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

• Main Authors: Zongbao Sun, Junkui Li, Jianfeng Wu, Xiaobo Zou, Chi-Tang Ho, Liming Liang, Xiaojing Yan, Xuan Zhou
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2021-05-01
• Series: Food Science and Human Wellness
• Subjects: SPME-MS; Strong aroma base liquor (SABL); Chemometrics; Grade identification; Ester compounds
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213453021000409

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.