Sliding-window enhanced olfactory visual images combined with deep learning to predict TVB-N content in chilled mutton.

A novel data enhancement method for olfactory visual images was proposed in this study, combined with deep learning to achieve the accurate prediction of total volatile basic nitrogen (TVB-N) content in chilled mutton. Specifically, the sliding-window was defined and used to separately extract different regions of interest from each sensing region by encoding and decoding the sliding position information, so the olfactory visual image was enhanced. This enhancement method considered the position shift and uneven colour presentation of sensitive points during the preparation and reaction of olfactory visualization sensor array.

Monitoring of veterinary drug residues in mutton based on hyperspectral combined with explainable AI: A case study of OFX.

Veterinary drug residues in meat seriously harm human health. Rapid and accurate detection of veterinary drug residues is necessary to minimize contamination. Taking ofloxacin (OFX) residues in mutton as an example, the near-infrared hyperspectral imaging combined with explainable AI was used to evaluate the importance of feature wavelengths in the convolutional neural network-stacked sparse auto-encoder (CNN-SSAE) model for chemical properties.

Establishment and comparison of detection models for foodborne pathogen contamination on mutton based on SWIR-HSI.

Introduction: Rapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers.

Objectives: The feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of (EC), (SA) and (ST) contaminated on mutton was explored.

Materials And Methods: The hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (10, 10, 10, 10, 10, 10 and 10 CFU/mL) of EC, SA and ST were acquired.