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改变大气包装条件对日质量的影响:使用人工神经网络进行实验研究和预测分析

Abdelrahman R Ahmed^1,2, Salah M Aleid¹, Maged Mohammed^3,4

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

概括

通过修改大气包装 (MAP) 和人工神经网络 (ANN) 优化日期存储,可以保持水果的质量. 在4°C的20%CO2+80%N的MAP显著降低了腐烂,而ANN准确地预测了质量变化.

科学领域:

食品科学食品科学食品科学
农业工程农业工程
数据科学数据科学数据科学

背景情况:

枣子很容易腐烂,需要有效的储存策略来保持质量.
对于食品行业来说,了解各种储存条件对枣品质的影响是必不可少的.
预测建模可以提高食品供应链管理,减少浪费.

研究的目的:

调查修改大气包装 (MAP),温度和时间对日期质量的影响.
开发和验证人工神经网络 (ANN) 模型,用于预测存储期间的日期质量属性.
在 Tamer 阶段确定 Khalas 和 Sukary 枣品种的最佳储存条件.

主要方法:

评估的质量属性包括水分,度,颜色,pH值,水活动,TSS和不同储存条件下的微生物负载.
应用了不同的气体成分 (CO2,O2,N) 和储存温度 (4°C) 的修改大气层 (MAP) 包装.
开发了人工神经网络 (ANN) 模型来预测质量变化,并使用RMSE,MAPE和R2值进行验证.

主要成果:

存储条件显著影响了数据质量 (p < 0.05).
在4°C的20%CO2+80%N的MAP有效地减少了颜色变化和微生物生长.
ANN模型准确地预测了高R2值 (0.7660.980) 的质量属性.

结论:

最佳的储存条件,特别是20%的CO2 + 80%的N在4°C的MAP,可以显著延长日期的保质期.
该模型为预测日期质量提供了可靠的工具,有助于库存管理和减少浪费.
研究结果支持改善食品质量和供应链效率,以改善日等易腐果的供应链效率.

关键词:

日期的特点,日期的特点.食品的保存食品的保存食品质量食品质量食品质量食品供应链中的食物供应链.机器学习 (ML) 是指机器学习.预测模型预测模型储存条件储存条件储存条件

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