A simple taste analyzing system design for visual and quantitative analysis of different tastes using multi-array chemical sensors and pattern recognition techniques

In this paper, we describe design of a simple taste analyzing system using sensory system based on a multi-array chemical sensor (MACS) and personal digital assistant (PDA) for visual and quantitative analysis of different tastes using pattern recognition techniques. The sensory system is communicated with PDA, which has several interesting benefits for data analysis and display, via wireless using the Bluetooth. A various pattern recognition techniques are adapted including spider map, principal component analysis (PCA) and fuzzy C-means (FCM) algorithm to classify visually data patterns detected by the sensory system. The proposed techniques can be determined the cluster centers and membership grade of patterns through the unsupervised way. The membership grade of an unknown pattern, which does not shown previously, can be visually and analytically determined. Throughout the experimental trails, the taste analyzing system is demonstrated robust performance through data acquisition via wireless communication and visual and quantitative analysis of different tastes for the liquids. The system, which is implemented as a simple hand-held taste analyzing instrument, can be applicable to on-site taste monitoring.