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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In order to develop an automatic and rapid detection method for enumeration of total bacteria in juice, biomimetic pattern recognition and machine vision were employed. The characteristic data, such as shape, texture and color features, were acquired by using the machine vision technology from bacteria images in varieties of juice. Based on multi-weight higher order neuron network, the recognition models were established which can achieve the imitation of human learning, memorizing and judging. By applying the principle of statistics, the detection results of new method showed no difference, compared with the traditional method in apple juice, tomato juice and carrot juice. The new method simplifies experimental preparation and shortens judgment time, especially in sample test on the spot and monitoring production site. Moreover, by using this rapid detection method, total bacteria counts in samples could be accurately enumerated within 1 h, which was much less than 24-48 h by using the traditional method.