Evidence map›Paper›PMID 38474628›Full record

ArticleMolecules (Basel, Switzerland)2024

Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration.

Eric Deconinck, Sybrien Lievens, Michael Canfyn, Peter Van Campenhout, Loic Debehault, Lies Gremaux, Margot Balcaen

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Eric DeconinckSciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.ORCID 0000-0001-6980-6684
Sybrien LievensSciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.ORCID 0009-0000-4308-4839
Michael CanfynSciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.
Peter Van CampenhoutSciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.
Loic DebehaultSciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.ORCID 0009-0006-6948-843X
Lies GremauxSciensano, Scientific Direction Epidemiology and Public Health, Service Lifestyle and Chronic Diseases, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.
Margot BalcaenSciensano, Scientific Direction Epidemiology and Public Health, Service Lifestyle and Chronic Diseases, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.ORCID 0000-0002-1596-6540

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.

Indexed as

HeroinCalibrationLeast-Squares AnalysisSpectrophotometry, InfraredHeroinadulterantsATR-IRchemometricsdiacetylmorphinediluentsmobile detection approaches

Identifiers

PMID38474628
PMCPMC10934774

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.