PMID- 38406188
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240227
IS  - 2296-861X (Print)
IS  - 2296-861X (Electronic)
IS  - 2296-861X (Linking)
VI  - 11
DP  - 2024
TI  - Establishment and comparison of in situ detection models for foodborne pathogen 
      contamination on mutton based on SWIR-HSI.
PG  - 1325934
LID - 10.3389/fnut.2024.1325934 [doi]
LID - 1325934
AB  - 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 Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella 
      typhimurium (ST) contaminated on mutton was explored. MATERIALS AND METHODS: The 
      hyperspectral images of uncontaminated and contaminated mutton samples with 
      different concentrations (10(8), 10(7), 10(6), 10(5), 10(4), 10(3) and 
      10(2) CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional 
      neural network (1D-CNN) model was constructed and the influence of structure 
      hyperparameters on the model was explored. The effects of different spectral 
      preprocessing methods on partial least squares-discriminant analysis (PLS-DA), 
      support vector machine (SVM) and 1D-CNN models were discussed. In addition, the 
      feasibility of using the characteristic wavelength to establish simplified models 
      was explored. RESULTS AND DISCUSSION: The best full band model was the 1D-CNN 
      model with the convolution kernels number of (64, 16) and the activation function 
      of tanh established by the original spectra, and its accuracy of training set, 
      test set and external validation set were 100.00, 92.86 and 97.62%, respectively. 
      The optimal simplified model was genetic algorithm optimization support vector 
      machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM 
      models established by full band spectra preprocessed by 2D and all 1D-CNN models 
      with the convolution kernel number of (32, 16) and the activation function of 
      tanh were 100.00%. In addition, the accuracies of all simplified models were 
      100.00% except for the 1D-CNN models. Considering the complexity of features and 
      model calculation, the 1D-CNN models established by original spectra were the 
      optimal models for pathogenic bacteria contamination status and species. The 
      simplified models provide basis for developing multispectral detection 
      instruments. CONCLUSION: The results proved that SWIR-HSI combined with machine 
      learning and deep learning could accurately detect the foodborne pathogen 
      contamination on mutton, and the performance of deep learning models were better 
      than that of machine learning. This study can promote the application of HSI 
      technology in the detection of foodborne pathogens on meat.
CI  - Copyright (c) 2024 Bai, Du, Zhu, Xing, Yang, Yan, Zhang and Kang.
FAU - Bai, Zongxiu
AU  - Bai Z
AD  - College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 
      China.
FAU - Du, Dongdong
AU  - Du D
AD  - Analysis and Test Center, Xinjiang Academy of Agricultural and Reclamation 
      Science, Shihezi, China.
FAU - Zhu, Rongguang
AU  - Zhu R
AD  - College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 
      China.
AD  - Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and 
      Rural Affairs, Shihezi University, Shihezi, China.
AD  - Engineering Research Center for Production Mechanization of Oasis Characteristic 
      Cash Crop, Ministry of Education, Shihezi University, Shihezi, China.
FAU - Xing, Fukang
AU  - Xing F
AD  - College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 
      China.
FAU - Yang, Chenyi
AU  - Yang C
AD  - College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 
      China.
FAU - Yan, Jiufu
AU  - Yan J
AD  - College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 
      China.
FAU - Zhang, Yixin
AU  - Zhang Y
AD  - College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 
      China.
FAU - Kang, Lichao
AU  - Kang L
AD  - Analysis and Test Center, Xinjiang Academy of Agricultural and Reclamation 
      Science, Shihezi, China.
LA  - eng
PT  - Journal Article
DEP - 20240209
PL  - Switzerland
TA  - Front Nutr
JT  - Frontiers in nutrition
JID - 101642264
PMC - PMC10884184
OTO - NOTNLM
OT  - deep learning
OT  - foodborne pathogens
OT  - hyperspectral imaging
OT  - machine learning
OT  - mutton
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2024/02/26 06:43
MHDA- 2024/02/26 06:44
PMCR- 2024/01/01
CRDT- 2024/02/26 04:54
PHST- 2023/10/27 00:00 [received]
PHST- 2024/01/22 00:00 [accepted]
PHST- 2024/02/26 06:44 [medline]
PHST- 2024/02/26 06:43 [pubmed]
PHST- 2024/02/26 04:54 [entrez]
PHST- 2024/01/01 00:00 [pmc-release]
AID - 10.3389/fnut.2024.1325934 [doi]
PST - epublish
SO  - Front Nutr. 2024 Feb 9;11:1325934. doi: 10.3389/fnut.2024.1325934. eCollection 
      2024.