Abstract: Objective: This study aimed to establish a new method for the evaluation of beer crispness using partial least squares-discriminant analysis (PLS-DA). Methods: The concentrations of the chemical constituents associated with beer crispness were determined by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and the obtained data were transformed to a matrix and analyzed by PLS-DA method using SIMCA-P software. Results: Beers with different crispness showed clear discrimination in the score plot of PLS-DA. According to the loading plot, ethyl caprylate, ethyl caprate and isoamyl acetate were the sensitive characteristic variables for the DA (1) set of samples with poor crispness. Fifty samples were selected as a validation set to establish a PLS-DA discrimination model. The correlation coefficients of the model for four beer samples with different crispness were 0.861, 0.798, 0.765 and 0.812, respectively, with a recognition rate of 92%. A total of 27 unknown samples were predicted by the PLS-DA model with root mean square errors of prediction (RMSEP) of 0.183, 0.321, 0.523 and 0.323 and a correct recognition rate of 74.07%. Conclusion: HS-SPME-GC-MS combined with PLS-DA is a simple and useful method for the evaluation of beer crispness.

Key words: beer crispness, headspace solid-phase microextraction (HS-SPME), gas chromatography-mass spectrometry (GC-MS), partial least squares-discriminant analysis (PLS-DA), characteristic components