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Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water
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使用基于纸张的色度测量传感器阵列与深度学习算法相结合,可见地检测冷牛肉的新鲜度.

Yuandong Lin1, Ji Ma1, Jun-Hu Cheng1

  • 1School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.

Food chemistry
|January 17, 2024
PubMed
概括

这项研究引入了色度传感器阵列和深度学习,以快速,准确地评估冷牛肉的新鲜度. 这种创新方法有效地检测氨基气体,并高精度地监测牛肉质量.

关键词:
冷藏的牛肉可以吃.颜值测量传感器阵列数组的颜色测量.深度学习是一种深度学习.食品的新鲜度 食品的新鲜度食品储存 食品的储存 食品的储存

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科学领域:

  • 食品科学与技术 食品科学与技术
  • 分析化学 分析化学
  • 人工智能在食品安全中的作用

背景情况:

  • 评估冷藏牛肉的新鲜度对于食品安全和质量控制至关重要.
  • 确定牛肉新鲜度的传统方法可能耗时且主观.
  • 氨基酸气体是肉制品腐败的关键指标.

研究的目的:

  • 开发一种用于检测氨基气体和评估冷藏牛肉新鲜度的创新方法.
  • 集成色度传感器阵列 (CSA) 与先进的算法,包括深度学习.
  • 为了实现冷牛肉质量的快速,稳健和准确的监测.

主要方法:

  • 使用十二种pH响应染料开发色度传感器阵列 (CSA).
  • 应用多变量统计分析来区分氨基气体和量化三甲基胺.
  • 使用深度学习模型 (ResNet34,VGG16,GoogleNet) 进行新鲜度评估,使用t-SNE进行可视化.

主要成果:

  • CSA有效地区分了五种氨基气体,三甲基胺的检测极限 (LOD) 为8.02ppb.
  • 深度学习模型在评估冷牛肉的新鲜度时,总体准确率达到98.0%.
  • t-分布式随机邻居嵌入 (t-SNE) 提供了对深度学习分类过程的见解.

结论:

  • 通过CSA和深度学习的结合方法,可以快速准确地评估冷藏牛肉的新鲜度.
  • 这项技术使可视监测和精确量化损坏指标成为可能.
  • 深度学习显著提高模式识别可靠的食品质量评估.