Development of an Integrated Soft E-Nose for Food Quality Assessment

Abstract

Food is one of the basic essential elements for human beings. Its quality is directly related to our physical, emotional, and social well-being. An electronic nose is a non-destructive instrument and is an effective solution for quick and easy determination of food quality. In this paper, an integrated soft E-nose methodology focusing on performance enhancement and complexity reduction is proposed and implemented for the effective classification of beef quality and prediction of microbial population in beef. The soft E-nose methodology is tested on 18 available datasets. Ours is a four-step approach that includes validated data collection, signal processing with a moving average filter and principal component analysis for feature reduction, efficient pattern recognition using machine learning and deep learning techniques like support vector machine, k-nearest neighbors, artificial neural network, extreme learning machine, and deep neural network for classification and regression, and an output stage for classification and prediction of the microbial population of beef. This methodology could provide accuracy greater than 98% using k-nearest neighbors, artificial neural network methods, and a correlation coefficient of greater than 0.98 using support vector machine and artificial neural network methods. The proposed methodology is validated using 10-fold cross-validation.