Optimization of a headspace solid-phase micro-extraction method to quantify volatile compounds in plain sufu, and application of the method in sample discrimination.

A rapid headspace solid-phase microextraction method was optimized to detect volatile components in three commercial plain sufu samples. The method was then used to qualify 14 aroma impact components that contribute significantly to the volatile profile in 12 commercial samples produced at three locations and laboratory-scale fermented samples aged for 3, 15, 30, and 90 days. Principal component analysis (PCA) was subsequently used to group the samples. The optimized method identified 148 volatile compounds in three commercial samples, and the concentrations of 14 aroma impact compounds varied significantly among commercial and laboratory samples. PCA confirmed that these samples could be discriminated according to their production location and aging time, thereby rendering this method a simple strategy for sample discrimination.