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Cod heads were hydrolysed using different enzymes (Alcalase, Flavourzyme, Papain, Bromelain, and Protamex) at concentration of 0.1% for 1 hour and different water/raw material ratios. The most promising processing conditions were further verified in large-scale trials in a pilot plant. The results showed that the water/raw material ratios had little effect on hydrolysis yield, protein content, or the molecular weight distribution in hydrolysate. However, different enzymes led to differences in chemical composition and molecular weight distribution. Protein recovery was lower in the pilot trial, primarily because pilot and industrial processing equipment must be adapted and optimized to the raw material and process. Upscaling proved that high-quality protein powder can be produced from cod heads. The product has a neutral smell, light color, contains more than 80% protein, and could serve as an excellent source of protein for human consumption.

Among the rest raw material in herring (Clupea harengus) fractions, produced during the filleting process of herring, there are high-value products such as roe and milt. As of today, there has been little or no major effort to process these by-products in an acceptable state, except for by manual separation and mostly mixed into low-value products. Even though pure roe and milt fractions can be sold for as much as ten times the value of the mixed fractions, the separation costs using manual techniques render this economically unsustainable. Automating this separation process could potentially give the pelagic fish industry better raw material utilization and a substantial additional income. In this paper, a robust classification approach is described, which enables separation of these by-products based on their distinct reflectance features. The analysis is conducted using data from image recordings of by-products delivered by a herring processing factory. The image data is divided into three respective classes: roe, milt, and waste (other). Classifier model tuning and analysis are done using multiclass support vector machines (SVMs). A grid search and cross-validation are applied to investigate the separation of the classes. Two-class separation was possible between milt/roe and roe/waste. However, separation of milt from waste proved to be the most difficult task, but it was shown that a grid search maximizing the precision—the true positive rate of the predictions—results in a precise SVM model that also has a high recall rate for milt versus waste. This is a submitted manuscript of an article published by Springer Verlag. The final publication is available at https://link.springer.com/article/10.1007%2Fs11947-016-1774-2

PurposeThe purpose of this paper is to describe a new slaughter line for industrial slaughtering of salmonid fish. Traditionally, slaughtering of farmed salmonids – salmon and rainbow trout – was done manually by bleed cutting with knives. Using the new slaughter line that includes 3D machine vision and a bleed‐cutting robot, slaughtering is almost completely automated – nominally requiring only one person to supervise the line and manually bleed cut the fish not handled by the robot.Design/methodology/approachThe design approach of the salmonid slaughter line focuses on using 3D machine vision and a bleed‐cutting robot with four biaxial pneumatic actuators to handle the slaughtering of pre‐anesthetized salmon and rainbow trout.FindingsUnder normal operating conditions, the slaughter line is capable of automatically slaughtering 85‐95 percent of all fish at an average feed rate of 30‐80 salmon/min, and the remaining 5‐15 percent are slaughtered manually. Several issues have been discovered, that should be addressed to improve the slaughter line.Originality/valueThis paper presents a new complete salmonid slaughter line that has reduced the need for manual labor in salmonid slaughtering plants.

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.