Organic and sustainable farming operations increasingly depend on biological soil amendments of animal origin to build soil health, improve nutrient availability, and sustain crop productivity without relying on synthetic fertilizers. Poultry litter, a mixture of manure, bedding material, feathers, and feed residues, is one of the most abundant and widely used of these amendments. In Georgia, one of the nation’s leading poultry-producing states, approximately two million tons of poultry litter are generated annually, making it an economically and agronomically important resource for vegetable growers across the state. Sweet onions, a signature Georgia crop, are frequently grown in soils amended with these materials.

The research team, which included Harsimran Kaur Kapoor of the University of Georgia and Abhinav Mishra of the university’s Department of Food Science & Technology, set out to answer a deceptively simple question: how long does E. coli remain viable in field soils treated with different poultry litter-based amendments, and how do environmental conditions modulate that survival? The answer matters because pathogenic strains of E. coli and related foodborne pathogens can persist on produce surfaces and within soil ecosystems long after the amendment is applied, creating a window of potential contamination that growers must manage.

Across two growing seasons, the researchers observed a consistent pattern. Soils amended with heat-treated poultry pellets and soils amended with composted poultry litter both supported longer survival of E. coli than untreated control soils. Heat treatment and composting are both intended to reduce pathogen loads in manure-based products, yet the study indicates that the amended soil environment itself, even when the amendment has been processed, can create conditions more favorable to bacterial persistence than unamended soil. Nutrient availability, moisture retention, and organic matter content are among the factors that researchers generally associate with improved microbial survival in amended soils, and the study’s results align with that broader understanding.

The weather findings add a layer of complexity that the authors argue is essential for risk management. The influence of weather differed between the two growing seasons analyzed, pointing to the importance of year-to-year variability. Higher humidity was associated with greater bacterial survival, while stronger winds generally reduced survival. Rain and warmer soil temperatures could promote bacterial persistence, whereas higher air temperature and wind speeds tended to reduce it. Taken together, the results suggest that extreme weather events, rather than average seasonal conditions alone, may play an important role in determining how long bacteria can survive in agricultural soils.

This distinction between averages and extremes carries real consequences for how food safety risk is modeled and managed. A season with a modest average temperature but several intense rainfall events may pose a different contamination profile than a season with a higher average temperature and steady winds. Risk-based approaches that rely solely on seasonal or monthly climate summaries could therefore underestimate the survival window of pathogens following specific extreme events. The authors note that understanding how amendment practices interact with extreme weather may help inform risk-based management approaches under real field conditions, where growers cannot control the weather but can adjust amendment type, timing, and application practices.

The crop at the center of the study is not an arbitrary choice. Onions have been linked to several major foodborne illness outbreaks in North America in recent years. In 2024, the U.S. Food and Drug Administration investigated an outbreak of E. coli O157:H7 associated with slivered onions served on McDonald’s Quarter Pounder burgers. Earlier incidents include a 2020 outbreak of Salmonella Newport linked to red onions across the United States and Canada, and a 2021 outbreak of Salmonella Oranienburg tied to whole fresh onions imported from Mexico. These outbreaks resulted in thousands of reported illnesses and hundreds of hospitalizations, underscoring that allium crops, long considered lower risk than leafy greens, can serve as vectors for serious foodborne disease.

Sweet onions present particular production considerations. They are typically grown in sandy soils with careful irrigation management, harvested after months in direct contact with the soil environment, and often consumed raw or with minimal processing, which means no kill step intervenes between field and consumer. Any pathogen that persists in the soil or on the bulb surface at harvest therefore represents a direct route into the food supply. Understanding whether and how poultry litter amendments extend the survival window of E. coli in these soils gives growers and regulators a concrete variable to work with when designing pre-harvest intervals, amendment application schedules, and water and soil testing regimes.

The regulatory context is also evolving. The authors note that regulators continue to seek data on the food safety implications of biological soil amendments of animal origin, a category that includes raw and processed manures, composts, and pelleted products. Field-scale studies like this one provide the kind of empirical evidence needed to calibrate standards that are both protective of public health and workable for growers who depend on these amendments for soil fertility. The interaction between amendment type and local environmental conditions that the study documents suggests that a one-size-fits-all national standard may be less effective than frameworks that account for regional climate, weather variability, and dominant amendment practices.

For Georgia’s sweet onion industry, and for produce growers in poultry-heavy regions more broadly, the study offers a practical takeaway: the combination of amendment choice and weather exposure deserves active attention in food safety planning. Growers who apply heat-treated poultry pellets or composted poultry litter may want to consider how upcoming rainfall, humidity, and wind patterns could extend or shorten pathogen survival in their fields. As extreme weather events become more frequent and more intense, the researchers’ central finding, that it is the extremes rather than the averages that shape bacterial persistence, is likely to grow in importance for anyone working at the intersection of soil health, agricultural productivity, and the safety of the fresh produce that reaches consumers’ tables.

Subject of Research: Survival of E. coli in poultry litter-amended soils under varying weather conditions in sweet onion production

Article Title: Study finds poultry litter-based soil amendments and weather influence E. coli survival in Georgia sweet onion production systems

Article References: Study finds poultry litter-based soil amendments and weather influence E. coli survival in Georgia sweet onion production systems. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: E. coli, poultry litter, soil amendments, sweet onions, food safety, extreme weather, Georgia agriculture, Risk Analysis, compost, foodborne illness, pathogen survival, produce safety

The stakes are enormous. Foodborne pathogens such as Salmonella, Escherichia coli, and Listeria pose particular dangers to children, the elderly, and immunocompromised individuals, and failures in quality control can trigger recalls, reputational collapse, and even criminal prosecution for food companies. Traditional verification methods, which rely heavily on laboratory analysis, are slow, expensive, and prone to human error. As global supply chains stretch across continents, contamination introduced at a farm in one country can spark illness outbreaks among consumers thousands of kilometers away, making consistent oversight a formidable challenge. The authors contend that AI, a hallmark of the Industry 4.0 revolution, offers the speed, scale, and precision that conventional approaches cannot match.

At the heart of this transformation lies machine learning, the branch of AI that allows algorithms to learn patterns from data rather than following fixed rules. The review describes how machine learning techniques, spanning supervised, unsupervised, semi-supervised, and reinforcement learning, are being deployed to monitor and predict food safety hazards. Convolutional neural networks excel at analyzing image data, while algorithms such as support vector machines and k-nearest neighbors have been applied to biosensors that detect antibiotic residues and even differentiate Salmonella concentrations in smartphone-based lateral flow assays. Unlike chromatography or spectroscopy, which deliver reliable results but demand lengthy sample preparation and technical expertise, machine learning models can flag suspicious samples in near real time, tracking contaminants from pesticides, additives, toxins, and pathogens.

Artificial neural networks, the most widely adopted AI architecture in food assessment, are proving especially versatile. The review notes their success in predicting quality attributes of products ranging from coconuts and potatoes to olive oil and dried mango, and in restoring nutritional parameters such as frying time, temperature, and oil content for fried fish. In agriculture, AI-enabled sensors evaluate soil quality and detect plant diseases before they spread, while studies cited in the paper report pest and disease detection in maize with around 75 percent accuracy and coffee bean grading reaching 96 percent accuracy. These predictive capabilities allow farmers and producers to intervene early, reducing waste and protecting harvests.

Computer vision brings a different dimension to quality control: sight. Automated inspection systems now detect and eject foreign objects, soil, and even aflatoxin mold in roasted peanuts by selectively distinguishing infected from normal grains through spectral imaging. Vision-guided robotic systems sort and pack food with a precision that human workers, laboring in hazardous processing environments, cannot safely match. The review highlights a meat processing case study in which AI-driven image recognition achieved contamination detection rates above 98 percent, automatically removing suspect products from the line. Beyond the factory, similar systems have been used to grade mushrooms on multiple quality attributes and even to track honeybee activity, coloration, and temperature in apiculture research.

A quieter revolution is unfolding in the realm of text. The food industry generates mountains of unstructured data, including regulatory reports, equipment maintenance logs, supplier certifications, and a torrent of customer reviews and social media posts. Natural language processing, the review explains, can automate the extraction and validation of compliance data, interpret complex standards such as FDA and ISO 22000 requirements, and perform sentiment analysis that reveals recurring complaints about freshness, taste, or packaging before they snowball into quality crises. In one cited beverage manufacturing case, an NLP-based document system cut audit preparation time by 40 percent while minimizing compliance errors, freeing quality teams to focus on the highest-risk areas.

Sensors and the Internet of Things supply the raw data that makes all of this intelligence possible. Electronic noses and tongues, once laboratory curiosities, now capture temperature, humidity, and aroma profiles in production and distribution, reporting sensory events as electronic signals. Cloud-connected IoT devices allow storage operators to maintain optimal transit conditions and estimate shelf life with unprecedented frequency, while integrated blockchain systems such as the Food Trail blockchain create immutable, decentralized records that trace products from fishing boats to retail shelves. A seafood supplier case study combining AI with blockchain reported a 45 percent improvement in traceability accuracy and a 30 percent reduction in fraud, demonstrating how transparency can be engineered into high-risk supply chains.

Perhaps the most striking application is AI’s role in predicting outbreaks before they happen. Predictive analytics models trained on historical and real-time data can forecast when and where foodborne illness risk will spike, accounting for temperature fluctuations, seasonal trends, and geographic patterns. NLP systems monitoring news, health forums, and social media have detected clusters of gastrointestinal complaints linked to fresh produce, enabling preventive action before formal reports were filed. In a dairy processing case study, AI sensors tracking temperature, pH, and bacterial counts reduced spoilage by 25 percent and improved quality consistency by 30 percent, while AI-enhanced environmental monitoring of processing plants has been credited with significant reductions in contamination rates.

The authors are candid about the obstacles. Globalized supply chains, shifting consumer preferences for additive-free foods, fragmented regulations across jurisdictions, climate-driven disruptions, chronic undertraining of workers, and the financial constraints facing small and medium-sized enterprises all complicate the AI transition. There are ethical dimensions too: because true contamination in any given sample is vanishingly rare, machine learning systems risk cascading false rejections, and biases embedded in training data can go unexamined. Existing AI regulatory frameworks, the review warns, often fail to address the unique requirements of food safety applications, and opaque or unaccountable models remain too hazardous to deploy outside controlled environments.

Still, the trajectory is unmistakable. From classifying barley grains and identifying fraudulent ingredients to automating cleaning verification in breweries and tracing contamination through blockchain-verified ledgers, AI technologies are reshaping what food safety means in the twenty-first century. The review concludes that continued progress will depend on developing consumer-friendly sensors that communicate product freshness directly to shoppers, harmonizing international standards, and building transparency into every algorithm. If those challenges are met, the researchers argue, Industry 4.0’s intelligent machines could deliver what traditional methods never could: safe, nutritious, and verifiably high-quality food for a global population at scale.

Subject of Research: Artificial intelligence applications for food safety and quality assurance in the food industry

Article Title: Artificial Intelligence for Food Safety and Quality Assurance: Technologies, Applications and Future Directions

Article References: Artificial Intelligence for Food Safety and Quality Assurance: Technologies, Applications and Future Directions. (n.d.). https://doi.org/10.1002/fsn3.72377

Image Credits: AI Generated

DOI: 10.1002/fsn3.72377

Keywords: artificial intelligence, food safety, machine learning, computer vision, natural language processing, Internet of Things, blockchain, food quality assurance, foodborne illness, smart sensors, traceability, Industry 4.0

The raw feeding movement traces its modern popularity to the 1993 publication of Ian Billinghurst’s book Give Your Dog a Bone, which argued that dogs thrive on bones and raw meat reminiscent of an ancestral canine diet. Today the category encompasses home-prepared BARF recipes, prey-model regimens, commercial frozen and chilled complete diets, freeze-dried products, premixes combined with raw meat, and raw meaty bones. The review stresses that these categories are not interchangeable. They differ in nutrient formulation, ingredient traceability, pathogen-control procedures, storage requirements and owner handling, and studies that lump all raw diets into a single exposure may obscure important variation in risk.

On the nutritional front, the review finds that adequacy is genuinely product-specific. In one analytical study of 33 preprepared raw dog foods labelled as complete, every single product had at least three mineral values outside the FEDIAF reference range. Selenium fell below the minimum recommendation in all 33 products, zinc, manganese and copper were frequently below recommended intakes, 45.5 percent of products exceeded the maximum calcium recommendation, and 57.6 percent exceeded the maximum iodine value. The authors caution that these deviations identify quality-control concerns within the sampled products but do not by themselves establish clinical deficiency or toxicity in the dogs consuming them, and the findings should not be generalized to every commercial raw product on the market.

Home-prepared raw diets fare less well in the literature. Analyses of published recipes, drawn from a broader body of work on home-prepared diets, have identified frequent deficiencies in calcium, zinc, copper, vitamin D, vitamin E and choline. Ingredient rotation, a common owner strategy, does not guarantee nutritional completeness, because adequacy depends on quantitative formulation and nutrient bioavailability rather than variety alone. Puppies are especially vulnerable to inappropriate calcium and phosphorus concentrations, and excessive liver or unbalanced supplementation can produce vitamin and trace-mineral excesses. Professional guidance therefore supports formulation and periodic review by a veterinarian with advanced nutrition training, particularly for growth, disease management or long-term exclusive feeding.

Where raw diets do show consistent short-term effects is in digestibility and faecal characteristics. Controlled studies have repeatedly reported higher apparent total-tract digestibility of protein and fat in raw, mildly cooked or human-grade fresh diets compared with extruded kibble, along with smaller, firmer stools and reproducible changes in faecal microbiota and fermentation products. Yet the review urges technical caution in interpreting these numbers. The diets being compared rarely differ only in processing; they typically vary simultaneously in ingredients, fat, fibre, starch, moisture and energy density, so higher digestibility coefficients cannot be attributed to the absence of heat treatment alone. Human-grade mildly cooked diets can produce similar effects, and no universally accepted healthy canine microbiome signature exists against which these compositional differences could be judged beneficial or harmful. Crucially, long-term clinical superiority of raw feeding over nutritionally complete conventional diets has not been demonstrated.

The microbiological picture is where the evidence is strongest and most troubling. Because raw meat receives no thermal kill step, contamination can persist from slaughter through processing, transport and household handling. Surveillance studies across Europe and the Americas have repeatedly detected Salmonella, Campylobacter, Listeria monocytogenes, Shiga toxin-producing Escherichia coli, Yersinia enterocolitica and other enteric bacteria in commercial raw pet foods. The largest recent United Kingdom retail survey tested 380 frozen raw products collected between March 2023 and February 2024. In the dog-food subset, Salmonella was detected in 24.2 percent of samples, Campylobacter in 14.4 percent, culture-confirmed STEC in 13.4 percent and MRSA in roughly 10 percent, while ESBL- or AmpC-producing E. coli were found in 21.5 percent of the dog-food samples tested for that outcome. Across the full dataset, 28.7 percent of products exceeded statutory microbiological criteria.

The review is equally clear that freezing and freeze-drying are not reliable microbial kill steps. Viable bacteria have been recovered from commercial freeze-dried raw products, and available evidence indicates that drying may reduce rather than eradicate contamination. Parasites add another layer of concern, including Toxoplasma gondii, Sarcocystis species, Neospora caninum and, depending on geographic origin and offal source, Echinococcus. An Italian observational study found that dogs reported to consume raw meat regularly had nearly threefold higher odds of Toxoplasma seropositivity, supporting exposure plausibility even though raw meat cannot be confirmed as the sole source. Domestic freezers may not achieve validated time-temperature combinations uniformly, so parasite control requires source-specific rather than generic assumptions about freezing.

Dogs fed raw diets can shed enteric pathogens without showing any clinical signs, creating a household exposure pathway through food preparation, contaminated bowls and surfaces, faeces and close contact. Multiple observational studies have linked raw feeding to increased faecal carriage of Salmonella and antimicrobial-resistant E. coli, including third-generation cephalosporin-resistant and multidrug-resistant strains in United Kingdom dog populations. Whole-genome sequencing has identified closely related resistant Enterobacterales in companion animals and their household members, supporting recent sharing or a common source, although genomic relatedness alone cannot establish the direction of transmission. Documented human outbreaks underscore that severe outcomes are possible: a cluster of Shiga toxin-producing E. coli O157:H7 infections was linked to raw tripe pet food, and Canadian authorities investigated an outbreak of extensively drug-resistant Salmonella associated with raw pet food and cattle contact. The review notes, however, that outbreak reports establish possibility and severity rather than the population-level burden of raw-pet-food-associated human disease.

On the environmental side, the evidence is the least raw-diet-specific. Life-cycle assessments of pet food generally show that impacts are driven principally by the type and quantity of animal-derived ingredients, with ruminant and human-edible meat dominating land use and greenhouse-gas emissions. A recent United Kingdom analysis of 996 dog foods, including 34 raw products, estimated a greater than 65-fold range in greenhouse-gas intensity across products, with prime-meat content an important driver. Frozen products require cold-chain energy during distribution and home storage, whereas freeze-drying demands substantial manufacturing energy but reduces transport mass and avoids frozen storage, and few matched assessments have quantified these trade-offs. The authors conclude that a poultry or by-product-based raw food may compare favourably with a beef-rich premium kibble, so the raw format itself cannot be assumed intrinsically more impactful.

The review’s overarching message is that raw meat-based diets warrant product-specific and household-specific risk assessment rather than categorical judgement. For owners who choose to continue raw feeding, veterinary counselling should prioritize nutritionally complete formulation appropriate for life stage, manufacturer quality assurance, validated pathogen-reduction processes such as high-pressure processing that reduce but do not eliminate risk, uninterrupted cold chains, strict hygiene including bowl cleaning and handwashing, prompt faeces disposal, and explicit consideration of household vulnerability. Households containing infants, pregnant individuals, older adults or immunocompromised people face a lower margin of safety, and raw feeding may be inappropriate when their exposure cannot be reliably prevented. The authors call for adequately powered prospective cohorts, batch-level surveillance that distinguishes raw diet categories, whole-genome-sequencing source-attribution studies, validation of pathogen-reduction technologies, and matched life-cycle assessments before the long-term benefits and risks of raw feeding can be compared with high certainty.

Subject of Research: Nutritional adequacy, microbiological safety, antimicrobial resistance and environmental sustainability of raw meat-based diets for dogs within a One Health framework

Article Title: Raw meat based diets for dogs and their nutritional and One Health implications

Article References: Tufarelli, V., & Bozzo, G. (2026). Raw meat based diets for dogs and their nutritional and One Health implications. Discover Animals, 3(1), Article 84. https://doi.org/10.1007/s44338-026-00249-0

Image Credits: AI Generated

DOI: 10.1007/s44338-026-00249-0

Keywords: raw meat-based diets, dog nutrition, One Health, Salmonella, antimicrobial resistance, zoonotic pathogens, pet food safety, BARF diet, foodborne illness, life cycle assessment, Toxoplasma gondii, veterinary nutrition