Bin Gao, Haotian Deng, Yuehua Wang, Chunhong Zhang, Jinyan Zhu
Burkholderia gladioli is a widely distributed Gram-negative bacterium. Under suitable environmental conditions, it can produce harmful toxins bongkrekic acid (BKA) and toxoflavin (TF) in food carriers (such as fermented cereal products, spoiled fresh Tremella and Auricularia auricula, and fermented potatoes), posing a serious threat to the safety of food producers and consumers. Food poisoning caused by B. gladioli can manifest as comprehensive symptoms such as diarrhea, and in severe cases, symptoms such as convulsions and shock may occur. Therefore, it is necessary to conduct a comprehensive and in-depth exploration of the food safety issues it causes. First, this article summarizes the clinical cases and prevalence caused by B. gladioli. Second, the detection methods for BKA and the methods for simultaneous detection of BKA and TF were reviewed. Finally, the methods that can inhibit the proliferation of B. gladioli and effective measures to control its production of BKA in different food carriers were reviewed. This review will help to comprehensively understand the spread of B. gladioli and its toxins and preventive measures, aiming to reduce their harm to human health.
{"title":"Detection methods and control measures of Burkholderia gladioli and its toxins: A review","authors":"Bin Gao, Haotian Deng, Yuehua Wang, Chunhong Zhang, Jinyan Zhu","doi":"10.1111/1750-3841.17668","DOIUrl":"https://doi.org/10.1111/1750-3841.17668","url":null,"abstract":"<p><i>Burkholderia gladioli</i> is a widely distributed Gram-negative bacterium. Under suitable environmental conditions, it can produce harmful toxins bongkrekic acid (BKA) and toxoflavin (TF) in food carriers (such as fermented cereal products, spoiled fresh Tremella and Auricularia auricula, and fermented potatoes), posing a serious threat to the safety of food producers and consumers. Food poisoning caused by <i>B. gladioli</i> can manifest as comprehensive symptoms such as diarrhea, and in severe cases, symptoms such as convulsions and shock may occur. Therefore, it is necessary to conduct a comprehensive and in-depth exploration of the food safety issues it causes. First, this article summarizes the clinical cases and prevalence caused by <i>B. gladioli</i>. Second, the detection methods for BKA and the methods for simultaneous detection of BKA and TF were reviewed. Finally, the methods that can inhibit the proliferation of <i>B. gladioli</i> and effective measures to control its production of BKA in different food carriers were reviewed. This review will help to comprehensively understand the spread of <i>B. gladioli</i> and its toxins and preventive measures, aiming to reduce their harm to human health.</p>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 2","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1750-3841.17668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ke Wen, Yan Chen, Zhengwei Zhu, Jinzhou Yang, Jinjin Bao, Dandan Fu, Zhigang Hu, Xianhui Peng, Ming Jiao
This study proposed a novel detection method for crayfish weight classification based on an improved Swin-Transformer model. The model demonstrated a Mean Intersection over Union (MIOU) of 90.36% on the crayfish dataset, outperforming the IC-Net, DeepLabV3, and U-Net models by 17.44%, 5.55%, and 1.01%, respectively. Furthermore, the segmentation accuracy of the Swin-Transformer model reached 99.0%, surpassing the aforementioned models by 1.25%, 1.73%, and 0.46%, respectively. To facilitate weight prediction of crayfish from segmented images, this study also investigated the correlation between the projected area and the weight of each crayfish part, and developed a multiple regression model with a correlation coefficient of 0.983 by comparing the total projected area and the relationship between the projected area and the actual weight of each crayfish part. To validate this model, a test set of 40 samples was employed, with the average prediction accuracy reaching 98.34% based on 10 representative data points. Finally, grading experiments were carried out on the crayfish weight grading system, and the experimental results showed that the grading accuracy could reach more than 86.5%, confirming the system's feasibility. The detection method not only predicts the weight based on the area but also incorporates the proportional relationship of the area of each part to improve the accuracy of the prediction further. This innovation makes up for the limitations of traditional inspection methods and shows higher potential for application. This study has important applications in industrial automation, especially for real-time high-precision weight grading in the aquatic processing industry, which can improve production efficiency and optimize quality control.
{"title":"A novel real-time crayfish weight grading method based on improved Swin Transformer","authors":"Ke Wen, Yan Chen, Zhengwei Zhu, Jinzhou Yang, Jinjin Bao, Dandan Fu, Zhigang Hu, Xianhui Peng, Ming Jiao","doi":"10.1111/1750-3841.70008","DOIUrl":"https://doi.org/10.1111/1750-3841.70008","url":null,"abstract":"<p>This study proposed a novel detection method for crayfish weight classification based on an improved Swin-Transformer model. The model demonstrated a Mean Intersection over Union (MIOU) of 90.36% on the crayfish dataset, outperforming the IC-Net, DeepLabV3, and U-Net models by 17.44%, 5.55%, and 1.01%, respectively. Furthermore, the segmentation accuracy of the Swin-Transformer model reached 99.0%, surpassing the aforementioned models by 1.25%, 1.73%, and 0.46%, respectively. To facilitate weight prediction of crayfish from segmented images, this study also investigated the correlation between the projected area and the weight of each crayfish part, and developed a multiple regression model with a correlation coefficient of 0.983 by comparing the total projected area and the relationship between the projected area and the actual weight of each crayfish part. To validate this model, a test set of 40 samples was employed, with the average prediction accuracy reaching 98.34% based on 10 representative data points. Finally, grading experiments were carried out on the crayfish weight grading system, and the experimental results showed that the grading accuracy could reach more than 86.5%, confirming the system's feasibility. The detection method not only predicts the weight based on the area but also incorporates the proportional relationship of the area of each part to improve the accuracy of the prediction further. This innovation makes up for the limitations of traditional inspection methods and shows higher potential for application. This study has important applications in industrial automation, especially for real-time high-precision weight grading in the aquatic processing industry, which can improve production efficiency and optimize quality control.</p>","PeriodicalId":193,"journal":{"name":"Journal of Food Science","volume":"90 2","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}