Journal of Food Safety最新文献

Food-related stresses such as heating and freezing may influence the biofilm formation ability of bacteria. This study aimed to investigate the main and interactive effects of food-related stresses on the biofilm formation potential of Salmonella strains isolated from meat. Salmonella enteritidis, Salmonella typhi, and Salmonella typhimurium were subjected to osmotic, acid, heat, cold, and freezing stresses. The colorimetric microtiter plate method was used to measure the biofilm formation ability as a response to the stresses. Among the main effects, freezing time had the most significant effect on the biofilm formation responses of three Salmonella serovars. Freezing reduced the biofilm formation ability of Salmonella typhimurium and Salmonella enteritidis but increased that of Salmonella typhi (p < 0.05). Among the interaction effects, the most significant effect on the biofilm formation response of Salmonella typhi was the interaction between pH and heat, which had a negative effect on the biofilm formation response. In contrast, the interaction between osmotic pressure and cold stresses was the most significant interactive effect on the biofilm formation responses of Salmonella enteritidis and Salmonella typhimurium, which had an increasing effect. This study concluded that the food-related stresses could change the biofilm formation capacity of Salmonella serotypes, and each serotype might show different biofilm formation abilities in response to different stresses.

Color, firmness, soluble solid content, and pH are important indices for assessing the quality and maturity of loquats. To explore the feasibility of rapid and non-destructive determination of loquat quality and maturity, this study utilized hyperspectral imaging combined with chemometrics to predict four quality indices of loquats and discriminate their maturity. Partial least squares regression models were developed using both raw and pre-processed spectral data to determine the optimal pre-processing method of multiple scattering correction and standard normal variate (SNV). The competitive adaptive reweighted sampling (CARS) and successive projection algorithms were used to extract spectral features. Feature wavelength models were subsequently developed using multiple linear regression (MLR) and error back propagation neural network. Finally, maturity determination models for loquats were developed by partial least squares discrimination analysis (PLS-DA), support vector machine, and random forest. The SNV-CARS-MLR model performed relatively better than the other models for predicting four quality indices. The PLS-DA model exhibited superior performance, with discrimination accuracies of 99.19% and 96.67% for the calibration and prediction sets. This study demonstrates that integrating hyperspectral imaging and chemometrics enables rapid and non-destructive determination of loquat quality and maturity.

Foodborne pathogens pose a persistent threat in broiler chicken production, particularly during the slaughter process, where contamination with zoonotic pathogens remains a concern. This study focuses on the potential of organic acids, such as formic and lactic acid, and the oxidizing agent peracetic acid, to decontaminate scalding water and enhance the hygiene of chicken carcasses. We conducted suspension tests introducing various organic loads to mirror the conditions of practical scalding water. Additionally, the surface tests were performed on chicken skin. Both methods were further tested in an experimental slaughtering facility. In suspension tests, the organic acids achieved impressive decontamination, with a 5-log₁₀ reduction of the test organisms Enterococcus hirae, Salmonella Typhimurium and Campylobacter jejuni at minimal concentrations (between 0.04% and 2% for formic acid; between 0.1% and 4.5% for lactic acid). Peracetic acid also effectively sanitized model water and chicken skin, even when used in low concentrations (between 0.001% and 0.1%), both in the laboratory-based testing and in the experimental slaughtering facility. These results suggest that the tested disinfectants can effectively sanitize process water, even under conditions mimicking practical scalding water with organic matter. Peracetic acid, in particular, proved highly effective in improving chicken skin hygiene even at low concentrations.

Mycotoxins are toxic secondary metabolites produced by certain molds and fungi that contaminate various food commodities, posing serious adverse effects on humans and animals. Aflatoxin, ochratoxin, trichothecene, fumonisin, zearalenone, patulin, and citrinin are some of the major mycotoxins affecting food and feed. The scientific community has focused on regulating food and feed materials due to their potential risks. Conventional techniques for mycotoxin detection have certain limitations in terms of sensitivity, specificity, and speed. In recent years, nanotechnology has emerged as a promising approach to revolutionize mycotoxin detection. This review provides an overview of nanotechnology-based detection methods for mycotoxins in food and feed, discussing the basic aspects of mycotoxins, their health hazards, and conventional methods. It also explores various nanosensors and nanodevices developed to improve the sensitivity, selectivity, and speed of mycotoxin detection, thereby enhancing food safety and security.

This systematic review (SR) and meta-analysis integrates the results of studies on the occurrence of staphylococcal enterotoxin (SE), the knowledge related to the profile of the foods most often involved, and the types of SE found. The study followed the guidelines of the Preferred Reporting Items for SR and Meta-Analyses, and its protocol was registered on the PROSPERO platform (CRD42021258223). Primary cross-sectional studies that report the analysis of SEs in food contaminated by Staphylococcus spp. were selected. All stages of study selection and data extraction were performed by two researchers independently, and in cases of conflicts, a third researcher was consulted. To assess the risk of bias, we used the critical appraisal checklist of the Joanna Briggs Institute. A total of 3012 reports were listed initially and 217 after the search update. After removing the duplicates, 2535 studies were found combining all databases and manual searches. Thus, 38 studies were included in this SR. In the meta-analysis, group 1 (dairy products) showed a prevalence of positive samples for SE of 15.38%, group 2 (meat products) 1.78% and group 3 (other foods) 27.11%. The results showed a considerable prevalence of SE in food contaminated by Staphylococcus spp., especially in group 3, which included mixed and ready-to-eat foods. This SR contributes to the study of the epidemiological profile and highlights the importance of adopting more effective prevention measures and policies aimed at improving public health.

Salmonella and Staphylococcus aureus are common pathogens that cause foodborne illnesses. Currently, the detection of these pathogens involves time-consuming procedures, namely isolation, cultivation, and biochemical identification, making it impossible for on-site real-time testing. In this study, we developed a compact hand-held real-time fluorescent nucleic acid testing device and specific lyophilized reagents to achieve rapid detection of Salmonella and S. aureus within 30 min. The detection sensitivity was 100 colony-forming units (CFU)/mL for Salmonella and 125 CFU/mL for S. aureus. This technique significantly reduced the detection time compared with the traditional cultivation method. Even at low initial concentrations of 5 CFU/mL for Salmonella and 15 CFU/mL for S. aureus, it demonstrated superior performance compared with traditional cultivation, detecting the target bacteria more than 2 days earlier than that method. Notably, we achieved 100% in the detection of Salmonella and S. aureus using spiked pastry samples. In addition, the proposed detection system exhibited excellent specificity when tested against 27 bacterial strains. In conclusion, the proposed nucleic acid detection system provides a viable, miniaturized solution for rapid detection of bacteria.

To ensure that food has been handled hygienically, manufacturers routinely examine the numbers of indicator bacteria, such as coliforms and Escherichia coli. Using the deep-learning algorithm YOLO, we developed a classifier that automatically counts the number of coliforms (red colonies) and E. coli (blue colonies) on a chromogenic agar plate. Using Citrobacter freundii IAM 12471^T and E. coli NBRC 3301, we trained our YOLO-based classifier with images of Petri dishes grown with each strain alone (10 images) and/or with a mixture of both strains (5 images). When the performance of the classifier was evaluated using 83 images, the accuracy rates for coliforms and E. coli reached 99.4% and 99.5%, respectively. We then investigated whether this classifier could detect other, non-trained coliform species (22 species) and E. coli strains (13 strains). The accuracy rates for coliforms and E. coli were 98.7% (90 Petri dishes) and 94.1% (46 Petri dishes), respectively. Furthermore, we verified the practical feasibility of the developed classifier using 38 meats (chicken, pork, and beef). The accuracy rates for coliforms and E. coli in meat isolates were 98.8% (80 Petri dishes) and 93.8% (35 Petri dishes), respectively. The time required to count coliforms/E. coli on a single plate was ~70 ms. This novel method should enable users to rapidly quantify coliforms/E. coli without relying on a human inspector's color vision, leading to improved assurance of food safety.