Rapid qualitative and quantitative analysis of strong aroma base liquor based on SPME-MS combined with chemometrics

Zongbao Sun, Junkui Li, Jianfeng Wu, Xiaobo Zou, Chi Tang Ho, Liming Liang, Xiaojing Yan, Xuan Zhou

Research output: Contribution to journalArticlepeer-review

Abstract

To objectively classify and evaluate the strong aroma base liquors (SABLs) of different grades, solid-phase microextraction-mass spectrometry (SPME-MS) combined with chemometrics were used. Results showed that SPME-MS combined with a back-propagation artificial neural network (BPANN) method yielded almost the same recognition performance compared to linear discriminant analysis (LDA) in distinguishing different grades of SABL, with 84% recognition rate for the test set. Partial least squares (PLS), successive projection algorithm partial least squares (SPA-PLS) model, and competitive adaptive reweighed sampling-partial least squares (CARS-PLS) were established for the prediction of the four esters in the SABL. CARS-PLS model showed a greater advantage in the quantitative analysis of ethyl acetate, ethyl butyrate, ethyl caproate, and ethyl lactate. These results corroborated the hypothesis that SPME-MS combined with chemometrics can effectively achieve an accurate determination of different grades of SABL and prediction performance of esters.

Original languageEnglish (US)
Pages (from-to)362-369
Number of pages8
JournalFood Science and Human Wellness
Volume10
Issue number3
DOIs
StatePublished - May 2021

All Science Journal Classification (ASJC) codes

  • Food Science

Keywords

  • Chemometrics
  • Ester compounds
  • Grade identification
  • SPME-MS
  • Strong aroma base liquor (SABL)

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