ABSTRACT
Herein we describe a novel route to indole derivatives from a variety of N-substituted 2-alkenylanilines. This route features three operationally simple steps: (1) oxidation to convert N-substituted 2-alkenylanilines into epoxide intermediates, (2) intramolecular cyclization, and (3) the acid-catalyzed elimination of water.
ABSTRACT
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.
ABSTRACT
This study aims to resolve one of the longest-standing problems in mass spectrometry, which is how to accurately identify an organic substance from its mass spectrum when a spectrum of the suspected substance has not been analyzed contemporaneously on the same instrument. Part one of this two-part report describes how Rice-Ramsperger-Kassel-Marcus (RRKM) theory predicts that many branching ratios in replicate electron-ionization mass spectra will provide approximately linear correlations when analysis conditions change within or between instruments. Here, proof-of-concept general linear modeling is based on the 20 most abundant fragments in a database of 128 training spectra of cocaine collected over 6 months in an operational crime laboratory. The statistical validity of the approach is confirmed through both analysis of variance (ANOVA) of the regression models and assessment of the distributions of the residuals of the models. General linear modeling models typically explain more than 90% of the variance in normalized abundances. When the linear models from the training set are applied to 175 additional known positive cocaine spectra from more than 20 different laboratories, the linear models enabled ion abundances to be predicted with an accuracy of <2% relative to the base peak, even though the measured abundances vary by more than 30%. The same models were also applied to 716 known negative spectra, including the diastereomers of cocaine: allococaine, pseudococaine, and pseudoallococaine, and the residual errors were larger for the known negatives than for known positives. The second part of the manuscript describes how general linear regression modeling can serve as the basis for binary classification and reliable identification of cocaine from its diastereomers and all other known negatives.
