Linking microbial contamination to food spoilage and food waste: the role of smart packaging, spoilage risk assessments, and date labeling

Ensuring a safe and adequate food supply is a cornerstone of human health and food security. However, a significant portion of the food produced for human consumption is wasted annually on a global scale. Reducing harvest and postharvest food waste, waste during food processing, as well as food waste at the consumer level, have been key objectives of improving and maintaining sustainability. These issues can range from damage during processing, handling, and transport, to the use of inappropriate or outdated systems, and storage and packaging-related issues. Microbial growth and (cross)contamination during harvest, processing, and packaging, which causes spoilage and safety issues in both fresh and packaged foods, is an overarching issue contributing to food waste. Microbial causes of food spoilage are typically bacterial or fungal in nature and can impact fresh, processed, and packaged foods. Moreover, spoilage can be influenced by the intrinsic factors of the food (water activity, pH), initial load of the microorganism and its interaction with the surrounding microflora, and external factors such as temperature abuse and food acidity, among others. Considering this multifaceted nature of the food system and the factors driving microbial spoilage, there is an immediate need for the use of novel approaches to predict and potentially prevent the occurrence of such spoilage to minimize food waste at the harvest, post-harvest, processing, and consumer levels. Quantitative microbial spoilage risk assessment (QMSRA) is a predictive framework that analyzes information on microbial behavior under the various conditions encountered within the food ecosystem, while employing a probabilistic approach to account for uncertainty and variability. Widespread adoption of the QMSRA approach could help in predicting and preventing the occurrence of spoilage along the food chain. Alternatively, the use of advanced packaging technologies would serve as a direct prevention strategy, potentially minimizing (cross)contamination and assuring the safe handling of foods, in order to reduce food waste at the post-harvest and retail stages. Finally, increasing transparency and consumer knowledge regarding food date labels, which typically are indicators of food quality rather than food safety, could also contribute to reduced food waste at the consumer level. The objective of this review is to highlight the impact of microbial spoilage and (cross)contamination events on food loss and waste. The review also discusses some novel methods to mitigate food spoilage and food loss and waste, and ensure the quality and safety of our food supply

Evaluating reuse of nontraditional water sources in agriculture and food production utilizing a scientometrics approach

Climate change is proving to be detrimental for agriculture and food production by depleting natural resources such as irrigation water. Researchers and growers are turning to alternative sources of irrigation water. Growers are potentially willing to accept nontraditional sources, provided they meet the chemical and microbial standards of existing sources. To help identify research gaps and suggest future research directions, a thorough analysis of existing literature needed to be done. The aim of this study was to categorize and analyze existing research on water reuse found on the Web of Science database using a scientometrics approach. The publication dataset comprising 3072 titles, published between 1990 and 2022, was analyzed for keywords and co-occurrence of commonly used phrase groups. The global and year-wise trends in publications were mapped and graphed to identify which countries were actively researching water reuse and whether the number of publications were progressing significantly per year. The highly cited publications were also analyzed for their content to understand what differentiated them from the other publications. Our results indicated that the numbers of publications have increased considerably over the years from 1990 to 2022 with a potential to further increase by 2060, indicating a growing interest in the area of water reuse. The global distribution of publications indicated that researchers across the globe have identified this as a potential future strategy and are actively working to understand various aspects of water reuse in agriculture and food production by using experimental and modeling based study methods. The current focus is on reclaimed water and roof harvested rainwater with other prospective sources being investigated. The findings indicate that a multidisciplinary approach is required to understand the multifaceted aspects of reusing nontraditional water sources as irrigation water for food crops. Based on our study, we suggest that collaborations between academic research, agricultural industries and government agencies could lead to the integration of nontraditional water sources as irrigation water, helping to alleviate the negative effects of climate change

Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends

Several studies have shown a correlation between outbreaks of Salmonella enterica and meteorological trends, especially related to temperature and precipitation. Additionally, current studies based on outbreaks are performed on data for the species Salmonella enterica, without considering its intra-species and genetic heterogeneity. In this study, we analyzed the effect of differential gene expression and a suite of meteorological factors on salmonellosis outbreak scale (typified by case numbers) using a combination of machine learning and count-based modeling methods. Elastic Net regularization model was used to identify significant genes from a Salmonella pan-genome, and a multi-variable Poisson regression developed to fit the individual and mixed effects data. The best-fit Elastic Net model (α = 0.50; λ = 2.18) identified 53 significant gene features. The final multi-variable Poisson regression model (χ2 = 5748.22; pseudo R2 = 0.669; probability > χ2 = 0) identified 127 significant predictor terms (p < 0.10), comprising 45 gene-only predictors, average temperature, average precipitation, and average snowfall, and 79 gene-meteorological interaction terms. The significant genes ranged in functionality from cellular signaling and transport, virulence, metabolism, and stress response, and included gene variables not considered as significant by the baseline model. This study presents a holistic approach towards evaluating multiple data sources (such as genomic and environmental data) to predict outbreak scale, which could help in revising the estimates for human health risk

Modeling the effects of infection status and hygiene practices on Mycobacterium avium subspecies paratuberculosis contamination in bulk tank milk

Infectious diseases in dairy cattle are of significant concern to dairy industries because of their huge impact on animal health, milk production, and economics. Mycobacterium avium subsp. paratuberculosis (MAP)is a pathogenic bacterium that causes Johne's disease, one of the important endemic infectious diseases in dairy cattle. Contamination of bulk tank milk with MAP can occur through direct shedding into milk by infected cows (internal route), fecal contamination (fecal route), or introduction of soil and water containing MAP (environmental route). Humans can be exposed to MAP via raw milk consumption; additionally, there are reports of MAP survival in milk after pasteurization. The risk of human consumption is particularly important due to an association between MAP and human Crohn's disease. In the current study, we used a probabilistic modeling framework to predict the level of MAP contamination per liter in the bulk tank milk and weigh the relative importance of each contamination route. Our model focused on several different infection statuses and the contribution of each group to environmental and fecal contamination, in addition to internal route shedding. We assessed the influence of common hygiene practices, such as washing of udders before milking and the use of milk filters, on the concentration of MAP in bulk tank milk. We extracted parameters and their distributions from national surveys and thorough literature search. Our baseline model comprising all hygiene practices provided an average estimate of 0.76 log CFU/L for the final concentration of MAP in bulk tank milk, with a maximum of 6.70 log CFU/L and a minimum of 0.04 log CFU/L depending on herd size and the ratio of infection statuses. Results from sensitivity analyses indicated that the average fecal contamination showed the greatest impact on the final MAP concentration per liter in bulk tank milk, followed by herd size and washing efficiency. This study emphasized that good hygiene practices are crucial for maintaining the quality of raw milk in an endemically-infected dairy herd.</p

A Machine Learning Model for Food Source Attribution of Listeria monocytogenes

Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.https://doi.org/10.3390/pathogens1106069

Modeling the effects of infection status and hygiene practices on Mycobacterium avium subspecies paratuberculosis contamination in bulk tank milk

Infectious diseases in dairy cattle are of significant concern to dairy industries because of their huge impact on animal health, milk production, and economics. Mycobacterium avium subsp. paratuberculosis (MAP)is a pathogenic bacterium that causes Johne's disease, one of the important endemic infectious diseases in dairy cattle. Contamination of bulk tank milk with MAP can occur through direct shedding into milk by infected cows (internal route), fecal contamination (fecal route), or introduction of soil and water containing MAP (environmental route). Humans can be exposed to MAP via raw milk consumption; additionally, there are reports of MAP survival in milk after pasteurization. The risk of human consumption is particularly important due to an association between MAP and human Crohn's disease. In the current study, we used a probabilistic modeling framework to predict the level of MAP contamination per liter in the bulk tank milk and weigh the relative importance of each contamination route. Our model focused on several different infection statuses and the contribution of each group to environmental and fecal contamination, in addition to internal route shedding. We assessed the influence of common hygiene practices, such as washing of udders before milking and the use of milk filters, on the concentration of MAP in bulk tank milk. We extracted parameters and their distributions from national surveys and thorough literature search. Our baseline model comprising all hygiene practices provided an average estimate of 0.76 log CFU/L for the final concentration of MAP in bulk tank milk, with a maximum of 6.70 log CFU/L and a minimum of 0.04 log CFU/L depending on herd size and the ratio of infection statuses. Results from sensitivity analyses indicated that the average fecal contamination showed the greatest impact on the final MAP concentration per liter in bulk tank milk, followed by herd size and washing efficiency. This study emphasized that good hygiene practices are crucial for maintaining the quality of raw milk in an endemically-infected dairy herd

Importance of artificial intelligence in evaluating climate change and food safety risk

Climate change is considered primarily as a human-created phenomenon that is changing the way humans live. Nowhere are the impacts of climate change more evident than in the food ecosphere. Climate-induced changes in temperature, precipitation, and rain patterns, as well as extreme weather events have already started impacting the yield, quality, and safety of food. Food safety and the availability of food is a fundamental aspect of ensuring food security and an adequate standard of living. With climate change, there have been increasing instances of observed changes in the safety of food, particularly from a microbiological standpoint, as well as its quality and yield. Thus, there is an urgent need for the implementation of advanced methods to predict the food safety implications of climate change (i.e., future food safety issues) from a holistic perspective (overall food system). Artificial Intelligence (AI) and other such advanced technologies have, over the years, permeated many facets of the food chain, spanning both farm- (or ocean-) to-fork production, and food quality and safety testing and prediction. As a result, these are perfectly positioned to develop novel models to predict future climate change-induced food safety risks. This article provides a roundup of the latest research on the use of AI in the food industry, climate change and its impact on the food industry, as well as the social, ethical, and legal limitations of the same. Particularly, this perspective review stresses the importance of a holistic approach to food safety and quality prediction from a microbiological standpoint, encompassing diverse data streams to help stakeholders make the most informed decisions

Special issue on 10th international conference of predictive modelling in foods : towards a new paradigm in predictive microbiology

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Elucidating Transmission Patterns of Endemic <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> Using Molecular Epidemiology

Mycobacterial diseases are persistent and characterized by lengthy latent periods. Thus, epidemiological models require careful delineation of transmission routes. Understanding transmission routes will improve the quality and success of control programs. We aimed to study the infection dynamics of Mycobacterium avium subsp. paratuberculosis (MAP), the causal agent of ruminant Johne&#8217;s disease, and to distinguish within-host mutation from individual transmission events in a longitudinally MAP-defined dairy herd in upstate New York. To this end, semi-annual fecal samples were obtained from a single dairy herd over the course of seven years, in addition to tissue samples from a selection of culled animals. All samples were cultured for MAP, and multi-locus short-sequence repeat (MLSSR) typing was used to determine MAP SSR types. We concluded from these precise MAP infection data that, when the tissue burden remains low, the majority of MAP infections are not detectable by routine fecal culture but will be identified when tissue culture is performed after slaughter. Additionally, we determined that in this herd vertical infection played only a minor role in MAP transmission. By means of extensive and precise longitudinal data from a single dairy herd, we have come to new insights regarding MAP co-infections and within-host evolution