文章基本信息

标题：MEAT FRESHNESS IDENTIFICATION SYSTEM USING GAS SENSOR ARRAY AND COLOR SENSOR IN CONJUNCTION WITH NEURAL NETWORK PATTERN RECOGNITION
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作者：MUHAMMAD RIVAI ; FAJAR BUDIMAN ; DJOKO PURWANTO 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：12
出版社：Journal of Theoretical and Applied
摘要：Meat freshness level is an important factor to determine meat quality for consumption. In this research, a sensor system has been designed to identify the freshness level of meat in fast, precise and non-destructive manners. The system is implemented into a Raspberry Pi equipped by gas and color sensors as the freshness identifier tools to replace the human olfaction and vision in determining a fresh meat. Pattern recognition powered by a neural network is used to identify the meat�s freshness. The neural network inputs are the odors sensed by the gas sensor array of MQ-136, MQ-137, TGS 2620 and Red, Green, Blue values sensed by TCS 3200 color sensor. Three levels of freshness have been tested, such as fresh meat, half-rotten meat, and rotten meat. The usage of the three gas sensors and one color sensor of the system is capable to acquire a distinct pattern for the three categories of freshness. The freshness identification of the meat has a high percentage of success up to 80%. The errors are caused by the small different of the pattern sensed by sensors for half-rotten meat and rotten meat; these two kinds of meat fortunately are not consumable. Thus, it may conclude that the system has 100% success degree to identify fresh meat and non-fresh meat. The implementation of the system is expected to replace the traditional measurement by the human senses (i.e. nose and eyes) to obtain equal measurement as different human examiner acquires different result, and to eliminate the impact of bacteria or virus from meats to examiner. It may also replace measurement system using chemical substances so the tested meat will be still consumable.
关键词：Color Sensor; Gas Sensor; Meat Freshness; Neural Network; Raspberry Pi.