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Research Article | Open Access

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

Zongbao Suna,c( ), Junkui Lia, Jianfeng Wuc, Xiaobo Zoua, Chi-Tang Hob( ), Liming Lianga, Xiaojing Yana, Xuan Zhoua,c
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Department of Food Science, Rutgers University, New Brunswick, New Jersey 08903, USA
Jiangsu King's Luck Brewery Co. Ltd., Lianshui 223411, China

Peer review under responsibility of KeAi Communications Co., Ltd

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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.

Keywords

SPME-MSStrong aroma base liquor (SABL)ChemometricsGrade identificationEster compounds

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Food Science and Human Wellness
Volume 10 Issue 3,
May 2021
Pages 362-369
DOI: 10.1016/j.fshw.2021.02.031
Cite this article:
Sun Z, Li J, Wu J, et al. Rapid qualitative and quantitative analysis of strong aroma base liquor based on SPME-MS combined with chemometrics. Food Science and Human Wellness, 2021, 10(3): 362-369. https://doi.org/10.1016/j.fshw.2021.02.031

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Received: 14 June 2020
Revised: 03 August 2020
Accepted: 10 August 2020
Published: 16 April 2021
© 2021 Beijing Academy of Food Sciences. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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