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Prediction of storage time in different seafood based on color values with artificial neural network modeling

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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 (R2) and mean square errors (MSE) and found to be 30 neurons in the layer (R2 = 0.81 and MSE = 0.2). The performance of ANN model was evaluated based on 6 criteria such as Mean Absolute Deviation (MAD), Mean Square Errors (MSE), Residual Mean Square Errors (RMSE), Correlation Coefficient (R2), Mean Absolute Error (MAE) and F-test statistics and found to be 0.2, 0.05, 0.002, 0.8, 0.71 and 1.06, respectively. Moreover, predicted and observed storage time values were fitted and regression coefficient was found to be 0.85. In accordance with the results of this study, the proposed ANN model is accurate, reliable, and proper for the estimation of storage time in seafood products.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The module developed during the current study is available from the corresponding author on reasonable request.

Abbreviations

ANN:

Artificial Neural Network

MLP:

Multi-Layer Perceptron

MSE:

Mean Square Errors

MAD:

Mean Absolute Deviation

RMSE:

Residual Mean Square Errors

MAE:

Mean Absolute Error

GDMALR:

Gradient Decent with Momentum and Adaptive Learning Rate Backpropagation

LS-SVM:

Least-Squares Support Vector Machine

PLSR:

Partial Least Square Regression

MLR:

Multiple Linear Regression

PPC:

Psychrotrophic Plate Count

TVB-N:

Total Volatile Basic Nitrogen

TMA-N:

Trimethylamine Nitrogen

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  1. Eğirdir Fisheries Faculty, Fishing and Processing Technology Department, Isparta University of Applied Sciences, Isparta, Turkey

    İsmail Yüksel Genç

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İsmail Yüksel GENÇ has compiled, analyzed the data, developed the model and module and constructed the article.

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Appendix

Appendix

Weights to layer 1 from input 1.

[0.215 1.0299 0.44519; 0.81905 − 0.25125 0.60449; 0.71524 − 0.40438 0.18169; 0.15737 0.53082 − 0.8139; − 1.0941 − 0.49792 0.13041; − 0.76441 0.89052 − 0.9176; 0.1436 0.79021 0.50513; 0.77111 − 0.18732 0.33336; 0.65459 − 0.29249 0.20499; 0.70678 0.48637 − 0.5326].

Biases to layer 1.

[0.26711; 0.41961; − 0.23041; − 0.84587; 0.53745; 0.59623; 0.15577; 0.24032; 0.52489; − 0.52791]

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Genç, İ.Y. Prediction of storage time in different seafood based on color values with artificial neural network modeling. J Food Sci Technol 59, 2501–2509 (2022). https://doi.org/10.1007/s13197-021-05269-0

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