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
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.