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Maximum-Likelihood Restoration Data Processing Techniques Applied to Matrix-Assisted Laser Desorption Mass Spectra

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Abstract:

Maximum-likelihood restoration (MLR) data processing is applied to matrix-assisted laser desorption time-of-flight mass spectra of several peptide/protein samples. It is shown that this technique can enhance mass resolution for peptides (porcine insulin; MW = 5777.58 Da) sufficiently to assist in the identification of various matrix adduction species formed in the laser desorption process. For higher-molecular-weight proteins, mass resolution enhancement is such that peak overlap contributions from partially resolved matrix adduct ion species can be minimized. Mass accuracies are improved by approximately a factor of six for a sample of a bacterial protease (Subtilisin Carlsberg; Subtilopeptidase A) from Bacillus licheniformis (MW = 27,288.4 Da). The MLR technique also supports the contention that the mass resolution degradation observed with increasing analyte mass is at least partially due to ion signal tailing, presumably caused by secondary ion species being produced in the detection process. This factor is also postulated as being at least partially responsible for the lack of success at resolution enhancement for higher mass analytes (bovine serum albumin; MW = 66,430.3 Da) where the multiple matrix adduction species produced can no longer be resolved from the protonated molecular ion species, thus limiting experimental mass measurement accuracies.

Keywords: Data processing techniques; Mass accuracies; Mass spectrometry; Matrix-assisted laser desorption

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/0003702934048514

Affiliations: Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523

Publication date: January 1, 1993

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