Skip to main content

A Machine Vision System for Robust Sorting of Herring Fractions


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 preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    NIR – Near infra-red


  1. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory (pp. 144–152). ACM.

  2. Ben-Hur, A., & Weston, J. (2010). A user’s guide to support vector machines. In Data mining techniques for the life sciences (pp. 223–239). Humana Press.

  3. Bottou, L., & Lin, C. J. (2007). Support vector machine solvers. Large scale kernel machines, 301–320.

  4. Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.

    Google Scholar 

  5. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Google Scholar 

  6. Egede-Nissen, H., Vogt, K. G., Haugen, J.-E., Høstmark, Ø., Oterhals, Å. (2013). Utvikling av høykvalitets pulverprodukt fra sildemelke. Sensorisk kvalitet på sildemelkepulver testet ved akselererte lagringsbetingelser-Fagrapport 2. Nofima Report 14/2013. ISBN: 978-82-8296-144-8.

  7. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

    Article  Google Scholar 

  8. Fossum, J. A., Mathiassen, J. R., Toldnes, B., Salomonsen, C. (2012). Teknologi for fraksjonert uttak og sortering av restråstoff fra sild – Fase 1, SINTEF Report A23065. ISBN: 978-82-14-05437-8.

  9. Friedman, J. (1996). Another approach to polychotomous classification (Vol. 56). Technical report, Department of Statistics, Stanford University.

  10. Guttormsen, E. (2015). Robust classification approaches to industrial sorting of herring fractions. Master’s Thesis, Norwegian University of Science and Technology (NTNU).

  11. Hastie, T., & Tibshirani, R. (1998). Classification by pairwise coupling. The Annals of Statistics, 26(2), 451–471.

    Article  Google Scholar 

  12. Hu, B. G., Gosine, R. G., Cao, L. X., & de Silva, C. W. (1998). Application of a fuzzy classification technique in computer grading of fish products. IEEE Transactions on Fuzzy Systems, 6(1), 144–152.

    Article  Google Scholar 

  13. Hsu, C. W., Chang, C. C., Lin, C. J. (2010). A practical guide to support vector classification. Online:

  14. Kjerstad, M., Larssen, W. E., Nystrand, B. T. (2014). Produkt- og markedsutvikling for restråstoff fra NVG-sild til konsum. Møreforskning Report MA 14–18. ISSN: 0804-54380.

  15. Lee, M. F. R., de Silva, C. W., Croft, E. A., & Wu, Q. J. (2000). Machine vision system for curved surface inspection. Machine Vision and Applications, 12(4), 177–188.

    Article  Google Scholar 

  16. Østvik, S. O., Grimsmo, L., Jansson, S., Dauksas, E., Bondø, M. (2009). Biråstoff fra filetering av sild-Kartlegging og analyse av råstoff og utnyttelsesmuligheter. RUBIN Rapport nr. 164. Accessed 5 July 2016.

  17. Richardsen, R., Nystøl, R., Strandheim, G., Viken, A. (2014). Analyse marint restråstoff. SINTEF report A26863, ISBN 978-82-14-05877-2.

  18. Wold, J. P. (2013). Individbasert kvalitetssortering og kvalitetsmerking av pelagisk fisk: Automatisk sortering basert på indre kvalitetsparametre. Nofima Report 35/2013. ISBN: 978-82-8296-112-7.

Download references


The work in this paper was financed by the Norwegian Research Council through project grant no. 219204. We thank the herring processing plant Nergård Sild for providing us with vacuum-packed herring fractions that were used in the experiments in this paper. We thank Henning Grande and Halgeir Hansen, Nergård Sild AS, for being the industry contacts for the project of which this paper is a part. We thank Cecilie Salomonsen for making the 3D illustration in Fig. 3.

Author information



Corresponding author

Correspondence to John Reidar Mathiassen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guttormsen, E., Toldnes, B., Bondø, M. et al. A Machine Vision System for Robust Sorting of Herring Fractions. Food Bioprocess Technol 9, 1893–1900 (2016).

Download citation


  • Machine vision
  • Support vector machines
  • Herring
  • Sorting