Milk Assessment using Potentiometric and Gas Sensors in Conjunction With Neural Network

Abstract

Currently, the identification of milk quality requires laboratory tests that are time-consuming because by analyzing the microorganisms commonly found in milk. In addition, milk quality can be directly detected by using the human nose and tongue. However, this is harmful because it can affect the human health. Moreover, the human senses have a different sensitivity that is not accurate in detecting the quality of milk. In this study has developed a sensor system to assess the quality of milk. The role of the human nose is replaced by gas sensor array for the identification of the smell or odor of milk. While the tongue is taken over by a potentiometric sensor array for identification of taste or compounds in the milk. The experimental result shows that this sensor array can produce different patterns to the fresh, sour, and spoiled milk samples. The Neural Network can be used to assess the quality of milk with a success rate of 83%. This technique is expected to be used as a tool to assess the quality of milk quickly, easily, and accurately.