Abstract
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.
Similar content being viewed by others
Notes
NIR – Near infra-red
References
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.
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.
Bottou, L., & Lin, C. J. (2007). Support vector machine solvers. Large scale kernel machines, 301–320.
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.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
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.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
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.
Friedman, J. (1996). Another approach to polychotomous classification (Vol. 56). Technical report, Department of Statistics, Stanford University.
Guttormsen, E. (2015). Robust classification approaches to industrial sorting of herring fractions. Master’s Thesis, Norwegian University of Science and Technology (NTNU).
Hastie, T., & Tibshirani, R. (1998). Classification by pairwise coupling. The Annals of Statistics, 26(2), 451–471.
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.
Hsu, C. W., Chang, C. C., Lin, C. J. (2010). A practical guide to support vector classification. Online: https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
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.
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.
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. http://www.rubin.no/images/files/documents/4411-164_birstoff_pelagisk_industri1.pdf. Accessed 5 July 2016.
Richardsen, R., Nystøl, R., Strandheim, G., Viken, A. (2014). Analyse marint restråstoff. SINTEF report A26863, ISBN 978-82-14-05877-2.
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.
Acknowledgments
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
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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). https://doi.org/10.1007/s11947-016-1774-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11947-016-1774-2