Machine Learning Approaches for GC–MS Data Interpretation in Flavour and Fragrance Analysis
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Wiley Open Access
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| Format: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867007223554637825 |
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| author | Jean‐Baptiste Coffin Romain Krafft Sylvain Antoniotti Sébastien Fiorucci |
| author_facet | Jean‐Baptiste Coffin Romain Krafft Sylvain Antoniotti Sébastien Fiorucci Jean‐Baptiste Coffin Romain Krafft Sylvain Antoniotti Sébastien Fiorucci |
| collection | Wiley Open Access |
| contents | Machine Learning Approaches for GC–MS Data Interpretation in Flavour and Fragrance Analysis Jean‐Baptiste Coffin Romain Krafft Sylvain Antoniotti Sébastien Fiorucci Flavour and Fragrance Journal ABSTRACT This review explores the integration of machine learning (ML) in the analysis of mass spectrometry data obtained by electronic impact after gas chromatography (GC–MS), with a focus on applications for the fragrance and flavour (FF) industry. It highlights recent advances in ML‐driven approaches to enhance the interpretation of complex GC–MS datasets, improving efficiency in ingredient identification, classification, and quality control. Furthermore, the paper discusses various ML techniques, including supervised and unsupervised learning, and also addresses challenges such as data scarcity and intellectual property concerns. 10.1002/ffj.70039 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1002/ffj.70039 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_ffj_70039 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Machine Learning Approaches for GC–MS Data Interpretation in Flavour and Fragrance Analysis Jean‐Baptiste Coffin Romain Krafft Sylvain Antoniotti Sébastien Fiorucci Flavour and Fragrance Journal Machine Learning Approaches for GC–MS Data Interpretation in Flavour and Fragrance Analysis Jean‐Baptiste Coffin Romain Krafft Sylvain Antoniotti Sébastien Fiorucci Flavour and Fragrance Journal ABSTRACT This review explores the integration of machine learning (ML) in the analysis of mass spectrometry data obtained by electronic impact after gas chromatography (GC–MS), with a focus on applications for the fragrance and flavour (FF) industry. It highlights recent advances in ML‐driven approaches to enhance the interpretation of complex GC–MS datasets, improving efficiency in ingredient identification, classification, and quality control. Furthermore, the paper discusses various ML techniques, including supervised and unsupervised learning, and also addresses challenges such as data scarcity and intellectual property concerns. 10.1002/ffj.70039 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Machine Learning Approaches for GC–MS Data Interpretation in Flavour and Fragrance Analysis |
| topic | Flavour and Fragrance Journal |
| url | https://onlinelibrary.wiley.com/doi/10.1002/ffj.70039 |