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 combi...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 samplingpartial 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.展开更多
基金The study was supported by the Key Research and Development Program of Jiangsu Province(BE2020312)National Natural Science Foundation of China(31671844)+2 种基金Open Project of National Engineering Laboratory for Agri-product Quality Traceability(AQT-2019-YB7)Science Foundation for Postdoctoral in Jiangsu Province(1501100C)Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘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 samplingpartial 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.
