Automated Fish Classification Using Unprocessed Fatty Acid Chromatographic Data: A Machine Learning Approach

Fish is approximately 40% edible fillet. The remaining 60% can be processed into low-value fertilizer or high-value pharmaceutical-grade omega-3 concentrates. High-value manufacturing options depend on the composition of the biomass, which varies with fish species, fish tissue and seasonally throughout the year. This paper investigates different classification and feature selection algorithms for their ability to automate the processing of Gas Chromatography data.