โก Quick Summary
This study explores the innovative use of ATR-FTIR spectroscopy combined with machine learning to detect fentanyl in human nails, achieving an impressive overall accuracy rate of 84.8% for distinguishing users from non-users. The findings suggest that human nails are a viable sample matrix for toxicological analysis.
๐ Key Details
- ๐ Sample Type: Human nails
- ๐ฌ Analyte of Interest: Fentanyl
- โ๏ธ Technology Used: ATR-FTIR spectroscopy and machine learning
- ๐ Prediction Models: PLS-DA and SVM-DA
- ๐ Accuracy Rates: PLS-DA: 84.8%, SVM-DA: 81.4%
๐ Key Takeaways
- ๐ Human nails can be effectively used for toxicological analysis.
- ๐ก ATR-FTIR spectroscopy combined with machine learning offers a novel approach to drug detection.
- ๐ The study achieved an overall accuracy of 84.8% in classifying fentanyl users.
- ๐ค Machine learning models demonstrated strong performance in differentiating between users and non-users.
- for non-invasive drug testing methods.
- ๐งช Fentanyl is a highly dangerous and abused substance, making accurate detection critical.
- ๐ Study published in the journal Sensors (Basel).
- ๐ PMID: 39797018.
๐ Background
The detection of drugs in biological samples is crucial for toxicological assessments, especially for substances like fentanyl, which has seen a rise in abuse and associated fatalities. Traditional methods often rely on blood or urine samples, which can be invasive and may not always provide a comprehensive picture of drug use. Recent studies have highlighted the potential of using human nails as a sample matrix due to their ability to retain drug metabolites over time.
๐๏ธ Study
This proof-of-concept study aimed to determine whether nail samples could effectively distinguish between individuals who have used fentanyl and those who have not. Researchers employed ATR-FTIR spectroscopy alongside machine learning techniques, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine Discriminant Analysis (SVM-DA), to analyze the spectral data obtained from the nail samples.
๐ Results
The results were promising, with the PLS-DA model achieving an overall accuracy of 84.8% and the SVM-DA model achieving 81.4%. Notably, when classification was performed at the donor level, all donors were correctly classified, indicating the robustness of the method. These findings suggest that ATR-FTIR spectroscopy, when combined with machine learning, can effectively differentiate between fentanyl users and non-users based on nail samples.
๐ Impact and Implications
The implications of this study are significant for the field of toxicology. The ability to detect fentanyl in human nails could lead to non-invasive testing methods that are both reliable and efficient. This advancement could enhance drug monitoring programs and contribute to public health efforts aimed at combating the opioid crisis. Furthermore, the integration of machine learning with traditional spectroscopy techniques represents a breakthrough in analytical chemistry.
๐ฎ Conclusion
This study highlights the potential of using human nails as a sample matrix for detecting fentanyl through the innovative combination of ATR-FTIR spectroscopy and machine learning. With an impressive accuracy rate, this approach could pave the way for more effective and less invasive drug testing methods in the future. Continued research in this area is essential to further validate these findings and explore additional applications in toxicology.
๐ฌ Your comments
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From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning.
Abstract
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.
Author: [‘Barney A’, ‘Trojan V’, ‘Hrib R’, ‘Newland A’, ‘Halรกmek J’, ‘Halรกmkovรก L’]
Journal: Sensors (Basel)
Citation: Barney A, et al. From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning. From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning. 2025; 25:(unknown pages). doi: 10.3390/s25010227