在大西洋鱼中虫的序列细分与多谱成像数据
Andrea Rakel Sigurðardóttir1, Hildur Inga Sveinsdóttir1,2, Nette Schultz3
1Faculty of Food Science and Nutrition, University of Iceland, Sæmundargata 12, 102 Reykjavík, Iceland.
Foods (Basel, Switzerland)
|September 28, 2024
概括
本研究引入了一种自动化方法,用于检测鱼片中的线虫,使用多谱成像 (MSI) 和机器学习. 该系统达到88%的精度和79%的召回率,提高了鱼类产品的安全性和质量.
科学领域:
- 食品科学与技术 食品科学与技术
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 在鱼类加工行业中,虫污染是一个重要的问题,特别是对于白鱼.
- 目前用于线虫的手动检查方法是劳动密集型的,容易出现错误.
- 需要技术进步来提高鱼片中线虫检测的效率和准确性.
研究的目的:
- 开发和评估一个自动化系统来检测和区分线虫和其他缺陷的鱼片.
- 调查多光谱成像 (MSI) 和机器学习的潜力,以根据其光谱特征识别线虫.
- 提高鱼类加工行业的工艺控制和产品安全.
主要方法:
- 使用视频仪实验室4获得大西洋鱼片的270张多谱图像.
- 使用细分任何模型 (SAM) 用最小的标记数据对线虫进行自动细分.
- 使用规范化法典区分分析 (nCDA) 开发细分模型,以区分线虫与皮肤残留物和血斑.
- 使用精度和回忆指标评估模型性能.
主要成果:
- 通过使用分段任何模型 (SAM) 成功标记了173种线虫.
- 在注释的测试数据中,在识别线虫方面,获得了88%的精度和79%的回忆.
- 证明了MSI和nCDA在鱼片中精确分化线虫的潜力.
结论:
- 使用MSI和nCDA开发的自动化系统有望改善鱼类加工中的线虫检测.
- 这种方法可以通过准确识别受污染的片来提高产品的安全性和质量.
- 该系统可以减少不必要的检查,优化处理工作流.
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