Criteria for comparing infrared spectra – A review of the forensic and analytical chemistry literature

The aim of this review is to summarize the forensic science and analytical chemistry literature related to the comparison of infrared spectra. Most of the peer-reviewed papers referenced in this review have been published during the past ten years. This review is divided into two major (although not all-inclusive) sections: library searching (comparing one spectrum or group of spectra representing a sample to a spectral database) and what has been called “pattern recognition” (differentiating one set of spectra from another set by identifying the spectral features that provide the necessary discrimination). In the absence of discriminating features, spectra may be determined to not be “significantly different” and, in the forensic context, the possibility of the spectra originating from the same source (e.g., assembly plant in the case of an automotive paint sample) cannot be discarded. Library searching algorithms report a “quality index” or “similarity score,” whereas pattern recognition techniques attempt to collect information from a training set in the form of a response function that can be applied independently to other spectra for the purpose of classification, discrimination or even association. Additional tools are also discussed within this review including the use of a likelihood ratio to estimate the strength of similarity between one spectrum and another. In forensic applications, library search algorithms and pattern recognition techniques can only provide class evidence and cannot individualize the sample, e.g., in the case of an automotive paint search, they cannot identify the specific vehicle, only the “make” and model of the vehicle.