Algorithms to analyze the quality test parameter values of seafood in the proposed ontology based seafood quality analyzer and miner (ONTO SQAM) model

Abstract

Ensuring the quality of food, particularly seafood has increasingly become an important issue nowadays. Quality Management Systems empower any organization to identify, measure, control and improve the quality of the products manufactured that will eventually lead to improved business performance. With the advent of new technologies, now intelligent systems are being developed. To ensure the quality of seafood, an ontology based seafood quality analyzer and miner (ONTO SQAM) model is proposed. The knowledge is represented using ontology. The domain concepts are defined using ontology. This paper presents the initial part of the proposed model – the analysis of quality test parameter values. Two algorithms are proposed to do the analysis – Comparison Algorithm and Data Store Updater algorithm. The algorithms ensure that the values of various quality tests are in the acceptable range. The real data sets taken from different seafood companies in Kerala, India, and validated by the Marine Product Export Development Authority of India (MPEDA) are used for the experiments. The performance of the algorithms is evaluated using standard performance metrics such as precision, recall, and accuracy. The results obtained show that all the three measures achieved good results.

Keywords

• Data store update
• Ontology
• Knowledge base
• Seafood
• Quality