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FDA Expands Artificial Intelligence Capabilities with Agentic AI Deployment
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Leveraging Automation for Enhanced Food Safety and Compliance
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.
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.
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.
Food Defense in the Age of AI: Are We Prepared?
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
Food Traceability and Authentication in the AI Era
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.
Ask the Expert: Five Steps for Success in Digitization and Technology Selection
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.
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.
Why Customized Food Safety Programs Featuring AI and Molecular Testing Are Essential
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.
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:
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.
Five Technologies Impacting the Beverage Industry
The beverage industry is undergoing a period of technological transformation. As consumer demand and supply chain pressures reveal the shortcomings of older systems and processes, beverage manufacturers are embracing new technologies at an unprecedented pace. While many of these trends are promising, some are more impactful than others. With that in mind, here are the five technologies making the biggest impact in the beverage industry.
Automation
While automation isn’t necessarily new, it is reaching new heights. Automation is becoming an essential part of the beverage sector as robotic systems become more accessible and versatile, and talent becomes harder to acquire.
The food and beverage industry currently has more than 4 million open positions and could add another 370,000 by 2031. With fewer young workers entering manufacturing, beverage facilities are turning to robotics to sustain productivity. The more automated a facility is, the more it can accomplish despite having fewer employees, offsetting the labor shortage.
Automation applies to more than just physical workflows, too. Robotic process automation is seeing increased adoption in back offices, where it can be used to boost productivity and reduce errors.
Artificial Intelligence
Another impactful technology in the beverage sector is artificial intelligence (AI). As beverage workflows become increasingly digitized, they generate more data. AI algorithms can analyze that data to turn it into actionable insights, helping beverage manufacturers predict and adapt to incoming changes and optimize their operations.
Common industry thinking holds that companies can only optimize two of three key variables—time, cost and quality—simultaneously. However, it’s often difficult for humans to determine which is the most valuable area for improvement in their businesses. AI can analyze workflow data to reveal weak points and highlight changes that would have the most significant impact, helping leaders make these decisions.
AI can also help predict future changes, including shifting consumer demand. With this insight, beverage producers can adjust to minimize losses and capitalize early on new trends. Those that don’t embrace AI analytics may quickly fall behind the competition as this technology becomes increasingly common.
The Internet of Things
As AI adoption grows, the Internet of Things (IoT) can help beverage companies make the most of these algorithms. IoT devices give previously unconnected machines wireless connectivity, providing more data points for AI models to analyze, improving their accuracy. This connectivity and data collection can also improve transparency.
One of the most impactful use cases for IoT sensors is in the supply chain. Connected tracking devices can provide real-time updates on shipment locations, temperature, vibrations and other factors. If anything falls out of acceptable parameters or schedules, they can alert relevant stakeholders so they can adapt to ensure safe, timely shipments.
IoT devices can also improve machine health by alerting workers to needed repairs. This data-driven, need-based approach prevents costly breakdowns while minimizing downtime from unnecessary maintenance.
Biotechnology
While many of the most impactful technologies in the beverage industry appear within manufacturing facilities, some focus on earlier workflows. Biotechnology, such as gene editing, can optimize the farming operations that produce the ingredients beverage companies need.
Some bioengineered crops require less water to grow or are pest-resistant, minimizing the need for pesticides. These upgrades reduce farms’ ongoing expenses, making beverage ingredients cheaper for production facilities. Other bioengineering processes can make certain ingredients healthier or less environmentally impactful, both of which appeal to consumers.
Emerging biotechnology solutions let beverage companies use specially designed enzymes to gauge milk contamination and spoilage better. With these biological markers, businesses can ensure they don’t send poor-quality products to market and can trace contamination issues, leading to long-term improvements.
Renewable Energy
Another increasingly impactful technology for beverage companies today is renewable energy. As climate issues become more prominent, consumer preferences lean towards sustainable companies and products, even if that means paying more for products. Switching to renewable power helps energy-hungry beverage factories adapt to this demand and protect the environment.
Because renewables such as solar and wind are technologies, not fuel sources, they will only become cheaper and more efficient over time. Consequently, switching to these technologies is becoming an increasingly viable option for companies. They can also reduce energy costs long-term, as facilities begin to generate their own power instead of buying it from the grid.
Additionally, growing climate urgency may lead to increased regulations around industrial energy sources. Shifting to renewable power now can ensure beverage companies minimize disruption from any changing legislation.
Virtually every industry today is undergoing a tech-driven transformation. Capitalizing on this movement means being able to separate the buzzwords from the technologies that hold the most promise. These five technologies are among the most impactful for beverage companies today. As their adoption grows, they could dramatically alter the face of the industry.
Food Crisis Backup Planning
We were collectively shocked by the Covid-19 crisis that disrupted the food industry. We didn’t see it coming and we weren’t prepared for the long-lasting, widespread repercussions of that crisis, including product and labor shortages, supply chain disruptions and record-setting inflation.
Many food businesses were reliant on certain suppliers, and if they couldn’t deliver necessary products, companies either had to go without or scramble to find an alternate solution. As an industry, we were reactive—not proactive—to the pandemic and the ensuing fallout.
Now that we have some perspective, a big takeaway is that food businesses need to have better backup plans to address supply chain disruptions, product shortages and delays. This is especially important because:
- Extreme weather is causing crop failures, livestock deaths and suboptimal soil conditions, resulting in more world hunger. Extreme drought conditions are destroying produce out west, including in California, a region that grows significant amounts of produce to ship nationwide. The Midwest, which produces approximately three-quarters of our country’s corn supply, is facing the opposite problem, as frequent floods wash away precious soil. Europe’s record-setting heat is torching vegetation, while India is pausing exports because of a severe heat wave.
- The ongoing Ukraine/Russian war is predicted to give rise to a “food catastrophe.” Our global food system relies on a few big food commodity exporters, and Ukraine and Russia are two of the biggest. Together, these two countries supply approximately 60% of the global sunflower oil production—a product that goes into hundreds of consumable goods. It is a significant threat to the global food supply that so many of these exports have stalled.
- Soaring inflation and resulting record high food prices are putting food out of the reach of many, leading to a worldwide rise in food insecurity, leading to increased hunger and malnutrition. The number of food insecure people is predicted to grow globally from 440 million to 1.6 billion, and nearly 250 million people are facing famine.
- The ongoing labor shortage is contributing to disruptions and food waste all along the supply chain. Crates of perishable foods are being left to rot in shipping containers, warehouses and trucks because there aren’t enough workers to get them safely to their final destinations.
Below are several steps food brands can take to address and prepare for these ongoing threats to the supply chain.
Use tech tools to manage your supply chain. Today’s digital solutions allow you to audit and evaluate your supply chain’s sustainability and resilience. These innovative tools can help you get a better handle on your supply chain by organizing supplier certifications into a system that offers better visibility and is easier manage.
Embrace sophisticated technologies. Advances in artificial intelligence (AI), robotics and other technologies may help solve some of our most pressing supply chain challenges. For instance, when the Suez Canal was blocked in 2021 it halted all shipments through that major passageway, causing a supply chain crisis. AI rerouted ships to avoid the blockage, so food deliveries could continue via a detour. AI can also monitor shipments to ensure safety and quality, notifying suppliers and buyers about any safety breaches.
The FDA’s New Era of Smarter Food Safety calls for a broader approach to food safety and traceability, and AI can help achieve those goals. Moving forward, AI will be instrumental in increasing transparency all along the supply chain, providing end-to-end visibility and predicting the path of foodborne outbreaks.
Develop back up plans. How are your suppliers pivoting to manage the simultaneous threats against our global food supply? How are they preparing for climate change? What will they do if they can’t get necessary produce from California, corn from the Midwest or grain from Ukraine? How will they recruit and retain enough labor to deliver necessary products safely to their final destinations? It’s smart to find backup suppliers, especially those closer to home, to ensure an uninterrupted supply of foods. Work with suppliers that are focused on solutions, safety and quality, and keep careful track of each supplier’s safety certifications.
Consider vertical farming. Increasingly, companies are looking for alternate supplier and agriculture solutions, such as vertical farming, which grows crops closer to their final destinations. Growing foods closer to their final destination helps reduce food deserts and safety risks, boost sustainability and minimize food wastage. Vertical farms are typically indoor climate-controlled spaces. These growing conditions protect crops from severe weather, and offer a viable solution to bypass a variety of current issues from the climate crisis to supply chain headaches.
Pivot to agroecological farming. Agroecological farming practices mitigate climate change and prioritize local supply chains. Using this approach, farmers adopt agricultural techniques based on the local area and its specific social, environmental and economic conditions. Agroecology focuses on sustainability, working to reduce emissions, recycle resources and minimize waste. Those that embrace this farming approach believe that traditional farming often faces—and contributes to—a variety of problems, including soil degradation and excessive use of pollutants. Intensive, traditional farming approaches typically focus on short-term output vs. long-term sustainability, which exhausts many natural resources, local resources and wildlife. Agroecological farmers adhere to strict standards that support animal welfare, fewer pesticides and antibiotics, healthier soil and no GMOs.
Be proactive. In hindsight, we should have been more proactive during the Covid-19 crisis, developing backup plans for the huge supply chain disruptions that were headed our way. Before the pandemic, we couldn’t possibly have anticipated the ramifications of a disrupted supply chain and we didn’t understand the need to have backup plans in place for alternative food sources and waste reduction. Today, we have a more realistic perspective and recognize the need to plan ahead for any eventuality.
Our food supply is being threatened by simultaneous crises—from climate change to war—so we must be proactive, prepared, resilient and flexible in developing a solid Plan B.
Five Advances in Food Processing Machinery Driving Growth
Food processing machinery is experiencing some incredible innovations, from intelligent robots to energy-efficient motors for food and beverage processing. Adopting these emerging technologies in your food and beverage processing facility can provide valuable benefits, such as improved food safety, greater efficiency and higher productivity. Following are five advances in food processing machinery that are transforming the industry.
Next Generation Energy-Efficient Motors
Energy efficiency is a growing concern across all industries, and it’s not just about reducing carbon footprints. Cutting back on emissions due to power consumption is certainly important, but food and beverage companies can also experience monetary benefits from optimizing their electricity usage.
Subscribe to the Food Safety Tech weekly newsletter to stay up-to-date on the latest news and information on food safety.Today’s next-gen motors for food and beverage processing are becoming much more energy-efficient right out of the box. The rise of soft-start and variable frequency drive engines is playing a key role in these innovations.
Soft-start motors cause less stress on machinery by protecting devices from sudden power surges. They start up using a slightly lower, limited initial charge rather than a sudden full charge. This can be compared to waking up with versus without an alarm clock—the former involves waking up abruptly while the latter is less stressful. The result is that soft-start motors allow machinery to warm up more gently and ease into operation, rather than straining electrical components with a sudden influx of energy.
Variable frequency drive motors use much less energy than other motor options. Unlike variable speed drive motors, variable frequency drive motor technology is limited specifically to AC motors. A variable frequency drive allows an AC motor to change its speed by changing the frequency of the power going through the motor. A variable frequency drive is essentially a control system for machinery engines, allowing them to start up with a lower voltage drop, similar to soft-start motors, and the speed can be adjusted to fit the unique needs of specific devices and tasks.
These energy-efficient motors also tend to be smaller in volume and weight than their conventional counterparts.
Soft Robotic Grippers
Automation, including the use of robotics, in the food and beverage industry is already happening. These technologies can deliver significant benefit as businesses struggle to keep up with demand even with fewer employees. However, processing foods like pastries, fruit or bread can be difficult with robots because their stiff grippers crush soft items when trying to pick them up. Soft grippers solve this problem.
One soft gripper designed for handling delicate food items was inspired by octopi and squids. The rubber fingers inflate and deflate using pressurized air so they open and close to precise dimensions. The gripper is nimble enough to lift items as delicate as marshmallows.
Autonomous AI Robots
Not only can automation help companies struggling with labor shortages, it can also help improve food processing efficiency. Autonomous robots, often powered by AI, are incredibly efficient at performing repetitive tasks. They can get more done in less time with fewer mistakes compared to the average employee. Food processing companies can use these robots to perform repetitive, mundane tasks that don’t appeal to employees. Workers can then be reskilled, upskilled or reassigned to more engaging and important roles.
IoT Machinery Monitoring
The Internet of Things (IoT) makes food processing machinery more intelligent and inter-connected. IoT can be used in various ways in the food and beverage industry, but it is especially helpful for monitoring and optimizing operations on the manufacturing floor. Sensors collect and relay data to a central hub in real-time. That information can be used to inform automated systems or production timelines.
IoT sensors can reveal inefficiencies and bottlenecks in production, giving companies concrete goals to act on. They can be used to monitor the health of food processing machinery, allowing for predictive maintenance, which involves performing tuneups on equipment as soon as signs of a potential malfunction appear.
The agriculture industry is exploring IoT, as well. For example, farmers and water management companies are using it in conjunction with AI algorithms to improve irrigation systems, cut energy costs and improve water usage.
Automated Food and Facility Safety
Health and safety are among the foremost priorities for every food and beverage company. Technological advances are making it easier for companies to stay on top of health and safety measures.
For example, food processing and storing companies can use AI to autonomously monitor and regulate temperature, helping prevent the growth and spread of E. coli and other diseases. This is achieved using IoT thermostats that relay real-time temperature data to an AI algorithm, which keeps an eye on temps throughout the facility and makes adjustments as needed.
Food processing machinery is in the midst of some truly exciting advancements that are helping businesses in the industry provide better service, products and working conditions. Cutting-edge motors for food and beverage equipment allow companies to save money on energy costs, while next-gen robotics open the door to a wealth of automation possibilities.
With the help of AI and IoT, food and beverage companies can ensure their operations are running as smoothly as possible. There will certainly be more incredible advancements in food processing technology in the years ahead.
The Future Is Now: AI Takes Journey from Supply Chain to Today’s Restaurant Kitchens
Futurist Ross Dawson has said that AI and automation will shape the future of work, and it also promises to transform our lives beyond the office. According to the World Economic Forum, when AI, which provides the ability to “enable devices to learn, reason and process information like humans,” is combined with Internet of Things (IoT) devices and systems, it creates AIoT. This super duo has the potential to power smart homes, smart cities, smart industries and even our smartwatches and fitness trackers, a market estimated by Gartner to be worth $87 billion by 2023. More importantly, this “interconnectedness” will change the way we interact with our devices as well as the way we will live and work in the future.
In the restaurant industry, we’re already seeing glimpses of this interconnectedness take shape, and in the past year, we’ve experienced major technological advancements that have transformed every facet of the way food establishments work. Reflecting on those advancements, I want to take a moment to share three areas of AI impact that are bubbling up in the restaurant sector in 2021.
1: AI-powered Intelligent Kitchens
From ghost kitchens to traditional kitchens, the “back of the house” continues to be a prime target for AI and automation. While great progress has been made, in many ways it seems like we’ve only scratched the surface when it comes to how far AI can take today’s restaurants. But every now and then, we hear examples of AI powering the future of our industry. For example, Nala Robotics, Inc. will be opening what it calls “the world’s first state-of-the-art intelligent restaurant” in Naperville, Illinois this year. The company says the AI-based robotic kitchen “can create dishes from any cuisine around the world, using authentic recipes from celebrated chefs”. A press release from Nala Robotics states that its flagship restaurant is taking “the first step in the food service industry with AI-powered service, addressing many of the issues affecting restaurant owners during COVID-19,” and it will “provide consumers an endless variety of cuisine without potential contamination from human contact.” This is the new frontier in intelligent kitchens, and it couldn’t have come at a better time, with the pandemic forcing restaurants to reimagine the way they do business.
2: AI-Driven Labor Shifts.
You can’t talk about AI in the restaurant industry without also having a conversation about the implications for the modern workforce. With AI in restaurant kitchens and beyond, the impact on the labor force is undeniable. By 2024, Gartner predicts “that these technologies will replace almost 69% of the manager’s workload.” But that’s not entirely a bad thing. Instead of manually filling out forms and updating records, managers can turn to AI to automate these and other tedious tasks. “By using AI…they can spend less time managing transactions and can invest more time on learning, performance management and goal setting,” Gartner adds.Managers can also use the extra time to focus more effort on the customer and employee experience. And indeed they should: In a recent Deloitte report, 60% of guests surveyed indicated that a positive experience would influence them to dine at a restaurant more frequently.
Looking at the impact of AI on labor at all levels, from the CEO to the entry-level wage earner, the shift, at its best, will be a transition to more meaningful—and less mundane—work. The evolution of humanity has taken us to the point we’re now at now, with food production and delivery processes becoming increasingly automated. This has been an evolution generations in the making. In an ideal world, everyone at every level of the organization should benefit from this new wave of technology. For example, automation can and should be used to open the door to new training and new opportunities for low-wage earners to learn new skills that elevate career paths, increase income and improve quality of life.
3: AI and Global Supply Chain Transformation
From the farm all the way to the table, AI is now poised to transform the global supply chain. From my perspective, the biggest impact will be around driving sustainability efforts. Restaurant and grocery brands are already beginning to leverage AI to forecast their food supply needs based on customer demand, leading to less over-ordering and less food waste to support sustainability initiatives. One company in this space, FourKites, is creating what it calls “the digital supply chain of the future.” Using real-time visibility and machine learning, FourKites powers and optimizes global supply chains, making them “automated, interconnected and collaborative—spanning transportation, warehouses, stores, trucks and more.”
In addition to predictive planning, more and more brands will start to use AI to create incident risk management models to identify trends and risks in the supply chain to determine whether bad or recalled products are originating from a specific supplier, distributor, or due to an environmental variable.With all of these changes, the need for comprehensive data standards will multiply as suppliers and distributors around the world work together to bring us produce and packaged food from all corners of the globe. Data standards will be critical to traceability and the exchange of critical tracking events and key data elements, and advances in data standards will power the meta-data needed to provide better insight for food quality and regulatory compliance, crisis management, and recalls—at scale.
Research firm Forrester states that, in the end, the greatest impact resulting from an investment in robotics and other technologies that automate operational tasks is improved customer experience (CX). “Most companies believe that investment in AI, automation, and robotics for engagement will decrease operational costs. While this is true, our research shows that the revenue upside from delivering better CX could deliver a greater impact on the bottom line over time,” Forrester states.
As a business engaged in digitizing and transforming supply chain operations, our team couldn’t agree with Forrester more. But we believe it will take striking the right balance between technology and the human touch to not only drive stronger CX, but to also create a world in which AI is implemented for the greater good—a world in which people, processes, business and technology all win.
