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相关概念视频

Taste Buds and Receptors01:20

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Gustation, or the sense of taste, is intrinsically linked to the anatomical structures located on the tongue. This organ's surface, along with the entirety of the oral cavity, is adorned with stratified squamous epithelium. Evident on the tongue are elevated structures known as papillae (singular = papilla), which house the mechanisms for the transduction of gustatory stimuli. Four distinct types of papillae exist, each identified by their unique morphological attributes: the circumvallate,...
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Gustation01:43

Gustation

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Gustation is a chemical sense that, along with olfaction (smell), contributes to our perception of taste. It starts with the activation of receptors by chemical compounds (tastants) dissolved in the saliva. The saliva and filiform papillae on the tongue distribute the tastants and increase their exposure to the taste receptors.
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Tactile and Chemical Senses01:27

Tactile and Chemical Senses

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Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
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Simple and Computer-assisted Olfactory Testing for Mice
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人工Q-Grader:机器学习支持的智能嗅觉和味觉感应系统.

Moonjeong Jang1,2, Garam Bae1,3, Yeong Min Kwon1

  • 1Thin Film Materials Research Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea.

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概括

这项研究开发了一个人工智能驱动的人工鼻子和舌头,使用氧化 (ZnO) 传感器来分析咖啡豆的味道和来源. 该系统在识别风味和区分咖啡类型方面取得了很高的准确性,为先进的食品质量监测铺平了道路.

关键词:
气体传感器是一个气体传感器.液体传感器是一种液体传感器.机器学习是机器学习.表面工程是什么?表面工程是什么?氧化氧化的使用方法

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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
  • 人工智能的人工智能

背景情况:

  • 便携式,个性化的人工智能驱动的传感器模仿物联网 (IoT) 应用程序的人类感官.
  • 氧化 (ZnO) 薄膜是为表面修改而设计的,使传感器发展成为可能.

研究的目的:

  • 使用ZnO薄膜和AI进行咖啡豆分析,开发一个人工Q分级器 (人工鼻子和舌头).
  • 识别咖啡豆中的香味和风味化学物质,并对其来源进行分类.

主要方法:

  • 制造一个聚乙烯化物-co-hexafluoropropylene) / ZnO薄膜晶体管 (TFT) 作为一个人工舌头.
  • 开发Au,Ag,或Pd纳米粒子/ZnO纳米混合气体传感器作为人工鼻子.
  • 使用TFT传输和动态响应曲线进行电传行为分析.
  • 实施主要组件分析 (PCA) 辅助的机器学习 (ML) 进行分类和回归.

主要成果:

  • 一个PCA辅助的ML模型在区分四种目标咖啡风味时实现了>92%的准确性.
  • 一个基于ML的回归模型以>99%的准确度预测了风味化学物质度.
  • 该分类模型成功地以100%的准确性区分了四种不同的咖啡豆类型.

结论:

  • 开发的人工Q分级器在分析咖啡豆风味概况和来源方面表现出高效率.
  • 人工智能驱动的传感器显示出在食品工业中自主监测和质量控制的巨大潜力.
  • 这项技术使咖啡特性的精确和高效分类成为可能,促进了食品科学应用.