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Deep Sea Microbial Ecology01:18

Deep Sea Microbial Ecology

The deep ocean and its underlying sediments represent vast, largely unexplored microbial habitats that extend far beyond the sunlit photic zone. The photic (euphotic) zone typically spans the upper ~100–200 meters of pelagic waters in the open ocean, but its depth varies geographically and seasonally, where sufficient light supports photosynthetic life. Below this lies the deep sea, spanning roughly 1000–6000 meters (bathypelagic to abyssal zones), with deeper hadal trenches extending beyond...

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相关实验视频

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DNA-based Fish Species Identification Protocol
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使用机器学习辅助微生物组分析防止非法海鲜贸易.

Luca Peruzza1, Francesco Cicala1, Massimo Milan2

  • 1Department of Comparative Biomedicine and Food Science, University of Padova, Viale Dell'Università 16, Legnaro, 35020, Italy.

BMC biology
|September 10, 2024
PubMed
概括

微生物组分析与机器学习相结合,可以准确追踪海鲜的来源,打击欺诈行为. 这种强大的工具验证了贝类的可追溯性,即使有季节性变化和净化.

关键词:
食品的可追溯性 食品的可追溯性非法未报告未规范 (IUU) 捕捞机器学习是机器学习.马尼拉是什么意思 马尼拉是什么意思微生物群16SS 微生物群16S北亚得里亚海北方的亚得里亚海.

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科学领域:

  • 海洋生物学 海洋生物学
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 海鲜供应链面临着严重的欺诈风险.
  • 准确的可追溯工具对于消费者信心和防止非法贸易至关重要.
  • 微生物组分析 (MP) 和机器学习 (ML) 为海产品原产地验证提供了一种新的方法.

研究的目的:

  • 开发和验证一种精确的方法来追踪马尼拉的起源,使用微生物组概况和机器学习.
  • 评估该方法在季节性变化,年间差异和净化过程中的稳定性.
  • 为防止非法捕捞或错误标记的贝类贸易提供一个常规实施的工具.

主要方法:

  • 从北亚得里亚海沿岸的各种地点和季节收集了马尼拉的样本.
  • 在鱼组织 (和消化腺) 上进行DNA提取和16S DNA元编码.
  • 运用机器学习算法在AMPLICON序列变体上进行原产地分类,具有独立的训练和测试数据集.

主要成果:

  • 微生物组分析与机器学习相结合,在区分来自禁区和养殖场的鱼方面取得了很高的准确性 (科恩K评分>0.95).
  • 四个不同的农业区的分类显示了良好的准确性 (得分为0.76).
  • 该方法证明了对季节性和跨年变化的稳定性,以及对净化处理的稳定性.

结论:

  • 微生物组分析和机器学习为追踪贝类来源提供了有效和强大的工具.
  • 开发的方法适用于日常实施,以打击海产品欺诈和错误标签.
  • 这项技术提高了海鲜可追溯性的可靠性,并支持生态标签的验证.