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Updated: May 20, 2025
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Fruit Volatile Analysis Using an Electronic Nose
Published on: March 30, 2012
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食品新鲜度预测平台利用基于深度学习的多式传感器融合挥发性有机化合物和水分分布
Zepeng Gu1,2, Qinyan Xu1,2, Xiaoyao Wang1,2
1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China.
ACS sensors
|March 24, 2025
概括
这项研究引入了一种新的深度学习方法,用于使用组合气体和湿度传感器监测牛肉变质. 这种方法提高了检测食品新鲜度的准确性.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 现有的食品腐烂传感器由于单一来源数据而面临准确性限制.
- 多式联运数据的交叉验证在实际应用中提出了重大挑战.
研究的目的:
- 用多维传感和深度学习开发一种精确的方法来监测牛肉腐烂.
- 克服单一来源数据的局限性,改善多式联运数据的交叉验证.
主要方法:
- 设计了一种表面增强的拉曼散射 (SERS) 气体传感器,用于分析挥发性有机化合物 (VOC).
- 利用低场核磁共振 (LF-NMR) 来评估牛肉的水分分布.
- 实施了多式联网深度学习模型,包括自我注意和SENet用于自适应功能融合.
主要成果:
- 该模型在预测牛肉储存时间时实现了大于0.98的R2值.
- 与单一模式方法相比,新鲜度评估的准确性从90%提高到97%以上.
- 该系统在各种条件下证明了可靠的牛肉新鲜度监测.
结论:
- 动态融合深度学习多式联网模型有效地集成各种传感器数据.
- 这种方法可以快速,可靠,准确地监测牛肉的新鲜度.
- 该研究强调了多式联络传感和人工智能在食品质量评估中的潜力.
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