Chemometrics and Machine Learning in Forensic Chemistry
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 162
Special Issue Editor
Interests: chemometrics; machine learning; forensic toxicology; metabolomics; hyperspectral imaging; forensic chemistry; forensic genetics; open-source software for data analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forensic chemistry increasingly relies on advanced analytical techniques, such as mass spectrometry, vibrational spectroscopy, hyperspectral imaging, and chromatographic methods, that generate complex, high-dimensional datasets. Extracting meaningful and reliable information from such data requires robust statistical frameworks that surpass traditional univariate approaches. Chemometrics and machine learning offer powerful tools for classification, regression, pattern recognition, and evidence evaluation in this context, yet their adoption in routine forensic casework remains limited.
This Special Issue aims to bridge the gap between data science and forensic chemistry by collecting original research and review articles that apply chemometric and machine learning methods to forensic problems. Topics of interest include, but are not limited to, the following: multivariate classification of forensic traces (drugs, biological fluids, materials); spectroscopic and spectral imaging methods combined with chemometric data processing for crime scene analysis; metabolomics and untargeted profiling in forensic toxicology; likelihood ratio frameworks and evaluative statistics for forensic evidence interpretation; predictive modeling for biomarker discovery, postmortem interval estimation, or biogeographical ancestry inference; and development of open-source tools and reproducible workflows for forensic data analysis.
Contributions employing supervised and unsupervised learning, deep learning, or hybrid approaches are equally welcome, provided that they address a concrete forensic question and demonstrate methodological rigor in model validation and interpretation. The overarching goal is to promote transparent, reproducible, and scientifically sound data-driven practices in forensic chemistry.
Dr. Eugenio Alladio
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- chemometrics
- machine learning
- forensic chemistry
- forensic toxicology
- multivariate analysis
- spectroscopy
- imaging
- evidence evaluation
- classification
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
