Assessing the quality and reliability of the DEA drug identification process

Expert Algorithm for Substance Identification Using Mass Spectrometry: Application to the Identification of Cocaine on Different Instruments Using Binary Classification Models

This is the second of two manuscripts describing how general linear modeling (GLM) of a selection of the most abundant normalized fragment ion abundances of replicate mass spectra from one laboratory can be used in conjunction with binary classifiers to enable specific and selective identifications with reportable error rates of spectra from other laboratories. Here, the proof-of-concept uses a training set of 128 replicate cocaine spectra from one crime laboratory as the basis of GLM modeling. GLM models for the 20 most abundant fragments of cocaine were then applied to 175 additional test/validation cocaine spectra collected in more than a dozen crime laboratories and 716 known negative spectra, which included 10 spectra of three diastereomers of cocaine. Spectral similarity and dissimilarity between the measured and predicted abundances were assessed using a variety of conventional measures, including the mean absolute residual and NIST's spectral similarity score. For each spectral measure, GLM predictions were compared to the traditional exemplar approach, which used the average of the cocaine training set as the consensus spectrum for comparisons. In unsupervised models, EASI provided better than a 95% true positive rate for cocaine with a 0% false positive rate. A supervised binary logistic regression model provided 100% accuracy and no errors using EASI-predicted abundances of only four peaks at m/z 152, 198, 272, and 303. Regardless of the measure of spectral similarity, error rates for identifications using EASI were superior to the traditional exemplar/consensus approach. As a supervised binary classifier, EASI was more reliable than using Mahalanobis distances.

A proper and systematic qualitative method validation procedure and its application to gas chromatography-mass spectrometry analysis of Chemical Weapons Convention related chemicals

Performing a detailed qualitative validation, which is carried out by many laboratories in the forensic community, has been the main goal of this study. In this study, a proper and systematic qualitative method validation procedure was proposed, and its application was shown on the analysis of Chemical Weapon Convention (CWC) related compounds in organic samples. All validation steps were described in detail. The study was carried out in pump oil and dichloromethane (DCM). The limit of detection values were determined for each compound and were found in the range of 0.5-2.0 µg mL^-1 in pump oil and 0.08-1.5 µg mL^-1 in DCM. The validation parameters were calculated, such as the rates of sensitivity, selectivity, false-negative, false-positive, also accordance and concordance. The predicted and obtained results were compared by using Cohen's Kappa Coefficient Test, and the compatibility of the results was found as "very good". After the validation procedure, all of the validation results were evaluated, and the proposed method was confirmed as appropriate for the analysis of CWC-related compounds in organic samples. The applicability of the validated method was proved by determining the CWC-related compounds in organic samples provided by the Organization for the Prohibition of Chemical Weapons during proficiency tests.