Application of Electronic Nose for Classification of Fish Sauces Using Transient Responses and Learning Vector Quantization Algorithm

Abstract

Fish sauce is one of the signature condiments in various cuisines in many countries. In this paper, fish sauces are successfully classified into groups depending on their quality indicated by the level of total nitrogen content. We introduce an electronic nose technology together with a neural network algorithm to the classification of fish sauces. The transient responses are used as features for the creation of pattern vectors for odor samples. The result of principal component analysis shows well-separated patterns of fish sauce. Furthermore, we also apply the learning vector quantization method for the classification. As a result, we obtain high accuracy of more than 90% in the classification of fish sauce based on the level of total nitrogen content.