Characterization of the volatile flavor profiles of Zhenjiang aromatic vinegar combining a novel nanocomposite colorimetric sensor array with HS-SPME-GC/MS

In this work, the feasibility of combining headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) with colorimetric sensor array (CSA) for flavor characterization of Zhenjiang aromatic vinegar (ZAV) from different grades was evaluated. Firstly, a new type of nanocomposite CSA that combines porous nanomaterials including hollow zeolitic imidazolate framework-8 (H-ZIF-8) and Universitetet i Oslo-66-NO2 (UiO-66-NO2) with chemically responsive dyes was successfully constructed. Then, the nanocomposite CSA was applied to effectively discriminate ZAV of different grades and further quantitively predict the characteristic aroma components by using multivariate data analysis. Compared with other pattern recognition methods, support vector machine (SVM) model achieved the highest recognition rate both for training set (100%) and prediction set (94.44%). Furthermore, a good performance of quantitative prediction of characteristic aroma components of ZAV including acetic acid, total volatile acids, furfural, aldehyde ketones, ethyl acetate and esters combining CSA with partial least square (PLS) regression was achieved with all the correlation coefficients being over 0.80 for training and prediction sets. Therefore, the nanocomposite CSA combined with chemometrics could be an effective tool for the rapid and nondestructive assessment of flavor and quality of vinegar.