Optimizing machine learning parameters for classifying the sweetness of pineapple aroma using electronic nose

Electronic nose (e-nose) has been widely used to distinguish various scents in food. The output of e-nose is a signal that can be identified, compared, and analyzed. However, many researchers use e-nose without using standardization tools, therefore e-nose is still often questioned for its validity. This paper proposes an electronic nose (e-nose) to classify the sweetness of pineapples. The standard sweetness levels are measured by using a Brix meter as a standardization tool. The e-nose consists of a series of gas sensors MQ Series which are connected to the Arduino micro-controller. The sweetness levels measured by the Brix meter are then ordered into low, medium, high sweetness groups. These sweetness groups are used as label ground-truth for the e-nose. Signal processing PCA and mother wavelet is employed to reduce noise from the e-nose signals. The signal processing methods obtain optimal parameters to find the characteristics of each signal. Machine learning methods were successfully carried out with optimized parameters for the classification of three levels of sweetness of pineapple. The best accuracy is 82% using KNN with 3 neighbors.