Stina Frosch

Multispectral Imaging for Determination of Astaxanthin Concentration in Salmonids

Multispectral imaging has been evaluated for characterization of the concentration of a specific cartenoid pigment; astaxanthin. 59 fillets of rainbow trout, Oncorhynchus mykiss, were filleted and imaged using a rapid multispectral imaging device for quantitative analysis. The multispectral imaging device captures reflection properties in 19 distinct wavelength bands, prior to determination of the true concentration of astaxanthin. The samples ranged from 0.20 to 4.34 g per g fish. A PLSR model was calibrated to predict astaxanthin concentration from novel images, and showed good results with a RMSEP of 0.27. For comparison a similar model were built for normal color images, which yielded a RMSEP of 0.45. The acquisition speed of the multispectral imaging system and the accuracy of the PLSR model obtained suggest this method as a promising technique for rapid in-line estimation of astaxanthin concentration in rainbow trout fillets.

Opportunities for the Herring Industry to Optimize Operations Through Information Recording, Effective Traceability Systems, and Use of Advanced Data Analysis

ABSTRACT Two case studies of the herring industry reveal that insufficient data on processes and traceability are currently recorded onboard fishing vessels, at primary processing into marinated herring fillets and at secondary processing into small jars as pickled herring products. This means that the traceable unit of a batch of herring is at the level of a whole fishing trip, and that there is a disconnection in traceability along the chain. Data on process and product parameters are insufficient and precludes the use of modern data analysis to examine where improvements and greater efficiencies can be obtained. Suggestions and procedures to overcome these deficiencies are outlined and discussed.

Image Analysis of Pellet Size for a Control System in Industrial Feed Production

When producing aquaculture fish feed pellets, the size of the output product is of immense importance. As the production method cannot produce pellets of constant and uniform size using constant machine settings, there is a demand for size control. Fish fed with feed pellets of improper size are prone to not grow as expected, which is undesirable to the aquaculture industry. In this paper an image analysis method is proposed for automatic size-monitoring of pellets. This is called granulometry and the method used here is based on the mathematical morphological opening operation. In the proposed method, no image object segmentation is needed. The results show that it is possible to extract a general size distribution from an image of piled disordered pellets representing both length and diameter of the pellets in combination as an area.

Spectral Imaging as a Tool in Food Research and Quality Monitoring of Food Production

A forward-looking food industry must obviously continue to develop its production technology to be able to produce foods that meet both present legislation and consumers’ expectations and demands. Ensuring a healthy, secure and sensory food quality, as well as ensuring cost competitiveness / effectiveness is of high importance to survive the strong competition within the field. The high costs in many food-processing areas are primary caused by the extensive use of manual work e.g. for visual inspection of quality parameters and the subsequent sorting or removal of products. However, new production and / or distribution technologies in themselves neither create nor ensure high quality of products or the optimization of the production. This requires knowledge about both new opportunities to create specific production and distribution conditions in combination with knowledge about product response given the production and distribution conditions. Food and food production covers a broad variety of both raw materials and production processes. Therefore, application of new technology cannot be regarded as a simple procurement of accessible standard products but requires research and development including several tests to ensure optimal outcome. The assessment of the visual appearance of food products from size and colour of the product to uniformity of packaging is an important part of the control system in the food supply chain. To ensure that the required standards are met, inspections at all stages from primary production to final retail distribution are needed. However, visual inspection of quality parameters and the subsequent sorting and sometimes also rejection of products by manual work are significant contributors to the total production costs in the food industry. To save costs and to enhance visual quality assessments, automatic vision systems are introduced and tested in many food-manufacturing operations. Vision systems are attractive for online quality assessment and process control, because the methods are rapid, contact free and non-destructive. The introduction of vision systems for quality monitoring in the food manufacturing industry is challenged by the relatively harsh production environment, typically in the form of a high humidity, low / high temperatures, routine wash down and sanitation. The technology is maturing, however, and the special hardware requirements on system design are now met, so that computer-based vision systems are now gaining wider application for quality monitoring in food processing.

The influence of processing conditions on the weight change of single herring (Clupea herengus) fillets during marinating

One of the main issues in the manufacturing of marinated herring is the variation in yield, which in turn, is affected by the processing conditions and the variance in fat content. In the present work, we study these effects on individual herring fillets, with focus on the intermediate brining process. Brining time, brine concentration, marinade composition and storage time were varied. For brine concentrations 8%, 16% and 26%, the diffusion coefficient was 2.31 × 10-9 m2 s-1, which was used for model development of salt change prediction in herring during brining. Conducting experiments on single fillets revealed a correlation between the fat content and the weight change after 35 days of marinating. The greatest change occurred within the first few days and only minor changes were seen during the storage period of up to one year. These results contribute to a better understanding of the herring marinating process, which can aid the optimization process in the industry.

Muscle Protein Profiles Used for Prediction of Texture of Farmed Salmon (Salmo salar L.)

A soft texture is undesired in Atlantic salmon as it leads to downgrading and reduced yield, yet it is a factor for which the cause is not fully understood. This lack of understanding highlights the need for identifying the cause of the soft texture and developing solutions by which the processing industry can improve the yield. Changes in muscle protein profiles can occur both pre- and postharvest and constitute an overall characterization of the muscle properties including texture. The aim of this study was to investigate this relationship between specific muscle proteins and the texture of the salmon fillet. Samples for 2D-gel-based proteomics were taken from the fillet above the lateral line at the same position as where the texture had been measured. The resulting protein profiles were analyzed using multivariate data analysis. Sixteen proteins were found to correlate to the measured texture, showing that it is possible to predict peak force based on a small subset of proteins. Additionally, eight of the 16 proteins were identified by tandem mass spectrometry including serum albumin, dipeptidyl peptidase 3, heat shock protein 70, annexins, and a protein presumed to be a titin fragment. It is contemplated that the identification of these proteins and their significance for the measured texture will contribute to further understanding of the Atlantic salmon muscle texture.

Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the users engagement is restricted to a minimum, which might be beneficial for certain applications. We conclude that the subsequent parallel factor analysis models fitted to excitation–emission data after scatter identification based on either ROBPCA or S‐PCA are comparable; however, the modified method based on S‐PCA clearly outperforms the original approach in relation to computational time. Copyright