Abstract
Polymer modified Quartz Tuning Fork sensor array with three sensors has been developed to classify 1,4-dimethoxy-2, 3-butanediol (BD), cyclohexanone (CH), ethanol, methanol and acetone with various concentrations. Linear Discriminant Analysis (LDA), Decision Tree (DT), and Random Forrest (RF) have been used and evaluated as the classifiers predicting the VOCs and their concentration. The classifiers were trained with three features: response time, recovery time, and shift in the resonant frequency of the sensors. The results have been compared and analyzed for the performance of each classifier for the present system. RF gives the best performance as a classifier with an accuracy of classification more than 95%. We present results of VOC classifier performance for the QTF sensor array.
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