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

• Published in: Analytical and bioanalytical chemistry Vol. 415; no. 18; pp. 4111 - 4123
• Main Authors: Renner, Gerrit, Reuschenbach, Max
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2023; Springer; Springer Nature B.V
• Subjects: Algorithms; Analytical Chemistry; automation; Biochemistry; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; chromatography; Confidence intervals; Critical Review; Data analysis; Data exchange; Data processing; data quality; Electronic data processing; Feature extraction; Food Science; Laboratory Medicine; Mass spectrometry; Mass spectroscopy; Mathematical analysis; Methods; Monitoring/Environmental Analysis; Optimization; Parameters; prioritization; Quality assessment; Quality control; Screening; Statistical analysis; Technology application; uncertainty; Young Investigators in (Bio-)Analytical Chemistry 2023; Germany; Data processing; Algorithms; Non-target screening; Challenges
• ISSN: 1618-2642; 1618-2650; 1618-2650
• DOI: 10.1007/s00216-023-04776-7

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.