Utilizing Artificial Neural Networks for Predictive Modeling Physicochemical Attributes in Maltodextrin-Coated Grapes with Potassium Carbonate and Pyracantha Extract in Storage

Document Type : Research paper

Authors

1 Grape Processing and Preservation Department, Faculty of Agriculture, Research Institute of Grape and Raisin, Malayer University, Malayer, Iran

2 Department of Horticulture and Landscape Engineering, Faculty of Agriculture, Malayer University, Malayer, Iran

3 Department of Food Science and Technology, Toyserkan Faculty of Engineering and natural resources, Bu-Ali Sina University, Hamadan, Iran

4 Department of Food Science and Technology, Bahar Faculty of Food Science and Technology, Bu-Ali Sina University, Hamadan, Iran

Abstract

Artificial neural networks (ANN) are a nondestructive method for estimating fruit and vegetable shelf life and quality attributes. This research used artificial neural networks to model a storage process for fruit grapes (Vitis vinifera cv. Rishbaba) coated with maltodextrin, including different levels of potassium nanocarbonate (0-2%) and pyracantha extract (0-1.5%). After applying these coatings, the fruits were stored for 60 days in cold storage (-1 °C), with a relative humidity of 90%. Measurements considered weight loss percentage, titrable acidity (TA), pH, texture firmness, color index (a), and general fruit acceptance. Artificial neural networks predicted changes in fruits during the storage process. By examining different networks, the feedforward backpropagation network had 3-10-6 topologies with a coefficient of determination (R2) greater than 0.988 and a mean square error (MSE) less than 0.005. With a hyperbolic sigmoid tangent activation function, a resilient learning pattern and 1000 learning process were determined as the best neural method. On the other hand, the results of the optimized models showed that this model had the highest and lowest accuracy for predicting the weight loss percentage (R2 = 0.9975) and a (R2 = 0.5671) of the samples, respectively.

Keywords

International Journal of Horticultural Science and Technology
Volume 11, Issue 4
October 2024
Pages 491-502

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History

  • Receive Date: 10 September 2023
  • Revise Date: 17 November 2023
  • Accept Date: 29 November 2023

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