Tag Archives: AI

Big data

AI Is Becoming a Practical Food Safety Equalizer for Small and Mid Sized Manufacturers

By Matthew Kang
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Big data

For small and mid sized food manufacturers, the real food safety challenge is often not the absence of programs. It is the difficulty of executing them consistently with limited people, limited time, and limited system support. AI does not replace food safety culture, trained employees, or management accountability. What it can do is reduce documentation drag, connect fragmented records, and give small plants better visibility into the daily factors that affect both compliance and performance.

Most small food plants already have some version of HACCP, sanitation procedures, allergen controls, supplier documentation, and corrective action forms. On paper, the structure exists. The problem is that these programs often live in separate places. Some information is in handwritten logs. Some is in Excel files. Some sits in email trails. Some remains in the memory of one or two experienced employees. In a large company, those gaps are often absorbed by specialized teams. In a 20 person plant, they become part of the day’s friction. USDA FSIS guidance for small and very small establishments reflects that reality by offering practical compliance guidance for smaller operations rather than assuming large company infrastructure.¹ ²

That is one reason AI matters. Not because it is futuristic, but because small food companies and facilities need tools that help them execute what they are already supposed to be doing.

FDA’s Food Traceability Final Rule makes that challenge even more visible. For foods on the Food Traceability List, firms are expected to maintain linked records around Critical Tracking Events and Key Data Elements so food can be identified and removed from the market more quickly when necessary. FDA has also said that, under current law, it does not intend to enforce the rule before July 20, 2028.³ ⁴

I did not introduce AI as a food safety system. At first, I was simply trying to make our ordinary plant records easier to use. What surprised me was how quickly those same operating records turned into food safety records once they were organized properly.

The first and most important use case was the daily production report.

A typical report includes labor hours, raw material use, number of batches, yield, run time, and overhead assumptions. But what makes that report valuable is the context around the numbers. A forming machine goes down and creates a one hour delay. A new operator joins the line and throughput drops. A raw material lot arrives with inconsistent quality and forces rework or a change in handling. Before AI, those details usually existed as loose comments. They were written down, but not really used.

That changed once we started combining the numbers and the narrative in one place. After a few weeks, I started noticing which problems were truly random and which ones kept coming back. A yield problem was not always just a yield problem. Sometimes it pointed to operator inconsistency. Sometimes it pointed to equipment instability. Sometimes it started with raw material quality. In a small plant, those issues do not stay in their own lane. They spill into sanitation timing, rushed handling, delayed changeovers, and rework decisions. That is when I realized AI was doing more than saving time. It was helping us see operational patterns we had been living with but not fully recognizing.

A second use case involved incoming raw materials.

In a small food company or facility, receiving is one of the most important control points, but also one of the easiest places for information to become fragmented. We began using simple photo capture of ingredient statements and specification sheets to pull out allergen information, compare those ingredients against non allergen counterparts, and flag price changes. If a supplier raised a price or changed a formulation, that information could be reflected back into costing and into the same day’s production analysis.

This mattered more than I expected. In the past, allergen characteristics, lot information, and pricing changes could all be reviewed by different people at different times. That made it too easy for something important to be noticed late. Once those pieces were pulled together, receiving became much more useful as an early warning point instead of just a paperwork step.

A third application involved process data and compliance follow through.

Post process data logger outputs, for example, became more useful when we reviewed them for patterns instead of as isolated records. If a cooling trend began to drift or a cook step started landing too close to the lower end of a target range, we could see it earlier. The same logic applied when a USDA or FSIS noncompliance record was issued. What used to require digging through prior records, emails, and deadlines could be organized into a more structured workflow. That did not remove the need for qualified review. It still required human judgment and human sign off. But it cut down the time spent assembling information that already existed in scattered places.

Monthly closing and costing created another layer of value. By comparing accounting data with production report trends, it became easier to see whether a margin decline was being driven by labor inefficiency, unstable yield, supplier inflation, or poor scheduling. In a small plant, food safety discipline and operational discipline are closely tied together. Rework, spoilage, excessive changeovers, and weak lot visibility are cost problems. They are also signals of weak execution. Once those signals become visible earlier, management decisions improve.

Production scheduling turned out to be one of the clearest examples of AI’s practical value. In a small facility, the best schedule is not simply the one that fills the day. It is the one that balances labor availability, sanitation windows, equipment uptime, maintenance timing, raw material readiness, and product mix. We began reviewing historical combinations of labor, line setup, batch sequence, and uptime that had previously produced stronger margins and smoother runs. It was not perfect. But it did stop us from planning only by instinct.

That also created a sustainability benefit. Better schedules can reduce avoidable changeovers, overproduction, product loss, and inefficient use of labor and energy. For small plants, sustainability does not begin with a polished ESG report. It begins with running a tighter operation. When inventory is more visible, fewer ingredients expire unnoticed. When schedules are better sequenced, fewer unnecessary runs are made. When traceability is better structured, edible surplus is easier to identify and donate instead of discard. In California, where edible food recovery and organic waste diversion obligations under SB 1383 are part of the operating landscape, those improvements are not abstract. They can affect whether product is simply written off or handled more responsibly.⁹

None of this means AI should be treated casually.

The stronger its role becomes, the more important governance becomes. That is why the NIST AI Risk Management Framework is useful even though it is not a food law. It gives smaller organizations a practical framework for thinking about trustworthiness, transparency, validation, human oversight, and risk management. Published as NIST AI 100-1 in January 2023, it was developed under the National Artificial Intelligence Initiative Act of 2020 and is voluntary, non sector specific, and broadly applicable across sectors.⁸

For a small food company or facility, that does not require a long policy manual. It does require a few clear rules. Which decisions require human sign off. Which records are AI assisted but still human verified. How outputs are checked against current FDA regulations, USDA FSIS guidance, customer requirements, and plant procedures. What data may be uploaded into external tools, and by whom. These questions matter because AI can produce text that sounds authoritative even when it is wrong. In food safety, that is not a minor issue. It is a governance issue.

The same caution applies to digital records. FDA’s Part 11 guidance makes clear that electronic records used in regulated settings remain subject to the applicable predicate rules.⁵ USDA FSIS has also made clear that electronic monitoring and recording records may be used to satisfy HACCP, sanitation, and related requirements, and that electronic records are treated the same as paper records.⁶ ⁷

The food safety world often talks in terms of programs, plans, and frameworks. Those matter. But in small and mid sized manufacturing, the real test is whether those systems can still be executed on an ordinary Tuesday while labor is tight, equipment is acting up, and a late shipment has already disrupted the day. That is where food safety often breaks down. Not in theory, but in execution.

That is why I see AI less as a replacement for expertise and more as a practical equalizer. In a 20 person plant, it can create better visibility, better consistency, and better follow through than the staffing level would otherwise allow.

References

¹ U.S. Department of Agriculture, Food Safety and Inspection Service. Small & Very Small Plant Guidance.
² U.S. Department of Agriculture, Food Safety and Inspection Service. HACCP Guidance. Last updated Jan. 12, 2022.
³ U.S. Food and Drug Administration. FSMA Final Rule on Requirements for Additional Traceability Records for Certain Foods.
⁴ U.S. Food and Drug Administration. Food Traceability List.
⁵ U.S. Food and Drug Administration. Part 11, Electronic Records; Electronic Signatures — Scope and Application. Guidance for Industry. September 2003.
⁶ U.S. Department of Agriculture, Food Safety and Inspection Service. Verifying Video or Other Electronic Monitoring Records. FSIS Directive 5000.9. Aug. 26, 2011.
⁷ U.S. Department of Agriculture, Food Safety and Inspection Service. Compliance Guidelines for Use of Video or Other Electronic Monitoring or Recording Equipment in Federally Inspected Establishments. Guideline ID FSIS-GD-2011-0001. August 2011.
⁸ National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. Jan. 26, 2023.
⁹ California Department of Resources Recycling and Recovery. Food Recovery Questions and Answers.

Food fraud
FST Soapbox

Harnessing AI can help to ensure safe food for consumers across the US and beyond

By Wesley Wilson
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Food fraud

With consumer confidence in the safety of US food hitting a 13-year-low last summer, there is clearly work to do for consumer goods companies. There are multiple drivers for this loss in confidence, including concerns about insufficient government regulation, fears around foodborne illnesses and contaminants, and a rise in the number of food and beverage recalls.

Given that four in 10 Americans say clearer information about food safety practices would improve their confidence, brands are well positioned to regain momentum by proactively demonstrating how they safeguard consumers.

For the food industry, safety is the cornerstone of consumer trust. Without clear commitments to safety, food companies face a consumer base – including previously loyal customers – that may take their business elsewhere. In this digital age, consumers have access to more product news and information than ever before. The widescale need for robust food safety procedures has never been clearer.

The food sector is already using AI to optimize its supply chains, reduce waste and improve demand forecasting. Now, pioneering companies are also using AI for food safety processes. Its capabilities are significant – including switching safety approaches from reactive to proactive, using data-driven systems that focus on real-time pathogen detection, predictive risk analysis, and automated quality control. Computer vision can be harnessed to inspect contamination, AI-driven sensors can be used for microbial detection, and machine learning can analyze supply chain data to prevent recalls.

The potential for AI to be harnessed is substantial: several AI food safety startups are now partnering with regulatory bodies to co-develop compliance-ready platforms, blurring the line between enforcement and innovation.

However, while some high-profile food companies are jumping on rapid innovation to lead the charge, take-up across the sector remains limited, with less than 30% of global food manufacturers adopting AI for food safety processes. Even though manual systems are no longer efficient for today’s supply demands, paper-based recordkeeping largely prevails. This means that companies reluctant to embrace digitization are leaving themselves exposed to risk.

One of the key barriers is cost. Many businesses struggle to quantify the return on investment for AI safety initiatives, making it hard to justify high initial expenditure. But this approach comes with its own price. According to one study, the business cost of a food recall averages around $10 million – and in 23% of cases, the cost exceeds $30 million.

These enormous sums include assembling crisis management teams, issuing recall notifications, retrieving contaminated products, and conducting investigations to prevent future reoccurrences. But they’re only the tip of the iceberg, as further expenses such as legal fees and lost sales add to the financial burden. Reputational damage is harder to measure, but its impacts are no less severe, as rebuilding consumer confidence is a costly and intensive process, often taking years to build back trust.

AI systems can mitigate these risks, and for a fraction of the cost of an expensive food safety incident. One case study of a collaboration between Walmart and IBM Food Trust, for example, shows how AI was able to reduce the time required to trace the origin of contaminated lettuce from seven days to just 2.2 seconds. Such rapid traceability means companies can pinpoint and remove only affected batches, preventing widespread recalls that waste huge volumes of food and cost – potentially – millions.

AI offers benefits beyond safety management, too. Automation can negate administrative and time-intensive tasks within product development, creating capacity to strengthen product portfolios, elevate brand reputation and deeper consumer connections – heightening confidence alongside solid safety performance.

Consumer trust is the cornerstone of a food company’s success – but it’s never been easier to lose it. Amid global uncertainties, volatile supply chains, increased consumer awareness and a growing focus on health and wellbeing, food safety needs to be a paramount priority.

Harnessing innovation to improve food safety is one of the topics discussed at the Global Food Safety Initiative (GFSI) Conference, in Vancouver, B.C. last month. Of course, AI and tech are not a magic solution. Their impact depends on how they are implemented, and a commitment to the ethos that food safety is everyone’s business.

AI systems empower companies to deliver safety excellence smoothly, positioning them as trusted, reputable brands. Those that continue to rely on fallible safety systems risk events that could cost them their reputation forever.

Collaboration Graphic
In the Food Lab

How Rapid Microbiology and AI Are Transforming Modern Food Safety Laboratories

By Wesam Al-Jeddawi, Ph.D.
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Collaboration Graphic

Food safety laboratories are undergoing a significant shift as the food industry faces increasing pressure for speed, accuracy, and transparency. Traditional microbiological methods remain foundational, but they are often too slow to support today’s accelerated production cycles and complex supply chains. As a result, laboratories are adopting rapid microbiological methods, digital data systems, and artificial intelligence to enhance decision-making and reduce risk.

These technologies are not replacing scientific expertise—they are expanding what laboratories can deliver. When integrated thoughtfully, they improve efficiency, strengthen data integrity, and help manufacturers identify issues earlier in the production process.

The Growing Role of Rapid Microbiological Methods

Rapid microbiology has become essential for processors seeking faster turnaround times and more consistent results. Technologies such as molecular assays, automated enumeration systems, and ATP bioluminescence offer several advantages:

  • Faster results, enabling earlier product release
  • Reduced manual handling, lowering variability and labor demands
  • Improved sensitivity, especially for stressed or low-level organisms
  • Digital traceability, supporting audit readiness and data integrity

For many facilities, the value lies not only in speed but in the ability to intervene earlier. Rapid methods allow quality teams to detect deviations before they escalate into waste, rework, or regulatory action.

Artificial intelligence is beginning to influence how laboratories interpret and manage microbiological data. Its most impactful applications include:

  • Identifying patterns across historical testing data
  • Predicting spoilage and contamination risks
  • Automating data checks, reducing transcription errors
  • Supporting root-cause analysis with more complete datasets

When paired with a laboratory information management system (LIMS), AI helps laboratories transition from reactive testing to proactive risk management. Instead of simply reporting results, labs can provide insights that help manufacturers prevent issues before they occur.

Quality Systems and Accreditation Expectations

As laboratories adopt new technologies, accreditation bodies are raising expectations around method validation, documentation, and data integrity. This shift is encouraging labs to:

  • Strengthen quality management systems
  • Standardize workflows to reduce analyst-to-analyst variation
  • Improve documentation for regulatory and customer audits
  • Integrate digital tools that support real-time monitoring

The laboratories that excel are those that combine scientific rigor with operational discipline.

A Changing Role for Food Safety Laboratories

The modern laboratory is evolving from a testing provider to a strategic partner. Today’s labs increasingly support manufacturers by offering:

  • Technical guidance on sampling and environmental monitoring
  • Data-driven insights for continuous improvement
  • Training for quality and production teams
  • Support for regulatory readiness and risk mitigation

This expanded role reflects the growing importance of laboratory expertise in ensuring food safety across the supply chain.

Conclusion

Rapid microbiology, digitalization, and artificial intelligence are reshaping the capabilities of food safety laboratories. These tools enhance—not replace—scientific judgment, enabling laboratories to deliver faster, more reliable, and more actionable information. As the industry continues to evolve, laboratories that embrace innovation while maintaining strong scientific foundations will play a central role in building a safer and more resilient food system.

From Chaos to Cruise Control: Generative AI and the Next Era of Human-Centric Warehouse Management

By Michelle Jones
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Managing a warehouse today can feel a lot like going on a beach vacation without a plan. You’ve packed your suitcase, brought sunscreen, and maybe even remembered the snacks, but the tides, the jellyfish, and the long lines at the rental shop all catch you off guard. Traditional warehouse management systems (WMS) can feel the same way. They enforce rules, track inventory, and issue alerts, but when something goes wrong, employees often find themselves scrambling to figure out what to do next. Searching manuals, calling support, or tracking down a colleague who “knows the system” can feel like running down the beach in flip-flops after a wayward volleyball.

Generative AI is changing that experience. Instead of being a rigid, static tool, the WMS becomes a co-pilot, a system that anticipates challenges, responds intelligently, and guides employees in real time. The difference is like stepping from a chaotic, sunburned first day into a perfectly planned vacation, where every detail is accounted for and the experience flows effortlessly.

The Challenges of Traditional WMS: A Chaotic Vacation

Modern warehouses are complex operations under tremendous pressure. Orders continue to climb, delivery windows shrink, and labor shortages make every team member essential. Yet many WMS environments still operate with interfaces and workflows designed for a very different era. The result is:

  • New employees spend weeks learning where to click rather than focusing on their work.
  • Experienced staff hunt for data buried somewhere in the system.
  • Simple questions can spiral into hours of inefficiency.

Day after day, these small frustrations erode productivity, accuracy, and employee satisfaction, just like a beach day where everything goes slightly wrong, leaving you exhausted and frustrated by the end.

Generative AI WMS: The Perfectly Planned Trip

Generative AI flips the traditional WMS experience on its head. Rather than forcing employees to adapt to rigid workflows, AI adapts to them. Questions can be asked in natural language, data is delivered instantly, and repetitive tasks no longer steal attention from high-value work.

Imagine asking your WMS a simple question: “Are any Costco orders at risk of missing their shipment deadlines today?”

Behind the scenes, the system:

  • Pulls real-time inventory data and status of shipment fulfillment tasks
  • References data from labor resource planning, transportation management, dock scheduling, and other warehouse systems
  • Gathers data and context for the orders to identify and escalate at risk shipments
  • Proposes issue resolution scenarios such as changing shipment mode from ground to air

From the employee’s perspective, it’s effortless, like having a vacation guide who already knows the tides, the sunscreen, and the best route to avoid crowds. Operationally, it’s transformational.

Empowering People

The most profound impact of Generative AI is on the human experience. A WMS that can listen, learn, and respond acts like a thoughtful vacation planner who is always available, patient, and proactive. Employees can:

  • Resolve questions independently without interrupting colleagues
  • Learn as they work, shortening onboarding times
  • Access insights via voice commands, mobile devices, or multilingual interfaces

The result is a more confident, engaged workforce, able to focus on meaningful tasks rather than firefighting. Just as a well-planned vacation lets you relax and enjoy the beach, a Generative AI-powered WMS allows employees to focus on execution instead of struggling with the system.

From Reactive to Proactive: Anticipating the Waves

While Generative AI quickly responds to questions, it simultaneously anticipates challenges. By continuously analyzing historical and live data, AI can identify trends, anomalies, and potential risks before they disrupt operations.

It’s like having a travel planner who already knows the best beaches, the shortest lines, and when the tide will turn. With this foresight, teams can prevent small annoyances from ruining the day like inventory bottlenecks, process delays, and service issues are addressed before they escalate, keeping operations running smoothly.

Rolling Out AI Without a Tan Line

Replacing a WMS doesn’t have to mean a disruptive, all-at-once change. While a Generative AI–powered WMS represents a new foundation, its value doesn’t need to arrive in a single “big bang” moment.

The most effective transformations roll out AI capabilities incrementally, starting with focused, high-impact use cases that align to real operational needs. Early wins build confidence, demonstrate value, and allow teams to adapt naturally as the platform expands into deeper optimization and autonomy.

This approach helps ensure adoption feels supportive rather than forced. Employees experience AI as a co-pilot that improves how they work from day one, not a system imposed on them overnight. Best practices include:

  • Starting with small pilot use cases in real operational scenarios
  • Providing clear, practical training instead of abstract theory
  • Involving employees early to build trust and familiarity

When introduced thoughtfully, AI-driven WMS transformation delivers lasting value without leaving teams feeling burned out in the process.

The Future of Autonomous, Adaptive Warehouses

The WMS of the near future is becoming increasingly autonomous and adaptive. Visual AI, real-time optimization, and dynamic route planning are not science fiction, but they are tools already helping reduce waste, improve throughput, and make warehouses more resilient.

A Generative AI WMS can adjust strategies on the fly, allocate resources in real time, and anticipate workflow disruptions. Much like a cruise ship that reroutes its course smoothly to avoid storms and crowded ports, these systems keep operations on track, even in complex, high-pressure environments.

From System of Records to system of Intelligence

Generative AI is transforming what a WMS is expected to do. Instead of just recording transactions and enforcing rules, it becomes an intelligent, human-centered partner that listens, learns, and acts.

Traditional WMS are like chaotic, poorly planned vacations that are functional, but stressful and full of surprises. Generative AI WMS are like meticulously planned trips where everything is anticipated, every decision is guided, and every moment is optimized. Employees are empowered, decisions happen faster, errors decrease, and operations flow smoothly.

The next era of warehouse management goes beyond automation. It’s adaptive, collaborative, and designed to make the complex feel effortless. When your WMS can “ask and answer questions” like a seasoned travel planner, the whole operation runs better, and everyone enjoys the experience along the way.

FDA logo
Beltway Beat

FDA Expands Artificial Intelligence Capabilities with Agentic AI Deployment

By Food Safety Tech Staff
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FDA logo
Seafood Analytics CQR

Leveraging Automation for Enhanced Food Safety and Compliance

By Ainsley Lawrence
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Seafood Analytics CQR

The food industry faces increasing customer demand on top of snowballing regulatory concerns, and many are calling for automation to overcome these obstacles. Automation technologies reinforce food safety practices from processing to packaging by revamping sanitation, quality control, and more.

To begin leveraging automation for food safety in your sector today, the most important areas to focus on are automated monitoring systems, growing AI/ML capabilities, and exceeding regulatory compliance.

Automated Monitoring Systems in Food Safety

Automated monitoring systems have become the titanium backbone of modern food safety, offering greater control over critical processes. With human error as a prevalent risk factor for safety incidents, companies can mitigate accidents with automated systems to mitigate this risk by standardizing processes and enforcing predefined protocols.

This paradigm shift in the way we produce food makes food safer, helps keep workers safe, and makes food quality more consistent at large. Automated monitoring systems can help reduce common errors, drive more effective sanitation, and track your most sensitive critical control points.

Error Reduction through Automated Processes

Many small, common errors can be reduced or outright eliminated with automation. In seafood processing, for example, optical sorting machines consistently identify and remove substandard products. Rather than relying on the inconsistent human eye, machines can rapidly assess each item based on precise criteria such as size, color, and texture. Automation enhances human capabilities in this way by minimizing errors due to fatigue, such as in high-volume production sites.

Seafood Analytics CQR
The CQR device from Seafood Analytics measures the freshness and quality of seafood.

 

Consistent Sanitation Procedures

Maintaining sanitary conditions is critical for safety and regulatory compliance in food production environments. Automated cleaning systems, programmed with precise chemical concentrations and application methods, guarantee thorough and consistent sanitation. These systems meticulously track each cleaning cycle, providing auditable records for compliance purposes. In food packaging, robots can make wrapping products safer, identify foreign objects like bone/shell, and greatly reduce fatigue on workers.

Real-time Critical Control Point Tracking

Automated systems excel at monitoring critical control points (CCPs) in food production, dramatically reducing spoilage. Temperature sensors in cold storage facilities transmit continuous data streams, alerting staff to deviations before spoilage occurs. Meanwhile, automated pH meters and metal detectors in processing areas operate tirelessly with pinpoint precision to ensure consistent product quality and safety.

AI and Machine Learning Applications

Automation can only go so far without insight. AI and ML are carving a niche alongside automation, supplementing raw power with vast datasets and analytic powers to identify anomalies. Together, they enable systems to recognize patterns, flag issues, and optimize processes in ways previously unfeasible.

These technologies integrate with automated systems to monitor complex food production networks, uncovering subtle irregularities that might be missed by human inspection or conventional algorithms.

Traceability in Food Supply Chains

Supply chains are notoriously complex and unpredictable to track because they often involve multiple stages, from raw material sourcing to processing, packaging, distribution, and retail. Each step can involve different suppliers, locations, and regulations, making it difficult to maintain a clear, real-time view of where a product has been and what conditions it has encountered.

AI and machine learning address this by continuously analyzing data from various points, creating an interconnected web of information that companies can use to trace products with greater accuracy than ever before. Whether it’s identifying the origin of a raw ingredient or tracking environmental conditions during transportation, AI-driven traceability systems provide granular insights that facility managers can use to make improvements.

Predictive Analytics

Machine learning models trained on historical data and real-time inputs can predict food safety risks before they appear. In food packaging operations, these systems analyze factors such as temperature fluctuations and microbial growth rates to track CCPs and identify issues. Across departments, predictive maintenance algorithms anticipate equipment failures that could lead to contamination. With this insight, managers can reduce accidents, cut waste, and intervene before incidents occur.

Setting Up for AI and ML

Preparation and a solid foundation in data management are essential to make the most of what AI and machine learning have to offer. Food processing facilities must prioritize data quality, storage capacity, and scalability to harness these technologies. Companies looking to adopt AI and machine learning should:

  • Invest in Quality Data Collection: AI and ML require high-quality data, so IoT devices and sensors are deployed to gather accurate, real-time data across production stages.
  • Choose Scalable Storage: Opt for cloud-based storage to handle increasing data volumes and facilitate easy access and integration.
  • Select Flexible AI Tools: Choose AI and machine learning platforms that can adapt to changing business needs and integrate with existing systems as smoothly as possible.
  • Train Staff with AI/ML: These technologies are only as good as the workers using them – provide training for employees on how to use AI tools effectively to maximize their potential.

AI can make workflows more efficient, but introducing it should always be met with deliberate planning and testing.

Regulatory Compliance and Automation

Automation tech plays a crucial role in helping food businesses navigate the complex regulatory landscape, which is subject to change. As food safety standards evolve, management should look to not just match but exceed regulatory compliance in anticipation of tightening requirements.

Robust food safety standards are essential for maintaining product integrity and consumer trust, but they only work when combined with automated documentation and reporting. Lastly, a new challenge facing food production is handling human-robot interaction in a Wild West-esque tech frontier.

Food Safety Standards

Regulatory bodies frequently update food safety standards to identify emerging risks and incorporate new scientific findings. Automation helps streamline this process for companies fighting a web of red tape by allowing for swift reconfiguration of monitoring parameters and control processes. For instance, AI-powered testing equipment can be remotely updated to detect new microbial threats without overhauling entire production lines. This flexibility helps companies stay ahead of the regulatory curve and slim costs simultaneously.

Automated Reporting and Documentation

Automated systems are stellar at simplifying food safety compliance, able to effortlessly generate and update detailed, real-time records of every aspect of food production and handling. From temperature logs to sanitation schedules, automated reporting tools compile data into a proper regulatory format and ease administrative burdens. While the primary goal is to demonstrate regulatory compliance, this data also proves itself a treasure trove for companies to improve their practices ahead of regulatory change.

Tackle Human-Robot Interaction

The concept of human-robot collaboration isn’t new, but it’s becoming increasingly more common, and the average food production worker is more likely than ever to work with a robot. This paradigm shift requires a new approach to work, which prioritizes streamlining repetitive or laborious tasks, clear communication, and continuous training as capabilities increase. It’s also worth noting that managers can alleviate worries about ‘being replaced with a machine’ by focusing on how technology supplements humans rather than wholesale replacing them in the workplace.

Workers production line
Workers in a factory sorting food by hand, could be assisted by new robot technology. (Unsplash image)

Final Thoughts

Automation, including robotics, AI, and machine learning, is pivotal in enhancing food safety and compliance across the industry. By using automated monitoring systems, food production sites can reduce human error and standardize processes. At the same time, AI and machine learning provide real-time data analysis and predictive insights if companies are willing to put in the work needed to prepare for automation. In that case, they can help reduce accidents, enhance efficiency, monitor food quality, and keep up with regulatory compliance at a fraction of their previous efforts.

Cybersecurity

Food Defense in the Age of AI: Are We Prepared?

By Radojka Barycki
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Cybersecurity

I watched a movie last year where a woman was being framed for murder using her facial features that were captured by a technology used in a bus that allowed passengers to get in based on facial recognition. In the movie, the woman, who was a cop, was investigating suspicious activity relating to the research of the facial recognition self-driven bus that a high-profile tech company was trying to approve for massive production and introduction into the market. The cop was getting too close to confirm her suspicions. So, the tech company got her face profile and embedded it in a video where another person was killing an executive of the company. This got me thinking about how we use face recognition nowadays and how technology is included in everything we do. So, I pose the question: are we at risk in the food industry in terms of Food Defense?

Recent cybersecurity attacks in the food industry have highlighted the urgency of this question. For instance, in 2021, the world’s largest meat processing company fell victim to a ransomware attack that disrupted its operations across North America and Australia. The company had to shut down several plants, leading to significant financial losses and potential supply chain disruptions.

Similarly, earlier that year, a cyberattack targeted a U.S. water treatment facility, where hackers attempted to alter the chemical levels in the water supply. Although this attack was prevented, it underscored the vulnerabilities within critical infrastructure systems, including those related to food production and safety.

Additionally, in 2022, a large fresh produce processing company experienced a cyber incident that disrupted its operations. The attack temporarily halted production and distribution of packaged salads and other products, causing delays and financial losses. The company paid $11M in ransom to the hackers to restitute order for their operations. This incident further underscores the importance of cybersecurity in the food industry and the potential risks posed by inadequate security measures.

These incidents illustrate the growing threat of cyberattacks in the food industry and the potential consequences of inadequate cybersecurity measures. As technology becomes more integrated into food production, processing, and distribution, the need for robust food defense strategies that encompass cybersecurity has never been more critical.

Understanding Food Defense
Food defense refers to the protection of food products from intentional contamination or adulteration by biological, chemical, physical, or radiological agents. Unlike food safety, which focuses on unintentional contamination, food defense addresses the deliberate actions of individuals or groups aiming to cause harm. In an era where technology permeates every aspect of food production, processing, and distribution, ensuring robust cybersecurity measures is crucial for effective food defense.

The Intentional Adulteration Rule, part of the FDA’s Food Safety Modernization Act (FSMA), mandates measures to safeguard the food supply from deliberate adulteration aimed at causing large-scale public health harm. Key requirements of this rule include conducting vulnerability assessments, implementing mitigation strategies, performing monitoring, verification, and corrective actions, as well as providing employee training and maintaining thorough records.

The Intersection of Technology and Food Defense
The integration of advanced technology into the food industry brings numerous benefits, such as increased efficiency, improved traceability, and enhanced quality control. However, it also introduces new vulnerabilities that can be exploited by cybercriminals. As technology becomes more sophisticated, so do the methods employed by those who seek to manipulate or sabotage our food supply.

AI and Technology: A Double-Edged Sword
Artificial intelligence (AI) and other advanced technologies are revolutionizing the food industry. Automated systems, IoT devices, and data analytics enhance productivity and provide real-time monitoring capabilities. However, these technologies also present new avenues for white-collar crime and cyberattacks. For instance, a cybercriminal could hack into a food processing plant’s control system, altering ingredient ratios or contaminating products, which could lead to widespread public health crises.

Pros and Cons of Using AI and Technology in Food Safety
The adoption of AI and technology in the food industry has both advantages and disadvantages:
Pros:
1. Enhanced Efficiency: Automation and AI can streamline food production processes, reducing human error and increasing output. This leads to more consistent product quality and improved overall efficiency.
2. Improved Traceability: Advanced tracking systems allow for real-time monitoring of food products throughout the supply chain. This enhances the ability to trace the source of contamination quickly, thereby reducing the impact of foodborne illness outbreaks.
3. Predictive Analytics: AI can analyze vast amounts of data to predict potential risks and prevent contamination before it occurs. This proactive approach can significantly enhance food safety.
4. Real-Time Monitoring: IoT devices and sensors can provide continuous monitoring of environmental conditions, ensuring that food storage and transportation are maintained within safe parameters.

Cons:
1. Cybersecurity Risks: As seen in recent cyberattacks, the integration of technology introduces new vulnerabilities. Hackers can exploit these weaknesses to disrupt operations or intentionally contaminate food products.
2. High Implementation Costs: The initial investment in AI and advanced technologies can be substantial. Small and medium-sized enterprises may find it challenging to afford these technologies.
3. Dependence on Technology: Over-reliance on technology can be problematic if systems fail or are compromised. It is essential to have robust backup plans and manual processes in place.
4. Privacy Concerns: The use of AI and data analytics involves the collection and processing of large amounts of data, raising concerns about data privacy and the potential misuse of sensitive information.

The Role of Cybersecurity in Food Defense
To safeguard against such threats, the food industry must prioritize cybersecurity as an integral component of food defense strategies. Here are key strategies to consider:
1. Conduct Regular Risk Assessments: Identify potential vulnerabilities within your technological infrastructure. Regular risk assessments can help detect weaknesses and prioritize areas needing immediate attention.
2. Implement Robust Access Controls: Ensure that only authorized personnel have access to critical systems and data. Use multi-factor authentication and monitor access logs for suspicious activity.
3. Invest in Employee Training: Employees are often the first line of defense against cyber threats. Provide comprehensive training on cybersecurity best practices, including recognizing phishing attempts and other common attack vectors.
4. Update and Patch Systems Regularly: Ensure that all software and hardware are up-to-date with the latest security patches. Regular updates can mitigate the risk of exploitation through known vulnerabilities.
5. Develop Incident Response Plans: Prepare for potential cyber incidents by developing and regularly updating incident response plans. These plans should outline specific steps to take in the event of a security breach, including communication protocols and recovery procedures.
6. Utilize Advanced Threat Detection Systems: Employ AI-driven threat detection systems that can identify and respond to unusual activity in real-time. These systems can provide an added layer of security by continuously monitoring network traffic and system behavior.
7. Collaborate with Cybersecurity Experts: Partner with cybersecurity professionals who can provide insights into emerging threats and recommend best practices tailored to the food industry’s unique challenges.

Current Efforts to Standardize the Use of AI
Recognizing the critical role of AI and technology in modern industries, including food production, international efforts are underway to standardize their use and ensure safety, security, and reliability. Two notable standards introduced recently are ISO/IEC 23053:2022 and ISO/IEC 42001:2023.
• ISO/IEC 23053:2022: This standard focuses on the transparency and interpretability of AI systems. It aims to make AI-driven processes understandable and explainable to users, which is crucial for maintaining trust and accountability. In the context of food safety, this standard can help ensure that AI decisions, such as those related to quality control and contamination detection, are transparent and can be audited.
• ISO/IEC 42001:2023: This standard provides guidelines for the governance of artificial intelligence, ensuring that AI systems are developed and used responsibly. It addresses ethical considerations, risk management, and the continuous monitoring and improvement of AI systems. For the food industry, adhering to this standard can help ensure that AI technologies are implemented in a way that supports food safety and defense.

As the food industry continues to embrace technological advancements, the importance of integrating robust cybersecurity measures into food defense strategies cannot be overstated. By understanding the potential risks and implementing proactive measures, we can protect our food supply from malicious actors and ensure the safety and security of the public. The scenario depicted in the movie may seem far-fetched, but it serves as a stark reminder of the potential consequences of unchecked technological vulnerabilities. Let us learn from fiction to fortify our reality

The author will be presenting Food Defense in the Digital Era at the Food Safety Consortium Conference. More Info

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Food Traceability and Authentication in the AI Era

By Maria-Eleni Dimitrakopoulou
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Food traceability provides comprehensive information about a product’s history and origin, facilitating efficient recalls and supply chain management. However, distinct types of food fraud, such as concealment, counterfeit, and mislabelling, pose significant challenges. The integration of Artificial Intelligence (AI) and new regulatory measures, like the FDA’s traceability rule, enhance food safety and authenticity, fostering a more transparent and reliable food supply chain.

In the intricate web of the global food supply chain, ensuring the safety and authenticity of consumables stands as a paramount concern. Food traceability, defined as the ability to provide comprehensive information about the history and origin of a product throughout its journey, emerges as a cornerstone in this endeavour. This meticulous documentation not only facilitates supply chain management but also empowers swift actions such as recalls in the event of safety or quality breaches.

Beyond its logistical benefits, food traceability assumes a pivotal role in safeguarding consumer interests. By serving as a fundamental component of food safety and quality assurance, traceability ensures transparency and accountability at every stage of production and distribution. However, the efficacy of a traceability system is inherently tied to the credibility of its origins, paving the way for the convergence of food traceability and authentication.

Unveiling the Shadows: The Challenge of Food Adulteration

In an era plagued by instances of food adulteration and mislabelling, the imperative for robust authentication mechanisms becomes increasingly apparent. Reports from international and national research bodies shed light on a myriad of cases spanning various food categories, from wine and spirits to olive oil, fish, meat, and beyond. This pervasive challenge underscores the need for stringent standards and regulatory frameworks to combat fraudulence and uphold consumer trust.

Food fraud manifests in several forms, each presenting unique challenges for detection and prevention. For example:

  • Concealment involves hiding inferior or harmful ingredients within a product to avoid detection. An example of this is the addition of melamine in milk to falsely increase protein content readings, which led to a major scandal in China.
  • Counterfeit products replicate and sell a product under the guise of a well-known brand, often with substandard quality. These fake products can range from everyday items like bottled water to high-end goods like wines and spirits. Counterfeiting not only deceives consumers but also damages brand reputations and violates intellectual property rights.
  • Botanical Authentication ensures that plant-based products are derived from the claimed species and not substituted with cheaper alternatives. This is particularly important for products like herbal supplements, teas, and spices. For instance, saffron, one of the most expensive spices in the world, is often adulterated with less expensive substances such as dyed corn stigmas or safflower.
  • Geographical Origin fraud involves misrepresenting the region from which a product originates. Certain regions are known for producing specific high-quality foods and beverages, such as Champagne from France or Parmigiano Reggiano cheese from Italy. Mislabelling products to benefit from these reputations deceives consumers and undermines genuine producers.
  • Substitution entails replacing a high-value ingredient with a lower-cost one. This is common in products like olive oil, honey, and seafood. For example, extra virgin olive oil might be diluted with cheaper oils, or expensive fish species like tuna might be replaced with less costly ones like escolar. This not only cheats consumers but can also pose health risks.
  • Mislabelling involves incorrectly listing ingredients or nutritional information on labels. An example is claiming a product is organic when it is not.
  • Dilution involves adding water or other substances to increase the volume of a product. For instance, diluting fruit juices with water and not declaring it.
  • Unapproved Enhancements involve using unauthorized substances to enhance the appearance or quality of a product. An example is adding unauthorized dyes to make a product look fresher or more appealing.
  • Theft and Resale refers to stealing products and reintroducing them into the market through unauthorized channels. For example, reselling stolen goods without proper storage conditions.
  • Artificial Additives involves using artificial ingredients to mimic the qualities of a natural product. For example, adding synthetic vanilla flavor instead of natural vanilla extraction

The New Traceability Rule of FDA

The Food and Drug Administration (FDA) has introduced a new traceability rule aimed at enhancing the ability to trace the origin of foods throughout the supply chain more efficiently. This rule mandates that companies maintain more rigorous records of their supply chains, focusing on high-risk foods. The implementation of this rule is expected to significantly improve the speed and accuracy of traceability in the event of a foodborne illness outbreak or contamination incident, thus ensuring faster recalls and reducing the risk to public health.

The Dawn of a New Era: Advancements in Food Fraud

As the spectre of food fraud looms large, there arises an urgent demand for sophisticated analytical techniques to authenticate foodstuffs with precision and reliability. Here, the advent of Artificial Intelligence (AI) heralds a new era of innovation. AI-driven algorithms can sift through vast datasets, identifying patterns and anomalies that elude traditional methods. Machine learning models can analyse complex chemical compositions, flagging deviations indicative of adulteration or mislabelling. By harnessing the power of AI, authorities can fortify their efforts in safeguarding consumer interests and preserving the integrity of the global food market.

Charting the Course Ahead: Toward a Safer, More Authentic Future

In the pursuit of food safety and quality, the symbiotic relationship between traceability and fraud, bolstered by AI technologies, emerges as a beacon of hope. By fortifying supply chain transparency and deploying cutting-edge analytical methods, stakeholders can navigate the complexities of the modern food landscape with confidence and integrity. The integration of the FDA’s new traceability rule further strengthens this endeavour, ensuring a safer and more reliable food supply chain for all.

Paul Bradley
Ask The Expert

Ask the Expert: Five Steps for Success in Digitization and Technology Selection

By Paul Bradley
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Paul Bradley

Across the food and beverage industry, organizations are undertaking a wide variety of data-oriented technology initiatives. There are a host of reasons for the trend, and indeed the convergence of multiple factors is likely behind the growing urgency for digitization within many food and beverage brands, manufacturers and supplier organizations. To be sure, ongoing supply chain instability over the last three or more years has put a focus on supply chain resilience and the need for more nimble and flexible supply networks. A dynamic and ever-changing global regulatory landscape is driving compliance and reporting requirements that are increasingly difficult to meet without a solid digital strategy in place. ESG initiatives are driving the need for increased visibility into global supply chains. Evolving consumer preferences create pressures on R&D organizations for continued product innovation, all of which needs to take place within acceptable safety, quality and risk management parameters. And of course, hovering over all of this is a tight (and increasingly costly) labor market, putting increased focus on opportunities for automation and increased efficiently.

Alongside these macro-level global trends, technology itself is moving forward at a rapid pace. The global food and beverage value chain has become more interconnected than ever before, with massive amounts of information moving around the world at remarkable speed. And of course, no discussion of technology is complete without a mention of artificial intelligence (AI). While by no means a new idea—many mature AI-based technologies have existed within the industry for years—AI is evolving quickly. Generative AI technologies, hardly known prior to 2023, are now appearing across the technology landscape, and dominating discussions around technology investment and strategy.

Confronted with all of this, food and beverage industry leaders could be forgiven for feeling a bit overwhelmed. Not only is more information (some valuable, some less so) available than ever before, but a profusion of technology solutions are vying for attention, nearly all promising new levels of insight and productivity. The landscape is complex, but there are a few basic steps that teams can take to help ensure that any potential technology investments are pointed in the right direction and are set up for long term success. Let’s examine five basic, but important steps that can help guide digitization efforts to a strong outcome.

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1. Starting with the end in mind. The objective of a technology implementation should never be to implement a platform. Usually, technology investments start with a business problem that needs to be solved. For food safety teams, this can encompass a range of possibilities, from a desire to reduce error and gain efficiencies in processes, to a need for better real-time monitoring of processes already in place, to a desire to decrease global risk exposure in an increasingly diverse supplier environment. Whatever the situation, teams can substantially de-risk technology investments by being crystal clear on the business objectives (not simply the implementation goals) of a given initiative. Clearly defining a “north star” in terms of expected business outcomes, and revisiting those goals often, can help keep projects focused, and avoid costly missteps and poor prioritization decisions along the way.

2. Defining stakeholders. Though seemingly obvious, it can be surprisingly easy for teams to launch an initiative without a clear view of impacted stakeholders. Typically, a given technology solution will have relatively well-understood functional owners within an organization. But it’s equally important to understand downstream groups that may have to interact with the solution or its outputs. Direct users, too, are a stakeholder community that can easily be overlooked. A solution that does its job on paper but doesn’t align with the working conditions of an end-user community is going to run into challenges. External stakeholders may also need to be considered, as suppliers, customers, contract manufacturers and other entities can all become obstacles to program success if their buy-in hasn’t been considered early in the process.

3. Supplementing (vs. replacing) human intelligence. With all the buzz around AI, it’s easy to get excited about the longer-term possibilities of the technology. And that’s appropriate – AI has already had notable effects on industry technologies and will continue to do so in the years to come. But it’s equally important to consider the current state of generative AI solutions, and be realistic about the limitations and risks of the technology as it exists today. A useful framework for this approach can be to think in terms of how AI can help supplement, even maximize, the intelligence and expertise of human users. Can it consolidate data that would be cumbersome to organize and collate? Can it scan information and flag likely priorities for further investigation?

In the high-stakes environment of food safety and quality, the overlay of hard-earned human knowledge and awareness is going to remain necessary for a long time to come. At the same time, AI-based solutions are already present in the space, and those who use them wisely may very well realize a significant market advantage over those who shy away entirely.

4. Getting real about data quality. Whether the discussion is about AI, data insights, analytics, compliance reporting or automation, most technologies run on data. Put another way, most technologies aren’t any better than the data they consume. The ancient saying, “garbage in, garbage out” remains depressingly current, many decades after the dawn of computing.  As a result, it’s important to take a hard look at the quality, completeness, consistency and structure of the information that a potential technology solution will need to access in order to deliver on its promise. On the positive side, qualified technology providers should be able to provide assistance and clear guidance through the data side of any implementation, and in an increasingly networked world, providers may even be able to come to the table with useful industry data and data management practices that make this part of the digitization journey easier and faster. But it’s important not to skip this step; many are the solutions that never lived up to their potential because the data they needed to consume wasn’t workable.

5. Lastly, as initiatives come together, it’s important to loop back to the original business objectives that were clarified in the first step. Have those objectives been met and, crucially, can that be measured? If it can, the project has likely succeeded, and is positioned to yield insights toward the next step in the technology journey.

The good news is that as digitization continues across the food and beverage industry, it creates a greater opportunity for brands, manufacturers and suppliers to move away from the antiquated model of static, linear supply chains, and toward a more interconnected future based both on shared data and shared values. Explore the world’s largest network of F&B brands and suppliers at TraceGains Gather™, and learn more about the growing community of committed safety professionals worldwide.

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Rodrigo Malig
Ask The Expert

Why Customized Food Safety Programs Featuring AI and Molecular Testing Are Essential

By Food Safety Tech Staff
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Rodrigo Malig

Everyone understands the importance of a robust food safety program. It should ensure the safety of the product and environment, backed by solid, traceable data. The food industry is vast, stretching from the farm all the way to our plates, and includes a diverse array of foods and drinks. Different segments of this industry have specific needs, whether it’s unique spoilage tests or specialized predictions based on distinct data. Unfortunately, current services haven’t delivered a trustworthy solution for these needs.

Rodrigo Malig is the Chief Product Officer at TAAG Genetics. He oversees both the artificial intelligence and molecular diagnostic teams. In this column, Rodrigo discusses the crucial roles of AI and molecular testing in crafting a reliable, tailored solution for food safety.

What are common deficiencies in current food safety and quality programs?

Malig: Common shortcomings in Food Safety and Quality programs (and frustrations for hardworking FSQA professionals) include:

Lack of Customization: Many programs don’t adapt or customize to specific industry needs.

Routine Sampling Issues: Environmental sampling is often random, lacking intelligent risk-based criteria. There’s also an insufficient adaptive process after each sampling cycle.

Paper files
Relying on paper files makes keeping track of data, trending it and analyzing it more difficult and time consuming.

Testing Targets: The targets for environmental and finished product testing are often insufficient. For instance, industries need specific tests for spoilage microorganisms, but many don’t have access to these tests and rely instead on general aerobic plate counts, and yeast and mold.

LIMS (Laboratory Information Management System) Limitations: These systems often don’t offer accurate digitized mapping, customization or ability to adapt, leading them to inaccurately represent a facility or its changing needs.

Outdated Methods: Some programs still rely on outdated technologies and methods. Let’s take plate counts for example. There’s a focus on mere quantitative results without the specificity of what those organisms are. This prevents facilities from taking precise corrective and preventive actions. Additionally, we all know plate counts can be time consuming with long incubation times, have limited sensitivity, lack genetic information, require manual labor (thereby creating additional risk for contamination) and increase overall costs. It is essential to determine when plate counts need additional support or substitution, such as with PCR (Polymerase Chain Reaction).

Comparison to FDA Standards: Many confirmation methods are inferior compared to the FDA’s Whole Genome Sequencing.

PCR Kit Issues: When using PCR, many kits test for only a single microorganism. This limitation requires multiple tests to be run, leading to increased turnaround times and costs.

Traceability Concerns: A significant deficiency is the lack of traceability in many programs, requiring additional documentation to be performed on paper.

Incomplete data and analysis: Antiquated data management systems result in insufficient data collection and digitization. Many in the industry still manually write on paper or use Excel spreadsheets, which makes keeping track of data, trending it and analyzing it more difficult and time consuming.

Reactive and not predictive: Because of the deficiencies detailed above, food safety programs become reactive and insufficient to address risk.

How can we improve current food safety and quality programs?

Malig: An improved food safety and quality program must become predictive (and not reactive), by embracing and implementing technology featuring customization, molecular testing and AI. Below is a basic checklist for food companies to follow:

Data analysis smartphone
An ideal food safety and quality program should be digital, implement artificial intelligence and molecular testing, be comprehensive, and most importantly be simple and mobile!

Customized Software & Testing: Utilize software and tests tailored to your unique requirements.

Advanced Environmental Sampling: Embrace sampling that’s customized, risk-based, predictive and adaptive. Employ digitization and AI to efficiently map, record, analyze and predict sampling schemes. This system should also adapt after each cycle and accommodate changes in the environment, equipment and processes.

Molecular Testing: Polymerase Chain Reaction (PCR) testing is a molecular biology technique with several advantages, including:

  • Sensitivity: PCR is highly sensitive and can detect very small amounts of genetic material (DNA or RNA) in a sample. This makes it effective for detection even when the pathogen is present in low concentrations.
  • Specificity: PCR is highly specific, meaning it can accurately identify and differentiate between different microorganisms or genetic variants. This specificity reduces the likelihood of false-positive results.
  • Speed: PCR can provide results relatively quickly, often within a few hours, depending on the type of PCR used (e.g., real-time PCR or RT-PCR). This rapid turnaround time is crucial for time sensitive decisions in the food industry.
  • Cost: PCR can be cost efficient, especially with multiplex PCR kits that detect multiple pathogens in a single reaction, which essentially cuts time, labor, use of lab equipment and space, and overall cost.

Industry-Specific Microorganism Testing: Ensure you’re testing for microorganisms relevant to your industry, processes and products. This is especially crucial if your products are susceptible to spoilage by specific microorganisms.

Adaptive LIMS: Your Laboratory Information Management System (LIMS) should be both customizable and adaptive. It should digitally represent your facility with accuracy and adapt to any changes or needs.

Dynamic Microbiological Programs: Move away from reactive and repetitive testing schemes. Most current microbiological programs tend to test the same samples repeatedly. With the help of AI algorithms, we can now implement preventive and risk-based microbiological programs.

This real-life case study illustrates how a Fortune 100 Company implemented the solutions above to improve their food safety and quality program.

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