗口，将每个互相关的IR光谱与未知光谱的自相关IR光谱同时进行比较。汇总每个搜索窗口中识别出的前五个匹配结果，并计算直方图，总结每个选定库样本的出现频率。选择出现频率最高的五个库样本作为潜在匹配结果。即使在具有挑战性的试验中，即所评估的清漆样本在有限的生产年份范围内均为同一品牌（例如通用汽车），也可以从其IR光谱中识别出获得未知油漆样本的汽车型号。
