GDL

One problem frequently faced by consumers in traditional markets is the addition of dangerous preservative substance in food, especially formalin. The formalin, which commonly used, is difficult to detect its presence by human since it is colorless. It typically requires laboratory based testing to detect the formalin using chemical compound. This study aims to build a formalin detector system in food which employs a series of polymer based gas sensor. The sensor used is made of polymer mixed with active carbon to form polymer-carbon composite. There are four types of polymer used, namely Polyethylene Glycol (PEG) 6000, PEG200, PEG20M, and PEG1450. The used of polymer-carbon composite as the gas sensor led a unique characteristic when it is exposed to vapor of food with or without formalin. The resistance of each polymer is different for each detected vapor. The combination of those sensors give a pattern of voltage output on the sensors when they are exposed certain gas so that every gas has its unique voltage pattern. The method of detection uses algorithm of back-propagation of neural network. That voltage pattern of sensors serve as input to an artificial intelligence program. The result shows that the system has the accuracy of 75% in detecting formalin in food

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