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Forensic chemistry is an important and rapidly growing branch of analytical chemistry. As a part of forensic practices, phenotype profiling is beneficial to help narrow down suspects. The goal of this study is to identify a person’s age range using dried bloodstains. Attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy is the technique used to acquire information about the total (bio)chemical composition of a sample. For the purpose of this proof-of-concept study, a diverse pool of donors including those in newborn (<1), adolescent (11–13), and adult (43–68) age ranges was used. Different donor age groups were found to have different levels of lipids, glucose, and proteins in whole blood, although the corresponding spectral differences were minor. Therefore, the collected data set was analyzed using chemometrics to enhance discrepancy and assist in donors’ classification. A partial least squares discriminant analysis (PLSDA) was used to classify ATR FT-IR spectra of blood from newborn, adolescent, and adult donors. The method showed a 92% correct classification of spectra in leave-one-out cross-validation (LOOCV) of the model. Overall, ATR FT-IR spectroscopy is nondestructive and can be an infield method that can be used for a variety of forensic applications. In general, the developed approach combining ATR FT-IR spectroscopy and advanced statistics shows the great potential for classifying (bio)chemical samples exhibiting significant intra-class variations.

Forensic chemistry is an important area of analytical chemistry. This field has been rapidly growing over the last several decades. Confirmation of the human origins of bloodstains is important in practical forensics. Current serological blood tests are destructive and often provide false positive results. Here, we report on the development of a nondestructive method that could potentially be applied at the scene for differentiation of human and animal blood using attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy and statistical analysis. The following species were used to build statistical models for binary human–animal blood differentiation: cat, dog, rabbit, horse, cow, pig, opossum, and raccoon. Three other species (deer, elk, and ferret) were used for external validation. A partial least squares discriminant analysis (PLSDA) was used for classification purposes and showed excellent performance in internal cross-validation (CV). The method was externally validated first using blood samples from new donors of species used in the training data set, and second using donors of new species that were not used to construct the model. Both validations showed excellent results demonstrating potential of the developed approach for nondestructive, rapid, and statistically confident discrimination between human and animal blood for forensic purposes.