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Automation of Data Collection for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Using a Correlative Analysis Algorithm

机译:基于相关分析算法的基质辅助激光解吸/电离质谱数据收集自动化

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摘要

Automation of data collection in matrix-assisted laser desorption/ionization (MALDI) mass spectrometry using a correlative analysis algorithm is demonstrated. This algorithm was employed to compensate for mass spectral jittering in MALDI data collection (e.g., peak shifts along the m/z axis, signal intensity deviations, etc.). Several important parameters for performing correlative analysis, such as the minimum correlation coefficient to be used and number of mass spectra to acquire prior to correlation, have been investigated and optimized. In addition, the correlation algorithm improved mass resolution of low-and high-molecular-weight compounds by as much as a factor of 4. Signal reproducibility in MALDI quantitative analysis also is improved when correlation is employed for data collection. This data collection algorithm can be used in conjunction with other instrumental optimization programs to allow for fully automated MALDI analysis, which is required for the routine applications carried out in many analytical laboratories.
机译:演示了使用相关分析算法在基质辅助激光解吸/电离(MALDI)质谱中自动进行数据收集的过程。该算法用于补偿MALDI数据收集中的质谱抖动(例如,沿m / z轴的峰位移,信号强度偏差等)。已经研究和优化了用于执行相关分析的几个重要参数,例如要使用的最小相关系数和相关之前要采集的质谱数。此外,相关算法将低分子量和高分子量化合物的质量分辨率提高了多达4倍。当使用相关性进行数据收集时,MALDI定量分析中的信号重现性也得到了改善。该数据收集算法可以与其他仪器优化程序结合使用,以实现全自动MALDI分析,这是许多分析实验室进行常规应用所必需的。

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