From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning
Language English Country Switzerland Media electronic
Document type Journal Article
PubMed
39797018
PubMed Central
PMC11722954
DOI
10.3390/s25010227
PII: s25010227
Knihovny.cz E-resources
- Keywords
- ATR–FTIR, PLS-DA, SVM-DA, fentanyl, fingernails, machine learning, toenails,
- MeSH
- Fentanyl * analysis MeSH
- Humans MeSH
- Nails * chemistry MeSH
- Spectroscopy, Fourier Transform Infrared methods MeSH
- Machine Learning * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Fentanyl * MeSH
Human nails have recently become a sample of interest for toxicological purposes. Multiple studies have proven the ability to detect various analytes within the keratin matrix of the nail. The analyte of interest in this study is fentanyl, a highly dangerous and abused drug in recent decades. In this proof-of-concept study, ATR-FTIR was combined with machine learning methods, which are effective in detecting and differentiating fentanyl in samples, to explore whether nail samples are distinguishable from individuals who have used fentanyl and those who have not. PLS-DA and SVM-DA prediction models were created for this study and had an overall accuracy rate of 84.8% and 81.4%, respectively. Notably, when classification was considered at the donor level-i.e., determining whether the donor of the nail sample was using fentanyl-all donors were correctly classified. These results show that ATR-FTIR spectroscopy in combination with machine learning can effectively differentiate donors who have used fentanyl and those who have not and that human nails are a viable sample matrix for toxicology.
Department of Environmental Toxicology Texas Tech University Lubbock TX 79409 USA
Department of Natural Drugs Faculty of Pharmacy Masaryk University 60200 Brno Czech Republic
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