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深度学习模型用于预测各种食品的保质期和规范:系统性审查

Miao Liang¹, Yongbiao Ni², Xuhang He¹

¹School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China.

Journal of food science

|February 28, 2026

概括

深度学习 (人工智能) 提高了食品保质期的预测,提高了安全性,减少了浪费. 这种人工智能技术使供应链现代化,并优化食品质量评估,以改善监管.

科学领域:

食品科学食品科学食品科学
人工智能的人工智能
机器学习机器学习

背景情况:

准确的食品保质期预测对于食品安全,减少浪费和消费者信任至关重要.
在复杂的食品系统中,传统方法往往不足.
先进的人工智能 (AI) 提供了有前途的解决方案.

研究的目的:

系统地审查食品保质期预测和监管中的深度学习应用.
分析预测模型架构和食品质量评估标准.
探索混合方法和基于模型的优化策略.

主要方法:

审查深度学习模型 (例如神经网络) 用于预测保质期.
对食品质量评估指标的分析.
探索混合数据驱动和机械模型.
对反向调节概念的研究.

主要成果:

深度学习模型显示了准确的保质期预测的巨大潜力.
结合人工智能和机械方法的混合方法提供了强大的解决方案.
人工智能的进步可以加强食品安全系统和资源效率.
反向监管提供了创新的供应链解决方案.

结论:

深度学习正在彻底改变食品保质期的预测和监管.
人工智能集成提高了食品安全,减少了浪费,并使供应链现代化.
未来的研究应该专注于混合模型和智能供应链物流.

关键词:

深度学习是一种深度学习.食品质量食品质量食品质量神经网络 (NN) 是一个神经网络.预测和监管预测和监管保质期货架上使用寿命

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