Rapid qualitative and quantitative analysis of strong aroma base liquor based on SPME-MS combined with chemometrics
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2021-05-01
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| Series: | Food Science and Human Wellness |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213453021000409 |
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| Summary: | 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. |
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| ISSN: | 2213-4530 |