Abstract
In this paper, a novel close to real-time artificial intelligent system for enumerating Total Viable Bacteria (TVB) in drinking water was developed by using pattern recognition and machine vision technology. In order to identify the viable bacteria accurately, four shape features including circularity ratio, eccentricity, rectangularity, and compact degree, and four color features (GRsd, BRsd, HRsd, SRsd) of the stained viable bacteria image were extracted. An optimal artificial neural network was used as the bacterial recognition classifier, whose inputs were the extracted feature parameters and output was bacteria signal or non-bacteria signal. By using this intelligent system, TVB counts in each sample can be enumerated within 1 h, but the traditional Aerobic Plate Count (APC) method will take us 48 h. The comparative test also indicated that the counting results by two methods are closely correlated (R2=0.9942). This close to real-time accurate information may contribute to melioration and instauration of drinking water safety systems and risk management for TVB.
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