A general procedure for finding potentially erroneous entries in the database of retention indices

The NIST retention index database is one the most widely used sources of retention indices. In both untargeted analysis and machine learning studies filtering for potential errors is rather lacking or nonexistent. According to our estimates about 80% of the compounds from both NIST 17 and NIST 20 re...

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Bibliographic Details
Published in:Analytica chimica acta 2024-04, Vol.1297, p.342375-342375, Article 342375
Main Authors: Khrisanfov, Mikhail D., Matyushin, Dmitriy D., Samokhin, Andrey S.
Format: Article
Language:English
Subjects:
Deep learning
Gas chromatography
Machine learning
Retention index database
Retention indices
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Summary:The NIST retention index database is one the most widely used sources of retention indices. In both untargeted analysis and machine learning studies filtering for potential errors is rather lacking or nonexistent. According to our estimates about 80% of the compounds from both NIST 17 and NIST 20 retention index databases have only one RI value per stationary phase, which makes searching for erroneous values with statistical methods impossible. Manual inspection is also impractical because the database contains more than 300 000 entries. We suggest a two-step procedure to find potentially erroneous retention indices based on machine learning. The first step is to use five predictive models to obtain predicted retention index values for the whole database. The second one is to compare these predicted values against the experimental ones. We consider a retention index erroneous if its accuracy (the difference between predicted and experimental value) is in the bottom 5% for each of the five models simultaneously. Using this method, we were able to detect 2093 outlier entries for standard and semi-standard non-polar stationary phases in the NIST 17 retention index database, 566 of those were corrected or removed by the developers in the NIST 20. This is a novel approach to find potentially erroneous entries in a large-scale database with mostly unique entries, which can be applied not only to retention indices. The procedure can help filter and report mishandled data to improve the quality of the dataset for machine learning applications and experimental use. [Display omitted] •NIST 17 retention index database contains erroneous entries.•A method for finding potentially erroneous entries was proposed.•Five machine and deep learning models were constructed to predict retention indices.•If all five predictions significantly differ from an entry, it may be incorrect.•2093 outlier entries were detected in NIST 17, some were corrected in NIST 20.
ISSN:0003-2670
1873-4324
DOI:10.1016/j.aca.2024.342375