ArticleAnalytical and bioanalytical chemistry2020
Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.
Article in Analytical and bioanalytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Machine Learning and SHAP Feature Analysis: Classification Model for Aroma Components in Green Plum Wine.Foods (Basel, Switzerland) · 2026Article
- Machine learning unveils three layers of food complexity.NPJ science of food · 2026Review
- Comparison of the quantification performance of thermal desorption GC-IMS and GC-MS in VOC analysis.Analytical and bioanalytical chemistry · 2025Article
- Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry as a Tool for the Untargeted Study of Hop and Their Metabolites.Metabolites · 2024Article
- Differentiation of Monofloral Honey Using Volatile Organic Compounds by HS-GCxIMS.Molecules (Basel, Switzerland) · 2022Article
- Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning.Molecules (Basel, Switzerland) · 2021Review
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4 authors.
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Abstract
For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops. Graphical abstract.
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