Applications of MALDI mass spectrometry in forensic science.

Forensic chemistry literature has grown exponentially, with many analytical techniques being used to provide valuable information to help solve criminal cases. Among them, matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS), particularly MALDI MS imaging (MALDI MSI), has shown much potential in forensic applications. Due to its high specificity, MALDI MSI can analyze a wide variety of compounds in complex samples without extensive sample preparation, providing chemical profiles and spatial distributions of given analyte(s). This review introduces MALDI MS(I) to forensic scientists with a focus on its basic principles and the applications of MALDI MS(I) to the analysis of fingerprints, drugs of abuse, and their metabolites in hair, medicine samples, animal tissues, and inks in documents.

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 µL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.