ArticleJournal of food science and technology2022
Prediction of storage time in different seafood based on color values with artificial neural network modeling.
Article in Journal of food science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Sensor to Analyze Fish Freshness: A Virtual Sensor Array Based on an Electrochemical Chemotransistor.Sensors (Basel, Switzerland) · 2026Article
- AI verification for spirulina's antimicrobial power in total coliform and Staphylococcus aureus isolated from tilapia fillet.Scientific reports · 2025Article
- Quality Differences in Frozen Mackerel According to Thawing Method: Potential Classification via Hyperspectral Imaging.Foods (Basel, Switzerland) · 2024Article
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Authors and funding
1 author.
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Abstract
The determination of storage time in seafood could be performed by microbiological, chemical and sensory analysis. Among these mentioned methods color changes are one part of sensory analysis and are prior acceptance criteria from the point of consumers' view. In this study, a feedforward artificial neural network (ANN) model was developed to predict the storage time of seafood based on L*, a* and b* values. A total of 205 data set were compiled from the literature that represents the color changes of different seafood products to train and test the ANN model. Another set of data (n = 45) were used for the validation of developed ANN model. A multi-layer perceptron (MLP) was applied for the determination of agreements between input and output data. The most accurate topology were determined in accordance with the changes in the values of correlation coefficients (R
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