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.