Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS
LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the r...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/27/3/1013 |
| _version_ | 1797485969956405248 |
|---|---|
| author | Emma Palm Anneli Kruve |
| author_facet | Emma Palm Anneli Kruve |
| author_sort | Emma Palm |
| collection | DOAJ |
| description | LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches— one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information. |
| first_indexed | 2024-03-09T23:27:25Z |
| format | Article |
| id | doaj.art-89ec82cb5f114fc490a8e55cee5e7bd1 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T23:27:25Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-89ec82cb5f114fc490a8e55cee5e7bd12023-11-23T17:16:17ZengMDPI AGMolecules1420-30492022-02-01273101310.3390/molecules27031013Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMSEmma Palm0Anneli Kruve1Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18 Stockholm, SwedenDepartment of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18 Stockholm, SwedenLC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches— one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.https://www.mdpi.com/1420-3049/27/3/1013random forestnon-target analysissuspect screeningquantification |
| spellingShingle | Emma Palm Anneli Kruve Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS Molecules random forest non-target analysis suspect screening quantification |
| title | Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS |
| title_full | Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS |
| title_fullStr | Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS |
| title_full_unstemmed | Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS |
| title_short | Machine Learning for Absolute Quantification of Unidentified Compounds in Non-Targeted LC/HRMS |
| title_sort | machine learning for absolute quantification of unidentified compounds in non targeted lc hrms |
| topic | random forest non-target analysis suspect screening quantification |
| url | https://www.mdpi.com/1420-3049/27/3/1013 |
| work_keys_str_mv | AT emmapalm machinelearningforabsolutequantificationofunidentifiedcompoundsinnontargetedlchrms AT annelikruve machinelearningforabsolutequantificationofunidentifiedcompoundsinnontargetedlchrms |