Asset Details
Do you wish to reserve the book?
Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks
Do you wish to request the book?
Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks
2020
Overview
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.
