Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability

Abstract

Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.

qPeaks: A Linear Regression-Based Asymmetric Peak Model for Parameter-Free Automatized Detection and Characterization of Chromatographic Peaks in Non-Target Screening Data

“…This would at least somewhat exclude uncertainties in the estimated feature properties due to fluctuating data quality but the conventional algorithms need to be adapted to conserve the information on which data points build which feature. 5 Even if such an approach will only be applicable with different algorithms and the same data, it could improve the understanding of NTS result reliability and improve NTS data processing in a future perspective.…”

“…Algorithms and their settings must be set carefully to achieve meaningful results. 5 This challenge is due to the lack of performance metrics to optimize processing settings and the data's nonlinear nature.…”

“…19 The detection parameters commonly applied in peak-picking algorithms are the signal-to-noise ratio and noise level offset. 5 However, if the EIC snippet analyzed during peak picking only contains the chromatographic peak but no notable noise amplitude, a correct estimation of the peak boundaries is quite challenging, and automatization is hindered. This phenomenon typically occurs if there is some noise filter during data processing, e.g., for high-resolution mass spectra using Orbitrap systems.…”

“…This is due to low analyte concentration or low specific ionization efficiency strongly influenced by the polarity of the analyte, which leads to discrimination effects for these analytes during data processing. 5 Due to the modern day's increased computing performance, nonlinear regression (NLR) can also be applied for chromatographic peak modeling. However, the critical point in NLR is the choice of initial parameters so that convergence of the models can be achieved.…”

“…Further, there are substantial variations in the order of ion traces’ intensities due to the noise level offset. This is due to low analyte concentration or low specific ionization efficiency strongly influenced by the polarity of the analyte, which leads to discrimination effects for these analytes during data processing . Due to the modern day’s increased computing performance, nonlinear regression (NLR) can also be applied for chromatographic peak modeling.…”