Applying Electronic Nose System for Qualitative Classification of Iranian Black Tea

Tea is one of the strategic products in north of Iran. The tea produced in tea factories have different qualities as it is affected by various factors such as weather conditions during growth, soil, harvest time, as well as processing and preparation methods. In addition to its appearance, other essential properties of tea are its chemical compounds and aromatic characteristics. Investigating new and accurate methods for tea quality assessment has a significant effect on the development of tea processing industries. In this research, an electronic nose system was used to extract the characteristics of tea aroma and applying of these features for qualitative classification of black tea. Extracted Features from a sensor array, including ten different metal oxide gas sensors (MOS) were used for classification of five qualitative categories of black tea by means of chemometric methods. Results showed that the best classification performance was obtained by Artificial Neural Network (ANN) with a total classification accuracy of 88.00%. Also, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) resulted in accuracies of 78.00% and 86.67% respectively. Based on the results of Principle Components Analysis (PCA), it was found that MQ7 and MQ2 sensors had the highest effect on the separation of different classes of tea. Generally, the performance of electronic nose system was suitable for qualitative classification of Iranian black tea.