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.

Expert Algorithm for Substance Identification Using Mass Spectrometry: Statistical Foundations in Unimolecular Reaction Rate Theory.

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.

Dissociation Kinetics in Quadrupole Ion Traps: Effective Temperatures under Dipolar DC Collisional Activation Conditions.

Ions stored in an electrodynamic ion trap can be forced from the center of the ion trap to regions of higher radio frequency (RF) electric fields by exposing them to a dipolar DC (DDC) potential applied across opposing electrodes. Such ions absorb power from the trapping RF field, resulting in increased ripple motion at the frequency of the trapping RF. When a bath gas is present, ions undergo energetic collisions that result in "RF-heating" sufficient to induce fragmentation. DDC is therefore a broad-band (i.e., mass-to-charge-independent) means for collisional activation in ion traps with added bath gas. Under appropriate conditions, the internal energy distribution of an ion population undergoing dissociation can be approximated with an effective temperature, Teff. In such cases, it is possible to determine thermal activation parameters, such as Arrhenius activation energies and A-factors, by measuring dissociation kinetics. In this work, the well-studied thermometer ion, protonated leucine enkephalin, was subjected to DDC activation under rapid energy exchange conditions and in two separate bath gases, N2 and Ar, to measure Teff as a function of the ratio of DDC and RF voltages. As a result, an empirically derived calibration was generated to link experimental conditions to Teff. It was also possible to quantitatively evaluate a model described by Tolmachev et al. that can be used to predict Teff. It was found that the model, which was derived under the assumption of an atomic bath gas, accurately predicts Teff when Ar was used as the bath gas but overestimates Teff when N2 was the bath gas. Adjustment of the Tolmachev et al. model for a diatomic gas resulted in an underestimate of Teff. Thus, use of an atomic gas can provide accurate activation parameters, while an empirical correction factor should be used to generate activation parameters using N2.

Gas-Phase Covalent Bond Formation via Nucleophilic Substitution: A Dissociation Kinetics Study of Leaving Groups, Isomeric R Groups, and Nucleophilic Sites.

Nucleophilic substitution covalent modification ion/ion reactions were carried out in a linear quadrupole ion trap between the doubly protonated peptides KGAILKGAILR, RARARAA, and RKRARAA and isomers of either singly deprotonated 3- or 4-sulfobenzoic acid (n-SBA) esterified with either N-hydroxysuccinimide (NHS) or 1-hydroxy-7-aza-benzotriazole (HOBt). The cation/anion attachment product, through which the covalent reaction occurs, was isolated and subjected to dipolar DC (DDC) activation to generate covalently modified product over the ranges of DDC activation energies and times. The resulting survival yields were used to determine reaction rates, and Tolmachev's effective ion temperature was used to extract Arrhenius and Eyring activation parameters. It was found that the kinetics determined under these conditions are highly sensitive to the identities and locations of the nucleophilic sites on the peptides, the leaving groups on the reagent, and the location of the attachment sites on the reagent and analyte. Depending upon the identity of the analyte/reagent combination, significant variations in activation energy or entropy (or both) were both found to underlie the measured rate differences. The determination of dissociation kinetics under DDC conditions and application of Tolmachev's effective ion temperature treatment enables unique insights into the dynamics of gas-phase covalent bond formation via ion/ion reactions.