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Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis
Abdelrahman R Ahmed1,2, Salah M Aleid1, Maged Mohammed3,4
1Department of Food and Nutrition Sciences, College of Agricultural and Food Sciences, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.
Foods (Basel, Switzerland)
|October 28, 2023
Summary
Optimizing date storage with modified atmosphere packaging (MAP) and artificial neural networks (ANN) preserves fruit quality. MAP with 20% CO2 + 80% N at 4°C significantly reduces spoilage, while ANN accurately predicts quality changes.
Area of Science:
- Food Science
- Agricultural Engineering
- Data Science
Background:
- Dates are highly perishable, necessitating effective storage strategies to maintain quality.
- Understanding the impact of various storage conditions on date quality is essential for the food industry.
- Predictive modeling can enhance food supply chain management and reduce waste.
Purpose of the Study:
- To investigate the effect of modified atmosphere packaging (MAP), temperature, and time on date quality.
- To develop and validate artificial neural network (ANN) models for predicting date quality attributes during storage.
- To identify optimal storage conditions for Khalas and Sukary date cultivars at the Tamer stage.
Main Methods:
- Assessed quality attributes including moisture, firmness, color, pH, water activity, TSS, and microbial load under different storage conditions.
- Applied modified atmosphere packaging (MAP) with varying gas compositions (CO2, O2, N) and storage temperatures (4°C).
- Developed artificial neural network (ANN) models to predict quality changes and validated them using RMSE, MAPE, and R2 values.
Main Results:
- Storage conditions significantly impacted date quality (p < 0.05).
- MAP with 20% CO2 + 80% N at 4°C effectively reduced color change and microbial growth.
- ANN models accurately predicted quality attributes with high R2 values (0.766–0.980).
Conclusions:
- Optimal storage conditions, particularly MAP with 20% CO2 + 80% N at 4°C, can significantly extend date shelf life.
- ANN models provide a reliable tool for predicting date quality, aiding in inventory management and waste reduction.
- Findings support improved food quality and supply chain efficiency for perishable fruits like dates.
Keywords:
date characteristicsfood preservationfood qualityfood supply chainmachine learning (ML)prediction modelsstorage conditionsMore Related Videos
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