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Alabdullatif M. Evaluating the effects of temperature and agitation on biofilm formation of bacterial pathogens isolated from raw cow milk. BMC Microbiol 2024; 24:251. [PMID: 38977975 PMCID: PMC11229293 DOI: 10.1186/s12866-024-03403-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024] Open
Abstract
OBJECTIVES To study the effect of agitation and temperature on biofilm formation (cell aggregates embedded within a self-produced matrix) by pathogenic bacteria isolated from Raw cow milk (RCM). METHODS A 40 RCM samples were gathered from eight dairy farms in Riyadh, Saudi Arabia. After bacterial culturing and isolation, gram staining was performed, and all pathogenic, identified using standard criteria established by Food Standards Australia New Zealand (FSANZ), and non-pathogenic bacteria were identified using VITEK-2 and biochemical assays. To evaluate the effects of temperature and agitation on biofilm formation, isolated pathogenic bacteria were incubated for 24 h under the following conditions: 4 °C with no agitation (0 rpm), 15 °C with no agitation, 30 °C with no agitation, 30 °C with 60 rpm agitation, and 30 °C with 120 rpm agitation. Then, biofilms were measured using a crystal violet assay. RESULTS Of the eight farm sites, three exhibited non-pathogenic bacterial contamination in their raw milk samples. Of the total of 40 raw milk samples, 15/40 (37.5%; from five farms) were contaminated with pathogenic bacteria. Overall, 346 bacteria were isolated from the 40 samples, with 329/346 (95.1%) considered as non-pathogenic and 17/346 (4.9%) as pathogenic. Most of the isolated pathogenic bacteria exhibited a significant (p < 0.01) increase in biofilm formation when grown at 30 °C compared to 4 °C and when grown with 120 rpm agitation compared to 0 rpm. CONCLUSION Herein, we highlight the practices of consumers in terms of transporting and storing (temperature and agitation) can significantly impact on the growth of pathogens and biofilm formation in RCM.
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Affiliation(s)
- Meshari Alabdullatif
- Department of Pathology, College of Medicine, Imam Mohammad Ibn Saud Islamic University (IMSIU), Uthman Ibn Affan Rd, Riyadh, 13317-4233, Saudi Arabia.
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Zhang H, Zhou G, Zhang S, Yang Y, Dev S, Su Q, Deng X, Chen Q, Niu B. Risk assessment of heavy metals contamination in pork. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108793] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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Qian J, Dai B, Wang B, Zha Y, Song Q. Traceability in food processing: problems, methods, and performance evaluations-a review. Crit Rev Food Sci Nutr 2020; 62:679-692. [PMID: 33016094 DOI: 10.1080/10408398.2020.1825925] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing.
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Affiliation(s)
- Jianping Qian
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bingye Dai
- Beijing Technology and Business University, Beijing, China
| | - Baogang Wang
- Beijing Academy of Forestry and Pomology Sciences, Beijing, China
| | - Yan Zha
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qian Song
- Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
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Qian J, Ruiz-Garcia L, Fan B, Robla Villalba JI, McCarthy U, Zhang B, Yu Q, Wu W. Food traceability system from governmental, corporate, and consumer perspectives in the European Union and China: A comparative review. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.03.025] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Leclair RM, McLean SK, Dunn LA, Meyer D, Palombo EA. Investigating the Effects of Time and Temperature on the Growth of Escherichia coli O157:H7 and Listeria monocytogenes in Raw Cow's Milk Based on Simulated Consumer Food Handling Practices. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E2691. [PMID: 31357682 PMCID: PMC6696089 DOI: 10.3390/ijerph16152691] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 07/15/2019] [Accepted: 07/24/2019] [Indexed: 11/16/2022]
Abstract
Consumption of raw cow's milk (RCM) is increasing in popularity in developed countries despite the associated foodborne disease risks. While previous research has focused on consumer motivations for drinking RCM, there is limited research on how consumer handling practices may impact the microbiological safety of RCM. In this study, consumer handling practices associated with transport, storage, and freezing and thawing were simulated to investigate the impact of time and temperature variables on the concentrations of either Escherichia coli O157:H7 or Listeria monocytogenes in RCM. We found that the type of storage during simulated transport had a large (η2 = 0.70) and significant (p < 0.001) effect on both pathogens. The refrigeration temperature also had a large (η2 = 0.43) and significant (p < 0.001) effect on both pathogens during refrigerated storage. The interaction between pathogen species and initial pathogen inoculum level had a large (η2 = 0.20) and significant (p = 0.012) effect on the concentration of the pathogens during ambient temperature storage. We found that freezing and thawing practices did not have a significant effect on the pathogens (p > 0.05). However, we were able to recover L. monocytogenes, but not E. coli O157:H7, from RCM after freezing for 365 days. The results from this study highlight that consumer transport and storage practices can have significant effects on the growth of E. coli O157:H7 and L. monocytogenes in RCM. Consumer food handling practices should be considered when developing public health strategies aimed at reducing the risks of RCM consumption.
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Affiliation(s)
- Roselyn M Leclair
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.
| | - Sarah K McLean
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
| | - Louise A Dunn
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
| | - Denny Meyer
- Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
| | - Enzo A Palombo
- Department of Chemistry and Biotechnology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
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Li G, Xu L, Wu W, Wang D, Jiang J, Chen X, Zhang W, Poapolathep S, Poapolathep A, Zhang Z, Zhang Q, Li P. On-Site Ultrasensitive Detection Paper for Multiclass Chemical Contaminants via Universal Bridge-Antibody Labeling: Mycotoxin and Illegal Additives in Milk as an Example. Anal Chem 2018; 91:1968-1973. [PMID: 30509070 DOI: 10.1021/acs.analchem.8b04290] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Guanghua Li
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
| | - Lin Xu
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
| | - Wenqin Wu
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
| | - Du Wang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Natonal Reference for Biotoxin Detection, Wuhan 430062, People’s Republic of China
| | - Jun Jiang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
| | - Xiaomei Chen
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
| | - Wen Zhang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Natonal Reference for Biotoxin Detection, Wuhan 430062, People’s Republic of China
| | - Saranya Poapolathep
- Department of Pharmacology, Faculty of Veterinary Medicine, Kasetsart University, Bangkok 10900, Thailand
| | - Amnart Poapolathep
- Department of Pharmacology, Faculty of Veterinary Medicine, Kasetsart University, Bangkok 10900, Thailand
| | - Zhaowei Zhang
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, People’s Republic of China
| | - Qi Zhang
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Natonal Reference for Biotoxin Detection, Wuhan 430062, People’s Republic of China
| | - Peiwu Li
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan 430062, People’s Republic of China
- Natonal Reference for Biotoxin Detection, Wuhan 430062, People’s Republic of China
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Alegbeleye OO, Guimarães JT, Cruz AG, Sant’Ana AS. Hazards of a ‘healthy’ trend? An appraisal of the risks of raw milk consumption and the potential of novel treatment technologies to serve as alternatives to pasteurization. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.10.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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8
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Obaidat MM, Bani Salman AE, Davis MA, Roess AA. Major diseases, extensive misuse, and high antimicrobial resistance of Escherichia coli in large- and small-scale dairy cattle farms in Jordan. J Dairy Sci 2018; 101:2324-2334. [DOI: 10.3168/jds.2017-13665] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 11/13/2017] [Indexed: 12/11/2022]
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Qian J, Fan B, Wu X, Han S, Liu S, Yang X. Comprehensive and quantifiable granularity: A novel model to measure agro-food traceability. Food Control 2017. [DOI: 10.1016/j.foodcont.2016.11.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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10
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Bent Fiber Sensor for Preservative Detection in Milk. SENSORS 2016; 16:s16122094. [PMID: 27941703 PMCID: PMC5191074 DOI: 10.3390/s16122094] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 11/29/2016] [Accepted: 12/06/2016] [Indexed: 11/16/2022]
Abstract
A fiber optic sensor sensitive to refractive index changes of the outer region of the fiber cladding is presented. The sensor uses bent plastic optical fibers in different bending lengths to increase sensitivity. Measurements were made for low-fat milk, the refractive index of which is altered by some preservatives such as formaldehyde, hydrogen peroxide, and sodium carbonate. Concentrations of the preservatives in the milk were changed between 0% and 14.3% while the refractive indices occurred between 1.34550 and 1.35093 for the minimum (0%) and maximum (14.286%) concentrations of sodium carbonate, respectively. Due to bending-induced sensitivity, the sensor is able to detect refractive index changes less of than 0.4%. The results show that there is excellent linearity between the concentration and normalized response of the sensor.
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Kleboth J, Luning P, Fogliano V. Risk-based integrity audits in the food chain – A framework for complex systems. Trends Food Sci Technol 2016. [DOI: 10.1016/j.tifs.2016.07.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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