Detection and Classification of Indonesian Civet and Non-Civet Coffee Based on Statistical Analysis Comparison Using E-Nose
Institut Teknologi Sepuluh Nopember (ITS) Sukolilo, Sulaiman Wakhid, Riyanarto Sarno, Institut Teknologi Sepuluh Nopember (ITS) Sukolilo, Shoffi Izza Sabilla, Institut Teknologi Sepuluh Nopember (ITS) Sukolilo, Dike Maghfira, Institut Teknologi Sepuluh Nopember (ITS) Sukolilo
Abstract
Civet coffee is a highly priced premium beverage in Indonesia. Because of its high economic value, civet coffee is often falsified with non-civet coffee. The detection and classification of coffee aroma using an e-nose has been the subject of several researches. However, only few researches have been done on civet coffee and non-civet coffee detection using an e-nose. This study aimed to improve the classification between civet coffee and non-civet coffee by trying out different combinations of classification methods and statistical parameters. The coffee aroma data were taken from e-nose sensors with different sensitivity toward certain chemicals. There are a number of steps in the classification of coffee aroma: ground truth data acquisition, statistical feature extraction, classification, and performance evaluation. The experimental results of this study indicate that an e-nose can recognize and distinguish well between civet and non-civet coffee. Comparing 6 classes of coffee, the best performing combination was the decision tree algorithm with the average and standard deviation parameters, which obtained 97% accuracy.