Odor clustering using a gas sensor array system of chicken meat based on temperature variations and storage time

Abstract

Shelf life and temperature are two things that affect the freshness of meat. Generally, people identify the freshness of meat by looking at the texture, color, and even aroma of meat. These methods have less effective approaches to identify the freshness of meat. The limitations of the human sense of smell have led to the development of gas sensor array system technology. Research has been done on odor cluster analysis using gas sensor array with variations in shelf life and temperature in classifying the smell of chicken meat. The study used a sample of 20 g of chicken meat in a 150 ml bottle which was sensed using a gas sensor array system at a certain storage period and temperature. The shelf life used is a shelf life of 0 h, 6 h, 12 h, 18 h, and 24 h as well as variations in temperature 4 °C, 30 °C, 35 °C, 40 °C, 45 °C, 50 °C. The analysis is carried out using machine learning in the form of principal component analysis and deep neural network. In this study using the principal component analysis and deep neural network method, it can be seen that the gas sensor array is able to classify well. Meanwhile, the results of deep neural network model can be classified as fresh and unfresh chicken meat with a testing accuracy of 98.70%. The result showed that gas sensor array could classify chicken meat with high accuracy and the proposed method provides a significant improvement.