A. Duda,1 C. Duda,1 M. Szychta,2 Ł. Gierz,3 K. Przybył,2 Krzysztof Koszela4
1Poznan Univ. of Medical Sciences (Poland) 2Poznan Univ. of Life Sciences (Poland) 3Poznań Univ. of Technology (Poland) 4Poznan Univ of Life Sciences (Poland)
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The aim of the research presented in this work was to develop a model of Artificial Neural Networks (ANN) with the use of computer image analysis for the qualitative classification of deep-frozen raw material - breaded pollock cutlets (Backfisch). Shape and color discriminants were selected, by using a computer program, it was possible to obtain numerical data and build a learning set from them. This work is an example of using one of the methods of artificial intelligence in the food industry. The designed network was characterized by a very high ability for classification, its training was done by the technique of the so-called " with a teacher". Such actions are motivated by the requirements of consumers who are becoming more and more attentive to the products they consume and expect calorically balanced and very high quality products.
A. Duda,C. Duda,M. Szychta,Ł. Gierz,K. Przybył, andKrzysztof Koszela
"Nutritional properties and quality assessment of breaded deep-frozen pollock fish cutlets using neural image analysis", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118781W (30 June 2021); https://doi.org/10.1117/12.2602018
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A. Duda, C. Duda, M. Szychta, Ł. Gierz, K. Przybył, Krzysztof Koszela, "Nutritional properties and quality assessment of breaded deep-frozen pollock fish cutlets using neural image analysis," Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118781W (30 June 2021); https://doi.org/10.1117/12.2602018