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Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop
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在整个供应链中使用便携式多光谱成像设备进行海床质量监测.

Anastasia Lytou1, Lemonia-Christina Fengou1, Antonis Koukourikos2

  • 1Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.

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概括

一个便携式多谱成像传感器使用人工神经网络 (ANN) 模型快速预测鱼的微生物质量. 这项技术提供了高效的食品质量监测,减少浪费,提高整个供应链的可持续性.

关键词:
人工神经网络的人工神经网络鱼的新鲜度 鱼的新鲜度微生物的腐败是微生物的腐败.多光谱成像技术的使用.预测模型的预测模型.传感器 传感器 传感器

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科学领域:

  • 食品科学与技术 食品科学与技术
  • 感官分析 感官分析
  • 人工智能对食品质量的影响

背景情况:

  • 快速且具有成本效益的食品质量监测对于及时决策,减少浪费和可持续性至关重要.
  • 评估鱼类的微生物质量对于确保食品安全和消费者信任至关重要.
  • 传统的微生物学方法耗时,阻碍了供应链中的快速质量控制.

研究的目的:

  • 开发和验证人工神经网络 (ANN) 模型,以快速预测鱼中的总有氧计数 (TAC).
  • 评估便携式多谱成像传感器用于评估鱼片微生物质量的效率.
  • 调查包装条件 (有氧,真空) 和鱼类部分 (皮肤,肉) 对预测准确性的影响.

主要方法:

  • 获取海片 (水产养殖和零售) 的多光谱图像.
  • 微生物质量 (TAC) 的估计与图像采集并行.
  • 使用数据分区和零售样本的外部验证开发和验证ANN模型.

主要成果:

  • 从皮肤和肉面 (RMSE 0.402-0.547) 来预测TAC的ANN模型表现良好.
  • 模型显示有氧和真空包装鱼的性能相似,在两种条件相结合时,准确性略有降低.
  • 与内部验证相比,使用零售样本进行的外部验证结果表现较差 (RMSE 1.061-1.414).

结论:

  • 便携式多谱成像传感器是快速微生物质量评估鱼片的有效工具.
  • 对于实时质量监测,ANN模型显示出有前途的前景,有利于工业,当局和消费者.
  • 需要进一步的研究和模型改进,以提高外部验证场景的性能.