Monitoring the Vinegar Aging based on Electronic Nose
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Abstract:
Four kinds of vinegars were detected by PEN3 electronic nose. The volatile compositions emanating from the vinegars were collected by the systems, and their response values were obtained. Principal component analysis (PCA), linear discrimination analysis (LDA), fisher linear discrimination analysis (FDA) and multilayer perceptron neural network (MLPNN) were used to distinguish the vinegars from different aging time. The results showed that LDA was able to identify different aging time of vinegars and the contribution rate was above 90%. The identification of vinegars from different aging time by LDA was better than that by PCA. Moreover, LDA showed that the changes of vinegars volatile composition had an increasing tendency during the aging time, and had a great agreement with the total acid of the samples. FDA and MLPNN were also employed to predict the aging time of the samples, which indicated that the FDA prediction rates of Feng Xiang, Long Xian, Jin Tai, Wei Bin vinegar were 100%, 100%, 98% and 100% respectively, while the MLPN prediction rates of these samples were 100%, 100%, 96.92% and 100%, respectively. Therefore, the prediction rate monitored by electronic nose combined with FDA was better than that with MLPNN.
