We present a data processing approach based on the spectral dot product for evaluating spectral similarity and reproducibility. The method introduces 95% confidence intervals on the spectral dot product to evaluate the strength of spectral correlation; it is the only calculation described to date that accounts for both the non-normal sampling distribution of the dot product and the number of peaks the spectra have in common. These measures of spectral similarity allow for the recursive generation of a consensus spectrum, which incorporates the most consistent features from statistically similar replicate spectra. Taking the spectral dot product and 95% confidence intervals between consensus spectra from different samples yields the similarity between these samples. Applying the data analysis scheme to replicates of brain tubulin CNBr peptides enables a robust comparison of tubulin isotype expression and post-translational modification patterns in rat and cow brains. t is widely recognized that multiple acquisitions of MALDI spectra have extensive variability. Addressing this problem in an automated and robust way becomes vital to the analysis of complex spectra because such spectra often contain peaks that are both inconsistent in their presence or absence and show great variability in intensity. Evaluating differences between replicate spectra manually can be tedious, if not impossible, and automatic tests for discriminating similar and dissimilar spectra have several caveats that have only been partially elaborated to date. Our calculations use the spectral dot product to evaluate spectral similarity. While several papers [1][2][3][4] have used the spectral dot product for evaluating spectral similarity, particularly the similarity of a query spectrum against library references [1, 3, 4], only one paper [4] has addressed the critical issue of how an algorithm or investigator attributes significance to a spectral dot product-a nontrivial detail because the dot product alone can yield equivocal information. Dot product ambiguity arises both from the number of peaks, as has been described [4], and from the different sampling distribution of the dot product at different values. We introduce the 95% confidence intervals on the spectral dot product to address both these sources of ambiguity. To accommodate for spectral variance, multiple acquisitions of the same sample are used to generate a consensus spectrum which incorporates the consistent features from all the replicate spectra. The dot product and its 95% confidence intervals allow for validation of the consensus spectrum by evaluating the similarity of each replicate to the consensus spectrum. The same method is also used to compare and evaluate similarity or dissimilarity between consensus spectra from different sources.Microtubules comprise one of the major fiber systems of the cytoskeleton and are polymers of the heterodimeric protein tubulin. Microtubules and tubulin are present in all eukaryotic cells and are responsible for cellular structure, chromoso...