RESUMO
This study investigates the use of untapped mesopelagic species as a source of long-chain polyunsaturated omega-3 fatty acids (LC n-3 PUFAs) to meet the growing demand. The challenges faced by commercial fishing vessels, such as varying catch rates and species distribution affecting lipid levels, are addressed. Marine oils were produced post-catch using thermal separation and enzymatic hydrolysis during four commercial cruises, screening approximately 20,000 kg of mixed mesopelagic species. Maurolicus muelleri and Benthosema glaciale were the dominant species in the catch, while krill was the primary bycatch. The lipid composition varied, with B. glaciale having a higher prevalence of wax esters, while triacylglycerols and phospholipids were more predominant in the other species. LC n-3 PUFAs ranged from 19% to 44% of lipids, with an average EPA + DHA content of 202 mg/g of oil. Both processing methods achieved oil recoveries of over 90%. Estimates indicate that the mesopelagic biomass in the Northeast Atlantic could supply annual recommended levels of EPA + DHA to 1.5 million people, promoting healthy heart and brain functions. These findings offer valuable insights for considering mesopelagic species as a potential source of dietary marine lipids, laying the groundwork for further research and innovation in processing and obtaining valuable compounds from such species.
RESUMO
UNLABELLED: Weight is an important parameter by which the price of whole herring (Clupea harengus) is determined. Current mechanical weight graders are capable of a high throughput but have a relatively low accuracy. For this reason, there is a need for a more accurate high-speed weight estimation of whole herring. A 3-dimensional (3D) machine vision system was developed for high-speed weight estimation of whole herring. The system uses a 3D laser triangulation system above a conveyor belt moving at a speed of 1000 mm/s. Weight prediction models were developed for several feature sets, and a linear regression model using several 2-dimensional (2D) and 3D features enabled more accurate weight estimation than using 3D volume only. Using the combined 2D and 3D features, the root mean square error of cross-validation was 5.6 g, and the worst-case prediction error, evaluated by cross-validation, was ±14 g, for a sample (n = 179) of fresh whole herring. The proposed system has the potential to enable high-speed and accurate weight estimation of whole herring in the processing plants. PRACTICAL APPLICATION: The 3D machine vision system presented in this article enables high-speed and accurate weight estimation of whole herring, thus enabling an increase in profitability for the pelagic primary processors through a more accurate weight grading.
