Electronic Nose and Neural Network Algorithm for Multiclass Classification of Meat Quality

Abstract

Meat is a source of food that contains many nutrients. The nutritional content of meat consists of fat, calories, trans fat, saturated fat, calcium, protein, vitamin D, vitamin B6, vitamin B12, and magnesium. Due to its good nutritional content, the demand for meat in Indonesia has increased. However, there are problems with meat health. Meat is prone to spoilage and is quickly contaminated with microbes. The microbial population can spoil or spoil the meat. Checking the feasibility of meat is usually done by looking at the texture of the meat traditionally. However, this method is less effective in assessing the feasibility of meat. Therefore, another method is used to determine the feasibility of meat, namely using the Electronic Nose (e-nose) with the Neural Network (NN) algorithm. Because by using an e-nose, that can find out the smell or smell of decent meat. They are applying the NN algorithm for classification to work in a structured manner on each component needed to determine meat quality. These results can help people to get the meat of good quality. The experiment was carried out using a dataset that had a total of 2220 data. The experimental results show that using the NN algorithm with the e-nose sensor gets an accuracy of 0.92.