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深度学习驱动的智能光探头:用于准确检测食品安全的分子设计的进步

Yongqiang Shi¹, Sisi Yang¹, Wenting Li²

¹Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.

Foods (Basel, Switzerland)

|September 13, 2025

概括

深度学习 (DL) 通过改进探头设计和分析复杂的信号来增强对食品安全的光传感. 本综述绘制了全球食品供应链中先进检测技术的未来研究方向.

科学领域:

食品科学食品科学食品科学
分析化学分析化学
计算科学计算科学

背景情况:

全球食品供应链带来了公共卫生挑战,需要超越传统实验室技术的先进检测方法.
光传感为食品安全提供高灵敏度和快速响应,但在探针设计和复杂信号分析方面遇到障碍.
深度学习 (DL) 提供了强大的非线性建模和模式识别来克服这些局限性.

研究的目的:

审查最近应用深度学习 (DL) 对光传感食品安全方面的进展.
探索DL在加速高性能光探针开发中的作用.
讨论DL技术用于对食品矩阵中复杂光信号的智能分析.

主要方法:

对光传感中的深度学习应用的文献综述.
对DL方法进行分析,以预测光探针特性和生成分子结构.
检查DL技术用于食品分析中的智能信号处理.

主要成果:

深度学习加速了新型高性能光探测器的设计和开发.
DL能够对复杂的光信号进行复杂的分析,提高检测准确度和可靠性.
将DL与光传感集成为未来的食品安全技术提供了一个有希望的路线图.

结论:

深度学习在推进光传感用于食品安全应用中至关重要.
应对当前的挑战,并拥抱未来的轨迹将提高检测能力.
这种跨学科的方法为未来的智能食品安全监测研究提供了路线图.

关键词:

深度学习是一种深度学习.光探测器的光探测器食品安全食品安全智能感知智能感知智能感知反向设计的设计.分子设计分子设计.模式识别模式识别模式识别信号处理信号处理信号处理

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