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 atm/z152, 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.