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&#181;Tongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo
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深度学习辅助自动驱动的微流体生物电子语言

Zihan Jin1,2, Yuhang Xue2, Bowei Zhang2

  • 1Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.

ACS applied materials & interfaces
|February 24, 2025
PubMed
概括
此摘要是机器生成的。

一种新型的自动供电微流体生物电子舌头 (SMET) 使用液体-固体接触电气化和深度学习来实现可靠的味道检测. 这种先进的电子舌头在识别口味和度方面达到很高的准确性,克服了以前的稳定性问题.

关键词:
生物电子电子舌头 生物电子舌头接触电气化的电气化深度学习是一种深度学习.微流体学 在微流体学方面自动供电 自动供电 自动供电

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科学领域:

  • 材料科学 材料科学 材料科学
  • 传感器技术 传感器技术
  • 生物技术是生物技术.

背景情况:

  • 人工生物电子舌头模仿天然的味觉感知,用于味觉检测和分类.
  • 液体固体接触电气化 (LSCE) 对于自动供电的电子舌头是有效的,但基于滴滴的系统遭受干扰,影响稳定性和可重复性.

研究的目的:

  • 开发一个单体集成的自动供电微流体生物电子舌头 (SMET).
  • 通过使用LSCE和深度学习来提高样本识别和度检测可靠性.
  • 为了提高灵敏度和减少样品体积要求.

主要方法:

  • 开发了一个具有多重化微通道结构的SMET.
  • 利用微型激发器生成多个激发波形,以提高算法精度.
  • 集成的LSCE效应与数据分析的深度学习算法.

主要成果:

  • 通过单个波形,为五种味道元素和五种NaCl度实现了超过93%的分类准确性.
  • 通过融合多个波形信号,达到100%的准确性.
  • 在十多个不同的味道样本中显示出明显的信号变化,表明高灵敏度.

结论:

  • 该SMET提供高度可靠和智能样本识别和度检测.
  • 它的多重微通道设计提高了灵敏度,并减少了样本体积.
  • 由于其高灵敏度和可靠性,SMET是快速准确分析液体的有希望的工具.