It is well known that grape aromatic composition is directly correlated to the final wine quality. To determine this composition, a previous stage of selective extraction is necessary, since the aromatic compounds are found in very low concentrations in the grapes. Therefore, in this work, the thin film microextraction technique (TF-SPME) was optimized, for first time, with the aim to analyze the volatile composition of the grape musts. The results obtained with the two commercially available absorbent materials for TF-SPME, polydimethylsiloxane/carboxene (PDMS/CAR) and PDMS/divinylbenzene (PDMS/DVB), were optimized and compared. To carry out the optimization, a randomized factorial design was performed combining the following factors and levels: extraction mode (headspace (HS), or direct immersion (DI)), stirring speed (500 and 1000 rpm), extraction time (1, 3 and 6 h), and extraction temperature (20, 40 and 60 °C). After performing a principal component analysis (PCA) and an analysis of variance (ANOVA) multifactorial, it was concluded that the best conditions for TF-SPME with PDMS/CAR were: direct immersion (DI), 500 rpm, 6 h, and 20 °C, while for TF-SPME with PDMS/DVB no conditions were found that maximized the extraction of most compounds, therefore compromise conditions were chosen: headspace (HS), 500 rpm, 6 h, and 40 °C. Finally, the comparison between the results obtained with both absorbents indicated that the absorbent that extracted better the volatile compounds from the musts with the TF-SPME technique, was PDMS/CAR, under the conditions: direct immersion (DI), 500 rpm, 6 h, and 20 °C.
Optimization of thin film-microextraction (TF-SPME) method in order to determine musts volatile compounds.
S. Marín-San Román,J. Carot,I. Sáenz de Urturi,P. Rubio-Bretón,E. Pérez-Álvarez,T. Garde-Cerdán
Published 2022 in Analytica Chimica Acta
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- Publication year
2022
- Venue
Analytica Chimica Acta
- Publication date
2022-08-01
- Fields of study
Agricultural and Food Sciences, Medicine, Chemistry, Environmental Science
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Semantic Scholar, PubMed
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