TY - JOUR

T1 - Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra

AU - Reuschenbach, Max

AU - Hohrenk-Danzouma, Lotta L.

AU - Schmidt, Torsten C.

AU - Renner, Gerrit

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022/9

Y1 - 2022/9

N2 - 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.

AB - 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.

KW - Centroiding

KW - Data processing

KW - Data quality

KW - HRMS

KW - Chemistry

UR - http://www.scopus.com/inward/record.url?scp=85134558702&partnerID=8YFLogxK

U2 - 10.1007/s00216-022-04224-y

DO - 10.1007/s00216-022-04224-y

M3 - Journal articles

C2 - 35871703

AN - SCOPUS:85134558702

VL - 414

SP - 6635

EP - 6645

JO - Analytical and Bioanalytical Chemistry

JF - Analytical and Bioanalytical Chemistry

SN - 1618-2642

IS - 22

ER -