Characterization of Volatile Organic Compounds of Vinegars with Novel Electronic Nose System Combined with Multivariate Analysis

A novel electronic nose system (also called artificial olfaction system) based on colorimetric sensor array was developed for characterization and identification of the volatile organic compounds (VOCs) of vinegars fermented from different raw materials. Fifteen chemo-responsive dyes including nine metalloporphyrins and six pH indicators were selected according to their sensitivity to volatile compounds from vinegar samples. The colorimetric sensor array was made by printing selected chemo-responsive dyes on a silica gel plate. A color change profile for each sample was obtained by differentiating the images of the colorimetric sensor array before and after exposure to the odorant of vinegar sample. The digital data (i.e., red, green, and blue components of the image) representing the color change profiles for the vinegar samples were analyzed. Genetic algorithm partial least squares was employed to select sensitive image digital variable to build a calibration model. Several methods (i.e., linear discrimination analysis, LDA; partial least square discrimination analysis; artificial neural network) were also used comparatively for classification, the result was evaluated by the % correct identification of samples. The optimal model was achieved by LDA model with 14 image digital variables used, and all the vinegar samples were correctly identified both in training and testing sets. This research suggests that the system shows significant potential in vinegar VOCs characterization and discrimination.