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Evidential value of polymeric materials—chemometric tactics for spectral data compression combined with likelihood ratio approach
Analyst ( IF 4.2 ) Pub Date : 2017-08-30 00:00:00 , DOI: 10.1039/c7an00236j
A. Menżyk 1, 2, 3, 4 , A. Martyna 1, 2, 3, 4 , G. Zadora 1, 2, 3, 4, 5
Affiliation  

Polymers have become a ubiquitous element of our culture. Therefore, these materials may play an important role in forensic investigations, serving as mute witnesses of occurrences such as car accidents. In this study, the possibilities provided by the likelihood ratio (LR) approach to estimate the evidential value of observed similarities and differences, and to discriminate among NIR spectral data originating from polypropylene automotive parts and household items, were investigated. Since the construction of LR models requires the introduction of only a few variables, the main objective was to reduce the dimensionality of registered spectra, which are characterised by over a thousand variables. The applied strategy was based on compression of NIR signals using discrete wavelet transform (DWT) followed by use of the SELECT algorithm for the selection and decorrelation of the most informative DWT coefficients. Selected features eventually served as an input for LR models. The performance of the developed models was assessed by measuring the rates of false positive and false negative answers as well as by applying an empirical cross entropy approach. Despite relatively small databases of polymeric objects, both univariate and multivariate LR models showed acceptable performances. The latter, however, gave the most satisfactory results, as it enabled successful discrimination of compared samples and delivered the lowest error rates. In addition, in order to verify the potential of NIR spectroscopy, the obtained results were compared with those obtained after application of the proposed tactics to the FTIR data, which is a well-established method in the forensic sphere.

中文翻译:

聚合物材料的证据价值-结合似然比法进行光谱数据压缩的化学计量策略

聚合物已成为我们文化中无处不在的元素。因此,这些材料可能在法医调查中发挥重要作用,充当车祸等事件的无声证人。在这项研究中,对似然比(LR)方法提供的可能性进行了估计,以估计观察到的相似性和差异的证据价值,并区分源自聚丙烯汽车部件和家用物品的NIR光谱数据。由于LR模型的构建仅需引入几个变量,因此主要目的是减少已注册光谱的维数,该特征具有上千个变量。应用的策略基于使用离散小波变换(DWT)对NIR信号进行压缩,然后使用SELECT算法对信息最丰富的DWT系数进行选择和去相关。选定的功能最终用作LR模型的输入。通过测量错误肯定和错误否定答案的比率以及通过经验交叉熵方法,评估了开发模型的性能。尽管聚合对象的数据库相对较小,但是单变量和多变量LR模型均显示出可接受的性能。但是,后者可以提供最令人满意的结果,因为它可以成功地区分所比较的样本,并提供最低的错误率。此外,为了验证NIR光谱学的潜力,
更新日期:2017-09-15
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