Dataset Information

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.

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 ?-acid content of hops and resulted in a standard error of prediction of only 1.04% ?-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.

SUBMITTER: Brendel R

PROVIDER: S-EPMC7497504 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.

Brendel Rebecca R Schwolow Sebastian S Rohn Sascha S Weller Philipp P

Analytical and bioanalytical chemistry 20200804 26

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 ...[more]

PMID: 32754792

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Project description:Finger lime is receiving growing attention as an ingredient of gastronomic preparations of haute cuisine for its delicious flavor and fragrance and for its appealing aspect. Volatile compounds play a crucial role in determining the organoleptic characteristics of the fruit and its pleasantness for consumers. The aim of the present study was to investigate the volatile profiles by headspace solid phase micro-extraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) in the peel and, for the first time, in the pulp of three Australian finger lime cultivars grown in Sicily (southern Italy): Pink Pearl, Sanguinea, and Faustrime, allowing to overall identify 84 volatile organic compounds (VOCs). The analytical data showed that the three cultivars were characterized by distinct volatile chemotypes: limonene/sabinene/bicyclogermacrene in the Pink Pearl, limonene/γ-terpinene/bicyclogermacrene in the Sanguinea, and limonene/β-phellandrene/γ-terpinene in the Faustrime. Moreover, some volatiles, found exclusively in one cultivar, could be considered potential markers of the individual cultivar. PCA allowed us to clearly discriminate the samples into three clusters: the first related to the Sanguinea peel, the second to the Faustrime peel, and a third group associated with the Pink Pearl peel along with the pulp of the three cultivars. Accordingly, the VOCs that mostly contributed to the differentiation of the three finger lime cultivars were also identified. Among them, D-limonene, sabinene γ-terpinene, α-pinene, α-phellandrene, β-myrcene, p-cymene, linalool, δ-elemene, ledene, bicyclogermacrene β-citronellol, α-bergamotene, α-caryophillene, and β-bisabolene, have been previously reported to exhibit important biological activities, suggesting that these cultivars, in addition to possessing unique volatile profiles, can show promise for several applications in pharmaceutical and food industry, namely for development of functional foods.

Dataset Information

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.

Publications

Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning.

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