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Answers to questions about the time of bloodstains formation are often essential to unravel the sequence of events behind criminal acts. Unfortunately, the relevance of preserved evidence to the committed offence usually cannot be verified, because forensic experts are still incapable of providing an accurate estimate of the bloodstains' age. An antidote to this impediment might be substituting the classical dating approach-founded on the application of calibration models-by the comparison problem addressed using likelihood ratio tests. The key aspect of this concept involves comparing the evidential data with results characterizing reference bloodstains, formed during the process of supervised ageing so as to reproduce the evidence. Since this comparison requires data that conveys information inherent to changes accompanying the process of blood decomposition, this study provided a Raman-based procedure, designated for probing into the chemistry of ageing bloodstains. To circumvent limitations experienced with single-point measurements-the risk of laser-induced degradation of hemoglobin and subsampling errors-the rotating mode of spectral acquisition was introduced. In order to verify the performance of this novel sampling method, obtained spectra were confronted with those acquired during conventional static measurements. The visual comparison was followed by analysis of data structure using regularized MANOVA, which boosted the variance between differently-aged samples while minimizing the variance observed for bloodstains deposited at the same time. Studies of relation between these variances demonstrated the superiority of novel procedure, as it provided Raman signatures that enabled a better distinction between differently-aged bloodstains.

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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.

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