Building of a metal oxide gas sensor-based electronic nose to assess the freshness of sardines under cold storage

Abstract

Abstract We report on the building of a simple and reproducible electronic nose based on commercially available metal oxide gas sensors aimed at monitoring the freshness of sardines stored at 4 °C. Sample delivery is based on the dynamic headspace method and four features are extracted from the transient response of each sensor. By using an unsupervised method, namely principal component analysis (PCA), we found that sardine samples could be grouped into three categories (fresh, medium and aged), which corresponded to an increasing number of days that sardines had spent under cold storage. Then, supervised linear or non-linear pattern recognition methods (PARC) such as discriminant factor analysis (DFA) or fuzzy ARTMAP neural networks (FANN) were successfully applied to build classification models to sort sardine samples according to these three states of freshness. The success rate in classification was 96.88% for the neural network classifier. Additionally, 10 volatile species that indicated the evolution of sardines with the number of days of cold storage were identified by SPME/MS/GC.