A hair screening method has been developed for the detection of methamphetamine using an immunoassay analyzer (AxSYM) with a fluorescence polarization immunoassay (FPIA) technique. The method consisted of washing, cutting and digesting a hair sample (5 mg) with an enzymatic digestion solution. The digested hair sample was centrifuged, and then an aliquot of the supernatant was used to conduct the screening. The results obtained from FPIA, in most cases, showed concentrations above 70.0 ng/mL of methamphetamine for hair samples that contained 0.5 ng/mg of methamphetamine, determined by gas chromatography-mass spectrometry (GC-MS). The percent sensitivity, defined as the true positive rate of screened and confirmed results, and the percent specificity, defined as the true negative rate of screened and confirmed results, of the FPIA screening method were 100.0 and 96.7% (false positive rate of 3.3%), respectively, when the threshold level for FPIA analysis was set at 70.0 ng/mL (n = 60).The correlation coefficient (r) for the linear relationship between FPIA and GC-MS results was 0.91 in real hair samples. The recommended amount of hair sample was found to be 5.0 mg for FPIA screening analysis when the concentration of methamphetamine in hair samples determined by GC-MS was found to be more than 0.5 ng/mg. The method developed in this study was reliable and effective for the screening of methamphetamine in routine hair analysis.
High-resolution LC-MS/MS tandem mass spectra-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPS’s). Using a training set comprised of 770 LC-MS/MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (k-NN) models. In these models, controlled substances and NPS’s were classified into 13 subgroups (benzylpiperazine, opiate, benzodiazepine, amphetamine, cocaine, methcathinone, classical cannabinoid, fentanyl, 2C series, indazole carbonyl compound, indole carbonyl compound, phencyclidine, and others). Using 193 LC-MS/MS barcode spectra as an external test set, accuracy of the ANN, SVM, and k-NN models were evaluated as 72.5%, 90.0%, and 94.3%, respectively. Also, the hybrid similarity search (HSS) algorithm was evaluated to examine whether this algorithm can successfully identify unknown controlled substances and NPS’s whose data are unavailable in the database. When only 24 representative LC-MS/MS spectra of controlled substances and NPS’s were selectively included in the database, it was found that HSS can successfully identify compounds with high reliability. The machine learning models and HSS algorithms are incorporated into our home-coded AI-SNPS (artificial intelligence screener for narcotic drugs and psychotropic substances) standalone software that is equipped with a graphic user interface. The use of this software allows unknown controlled substances and NPS’s to be identified in a convenient manner.
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