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相关实验视频

Updated: Jun 12, 2025

Necropsy-based Wild Fish Health Assessment
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Necropsy-based Wild Fish Health Assessment

Published on: September 11, 2018

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基于监督学习的人工感官用于非破坏性鱼类质量分类.

Rehan Saeed1, Branko Glamuzina2, Mai Thi Tuyet Nga3

  • 1Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing, 100083, PR China; Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230027, PR China.

Biosensors & bioelectronics
|September 17, 2024
PubMed
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这项研究引入了一种人工感官系统,用于早期预测鱼类质量,其性能优于人类感官. 它使用气体和纹理数据准确检测腐烂,为自动化食品供应链铺平了道路.

科学领域:

  • 食品科学 食品科学 食品科学
  • 感官科学 感官科学
  • 人工智能的人工智能

背景情况:

  • 目前的人类感官方法不足以实现自动化鱼类质量监测和可控储存.
  • 单一质量指数监测未能预测新鲜度的损失,影响消费者接受度.
  • 自动化鱼类质量评估对于食品供应链至关重要.

研究的目的:

  • 开发和验证用于早期鱼类质量预测的综合人工传感系统.
  • 整合多个传感器数据,以提高新鲜度评估的准确性.
  • 为了实现鱼类供应链的自动化质量控制.

主要方法:

  • 使用了多参数方法,包括气体传感器,纹理计,pH计,摄像头和彩虹鱼的TVB-N分析.
  • 应用数据预处理和相关性分析,以确定关键的质量参数 (三甲基胺,氨,CO2,硬度,粘度).
  • 开发了一个反向传播的神经网络模型,使用已识别的气体和纹理参数进行质量分类.

主要成果:

  • 在使用气体和纹理数据对新鲜和腐烂的鱼类进行分类时,获得了大约99%的预测准确度.
  • 确定了 trimethylamine,氨,二氧化碳,硬度和粘性等关键参数,这些参数对于质量预测至关重要.
  • 证明了该系统在早期检测鱼类新鲜度丧失方面的能力.
关键词:
鱼的质量 鱼的质量机器学习 机器学习神经网络的神经网络传感器 传感器 传感器质地 质地 质地

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结论:

  • 开发的人工传感系统有效地预测鱼的质量,并检测到腐败的早期迹象.
  • 纹理和气体数据的多参数融合显著提高了预测准确性.
  • 该系统显示出在食品供应链中完全自动化鱼类质量监测的巨大潜力.