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MALDI-TOF MS has been shown capable of rapidly and accurately characterizing bacteria. Highly reproducible spectra are required to ensure reliable characterization. Prior work has shown that spectra acquired manually can have higher reproducibility than those acquired automatically. For this reason, the objective of this study was to optimize automated data acquisition to yield spectra with reproducibility comparable to those acquired manually. Fractional factorial design was used to design experiments for robust optimization of settings, in which values of five parameters (peak selection mass range, signal to noise ratio (S:N), base peak intensity, minimum resolution and number of shots summed) commonly used to facilitate automated data acquisition were varied.  Pseudomonas aeruginosa  was used as a model bacterium in the designed experiments, and spectra were acquired using an intact cell sample preparation method. Optimum automated data acquisition settings (i.e., those settings yielding the highest reproducibility of replicate mass spectra) were obtained based on statistical analysis of spectra of  P. aeruginosa . Finally, spectrum quality and reproducibility obtained from non-optimized and optimized automated data acquisition settings were compared for  P. aeruginosa , as well as for two other bacteria,  Klebsiella pneumoniae  and  Serratia marcescens . Results indicated that reproducibility increased from 90% to 97% (p-value    0.002) for  P. aeruginosa  when more shots were summed and, interestingly, decreased from 95% to 92% (p-value0.013 ) with increased threshold minimum resolution. With regard to spectrum quality, highly reproducible spectra were more likely to have high spectrum quality as measured by several quality metrics, except for base peak resolution. Interaction plots suggest that, in cases of low threshold minimum resolution, high reproducibility can be achieved with fewer shots. Optimization yielded more reproducible spectra than non-optimized settings for all three bacteria.

A Designed Experiments Approach to Optimization of Automated Data Acquisition during Characterization of Bacteria with MALDI-TOF Mass Spectrometry