Towards a decision support system for control of multiple food safety hazards in raw milk production

Evaluating the effects of temperature and agitation on biofilm formation of bacterial pathogens isolated from raw cow milk

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.

Traceability in food processing: problems, methods, and performance evaluations-a review

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.

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

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.

Bent Fiber Sensor for Preservative Detection in Milk

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.