One of the most common tasks in criminal investigation is to determine from which tissue source a biological fluid stain originates. As a result, there are many tests that are frequently used to determine if a stain is blood, semen or saliva by exploiting the properties of certain molecules present within the fluids themselves. These include chemical reagents such as the Kastle-Meyer or Acid Phosphatase tests, as well as other techniques like the use of alternative light sources. However, most of the tests currently available have some major drawbacks. In this study, a handheld near-infrared spectrometer is investigated for the specific identification of deposited bloodstains. First, a calibration was carried out by scanning over 500 positive (blood present) and negative (blood absent) samples to train several predictive models based on machine learning principles. These models were then tested on over 100 new positive and negative samples to evaluate their performance. All models tested were able to correctly classify deposited stains as blood in at least 81% of tested samples, with some models allowing for even higher classification accuracy at over 94%. This suggests that handheld near infrared devices could offer great opportunity for the rapid, low cost and non-destructive screening of body fluids at scenes of crime.

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The use of corrosive substances for criminal intent has recently increased in many countries, with 619 violent assaults recorded from 2019 to 2020 only in the UK. Criminals often conceal corrosive solutions, such as common household cleaners, in inconspicuous plastic bottles and splashing the content in order to incapacitate a victim while committing a robbery or to cause physical harm. There is currently no method available to law enforcement for the safe identification of these corrosive substances without being exposed to them. In this work, the feasibility of a near infrared (NIR) handheld spectrometer for the screening of corrosive inorganic solutions through plastic bottles is investigated. First, a training set comprising samples of five different corrosives was used to build a spectral library for data analysis and chemometric model design. Four models were then tested on three hundred samples of corrosive substances, as well as harmless substances such as water and soft drinks, to evaluate their performance. The models designed identified the corrosive substances in scenarios of concentrated solutions, showcasing the potential capability of this technique for the pre-screening of corrosive substances.

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