Journal of food protection最新文献

Foodborne illness continues to be a substantial public health concern in the United States with contaminated ground beef, chicken, and leafy greens identified as important sources of illnesses and outbreaks. These foods also have been identified by consumers as foods that are likely to carry germs that can make people sick. Food irradiation is a promising tool to enhance food safety. Despite this, there has been limited application of food irradiation in the U.S. During October 7-9, 2022, we licensed data from a Porter Novelli Public Services survey (N=1,008) to examine consumer risk perception of contamination of ground beef, chicken, and leafy greens with pathogens, and the potential influence risk perception has on purchasing irradiated versions of these foods. Among respondents familiar with food irradiation, a higher proportion of those who believed ground beef and leafy greens were likely contaminated also indicated they were likely to consider purchasing irradiated ground beef (55.6% vs 35.4%; p=0.0061) and leafy greens (60.8% vs 36.1%; p=<.0001) compared with those who did not; chicken was not significant (58.6% vs 45.4%; p=0.0727). This survey demonstrated the importance of risk perception as an influencer on a consumer's decision to purchase irradiated foods.

Some of the early applications of artificial intelligence (AI) for food safety appear to be intended for use at the level of manufacturing and distribution. Artificial intelligence applications to facilitate foodborne illness outbreak investigations, development of HACCP plans, and food safety root cause analyses at the retail level are needed. For example, the interview form in the International Association for Food Protection booklet, Procedures to Investigate Foodborne Illness, could be filled out by humans, but much of the rest of the forms could be completed by artificial intelligence applications. Humans would still have to do the environmental assessments. Most AI applications to date have consisted of pattern identification. Pattern recognition applications may not be capable of assisting in all the proposed retail applications, but it would not be helpful to propose these retail applications without offering a possible path forward. Progress in the proposed directions may require the development of more robust artificial intelligence based on cognitive models. Because this paradigm shift is less familiar to food safety professionals, a comparison between pattern recognition algorithms and cognitive models is offered. An explanation of cognitive models is included to raise awareness of this approach.

Pathogen control in the meat industry relies on the effectiveness of post-harvest interventions in reducing microbial populations. This study investigated differences in the survival of Salmonella serovars when exposed to organic acids used as antimicrobials on raw pork meat. Seven serovars were included in this study (S. Newport, S. Kentucky, S. Typhimurium, S. Dublin, S. Heidelberg, S. Infantis, and S. Enteritidis).Multi-strain serovar cocktails were prepared and tested against lactic acid (LA) and peracetic acid PAA at two concentrations, LA 2 and 4% and PAA 200 and 400 ppm. Pork samples were assigned to each serovar, inoculated with 6.0 Log CFU/cm²Salmonella (one serovar at a time), and treated with the corresponding antimicrobials. A two-way analysis of variance was conducted to examine the effects of serovar and antimicrobial concentrations on Salmonella survival. A significant main effect of serovar was identified, indicating that Salmonella concentration and reduction rate were significantly affected by serovar. Similarly, a significant main effect of antimicrobials was observed, suggesting that the treatment types impacted Salmonella concentration and reduction rate. However, the interaction effect between serovar and antimicrobial was not significant. Post-hoc comparisons indicate that PAA 400 ppm is more effective at reducing Salmonella concentrations and that S. Dublin may be more susceptible than S. Newport to antimicrobial sprays. Additionally, under PAA exposure, only S. Dublin, S. Kentucky, and S. Heidelberg showed statistically significant differences (P<0.05) compared with the control, indicating that these three serovars are more susceptible to PAA treatments than the rest. The behavior of different Salmonella serovars under stress conditions can give us an insight into how these pathogens survive processing.

As the interest in plant-based alternative food products has increased significantly in the last years, it is also important to investigate these products regarding microbiological aspects. The aim of this study was to assess the microbiological quality and the occurrence of selected foodborne pathogens in plant-based meat alternative products (PBMA) collected at retail level in Switzerland. A total of 100 PBMA (84 vegan and 16 vegetarian products) was analyzed qualitatively for the presence of Salmonella, Listeria monocytogenes and quantitatively for Staphylococcus aureus, Bacillus cereus group members, Enterobacteriaceae and the total viable count. Furthermore, pH measurements were carried out and the a_w-value was determined. Isolates were further analyzed with Whole Genome Sequencing. The total viable count before the expiration date was between < 2 log and 7 log CFU/g (median: 5.97 log CFU/g). In six (6 %) samples Enterobacteriaceae with 2 log to 3 log CFU/g were detected. No Salmonella and no Listeria monocytogenes were detected. However, seven products (7 %) were contaminated with other Listeria spp. (six L. innocua and one L. seeligeri). Further findings were two (2 %) Staphylococcus aureus ST8 with the presence of selx and tsst-1 genes, and five (5 %) Bacillus cereus group members (three B. paranthracis, one B. cereus sensu stricto, and one B. cytotoxicus) which all were diarrhea-associated strains. This study provides data that are relevant for HACCP concepts of companies that produce plant-based meat alternative products and helps to define microbiological parameters that should be included when testing such products.

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and Escherichia coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier (SVC) and Random Forest (RF), demonstrate high accuracy (>0.85) for detecting E. coli at 10² CFU/ml concentration within 6 h. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 10² CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodology, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

Cancer patients, who face increased foodborne illness susceptibility and severity, are often placed on neutropenic diets (NDs), which eliminate the consumption of fresh produce, among other foods perceived as high-risk. Such diets are clinically disputed because they have never been proven effective in reducing foodborne illness, leading to unstandardized dietary guideline content, format, and delivery methods. To inform a strategic communication approach, this study explored the produce safety handling behavior, barriers, motivators, and beliefs of pediatric cancer patient caretakers using a mixed methods convergent parallel design. A quantitative survey revealed high frequencies (>60%) for generally recommended produce safety behaviors, such as rinsing produce and washing cutting boards, and more mixed responses for restrictive produce safety behaviors, such as peeling produce and avoiding precut, self-serve, and school cafeteria produce. Total produce safety frequency scores were not significantly affected by demographic factors or Child Vulnerability Scale (CVS) scores. Qualitative interviews established a wide domain of caretaker produce safety experiences and beliefs, finding that eight of seventeen interview participants from different hospitals received produce restrictions typical of the ND. Ultimately, five caretaker archetypes were identified, with common motivators and barriers linked to materials received, child's health and perceived susceptibility, and self-efficacy beliefs. Finally, response-driven communication strategy improvements were recommended. Although sample sizes in this work were small, and further validation is advised, this work highlights the inconsistent use of the restrictive ND, advances understanding of the drivers of produce safety behaviors in cancer patient caretakers, and supports future endeavors to streamline communication strategy interventions.

Peracetic acid (PAA), a strong oxidizing agent, has been widely used as a disinfectant in food processing settings as it does not produce harmful chlorinated by-products. In the present study, the transcriptional response of Listeria monocytogenes to a sub-lethal concentration of PAA (2.5 ppm) was assessed using RNA-sequencing (RNA-seq). Our analysis revealed 12 differentially expressed protein-coding genes, of which nine were upregulated (ohrR, ohrA, rpsN, lmo0637, lmo1973, fur, lmo2492, zurM, and lmo1007), and three were down-regulated (argG, lmo0604, lmo2156) in PAA treated samples compared to the control samples. A non-coding small RNA gene (rli32) was also found to be down-regulated. In detail, the organic peroxide toxicity protection (OhrA-OhrR) system, the metal homeostasis genes fur and zurM, the SbrE-regulated lmo0636-lmo0637 operon and a carbohydrate phosphotransferase system (PTS) operon component were induced under exposure of L. monocytogenes to PAA. Hence, this study identified key elements involved in the primary response of L. monocytogenes to oxidative stress caused by PAA, including the expression of the peroxide detoxification system and fine-tuning the levels of redox-active metals in the cell. The investigation of the molecular mechanism of PAA response in L. monocytogenes is of utmost importance for the food industry, as residual PAA can lead to stress tolerance in pathogens.

We investigated a suspected norovirus outbreak associated with a wedding reception in Wisconsin in May 2015. Fifty-six of 106 (53%) wedding attendees were interviewed and 23 (41%) reported symptoms consistent with norovirus infection. A retrospective cohort study identified fruit salad as the likely vehicle of infection (risk ratio 3.2, 95% confidence interval 1.1- 8.3). Norovirus was detected by real-time reverse transcription polymerase chain reaction (RT-qPCR) in stool specimens collected from four attendees and one food handler and in 12 leftover fruit salad samples from both an opened and a sealed container. Norovirus-positive clinical samples (n=4) were genotyped as GII.4 Sydney and norovirus-positive fruit salad samples (n=2) confirmed the presence of GII.4 norovirus by Sanger sequencing with 98% nucleotide (n=236) similarity in 5' end of ORF2 between fruit salad and clinical specimens. In conclusion, this comprehensive norovirus outbreak investigation combined epidemiologic, virologic, and environmental findings to traceback the contaminated food as the source of the outbreak.

To enhance fast and accurate detection of pollution-free green apples for food safety, this paper uses the DETR network as a framework to propose a new method for pollution-free green apple detection based on a multi-dimensional feature extraction network and Transformer module. Firstly, an improved DETR network main feature extraction module adopts the ResNet18 network and replaces some residual layers with deformable convolutions (DCNv2), enabling the model to better adapt to pollution-free fruit changes at different scales and angles, while eliminating the impact of microbial contamination on fruit testing; Subsequently, the extended spatial pyramid pooling model (DSPP) and multi-scale residual aggregation module (FRAM) are integrated, which help reduce feature noise and minimize the loss of underlying features during the feature extraction process. The fusion of the two modules enhances the model's ability to detect objects of different scales, thereby improving the accuracy of near-color fruit detection; At the same time, in order to solve the problems of slow convergence speed and large calculation amount of the basic network model, the convergence speed of the overall network model is improved by replacing the attention mechanism of Transformer. Experimental results show that compared with the original DETR model, the proposed algorithm has improved in AP, AP50 and AP75 indicators, especially in the AP50 indicator, which has the most obvious improvement reaching a detection accuracy of 97.12%. In the meantime, the trained network model is deployed on the picking robot. Compared with the original DETR network model, its average detection accuracy is as high as 96.58%, and the detection speed is increased by about 51%. Mixed sample detection tests were carried out before and after the model deployment, and the detection rate of the proposed method for non-polluted fruits reached more than 0.95. enabling the picking robot to efficiently complete the task of picking green apples. The test results show that the algorithm proposed in this article exhibits great potential in the task of detecting pollution-free near-color fruits by the picking robot. It ensures pollution-free fruit picking and the application of AI in food safety.