{"id":71,"date":"2022-05-07T05:35:22","date_gmt":"2022-05-07T05:35:22","guid":{"rendered":"https:\/\/src.cikeys.com\/2022\/?page_id=71"},"modified":"2022-05-07T05:35:22","modified_gmt":"2022-05-07T05:35:22","slug":"a-comparison-of-beer-volatiles-extractions-using-dllme-for-analysis-by-gc-ms","status":"publish","type":"page","link":"https:\/\/src.cikeys.com\/2022\/a-comparison-of-beer-volatiles-extractions-using-dllme-for-analysis-by-gc-ms\/","title":{"rendered":"A Comparison of Beer Volatiles Extractions using DLLME for analysis by GC-MS"},"content":{"rendered":"\n

Carmen Cortez Ortega and Dr. Blake Gillespie<\/h3>\n\n\n\n\n\n\n\n
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Abstract<\/h2>\n\n\n\n

Beer styles have particular volatile profiles contributed by the yeast used during brewing. Understanding yeast\u2019s contribution to flavor development in beer brewing often requires rapid, reproducible extraction of volatiles from aqueous solution for mass spectral analysis. HS-SPME is a time consuming process in which reproducibility is a challenge. We are looking for a more efficient way of analyzing beer volatiles. A more efficient process is dispersive liquid-liquid microextraction (DLLME). In comparison to HS-SPME, DLLME allows many samples to be rapidly and simultaneously extracted. Using volatiles commonly found in beers as standards, we are optimizing DLLME extraction procedures. We show good recovery of volatiles from beer using DLLME. Principal component analysis (PCA) is a technique that interprets the information received in data sets and increases interpretability. Using principal component analysis, we are able to discover common and unique volatiles across different beers that contribute to the particular smell and taste profile.<\/p>\n<\/div>\n\n\n\n

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Presentation<\/h2>\n\n\n