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Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks

Abstract

Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.

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Acknowledgments

The authors would like to thank Dr. Nasreen Bano of the Quality and Safety Assessment Research Unit in Athens, Georgia, for assistance in this research.

Funding

Mr. Rui Kang received financial support from the China Scholarship Council for his study in the USDA, ARS laboratory in Athens, Georgia.

Author information

Correspondence to Bosoon Park.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with animals performed by any of the authors.

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Kang, R., Park, B., Eady, M. et al. Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks. Appl Microbiol Biotechnol (2020). https://doi.org/10.1007/s00253-020-10387-4

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Keywords

  • Hyperspectral microscopy
  • Foodborne pathogen
  • Rapid classification
  • Food safety
  • Machine learning
  • Convolutional neural network