Catching makers of crystal meth by chemometrics

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  • Published: Mar 15, 2017
  • Author: Ryan De Vooght-Johnson
  • Channels: Laboratory Informatics / Chemometrics & Informatics
thumbnail image: Catching makers of crystal meth by chemometrics

The high purity of methamphetamine makes impurities hard to detect

Methamphetamine is an addictive, illegal drug with serious side-effects, commonly used in many countries. The examination of the impurities present in seized methamphetamine can give vital information to law enforcement agencies as to the potential manufacturers and the chemical route used. However, much of the methamphetamine currently distributed has a high purity, making the detection of small traces of impurities difficult, particularly when the chromatographic peaks for these are hidden under the large main peak.

The researchers from Tehran attempted to use chemometric methods to identify the impurity peaks in a sample of seized methamphetamine. MCR-ALS (multivariate curve resolution-alternating least squares), an iterative computational technique, was applied to the mass spectrometer output in order to detect ‘hidden’ impurities.

Chemometric methods detect hidden GC-MS peaks

The researchers used an Agilent Hewlett Packard 6890 GC with an HP-5ms column, run with a 70-280 °C gradient. Mass analysis was carried out with an HP 5973N mass selective detector with a quadrupole mass filter. A complex TIC (total-ion chromatograph) was obtained, which was split into clusters of peaks for chemometric examination.

Matlab software was used for the chemometric analysis, in conjunction with MCRC software, which makes use of Matlab functions. The use of this software was outlined in a 2010 paper by Jalali-Heravi et al. Initial data processing was carried out to remove noise. Morphological score plots, in conjunction with a subspace comparison approach, were used to determine the chemical rank (i.e. the number of components present) for each cluster.

MCR-ASL was used to determine the peaks within each cluster, optimising the chromatographic profiles and the mass spectra alternately in an iterative process until they converged on an acceptable solution. Comparison with mass spectral database figures (NIST database) gave LOF (lack of fit) and R2 (explained variance) data, measures of how well the MCR-ALS output derived from the GC/MS data fitted the database spectra. For a ‘perfect fit’, the LOF should be zero, and R2 should be 100%. In practice, the values for LOF in the resolved clusters ranged from 0.51 to 1.05, while R2 ranged from 98.8% to 99.9%.

The MCR-ASL approach successfully resolved ‘clusters’ into individual peaks for specific species. For instance, a cluster containing the massive methamphetamine peak was also shown to contain peaks for two other species: N-benzyl-2-methyl aziridine and N,α-dimethylamphetamine (alternative name N,α,α-trimethylphenylethylamine). The aziridine by-product is specific for the ephedrine/pseudoephedrine route to methamphetamine. The N,α-dimethylamphetamine by-product had not been detected before in methamphetamine. Other clusters were also successfully resolved into their underlying peaks: an early running cluster was shown to contain ethyl propionate, n-propyl acetate and trichloroethylene.

Clonitazene, a synthetic opioid, was also detected in the methamphetamine. The authors of the paper referred to it as an ‘adulterant’, although it could also be present owing to accidental cross-product contamination in a production facility, rather than having being deliberately introduced. Criminals, even when producing relatively pure products, rarely operate to GMP standards.

MCR-ASL shows chemical route to methamphetamine sample

The technique of MCR-ASL successfully resolved the complex spectrum of the methamphetamine, showing the chemical route that had been used to make it. The ability to determine species that would normally be hidden by the main peak is a great advantage of this method. It is likely that such chemometric methods will see greater application in the future, both in the fight against illegal drugs and in other areas where finding trace components in complex mixtures is important, such as looking at environmental pollutants or complex mixtures of metabolites.

Related Links

Journal of Separation Science, 2017, Early View paper. Shekari et al. Chemometrics-assisted chromatographic fingerprinting: An illicit methamphetamine case study.

Chemometrics and Intelligent Laboratory Systems, 2010, 104, 155-171. Jalali-Heravi et al. MCRC software: A tool for chemometric analysis of two-way chromatographic data.

Wikipedia, Methamphetamine

Article by Ryan De Vooght-Johnson

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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