Bridging the Gap to Sustainable Salmon Farming: Overcoming the Gaping Problem
- *Corresponding Author:
- Karin Pittman
Department of Biology
University of Bergen
High Technology Centre
N-5020 Bergen, Norway
Tel: +47 55 58 44 72
E-mail: [email protected]
Received Date: February 23, 2013; Accepted Date: March 25, 2013; Published Date: March 27, 2013
Citation: Pittman K, Merkin GV, Brandebourg T (2013) Bridging the Gap to Sustainable Salmon Farming: Overcoming the Gaping Problem. J Fisheries Livest Prod 1:104. doi:10.4172/2332-2608.1000104
Copyright: © 2013 Pittman K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
As oily fish consumption has increased worldwide, farmed salmonid production has also dramatically increased. As such, farming and the satellite industries affiliated with the salmonid production chain form an increasingly important economic foundation for many communities in Norway and throughout Northern Europe. However, despite the successful growth of the European salmon industry, quality concerns pose significant challenges to the sustainability of farmed salmonid production. For instance, muscle gaping, the undesirable lace-like, irregular voids or gapes in the final product, can lead to the downgrade of up to 38% of salmon produced. These blemishes lead to consumer rejection of whole cuts at the fish counter while the resulting decrease in structural integrity of the meat also poses significant limitations to the further processing of value-added products. Because of such devastating losses, determining the underlying causes of gaping and developing better detection methods that allow evaluation of intervention strategies have become high research priorities for the industry and governmental agencies alike. Automated Image Analysis (IA) is one such technology that allows the objective measure of gaping on fish carcasses. Efforts to translate this technology to a platform that can be utilized efficiently in packing plants are progressing rapidly and producing promising results. The ability to objectively and rapidly detect graded differences in gaping of salmon products in commercial settings will allow the identification of critical points in the supply chain that impact upon product quality. Applying IA methods to identify these critical points and to assess the effectiveness of intervention strategies will ultimately allow salmon producers to bridge the quality gap that currently exists between the fish farm and the consumer.