Ocfentanil

Fentanyl analogs (fentalogs) share many structural and mass spectral similarities that make them difficult to differentiate and accurately identify without chromatog. data.In such situations, the expert algorithm for substance identification (EASI) provides superior classification relative to conventional approaches.Using a database of >57,000 replicate electron-ionization mass spectra of 76 fentalogs from ten laboratories, three challenging sets of isomers were studied in detail.To maximize limits of detection, only the 20 most abundant ions were considered.In each case, 50 % of the data from one laboratory served as the training set.On average, the mean absolute residuals between measured and modeled abundances of known positives were five times smaller using EASI than the consensus approach, which used the means of training sets as the exemplar spectra to which all query spectra were compared.With a conservative threshold of zero false positives, EASI identified isovalerylfentanyl from its two closest isomers with an accuracy of 96.7 %, which was ∼10 % better than the consensus approach.The associated pos. likelihood ratios increased from 366 for the consensus approach to more than 4,200 for EASI.When discriminating isovalerylfentanyl spectra from the other 72 fentalogs, EASI provided errorless results with a pos. likelihood ratio exceeding 50,000.For all 9 fentalogs, EASI outperformed the consensus approach and the use of Mahalanobis distance as a metric for identifying outliers.In the absence of retention time information, EASI improves confidence in drug identifications, enables inter-laboratory identifications, and reduces the need for acquiring concomitant spectra of standards