Vibriosis and its impact on microbiological food safety

Martins, Victória Gabrielle Pires; Nascimento, Janaı́na dos Santos; Martins, Flávia Myllena Da Silva; Vigoder, Hilana Ceotto

doi:10.1590/fst.65321

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Vibrio spp., prevalent in numerous types of seafood, are microbial foodborne pathogens of water sources that increase human vulnerability to hazards to public health [ 71 ]. V. parahaemolyticus stands out for its prevalence among pathogens carried by food of marine origin [ 72 ] and is recognized in 16% of inspected fishes, with incidences ranging from 28% in O. niloticus to 10% in S. lucioperca ( Figure 2 ). Differential biochemical analyses identified all isolates as V. parahaemolyticus at the species level ( Table 7 ).…”

Section: Discussionmentioning

confidence: 99%

Bacterial Quality and Molecular Detection of Food Poisoning Virulence Genes Isolated from Nasser Lake Fish, Aswan, Egypt

Elbarbary,

Dandrawy,

Hadad

et al. 2024

International Journal of Food Science

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The microbial analysis of fish is critical for ensuring overall health. Uncooked fish can serve as a conduit for transmitting several types of microbes; the current investigation sought to assess the bacterial levels in various kinds of fish from Nasser Lake, Aswan, Egypt, considered the chief source of potable water in Egypt. Two hundred and fifty fish samples, including 50 of each Oreochromis niloticus, Sander lucioperca, Lates niloticus, Clarias gariepinus, and Mormyrus kannume, from Nasser Lake, Aswan, Egypt, were collected to detect the bacterial load, isolation, and identification of Aeromonas hydrophila, Pseudomonas aeruginosa, and Vibrio parahaemolyticus and their virulence genes. The findings revealed that Oreochromis niloticus and Clarias gariepinus exhibited higher bacterial loads than other fish species. Incidences of bacterial contamination among examined fishes were 28.8%, 20.4%, and 16% for Aeromonas hydrophila, Pseudomonas aeruginosa, and Vibrio parahaemolyticus, respectively. Additionally, PCR analysis detected the presence of aerA (60%) and Act (40%) genes in A. hydrophila, rpoB (70%) and LasB (30%) genes in P. aeruginosa, and ToxR (70%) and tdh (50%) genes in V. parahaemolyticus. The study suggested that the bacterial contamination levels in Oreochromis niloticus and Clarias gariepinus could be notably more significant than in other species that could potentially be harmful to the consumers, especially considering the identification of particular bacteria known to cause foodborne illnesses. Further recommendations emphasized that regular monitoring and assessments are required to preserve their quality.

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Section: Discussionmentioning

confidence: 99%

Bacterial Quality and Molecular Detection of Food Poisoning Virulence Genes Isolated from Nasser Lake Fish, Aswan, Egypt

Elbarbary,

Dandrawy,

Hadad

et al. 2024

International Journal of Food Science

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“…Because food safety is very worthy of attention, there have been many scholars in the world from a variety of aspects working to improve the research on food safety. Pires Martins et al (2022) studied the vibriosis and its impact on microbiological food safety. Zhao & Talha (2022) studied the Evaluation of food safety problems based on the fuzzy comprehensive analysis method.…”

Section: Introductionmentioning

confidence: 99%

An apple leaf disease identification model for safeguarding apple food safety

LIU

Bai

et al. 2023

Food Sci. Technol

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Apples are the fourth most produced fruit in the world, so it is important to safeguard them from disease damage. Although there are many deep learning-based plant disease detection models, the existing deep learning networks have complex structures and require large amounts of computational resources for support. Lightweight networks such as MobileNet and ShuffleNet designed for mobile devices could achieve better classification and faster recognition on public datasets, but their accuracy rates are not yet up to the requirements of practical applications. To address these problems, we constructed an improved apple leaf disease recognition algorithm based on MobileNetV2. We used feature reuse to improve the network structure, added a dense connection structure to the inverse residual module, and an ECA-Net attention module to increase the model's focus on diseased regions. We trained the improved model on the network on a dataset expanded by a generative adversarial network. The results showed that the improved model had a smaller number of model parameters, only 3.3 M, and a higher accuracy rate of 96.23% compared to Resnet50, ShuffleNet, and MobileNet models. The improved model had only 0.34 M addition in the number of parameters compared with MobileNet-V2, and had a 2.2% improvement in accuracy.

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