Qualitative and Quantitative Prediction of Volatile Compounds from Initial Amino Acid Profiles in Korean Rice Wine (makgeolli) Model

Bo Sik Kang, Jang Eun Lee, Hyun Jin Park

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In Korean rice wine (makgeolli) model, we tried to develop a prediction model capable of eliciting a quantitative relationship between initial amino acids in makgeolli mash and major aromatic compounds, such as fusel alcohols, their acetate esters, and ethyl esters of fatty acids, in makgeolli brewed. Mass-spectrometry-based electronic nose (MS-EN) was used to qualitatively discriminate between makgeollis made from makgeolli mashes with different amino acid compositions. Following this measurement, headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (GC-MS) combined with partial least-squares regression (PLSR) method was employed to quantitatively correlate amino acid composition of makgeolli mash with major aromatic compounds evolved during makgeolli fermentation. In qualitative prediction with MS-EN analysis, the makgeollis were well discriminated according to the volatile compounds derived from amino acids of makgeolli mash. Twenty-seven ion fragments with mass-to-charge ratio (m/z) of 55 to 98 amu were responsible for the discrimination. In GC-MS combined with PLSR method, a quantitative approach between the initial amino acids of makgeolli mash and the fusel compounds of makgeolli demonstrated that coefficient of determination (R2) of most of the fusel compounds ranged from 0.77 to 0.94 in good correlation, except for 2-phenylethanol (R2 = 0.21), whereas R2 for ethyl esters of MCFAs including ethyl caproate, ethyl caprylate, and ethyl caprate was 0.17 to 0.40 in poor correlation.

Original languageEnglish
Pages (from-to)C1106-C1116
JournalJournal of Food Science
Volume79
Issue number6
DOIs
Publication statusPublished - 2014 Jun

Keywords

  • Amino acid
  • Electronic nose
  • Fusel alcohol
  • Makgeolli
  • Partial least-squares regression

ASJC Scopus subject areas

  • Food Science

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