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1) Ege University, Fisheries Faculty, Fish Processing Technology Department, 35100 Bornova-­Izmir, Türkiye; 2) Ege University, Agriculture Faculty, Animal Science Department, Biometry and Genetics Unit, 35100 Bornova-Izmir, Türkiye

Artificial neural networks models used for fishery products

Modelle künstlicher neuronaler Netze für die Fischereitechnik

Irem Kılınç1), Berna Kılınç1), Yakut Gevrekçi2), Çigdem Takma2)


The statistical methods have many benefits in acquiring results in a variety of areas such as the optimization the conditions of processing, estimating the shelf life of food products, predicting the bacteria growth on foods, and also predicting the risk formation of chemicals and pathogenic bacteria on food products. The Artificial Neural Network (ANN) model is just one of many mathematical models used today, and its applications in many areas of food and aquatic products will be enhanced in the future. In addition, in the near future, the most appropriate ANN model will be determined by combining the data produced with advanced processing technologies or obtained from food products and different models for each type of food product, and relible predictions can be made for the future. In the lights of the importance of the subject in this review; the meaning, importance, types, formulations of the ANN models were explained. Additionally, the usages of the ANN models not only in aquaculture and fisheries studies, but also the usages of the ANN models for evaluation of safety assessments of the processing technologies of fishery products were highlighted. Moreover, the usages of the ANN models for determining the freshness and shelf life of fishery products were reviewed. It is expected that future development of various mathematical models, as well as studies to adapt the use of these models together, will benefit the aquaculture, fisheries, food and seafood processing industries.

Keywords: fishery products, the artificial neural network, models

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Weitere Informationen zum Beitrag

Arch Lebensmittelhyg 75,
24–33 (2024)
DOI 10.53194/0003-925X-75-24


© M. & H. Schaper GmbH & Co.
ISSN 0003-925X

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