Alcohol consumption is associated with oxidative stress and an increased risk of carcinoma of the upper aero-digestive tract (UADT). Recently, it has been found that some microorganisms in the human oral cavity may locally metabolize ethanol, forming acetaldehyde, a carcinogenic metabolite of alcohol. In a cohort of patients first visited for UADT cancers, we estimated their alcohol consumption by measuring Ethyl Glucuronide/EtG (a long-lasting metabolite of ethanol) in the hair and carbohydrate-deficient transferrin/CDT (short-term index of alcohol intake) in the serum. Moreover, we analyzed, by culture-based methods, the presence of Neisseria subflava, Streptococcus mitis, Candida albicans, and glabrata (microorganisms generating acetaldehyde) in the oral cavity. According to the EtG values, we correlated drinking alcohol with endogenous oxidative stress and the investigated microorganism’s presence. We found that 55% of heavy drinkers presented microorganisms generating acetaldehyde locally. Moreover, we found that the presence of oral acetaldehyde-producing bacteria correlates with increased oxidative stress compared to patients without such bacteria. As for the study of alcohol dehydrogenase gene polymorphisms (the enzyme that transforms alcohol to acetaldehyde), we found that only the “CGTCGTCCC” haplotype was more frequent in the general population than in carcinoma patients. This pilot study suggests the importance of estimating alcohol consumption (EtG), the presence of bacteria producing acetaldehyde, and oxidative stress as risk factors for the onset of oral carcinomas.

Time elapsed since bloodstain deposition is a crucial aspect in forensic investigations, where non-destructive spectroscopic methods play a pivotal role. While extensive research has been conducted by UV-Vis spectroscopy, showcasing its utility in specific cases, there is still a paucity of studies based on NIR spectroscopy, which has the potential to overcome the limitations of the UV-Vis-based methods. To compensate for this disequilibrium, the present study aimed to evaluate the NIR applicability for estimating the age of forensic bloodstains and develop a performance comparison with UV-Vis spectroscopy methods. Capillary blood was sampled and subjected to a 16-day aging, during which it was repeatedly analyzed using both spectroscopic methods. Subsequently, chemometric analysis was applied to process the spectral data and independently assess the methods' performance. Classical preprocessing transforms (i.e., Savitzky-Golay derivatives and SNV transform) were used together with more targeted strategies, such as class centering, whose benefit was highlighted by PCA. Lastly, PLS regression models were computed to evaluate the effectiveness of both spectroscopic methods in estimating the time elapsed since blood trace deposition. Comparable root mean square errors in prediction (RMSEP) - 40 and 55 h for UV-Vis and NIR spectroscopy, respectively - were observed for both techniques, featuring an improvement with respect to the existing literature for NIR spectroscopy. Data fusion strategies for a multi-instrumental platform were also explored, evaluating advantages and disadvantages of low-level and mid-level approaches. The results indicated that NIR spectroscopy integrated with adequate chemometric strategies deserves increased appreciation in forensic bloodstain dating.

Fentanyl is a synthetic L-opioid receptor agonist, approximately 100 times more potent than morphine, that is experiencing an upward trend in the field of abuse. Fentanyl patches’ abusive consumption can occur either by transdermal absorption or through other atypical and ingenious routes. In the present case, a 29-year-old man with a history of illicit drug use was found dead in a suburban neighborhood of an Italian city. At autopsy, lungs appeared increased in weight and showed minute subpleural hemorrhages. Airways contained abundant reddish foamy material; in addition, a fentanyl patch protective film was found inside the left main bronchus. Toxicological analysis revealed the presence of morphine, fentanyl, BEG and ethyl alcohol in peripheric blood; 6-MAM was also revealed in urine. Findings collected during post-mortem investigations allowed us to identify fentanyl consumption as the cause of death. Fentanyl consumption presumably took place by chewing of a transdermal patch, with subsequent aspiration of the protective film. The pathophysiology of death can be identified as combined respiratory failure—both central suppression and a fentanyl-induced increase in muscular stiffness; a further minor contribution may be identified in the mechanical airflow obstruction caused by the presence of the protective film at the bronchial level.

The interest in assessing the behavior of rare-earth elements (REE) in the natural environment is constantly increasing due to their numerous applications in both environmental and technological fields. However, current methodologies for analyzing REE distributions are based on normalization of REE concentration profiles to lithological values, potentially resulting in different outcomes depending on which lithological values are used for normalization, affecting the interpretability of the data. The present work proposes an alternative approach for analyzing REE concentration profiles by applying principal component analysis (PCA) to create REE chemometric maps. The data compression allows the visualization of the REE distribution using a red-green-blue (RGB) color scale (PC1 = red channel; PC2 = green channel; PC3 = blue channel) directly on a geographical map, reflecting the chemical properties of rare-earth elements. This highlights similarities and differences in the compositional REE distribution of natural soils, facilitating the interpretability of REE data and potentially leading to new insights related to seemingly unrelated samples. Additionally, PCA applied to soil variables correlates with REE patterns and provides deeper insights into soil properties in an unsupervised manner, enhancing the interpretation of soil characteristics and implementing the ability to monitor environmental changes and study soil evolution processes. Of particular significance is the fact that applying the proposed methodology to non-normalized data yields results that are consistent with those derived from normalized data sets. Therefore, this approach not only overcomes normalization challenges but also supports the classical approach from a new methodological perspective, paving the way for broader applications.

Nitrite/nitrate poisoning is an emerging problem, with an ongoing escalation of reported self-administration with suicidal intent in several countries. Nitrites toxicity mainly consists of their interaction with hemoglobin (Hb), causing its oxidization to methemoglobin (MetHb). In order to give support to the correct procedures for the analysis of these cases, this study aims to evaluate spontaneous sample degradation and consequent MetHb formation in the typical storage conditions of a forensic toxicology laboratory. Two different types of samples have been used in this study: the first stage of our study consisted of a retrospective analysis of blood samples obtained by judicial autopsies already stored in the toxicology laboratory, collected over four years (2018–2021), while the samples used for the second stage were appositely collected during judicial autopsies. The data obtained by the application of a derivative spectrophotometry method on these samples suggest that there seems not to be a maximum threshold for MetHb formation within which it is possible to state with a sufficient grade of certainty that the concentration of MetHb found is consistent with an ante-mortem formation and is not the result of an artifact due to sample degradation and storage conditions. On the other hand, the results suggest that MetHb formation depends on the time passed between sample collection and analysis, so that a tempestive sample processing, performed as soon as the samples are received in the laboratory, is crucial to obtain the maximum reliability and diagnostic values from the data when MetHb quantitation is necessary.

Understanding how deeply near-infrared (NIR) radiation penetrates into matter is essential for interpreting (hyper)spectral imaging (SI) data, yet comprehensive assessments of penetration depth remain limited. Although NIR-SI is traditionally considered a surface analytical technique, subsurface contributions may affect spectral profiles, challenging this assumption. This study presents a systematic and quantitative investigation of NIR penetration in a controlled SI setup using multilayer polymeric samples manufactured by 3D printing. Layered cubes and cylinders composed of polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) were analyzed using two independent short-wave NIR-SI systems. Penetration behavior was evaluated through an integrated chemometric workflow combining exploratory analysis (principal component analysis, PCA), unmixing (classical least squares, CLS), supervised linear (partial least squares, PLS) and nonlinear (convolutional neural network, CNN) regression methods. The results demonstrated that NIR-SI can retrieve chemical information from subsurface layers to depths of ∼1 cm. Penetration depth was strongly influenced by material composition, internal structure, and illumination conditions. PCA and CLS revealed nonlinear attenuation and scattering phenomena, while regression models successfully retrieved subsurface information across the full sample height. CNN consistently outperformed PLS, highlighting the importance of nonlinear modeling approaches. The robustness of the proposed strategy arises from the use of designed ad hoc samples, two independent instruments, and multiple complementary chemometric methods converging toward consistent results. Overall, this work challenges the conventional surface-limited view of NIR-SI and provides a robust framework for investigating NIR penetration, supporting the development of depth-resolved and potentially tomographic NIR-SI approaches.