HS-SPME was adopted to enrich volatile terpenoid aroma compounds in grape berries and wine samples, and targeted quantification of characteristic monoterpenoid metabolites was performed. Combined with multivariate statistical analysis and machine learning algorithms, an origin discrimination model was established.
Key Findings
Significant differences were identified in the contents of free and bound monoterpenoid aroma compounds of ‘Muscat Hamburg’ grapes from various producing regions, with prominent overall terpenoid accumulation in specific regions.
Eighteen key aroma terpenoid markers were screened out, which could effectively distinguish grape raw materials from different origins based on odor activity values (OAVs).
A high-accuracy origin discrimination model was constructed, providing a rapid and efficient detection technical scheme for geographical indication certification and authenticity traceability of grapes and wine.
The full article entitled Targeted metabolomics analysis based on HS-SPME-GC-MS to discriminate geographical origin of ‘Muscat Hamburg’ grape and wine was published in Food Research International (Impact Factor = 8.8).