FDA has issued a Request for Information in an effort to gain information and data about how to properly label foods made with cultured seafood cells. The goal is to help FDA determine next steps in ensuring that products derived from cultured seafood cells are labeled consistently and transparently. The “Request for Information: Labeling of Foods Comprised of or Containing Cultured Seafood Cells” will be published on the Federal Register on October 7, and there is a 150-day comment period.
“The FDA invites comment, particularly data and other evidence, about names or statements of identity for foods made with cultured seafood cells. The agency is also interested in information on consumer understanding of those terms and how to determine material differences between cell cultured and conventionally produced seafood,” FDA stated in an email constituent update.
Recent food scandals around the world have generated strong public concerns about the safety of the foods being consumed. Severe threats to food safety exist at all stages of the supply chain in the form of physical, chemical and biological contaminants. The current pandemic has escalated the public’s concern about cross contamination between people and food products and packaging. To eliminate food risks, manufacturers need robust technologies that allow for reliable monitoring of key contaminants, while also facilitating compliance with the ISO 17025 standard to prove the technical competence of food testing laboratories.
Without effective data and process management, manufacturers risk erroneous information, compromised product quality and regulatory noncompliance. In this article, we discuss how implementing a LIMS platform enables food manufacturers to meet regulatory requirements and ensure consumer confidence in their products.
Safeguarding Food Quality to Meet Industry Standards
Food testing laboratories are continually updated about foodborne illnesses making headlines. In addition to bacterial contamination in perishable foods and ingredient adulteration for economic gains, chemical contamination is also on the rise due to increased pesticide use. Whether it is Salmonella-contaminated peanut butter or undeclared horsemeat inside beef, each food-related scandal is a strong reminder of the importance of safeguarding food quality.
Food safety requires both preventive activities as well as food quality testing against set quality standards. Establishing standardized systems that address both food safety and quality makes it easier for manufacturers to comply with regulatory requirements, ultimately ensuring the food is safe for public consumption.
In response to food safety concerns, governing bodies have strengthened regulations. Food manufacturers are now required to ensure bacteria, drug residues and contaminant levels fall within published acceptable limits. In 2017, the ISO 17025 standard was updated to provide a risk-based approach, with an increased focus on information technology, such as the use of software systems and maintaining electronic records.
The FDA issued a notice that by February 2022, food testing, in certain circumstances, must be conducted in compliance with the ISO 17025 standard. This means that laboratories performing food safety testing will need to implement processes and systems to achieve and maintain compliance with the standard, confirming the competence, impartiality and consistent operation of the laboratory.
To meet the ISO 17025 standard, food testing laboratories will need a powerful LIMS platform that integrates into existing workflows and is built to drive and demonstrate compliance.
From Hazard Analysis to Record-Keeping: A Data-Led Approach
Incorporating LIMS into the entire workflow at a food manufacturing facility enables the standardization of processes across its laboratories. Laboratories can seamlessly integrate analytical and quality control workflows. Modern LIMS platforms provide out-of-the-box compliance options to set up food safety and quality control requirements as a preconfigured workflow.
The requirements set by the ISO 17025 standard build upon the critical points for food safety outlined in the Hazard Analysis and Critical Control Points (HACCP) methodology. HACCP, a risk-based safety management procedure, requires food manufacturers to identify, evaluate and address all risks associated with food safety.
The systematic HACCP approach involves seven core principles to control food safety hazards. Each of the following seven principles can be directly addressed using LIMS:
Principle 1. Conduct a hazard analysis: Using current and previous data, food safety risks are thoroughly assessed.
Principle 2. Determine the critical control points (CCPs): Each CCP can be entered into LIMS with contamination grades assigned.
Principle 3. Establish critical limits: Based on each CCP specification, analytical critical limits can be set in LIMS.
Principle 4. Establish monitoring procedures: By defining sampling schedules in LIMS and setting other parameters, such as frequency and data visualization, procedures can be closely monitored.
Principle 5. Establish corrective actions: LIMS identifies and reports incidents to drive corrective action. It also enables traceability of contamination and maintains audit trails to review the process.
Principle 6. Establish verification procedures: LIMS verifies procedures and preventive measures at the defined CCPs.
Principle 7. Establish record-keeping and documentation procedures: All data, processes, instrument reports and user details remain secured in LIMS. This information can never be lost or misplaced.
As food manufacturers enforce the safety standards set by HACCP, the process can generate thousands of data points per day. The collected data is only as useful as the system that manages it. Having LIMS manage the laboratory data automates the flow of quality data and simplifies product release.
How LIMS Enable Clear Compliance and Optimal Control
Modern LIMS platforms are built to comply with ISO 17025. Preconfigured processes include instrument and equipment calibration and maintenance management, traceability, record-keeping, validation and reporting, and enable laboratories to achieve compliance, standardize workflows and streamline data management.
The workflow-based functionality in LIMS allows researchers to map laboratory processes, automate decisions and actions based on set criteria, and reduce user intervention. LIMS validate protocols and maintain traceable data records with a clear audit history to remain compliant. Data workflows in LIMS preserve data integrity and provide records, according to the ALCOA+ principles. This framework ensures the data is Attributable, Legible, Contemporaneous, Original and Accurate (ALCOA) as well as complete, consistent and enduring. While the FDA created ALCOA+ for pharmaceutical drug manufacturers, these same principles can be applied to food manufacturers.
Environmental monitoring and quality control (QC) samples can be managed using LIMS and associated with the final product. To plan environmental monitoring, CCPs can be set up in the LIMS for specific locations, such as plants, rooms and laboratories, and the related samples can then be added to the test schedule. Each sample entering the LIMS is associated with the CCP test limits defined in the specification.
Near real-time data visualization and reporting tools can simplify hazard analysis. Managers can display information in different formats to monitor critical points in a process, flag unexpected or out-of-trend numbers, and immediately take corrective action to mitigate the error, meeting the requirements of Principles 4 and 5 of HACCP. LIMS dashboards can be optimized by product and facility to provide visibility into the complete process.
Rules that control sampling procedures are preconfigured in the LIMS along with specific testing rules based on the supplier. If a process is trending out of control, the system will notify laboratory personnel before the product fails specification. If required, incidents can be raised in the LIMS software to track the investigation of the issue while key performance indicators are used to track the overall laboratory performance.
Tasks that were once performed manually, such as maintaining staff training records or equipment calibration schedules, can now be managed directly in LIMS. Using LIMS, analysts can manage instrument maintenance down to its individual component parts. System alerts also ensure timely recalibration and regular servicing to maintain compliance without system downtime or unplanned interruptions. The system can prevent users from executing tests without the proper training records or if the instrument is due for calibration or maintenance work. Operators can approve and sign documents electronically, maintaining a permanent record, according to Principle 7 of HACCP.
LIMS allow seamless collaboration between teams spread across different locations. For instance, users from any facility or even internationally can securely use system dashboards and generate reports. When final testing is complete, Certificates of Analysis (CoAs) can be autogenerated with final results and showing that the product met specifications. All activities in the system are tracked and stored in the audit trail.
With features designed to address the HACCP principles and meet the ISO 17025 compliance requirements, modern LIMS enable manufacturers to optimize workflows and maintain traceability from individual batches of raw materials all the way through to the finished product.
To maintain the highest food quality and safeguard consumer health, laboratories need reliable data management systems. By complying with the ISO 17025 standard before the upcoming mandate by the FDA, food testing laboratories can ensure data integrity and effective process management. LIMS platforms provide laboratories with integrated workflows, automated procedures and electronic record-keeping, making the whole process more efficient and productive.
With even the slightest oversight, food manufacturers not only risk product recalls and lost revenue, but also losing the consumers’ trust. By upholding data integrity, LIMS play an important role in ensuring food safety and quality.
Traditional approaches to food safety no longer make the grade. It seems that stories of contaminated produce or foodborne illnesses dominate the headlines increasingly often. Some of the current safeguards set in place to protect consumers and ensure that companies are providing the freshest, safest food possible continue to fail across the world. Poorly regulated supply chains and food quality assurance breakdowns often sicken customers and result in recalls or lawsuits that cost money and damage reputations. The question is: What can be done to prevent these types of problems from occurring?
While outdated machinery and human vigilance continue to be the go-to solutions for these problems, cutting-edge intelligent imaging technology promises to eliminate the issues caused by old-fashioned processes that jeopardize consumer safety. This next generation of imaging will increase safety and quality by quickly and accurately detecting problems with food throughout the supply chain.
How Intelligent Imaging Works
In broad terms, intelligent imaging is hyperspectral imaging that uses cutting-edge hardware and software to help users establish better quality assurance markers. The hardware captures the image, and the software processes it to provide actionable data for users by combining the power of conventional spectroscopy with digital imaging.
Conventional machine vision systems generally lack the ability to effectively capture and relay details and nuances to users. Conversely, intelligent imaging technology utilizes superior capabilities in two major areas: Spectral and spatial resolution. Essentially, intelligent imaging systems employ a level of detail far beyond current industry-standard machinery. For example, an RGB camera can see only three colors: Red, green and blue. Hyperspectral imaging can detect between 300 and 600 real colors—that’s 100–200 times more colors than detected by standard RGB cameras.
Intelligent imaging can also be extended into the ultraviolet or infrared spectrum, providing additional details of the chemical and structural composition of food not observable in the visible spectrum. Hyperspectral imaging cameras do this by generating “data cubes.” These are pixels collected within an image that show subtle reflected color differences not observable by humans or conventional cameras. Once generated, these data cubes are classified, labeled and optimized using machine learning to better process information in the future.
Beyond spectral and spatial data, other rudimentary quality assurance systems pose their own distinct limitations. X-rays can be prohibitively expensive and are only focused on catching foreign objects. They are also difficult to calibrate and maintain. Metal detectors are more affordable, but generally only catch metals with strong magnetic fields like iron. Metals including copper and aluminum can slip through, as well as non-metal objects like plastics, wood and feces.
Finally, current quality assurance systems have a weakness that can change day-to-day: Human subjectivity. The people put in charge of monitoring in-line quality and food safety are indeed doing their best. However, the naked eye and human brain can be notoriously inconsistent. Perhaps a tired person at the end of a long shift misses a contaminant, or those working two separate shifts judge quality in slightly different ways, leading to divergent standards unbeknownst to both the food processor and the public.
Hyperspectral imaging can immediately provide tangible benefits for users, especially within the following quality assurance categories in the food supply chain:
Pathogen detection is perhaps the biggest concern for both consumers and the food industry overall. Identifying and eliminating Salmonella, Listeria, and E.coli throughout the supply chain is a necessity. Obviously, failure to detect pathogens seriously compromises consumer safety. It also gravely damages the reputations of food brands while leading to recalls and lawsuits.
Current pathogen detection processes, including polymerase chain reaction (PCR), immunoassays and plating, involve complicated and costly sample preparation techniques that can take days to complete and create bottlenecks in the supply chain. These delays adversely impact operating cycles and increase inventory management costs. This is particularly significant for products with a short shelf life. Intelligent imaging technology provides a quick and accurate alternative, saving time and money while keeping customers healthy.
Characterizing Food Freshness
Consumers expect freshness, quality and consistency in their foods. As supply chains lengthen and become more complicated around the world, food spoilage has more opportunity to occur at any point throughout the production process, manifesting in reduced nutrient content and an overall loss of food freshness. Tainted meat products may also sicken consumers. All of these factors significantly affect market prices.
Sensory evaluation, chromatography and spectroscopy have all been used to assess food freshness. However, many spatial and spectral anomalies are missed by conventional tristimulus filter-based systems and each of these approaches has severe limitations from a reliability, cost or speed perspective. Additionally, none is capable of providing an economical inline measurement of freshness, and financial pressure to reduce costs can result in cut corners when these systems are in place. By harnessing meticulous data and providing real-time analysis, hyperspectral imaging mitigates or erases the above limiting factors by simultaneously evaluating color, moisture (dehydration) levels, fat content and protein levels, providing a reliable standardization of these measures.
Foreign Object Detection
The presence of plastics, metals, stones, allergens, glass, rubber, fecal matter, rodents, insect infestation and other foreign objects is a big quality assurance challenge for food processors. Failure to identify foreign objects can lead to major added costs including recalls, litigation and brand damage. As detailed above, automated options like X-rays and metal detectors can only identify certain foreign objects, leaving the rest to pass through untouched. Using superior spectral and spatial recognition capabilities, intelligent imaging technology can catch these objects and alert the appropriate employees or kickstart automated processes to fix the issue.
Though it may not be put on the same level as pathogen detection, food freshness and foreign object detection, consumers put a premium on food uniformity, demanding high levels of consistency in everything from their apples to their zucchini. This can be especially difficult to ensure with agricultural products, where 10–40% of produce undergoes mechanical damage during processing. Increasingly complicated supply chains and progressively more automated production environments make delivering consistent quality more complicated than ever before.
Historically, machine vision systems and spectroscopy have been implemented to assist with damage detection, including bruising and cuts, in sorting facilities. However, these systems lack the spectral differentiation to effectively evaluate food and agricultural products in the stringent manner customers expect. Methods like spot spectroscopy require over-sampling to ensure that any detected aberrations are representative of the whole item. It’s a time-consuming process.
Intelligent imaging uses superior technology and machine learning to identify mechanical damage that’s not visible to humans or conventional machinery. For example, a potato may appear fine on the outside, but have extensive bruising beneath its skin. Hyperspectral imaging can find this bruising and decide whether the potato is too compromised to sell or within the parameters of acceptability.
Intelligent imaging can “see” what humans and older technology simply cannot. With the ability to be deployed at a number of locations within the food supply chain, it’s an adaptable technology with far-reaching applications. From drones measuring crop health in the field to inline or end-of-line positioning in processing facilities, there is the potential to take this beyond factory floors.
In the world of quality assurance, where a misdiagnosis can literally result in death, the additional spectral and spatial information provided by hyperspectral imaging can be utilized by food processors to provide important details regarding chemical and structural composition previously not discernible with rudimentary systems. When companies begin using intelligent imaging, it will yield important insights and add value as the food industry searches for reliable solutions to its most serious challenges. Intelligent imaging removes the subjectivity from food quality assurance, turning it into an objective endeavor.
When it comes to mainstream consumer food brands, customers expect to receive the same product each time they buy it. That consistency brings consumers back to the same brands over and over again. Unfortunately, the same can’t be said about products sold in the cannabis industry. Consumers aren’t building long-term relationships with brands because consumers don’t have consistent product experiences and often take their business to other brands.
This inconsistency plaguing the cannabis industry can be attributed to an unreliable supply chain, which plays out in multiple ways.
First, cannabis companies are having difficulty meeting state regulations. This happens because the legal cannabis industry is still relatively young and there isn’t a substantial institutional knowledge about regulatory compliance, nor are there any standardized best practices in place. Regulation is expensive and requires human and financial capital that most cannabis companies don’t have in place. Complicating things further, regulations keep changing, making it more difficult for compliant businesses to keep up, even when they have the best intentions.
Second, testing of cannabis products has been complicated. Because cannabis isn’t federally legal, standardized testing guidelines have not been developed, leaving individual states in charge of dictating their own requirements and enforcement framework. There have been numerous reports in the past few years of labs in California either improperly reporting testing results, or worse, submitting fraudulent results.
Third, problems also arise on production end of the supply chain—not only with consistency, but also with consumer safety. According to an estimate from New Frontier Data, approximately 80% of sales are still conducted through the black market. Many growers are using banned pesticides in amounts way beyond recommended levels. In addition, as the recent vape issue has demonstrated, black market manufactured products are being adulterated with toxic substances that pose significant health hazards to consumers.
Given these consistency challenges, the standardization of the supply chain—especially compliance, testing and safety measures—should be a top priority for new cannabis brands. Luckily, many best practices and standardized procedures can be adopted from the food, agriculture and pharmaceutical industries, where companies have successfully developed protocols to ensure safe and reliable products.
In addition to standardization and best practices, cannabis companies should also utilize the following recent innovations in transaction technology to provide peace-of-mind to both new brands and consumers that cannabis products are tested and safe.
Modernized Retail POS systems. Common in other consumer packaged goods industries, such as food, wine, beverages and soft drinks, RFID tags can be used throughout the supply chain to track products from seed to sale. These tags, like the “chips” on credit cards, hold electronically stored information about a product that can be accessed to verify compliance and safety.
QR Codes. While QR codes are mostly used today as marketing gimmicks, they actually have potential to provide true value for curious customers. Batch-specific QR codes could be applied to cannabis products to show detailed information about when and where it was made, what strains of cannabis were used, and testing results. This technology could be used to increase transparency between companies and to consumers.
Data Informatics. A strong information technology infrastructure can be put in place to collect and store inventory and customer data. That data can then be run through algorithms, AI and machine learning systems to help cannabis brands make better decisions about how to optimize the production of their products and how to achieve better results on future batches.
Video Surveillance. Granted, this is a more ‘low-tech’ approach, but effective, nonetheless. Video cameras can go way beyond security purposes. Footage can be viewed and compared to collected data sets to gain a deeper understanding of product flows, personnel movement and logistics that might impact a company’s final product. Video can also be analyzed automatically using AI to provide important insight to help a company fine tune their business strategies.
Consumers want to know that the cannabis products they purchase are safe, compliant and tested. Consumers also have a right to know what they are buying and expect product consistency over time from companies they trust. Ensuring supply chain consistency is key to making this happen as the industry matures. An experienced and trusted supply chain partner can help companies across different cannabis sectors, ranging from medical to food, and ensure product safety and consumer trust today through standardization and consistency. Ultimately, cannabis businesses want to cultivate a culture of excitement, not fear or uncertainty, to help the market flourish and bring quality products to our customers.
The human behavior that surrounds us contagious. Read the article about Frank Yiannas’ presentation, Catch the Food Safety Culture Bug. In keeping with this theme, Frank Yiannas, vice president of food safety at Walmart, reviews behavioral science techniques that can be applied to a food safety management system. In part I of this video series from the 2015 Food Safety Consortium, Yiannas reviews the principles of consistency and commitment.
Strictly Necessary Cookies
Strictly Necessary Cookies should be enabled at all times so that we can save your preferences for these cookie settings.
We use tracking pixels that set your arrival time at our website, this is used as part of our anti-spam and security measures. Disabling this tracking pixel would disable some of our security measures, and is therefore considered necessary for the safe operation of the website. This tracking pixel is cleared from your system when you delete files in your history.
If you visit and/or use the FST Training Calendar, cookies are used to store your search terms, and keep track of which records you have seen already. Without these cookies, the Training Calendar would not work.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.
A browser cookie is a small piece of data that is stored on your device to help websites and mobile apps remember things about you. Other technologies, including Web storage and identifiers associated with your device, may be used for similar purposes. In this policy, we say “cookies” to discuss all of these technologies.
Data generated from cookies and other behavioral tracking technology is not made available to any outside parties, and is only used in the aggregate to make editorial decisions for the websites. Most browsers are initially set up to accept cookies, but you can reset your browser to refuse all cookies or to indicate when a cookie is being sent by visiting this Cookies Policy page. If your cookies are disabled in the browser, neither the tracking cookie nor the preference cookie is set, and you are in effect opted-out.
In other cases, our advertisers request to use third-party tracking to verify our ad delivery, or to remarket their products and/or services to you on other websites. You may opt-out of these tracking pixels by adjusting the Do Not Track settings in your browser, or by visiting the Network Advertising Initiative Opt Out page.
You have control over whether, how, and when cookies and other tracking technologies are installed on your devices. Although each browser is different, most browsers enable their users to access and edit their cookie preferences in their browser settings. The rejection or disabling of some cookies may impact certain features of the site or to cause some of the website’s services not to function properly.
The use of online tracking mechanisms by third parties is subject to those third parties’ own privacy policies, and not this Policy. If you prefer to prevent third parties from setting and accessing cookies on your computer, you may set your browser to block all cookies. Additionally, you may remove yourself from the targeted advertising of companies within the Network Advertising Initiative by opting out here, or of companies participating in the Digital Advertising Alliance program by opting out here.