Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra

• Published in: Analytical and bioanalytical chemistry Vol. 414; no. 22; pp. 6635 - 6645
• Main Authors: Reuschenbach, Max, Hohrenk-Danzouma, Lotta L., Schmidt, Torsten C., Renner, Gerrit
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Analytical Chemistry; Biochemistry; Centroids; Centroids (Geometry); Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Data processing; data quality; Food Science; High resolution; Laboratory Medicine; Mass spectra; Mass spectrometry; Mass spectroscopy; Mathematical analysis; Methods; Monitoring/Environmental Analysis; Parameters; Programming languages; Regression analysis; Regression coefficients; Research Paper; Robustness (mathematics); Statistical analysis; Germany; HRMS; Data processing; Centroiding; Data quality
• ISSN: 1618-2642; 1618-2650; 1618-2650
• DOI: 10.1007/s00216-022-04224-y

High-resolution mass spectrometry is widely used in many research fields allowing for accurate mass determinations. In this context, it is pretty standard that high-resolution profile mode mass spectra are reduced to centroided data, which many data processing routines rely on for further evaluation. Yet information on the peak profile quality is not conserved in those approaches; i.e., describing results reliability is almost impossible. Therefore, we overcome this limitation by developing a new statistical parameter called data quality score (DQS). For the DQS calculations, we performed a very fast and robust regression analysis of the individual high-resolution peak profiles and considered error propagation to estimate the uncertainties of the regression coefficients. We successfully validated the new algorithm with the vendor-specific algorithm implemented in Proteowizard’s msConvert . Moreover, we show that the DQS is a sum parameter associated with centroid accuracy and precision. We also demonstrate the benefit of the new algorithm in nontarget screenings as the DQS prioritizes signals that are not influenced by non-resolved isobaric ions or isotopic fine structures. The algorithm is implemented in Python, R, and Julia programming languages and supports multi- and cross-platform downstream data handling.