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Chocolate

Chocolate and Big Data: The Recipe for Food Safety Is Changing

By Steven Sklare
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Chocolate

Almost everybody loves chocolate, an ancient, basic, almost universal and primal source of pleasure. “The story of chocolate beings with cocoa trees that grew wild in the tropical rainforests of the Amazon basin and other areas in Central and South America for thousands of years… Christopher Columbus is said to have brought the first cocoa beans back to Europe from his fourth visit to the New World” between 1502 and 1504.1

Unfortunately, the production of chocolate and chocolate products today is as complex as any other global food product with supply chains that reach from one end of the world to the other. The complexity of the supply chain and production, along with the universal demand for the finished product, exposes chocolate to increasing pressure from numerous hazards, both unintentional and intentional. For example, we know that more than 70% of cocoa production takes place in West African countries, particularly the Ivory Coast and Ghana. These regions are politically unstable, and production is frequently disrupted by fighting. While production has started to expand into more stable regions, it has not yet become diversified enough to normalize the supply. About 17% of production takes place in the Americas (primarily South America) and 9% from Asia and Oceania.2

In today’s world of global commerce these pressures are not unique to chocolate. Food quality and safety experts should be armed with tools and innovations that can help them examine specific hazards and fraud pertaining to chocolate and chocolate products. In fact, the global nature of the chocolate market, requires fast reflexes that protect brand integrity and dynamic quality processes supported by informed decisions. Digital tools have become a necessity when a fast interpretation of dynamic data is needed. If a food organization is going to effectively protect the public’s health, protect their brand and comply with various governmental regulations and non-governmental standards such as GFSI, horizon scanning, along with the use of food safety intelligent digital tools, needs to be incorporated into food company’s core FSQA program.

This article pulls information from a recent industry report about chocolate products that presents an examination of the specific hazards and fraud pertaining to chocolate and chocolate products along with ways to utilize this information.

Cocoa and chocolate products rely on high quality ingredients and raw materials, strict supplier partnership schemes and conformity to clearly defined quality and safety standards. During the past 10 years there have been a significant number of food safety incidents associated with chocolate products. The presence of Salmonella enterica, Listeria monocytogenes, allergens and foreign materials in cocoa/chocolate products have been reported on a global scale. Today, information on food safety incidents and potential risks is quickly and widely available by way of the internet. However, because the pertinent data is frequently siloed, food safety professionals are unable to take full advantage of it.

Top Emerging Hazards: Chocolate Products (2013-2018)

Publicly available data, from sources such as European Union RASFF, Australian Competition and Consumer Commission, UK Food Standards Agency, FDA, Food Standards Australia New Zealand (FSANZ), shows a significant increase in identified food safety incidents for cocoa/chocolate products from 2013 to 2018. For this same time period, the top emerging hazards that were identified for chocolate products were the following:

  • Allergens: 51.60%
  • Biological: 16.49%
  • Foreign bodies: 13.83%
  • Chemical: 7.45%
  • Fraud: 6.38%
  • Food additives & flavorings: 4.26%
  • Other hazards: 2.66%

By using such information to identify critical food safety protection trends, which we define to include food safety (unintentional adulteration) and food fraud (intentional adulteration, inclusive of authenticity/intentional misrepresentation) we can better construct our food protection systems to focus on the areas that present the greatest threats to public health, brand protection and compliance.

A Data Driven Approach

Monitoring Incoming Raw Materials
Assessment and identification of potential food protection issues, including food safety and fraud, at the stage of incoming raw materials is of vital importance for food manufacturers. Knowledge of the associated risks and vulnerabilities allows for timely actions and appropriate measures that may ultimately prevent an incident from occurring.

Specifically, the efficient utilization of global food safety and fraud information should allow for:

  • Identification of prevalent, increasing and/or emerging risks and vulnerabilities associated with raw materials
  • Comparative evaluation of the risk profile for different raw materials’ origins
  • Critical evaluation and risk-based selection of raw materials’ suppliers

A comprehensive risk assessment must start with the consideration of the identified food safety incidents of the raw material, which include the inherent characteristics of the raw material. Next, the origin-related risks must be taken into account and then the supplier-related risks must be examined. The full risk assessment is driven by the appropriate food safety data, its analysis and application of risk assessment scientific models on top of the data.

Using food safety intelligent digital tools to analyze almost 400 unique, chocolate product related food safety incidents around the globe provides us with important, useful insights about cocoa as a raw material, as a raw material from a specific origin and as a raw material being provided by specific suppliers. The graph below represents the results of the analysis illustrating the trend of incidents reported between 2002 and 2018. It can be observed that after a significant rise between 2009 and 2010, the number of incidents approximately doubled and remained at that level for the rest of the evaluated period (i.e., from 2010 to 2018), compared to the period from 2002 to 2005.

Cocoa incidents, FOODAKAI
Graph from Case Study: Chocolate Products: lessons learned from global food safety and fraud data and the guidance it can provide to the food industry,
an industry report from FOODAKAI. Used with permission.

By further analyzing the data stemming from the 400 food safety incidents and breaking them down into more defined hazards, for incoming raw materials, we can clearly see that chemical hazards represent the major hazard category for cocoa.

  • Chemical: 73.46%
  • Biological: 16.49%
  • Organoleptic aspects: 5.93%
  • Other Hazards: 4.38%
  • Fraud: 2.32%
  • Foreign bodies: 2.06%
  • Food additives and flavorings: .77%
  • Allergens: .52%
  • Food contact materials: .52%

Using the appropriate analytical tools, someone can drill down into the data and identify the specific incidents within the different hazard categories. For example, within the “chemical hazard” category specific hazards such as organophosphates, neonicotinoids, pyrethroids and organochlorines were identified.

Comparative Evaluation of Risk Profiles for Different Origins of Raw Materials
The main regions of origin for cocoa globally are Africa, Asia and South America. After collecting and analyzing all relevant data from recalls and border rejections and the frequency of pertinent incidents, we can accurately identify the top hazards for cocoa by region.

The top five specific hazards for the regions under discussion are listed in Table I.

Africa South America Asia
1 Organophosphate 2,4-dinitrophenol (DNP) 2,4-dinitrophenol (DNP)
2 Molds Pyrethroid Poor or insufficient controls
3 Neonicotinoid Aflatoxin Aflatoxin
4 Pyrethroid Cadmium Spoilage
5 Organochlorine Anilinopyrimidine Salmonella
Table I.  Top Five Hazards By Region

After the first level of analysis, a further interpretation of the data using the appropriate data intelligence tools can help to reach to very specific information on the nature of the incidents. This provides additional detail that is helpful in understanding how the regional risk profiles compare. For example, the prevalence of chemical contamination, as either industrial contaminants or pesticides, has been a commonly observed pattern for all three of the regions in Table I. However, beyond the general hazard category level, there are also different trends with regard to specific hazards for the three different regions. One such example is the increased presence of mold in cocoa beans coming from Africa.

The primary hazard categories for cocoa, as a raw ingredient were identified and a comparison among the primary hazards for cocoa by region (origin-specific) should take place. The next step in a data-powered supplier assessment workflow would be to incorporate our use of global food safety data in evaluating the suppliers of the raw materials.

The Role of Global Food Safety Data

This article has been focused on chocolate products but has only touched the surface in terms of the information available in the complete report, which also includes specific information about key raw materials. Let’s also be clear, that the techniques and tools used to generate this information are applicable to all food products and ingredients. As we strive to produce food safely in the 21st Century and beyond, we must adapt our methods or be left behind.

The regulatory environment the food industry must operate in has never been more intense. The threats to an organization’s brand have never been greater. This is not going to change. What must change is the way in which food companies confront these challenges.

Global food safety data can contribute to the establishment of an adaptive food safety/QA process that will provide time savings and improve a quality team’s efficiency and performance.

Based on the continuous analysis of food recalls and rejections by key national and international food authorities, a food safety / quality assurance manager could establish an adaptive supplier verification process and risk assessment process by utilizing the knowledge provided by such data. In that way, QA, procurement, food safety and quality departments can be empowered with critical supplier data that will inform the internal procedures for incoming materials and ingredients (e.g., raw materials, packaging materials) and allow for adaptive laboratory testing routines and compliance protocols. Moreover, food safety systems can become adaptive, enabling quality assurance and safety professionals to quickly update points of critical control when needed, and intervene in important stages of the chocolate manufacturing process.

References

  1. Discovering Chocolate. The Great Chocolate Discovery. Cadbury website. Retrieved from https://www.cadbury.com.au/About-Chocolate/Discovering-Chocolate.aspx.
  2. Chocolate Industry Analysis 2020 – Cost & Trends. Retrieved from https://www.franchisehelp.com/industry-reports/chocolate-industry-analysis-2020-cost-trends/.
Sasan Amini, Clear Labs

2020 Expectations: More Artificial Intelligence and Machine Learning, Technology Advances in Food Safety Testing

By Maria Fontanazza
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Sasan Amini, Clear Labs

2018 and 2019 were the years of the “blockchain buzz”. As we enter the new decade, we can expect a stronger focus on how technology and data advances will generate more actionable use for the food industry. Food Safety Tech has highlighted many perspectives from subject matter experts in the industry, and 2020 will be no different. Our first Q&A of the year features Sasan Amini, CEO of Clear Labs, as he shares his thoughts on tech improvements and the continued rise consumer expectations for transparency.

Food Safety Tech: As we look to the year ahead, where do you see artificial intelligence, machine learning and blockchain advancing in the food industry?

Sasan Amini: AI, ML, and blockchain are making headway in the food industry through advances in supply chain management, food sorting and anomaly detection, and tracing the origin of foodborne outbreaks. On the regulatory side, FDA’s focus on its New Era of Smarter Food Safety will most likely catalyze the adoption of the above mentioned technologies. On the private side, a few of the companies leading the charge on these advancements are IBM and Google, working in partnership with food manufacturers and retailers across the world.

Along those same lines, another area that we expect to grow is the use of AI and ML in tandem with robotics—and the value of new troves of data that they collect, analyze and distribute. For example, robotics for the use of environmental monitoring of potential contaminants, sorting techniques and sterilization are valuable because they ensure that end products have been through thorough testing—and they give us even more information about the lifecycle of that food than ever before.

At the end of the day, data is only valuable when you can transform it into actionable insights in real-time with real-world applications, and we expect to see more and more of this type of data usage in the year ahead.

FST: Where do you think food safety testing technologies will stand out? What advancements can the industry expect?

Amini: In 2020, technology is going to begin to connect itself along the entire supply chain, bringing together disparate pieces and equipping supply chain professionals with action-oriented data. From testing advances that improve speed, accuracy and depth of information to modular software solutions to promote transparency, the food safety industry is finally finding its footing in a data-driven sea of technological and regulatory advances.

Right now, legacy testing solutions are limited in their ability to lead food safety and quality professionals to the source of problems, providing insights on tracking recurring issues, hence having a faster response time, and being able to anticipate problems before they occur based on a more data heavy and objective risk assessment tools. This leaves the industry in a reactive position for managing and controlling their pathogen problems.

Availability of higher resolution food safety technologies that provide deeper and more accurate information and puts them in context for food safety and quality professionals provides the food industry a unique opportunity to resolve the incidents in a timely fashion with higher rigour and confidence. This is very in-line with the “Smarter Tools and Approaches” that FDA described in their new approach to food safety.

FST: How are evolving consumer preferences changing how food companies must do business from a strategic as well as transparency perspective?

Amini: Consumers are continuing to get savvier about what’s in their food and where it comes from. Research suggests that about one in five U.S. adults believe they are food allergic, while only 1 in 20 are estimated to have physician-diagnosed food allergies. This discrepancy is important for food companies to consider when making decisions about transparency into their products. Although the research on food allergies continues to evolve, what’s important to note today is that consumers want to know the details. Radical transparency can be a differentiator in a competitive market, especially for consumers looking for answers to improve their health and nutrition.

Consumers are also increasingly interested in personalization, due in part to the rise in new digital health and testing companies looking to deliver on the promise of personalized nutrition and wellness. Again, more transparency will be key.

FST: Additional comments are welcome.

Amini: Looking ahead, we expect that smaller, multi-use, and hyper-efficient tools with reduced physical footprints will gain market share. NGS is a great example of this, as it allows any lab to gather millions of data points about a single sample without needing to run it multiple times. It moves beyond the binary yes-no response of traditional testing, and lets you get much more done, with far less. Such wealth of information not only increases the confidence about the result, but can also be mined to generate more actionable insights for interventions and root cause analysis.

This “multi-tool” will be driven by a combination of advanced software, robotics, and testing capabilities, creating a food safety system that is entirely connected, driven by data, and powerfully accurate.

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Top 10 Food Safety Articles of 2019

By Food Safety Tech Staff
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#10

Lessons Learned from Intentional Adulteration Vulnerability Assessments (Part I)

#9

Lead in Spices

#8

Three Practices for Supply Chain Management in the Food Industry

#7

Changes in the Food Safety Industry: Face Them or Ignore Them?

#6

How Technology is Elevating Food Safety Practices & Protocols

#5

Five Tips to Add Food Fraud Prevention To Your Food Defense Program

#4

2019 Food Safety and Transparency Trends

#3

Sustainability Strategies for the Food Industry

#2

Is Food-Grade always Food-Safe?

#1

E. Coli Update: FDA Advises Consumers to Avoid All Romaine Lettuce Harvested in Salinas, California

Production line, NiceLabel

Farm-to-Fork Transparency: How Digitized Labeling Can Prevent a Major Allergen Recall

By Lee Patty
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Production line, NiceLabel

For consumers and brands alike, the damaging impact of mislabeling or neglecting to clearly outline an allergen can be colossal. Therefore, to prevent a health and business disaster, best practices around allergen labeling must be top of mind. Luckily, technology can help, and the farm-to-fork transparency provided by a centralized and digitized modern label management system can ensure organizations improve responsiveness and accuracy while reducing costs beyond those saved by mitigating recalls.

No one wants to face a recall, but have you done enough to prevent one from happening to you? More than 650 food products were recalled last year in the United States alone. And one of the leading causes might just be the easiest to prevent: Undeclared allergens.

According to the Q2 2019 Stericycle Recall Index, undeclared allergens are the leading cause of U.S. food recalls, accounting for 48.4% of food recalls from the FDA and 62.9% of food pounds recalled by the USDA. This statistic becomes more alarming considering that roughly 11% of US adults have a food allergy, according to JAMA.

Enacted in 2004, the Food Allergen Labeling and Consumer Protection Act (FALCPA) stipulates that all packaged food regulated under the Federal Food Drug and Cosmetic Act (FFD&C) comply by listing major food allergens. “Major allergens” refers to milk, eggs, fish, shellfish, tree nuts, peanuts, wheat, and soybeans, and for nuts and shellfish, the species must be declared.

For brands, the damaging impact of mislabeling or neglecting to clearly outline an allergen can be colossal, resulting in costly recalls or litigation. However, the impact to consumers can be even greater when one small mistake can cause serious illness, or worse, death. To prevent a health and business nightmare, best practices around allergen labeling must be top of mind.

However, with constantly changing legislation, this can be easier said than done. For instance, in a move that outpaced the FDA, Illinois issued a state law requiring sesame labeling. And in the UK, Natasha’s Law was recently introduced, requiring companies to label all food ingredients on fresh pre-packaged food after 15-year-old Natasha Ednan-Laperouse died of a sesame allergy from a sandwich that didn’t list all the ingredients.

The need for optimal allergen labeling is clear, so how can organizations ensure allergens are clearly labeled on their products and meet existing standards while preparing for future requirements?

Though the underlying principle behind a clear label is simple, the process of designing such labels can be multifaceted and difficult to streamline—especially if labels are designed, printed and managed by separate users across a franchise or store network. And this challenge is multiplied further when products reach across international boundaries. But technology can help, and the farm-to-fork transparency provided by a centralized and digitized modern label management system can ensure organizations improve responsiveness and accuracy while reducing costs beyond those saved by mitigating recalls.

Disorganized Sprawl: A Major Hurdle to Effective Labeling

When implemented properly, modern label management can cost-effectively centralize labeling, reducing inefficiencies and human error. However, before this can happen, there are a few common roadblocks that may make standardizing the labeling process challenging.

One issue may be a sprawl of legacy equipment that is not integrated into a cohesive network. For instance, a legacy labeling system may only support certain label printers while certain manufacturers of direct marking equipment may only support their own propriety brand of printers. In another sense, a lack of standardization can also make it difficult to efficiently integrate labeling with other business solutions like manufacturing execution systems (MES) and enterprise resource planning (ERP) systems.

A damaging impact of sprawl is adoption of a wide range of different labeling applications across various facilities. This will result in inconsistent label formatting, the need to create the same label multiple times, and the need to accommodate different systems and printers. Consequences of this may be a lack of centralized storage when everything is saved locally, complex user training encompassing many software programs, an increased burden on IT, and a great deal of extra administration and human intervention to maintain and update labels.

Another problem with a disorganized ecosystem for labeling is that quality assurance inevitably suffers because tracing a label’s history or implementing standardized approval processes can be difficult or impossible. To accurately track labeling, it’s necessary to have a production log stating where and when labels were produced and who produced them. Having such a log and using it effectively requires centralization or else it can become difficult to track different versions or enforce universal approval processes for altering templates.

Implementing Modernized Labeling to Improve QA

Modern label management systems can help suppliers and manufacturers standardize and control marking packaging or label production across an entire organizational ecosystem. These solutions feature a central, web-based document management system and provide a reliable storage space for label templates and label history. This will enable changes and updates to be tracked centrally, so local facilities can access uniform and accurate templates to produce labels.

An ideal label management system can also interface with a multitude of direct marking and labeling printers, even if they are from different manufacturers, and it can integrate labeling and direct marking with a business system’s master data, which eliminates manual data entry errors. This decreases upfront capital expenditures in more costly efforts to standardize equipment, provides a system that is easy to integrate with partners, saves costs generated from having to discard product or rework labels, and increases a company’s ability to implement unified, organization-wide labeling processes.

Centralized Labeling is Easily Delivered Through Cloud

To many, the thought of migrating legacy labeling to a centralized system or investing a large sum of resources into centralizing labeling may seem inordinate or daunting. However, cloud technology makes migrating to a modern label management system feasible for organizations of all sizes.

With the cloud, designing labels and ensuring quality assurance becomes far more accessible. Additionally, the software-as-a-service (SaaS) model doesn’t require the capital investments or operations and maintenance upkeep associated with costly IT infrastructure and is easily scalable depending on business needs. This is a game changer for small to medium sized businesses who can now benefit from a centralized labeling system because of the cloud.

The Benefits of a “Single-source-of-truth”

In addition to other benefits, integrating a modern label management solution with other business systems allows users to access a “single-source-of-truth.” This allows for enforceable, specific user roles with logins for each user as well as traceability and transparency across all factories that produce products. The traceability from being able to monitor a “single-source-of-truth” is a critical component to farm-to-fork transparency because it can provide an accurate production log overviewing label versions and changes, so companies can pinpoint the locations and causes of labeling inaccuracies and fix them instantly.

A modern label management system also enables organizations to nimbly respond to new regulatory requirements because alterations only need to be made in one location, new templates can be previewed before going to production, and nutrition and allergen functionality can be easily formatted so that it is clear and stands out to the consumer. This increases labeling consistency and accuracy, and saves time when rules change and when new products need to be incorporated during a merger or acquisition.

Futureproofing and Ensuring Consumer Safety with Allergen Labeling

In today’s world, food and beverage manufacturers must rise to the challenge of changing regulations while meeting the call of shifting customer demands and integrating themselves within greater business ecosystems and extended supply chains. In the case of allergen labeling, this may mean preparing labels for different countries, which have varying standards for labeling allergens like sesame, royal jelly, bee pollen, buckwheat and latex, or ensuring labels can be altered quickly when new products are rolled out or when bodies like the FDA revamp standards.

Companies that implement modern label management solutions position themselves to adapt to competition and regulations quickly, implement solutions that can easily be integrated with partners in a supply chain, and streamline quality control. This can help improve productivity, reduce labeling errors, increase collaboration, and prevent product recalls. But most importantly, it helps ensure the safety of consumers everywhere.

Megan Nichols
FST Soapbox

How Will AR and VR Improve Safety in the Food Industry?

By Megan Ray Nichols
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Megan Nichols

The food and beverage sector is a huge presence in the U.S. economy. As of 2017, the industry employed 1.46 million people across 27,000 different establishments. Total food and beverage sales stand at around $1.4 trillion and add $164 billion in value to the economy as a whole.1 This presents significant opportunities and risks alike. Companies that trade in food products are held to some of the highest regulatory standards. With globalization ongoing and a higher demand than ever for variety and niche products, companies find they need to expand the mobility of their services. They must also broaden their product choices without missing a beat when it comes to quality.

Augmented reality (AR) and virtual reality (VR) have emerged as unlikely allies in that quest. These technologies are already having a positive impact on food and worker safety in the industry.

Improves New Employee Training

Onboarding and training new employees is a costly and time-consuming endeavor in any industry. Moreover, failure by companies to impart the necessary skills, and failure by employees to retain them, can have ghastly consequences. Errors on assembly lines may result in faulty products, recalls, worker and customer injuries, and worse.

The stakes in the food and beverage sector are just as high as they are in other labor- and detail-oriented industries. VR provides an entirely new kind of training experience for employees, whether they’re working on mastering their pizza cutting technique or brewing the perfect cappuccino. Other times, “getting it right” is about much more than aesthetic appeal and immediate customer satisfaction.

Animal slaughtering and processing facilities represent some of the more extreme examples of potentially dangerous workplaces in the larger food and beverage industry. Between 2011 and 2015, this U.S. sector experienced 73 fatal workplace injuries. Excepting poultry processing, 2015 saw 9,800 recordable incidents in animal processing, or 7.2 cases for every 100 full-time employees.

Some adopters of VR-based employee training claim that virtual reality yields up to an 80% retention rate one year after an employee has been trained. This compares extremely favorably to the estimated 20% retention rate of traditional training techniques.

Training via VR headset can help companies get new hires up to speed faster in a safe, detailed and immersive environment. Food processing and service are high-turnover employment sectors. The right training technology can help workers feel better prepared and more engaged with their work, potentially reducing employee churn.

Helps Eliminate Errors in Food Processing

Augmented reality is already demonstrating great promise in manufacturing, maintenance and other sectors. For instance, an AR headset can give an assembly line worker in an automotive plant detailed, step-by-step breakdowns of their task in their peripheral vision through a digital overlay.

The same goes for food and beverage manufacturing. AR headsets can superimpose a list of inspection or processing tasks for workers to follow as they prepare food items in a manufacturing or distribution facility.

In 2018, there was an estimated 382 recalls involving food products. Augmented reality alone won’t bring that number down to zero. However, it does help reduce instances of line workers and inspectors missing critical steps in processing or packaging that might result in contamination or spoilage.

Eases the Learning Curve in Food Preparation

There are lots of food products in the culinary world that are downright dangerous if they’re not prepared properly and by following specific steps. Elderberries, various species of fish, multiple root vegetables, and even cashews and kidney beans can all induce illness and even death if the right steps aren’t taken to make them fit for consumption.

In early 2019, inspectors descended on a Michelin-starred and highly respected restaurant in Valencia, Spain. The problem? A total of 30 patrons reported falling ill after eating at El País, one of whom lost her life. Everyone reported symptoms similar to food poisoning.

The common element in each case appeared to be morel mushrooms. These are considered a luxury food item, but failure to cook them properly can result in gastric problems and worse. Augmented reality could greatly reduce the likelihood of incidents like this in the future by providing ongoing guidance and reminders to new and veteran chefs alike, without taking the bulk of their attention away from work.

Brings New Efficiencies to Warehousing and Pick-and-Pack

Consumers around the globe are getting used to ordering even highly perishable foodstuffs over the internet—and there’s no putting that genie back in the bottle. Amazon’s takeover of Whole Foods is an indicator of what’s to come: Hundreds of freezer-equipped and climate-controlled warehouses located within a stone’s throw from a majority of the American population.

Ensuring smooth operations in perishable food and beverage supply chains is a major and ongoing struggle. It’s not just a practical headache for companies—it’s something of a moral imperative, too. The World Health Organization finds that around 600 million individuals worldwide fall ill each year due to foodborne illnesses.

Augmented reality won’t completely solve this problem, but it may greatly reduce a major source of potential spoilage and contamination: Inefficiencies in picking and packing operations. Order pickers equipped with AR headsets can:

  • Receive visual prompts to quickly find their way to designated stow locations in refrigerated warehouses after receiving refrigerated freight.
  • Locate pick locations more efficiently while retrieving single items or when they already have a partial order of perishable goods picked.

In both cases, the visual cues provided by AR help employees navigate warehousing locations much more quickly and efficiently. This substantially lowers the likelihood that food products are stuck in limbo in unrefrigerated areas, potentially coming into contact with noncompliant temperatures or pathogens. The FDA recognizes mispackaged and mislabeled food products as a major public health risk.

For food and beverage companies, AR should be a welcome development and a worthy investment. FSMA recognized that 48 million Americans get sick each year from compromised foods. The act required these entities to be much more proactive in drawing up prevention plans for known sources of contamination and to be more deliberate in standardizing their processes for safety’s sake.

AR and VR Boost Food, Worker and Customer Safety

Augmented and virtual reality may seem like an unusual ally in an industry where most consumers are primarily focused on the aesthetic and sensory aspects of the experience. However, there’s a whole world that lives and dies according to the speed and attention to detail of employees and decision-makers alike. Augmented realities, and entirely new ones, point the way forward.

Reference

  1. Committee for Economic Development of The Conference Board. (March 2017). “Economic Contribution of the Food and Beverage Industry. Retrieved from https://www.ced.org/pdf/Economic_Contribution_of_the_Food_and_Beverage_Industry.pdf.
Allison Kopf, Artemis

How Technologies for Cultivation Management Help Growers Avoid Food Safety Issues

By Maria Fontanazza
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Allison Kopf, Artemis

Visibility, accountability and traceability are paramount in the agriculture industry, says Allison Kopf, founder and CEO of Artemis. In a Q&A with Food Safety Tech, Kopf explains how growers can take advantage of cultivation management platforms to better arm them with the tools they need to help prevent food safety issues within their operations and maintain compliance.

Food Safety Tech: What are the key challenges and risks that growers face in managing their operations?

Allison Kopf: One of the easiest challenges for growers to overcome is how they collect and utilize data. I’ve spent my entire career in agriculture, and it’s been painful to watch operations track all of their farm data on clipboards and spreadsheets. By not digitizing processes, growers become bogged down by the process of logging information and sifting through old notebooks for usable insights—if they even choose to do that.

Allison Kopf, Artemis
Allison Kopf is the founder and CEO of Artemis, a cultivation management platform serving the fruit, vegetable, floriculture, cannabis, and hemp industries. She is also is an investment partner at XFactor Ventures and serves on the boards of Cornell University’s Controlled Environment Agriculture program and Santa Clara University’s College of Arts and Sciences.

I was visiting a farm the other day and the grower pulled out a big binder. The binder contained all of his standard operating procedures and growing specifications for the varieties he’s grown over the past 20 years. Then he pulled out a pile of black notebooks. If you’ve ever worked on a farm, you’d recognize grower notebooks anywhere. They’re used to log data points such as yield, quality and notes on production. These notebooks sit in filing cabinets with the hopeful promise of becoming useful at some point in the future—to stop production from falling into the same pitfalls or to mirror successful outcomes. However, in reality, the notebooks never see the light of day again. The grower talked about the pain of this process—when he goes on vacation, no one can fill his shoes; when he retires, so does the information in his head; when auditors come in, they’ll have to duplicate work to create proper documentation; and worse, it’s impossible to determine what resources are needed proactively based on anything other than gut. Here’s the bigger issue: All of the solutions are there; they’re just filed away in notebooks sitting in the filing cabinet.

Labor is the number one expense for commercial growing operations. Unless you’re a data analyst and don’t have the full-time responsibilities of managing a complex growing operation, spreadsheets and notebooks won’t give you the details needed to figure out when and where you’re over- or under-staffing. Guessing labor needs day-to-day is horribly inefficient and expensive.

Another challenge is managing food safety and compliance. Food contamination remains a huge issue within the agriculture industry. E. coli, Listeria and other outbreaks (usually linked to leafy greens, berries and other specialty crops) happen regularly. If crops are not tracked, it can take months to follow the contamination up the chain to its source. Once identified, growers might have to destroy entire batches of crops rather than the specific culprit if they don’t have appropriate tracking methods in place. This is a time-consuming and expensive waste.

Existing solutions that growers use like ERPs are great for tracking payroll, billing, inventory, logistics, etc., but the downside is that they’re expensive, difficult to implement, and most importantly aren’t specific to the agriculture industry. The result is that growers can manage some data digitally, but not everything, and certainly not in one place. This is where a cultivation management platform (CMP) comes into play.

FST: How are technologies helping address these issues?

Kopf: More and more solutions are coming online to enable commercial growers to detect, prevent and trace food safety issues, and stay compliant with regulations. The key is making sure growers are not just tracking data but also ensuring the data becomes accessible and functional. A CMP can offer growers what ERPs and other farm management software can’t: Detailed and complete visibility of operations, labor accountability and crop traceability.

A CMP enables better product safety by keeping crop data easily traceable across the supply chain. Rather than having to destroy entire batches in the event of contamination, growers can simply trace it to the source and pinpoint the problem. A CMP greatly decreases the time it takes to log food safety data, which also helps growers’ bottom line.

CMPs also help growers manage regulatory compliance. This is true within the food industry as well as the cannabis industry. Regulations surrounding legal pesticides are changing all the time. It’s difficult keeping up with constantly shifting regulatory environment. In cannabis this is especially true. By keeping crops easily traceable, growers can seamlessly manage standard operating procedures across the operation (GAP, HACCP, SQF, FSMA, etc.) and streamline audits of all their permits, licenses, records and logs, which can be digitized and organized in one place.

FST: Where is the future headed regarding the use of technology that generates actionable data for growers? How is this changing the game in sustainability?

Kopf: Technology such as artificial intelligence and the internet of things are changing just about every industry. This is true of agriculture as well. Some of these changes are already happening: Farmers use autonomous tractors, drones to monitor crops, and AI to optimize water usage.

As the agriculture industry becomes more connected, the more growers will be able to access meaningful and actionable information. Plugging into this data will be the key for growers who want to stay profitable. These technologies will give them up-to-the-second information about the health of their crops, but will also drive their pest, labor, and risk & compliance management strategies, all of which affect food safety.

When growers optimize their operations and production for profitability, naturally they are able to optimize for sustainability as well. More gain from fewer resources. It costs its customers less money, time and hassle to run their farms and it costs the planet less of its resources.

Technology innovation, including CMPs, enable cultivation that will provide food for a growing population despite decreasing resources. Technology that works both with outdoor and greenhouse growing operations will help fight food scarcity by keeping crops growing in areas where they might not be able to grow naturally. It also keeps production efficient, driving productivity as higher yields will be necessary.

Beyond scarcity, traceability capabilities enforce food security which is arguable the largest public health concern across the agricultural supply chain. More than 3,000 people die every year due to foodborne illness. By making a safer, traceable supply chain, new technology that enables growers to leverage their data will protect human life.

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Food Fraud and Adulteration Detection Using FTIR Spectroscopy

By Ryan Smith, Ph.D.
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Producers of food-based products are faced with challenges of maintaining the safety and quality of their products, while also managing rapid screening of raw materials and ingredients. Failure to adequately address both challenges can be costly, with estimated recall costs alone starting around $10 million, in addition to any litigation costs.1 Long-term costs can accumulate further as a result of damage to brand reputation. A vast array of methods has been employed to meet these challenges, and adoption continues to increase as technology becomes smaller, cheaper and more user friendly. One such technique is Fourier transform infrared (FTIR) spectroscopy, an analytical technique that is widely used for quick (typically 20–60 seconds per measurement) and non-destructive testing of both man-made and natural materials in food products. The uniformity and physical state of the sample (solid vs. liquid) will dictate the specifics of the hardware used to perform such analyses, and the algorithm applied to the identification task will depend, in part, on the expected variability of the ingredient.

Infrared spectral measurements provide a “compositional snapshot”— capturing information related to the chemical bonds present in the material. Figure 1 shows an example of a mid-infrared spectrum of peppermint oil. Typically, the position of a peak along the x-axis (wavenumber) is indicative of the type of chemical bond, while the peak height is related either to the identity of the material, or to the concentration of the material in a mixture. In the case of peppermint oil, a complex set of spectral peaks is observed due to multiple individual naturally occurring molecular species in the oil.

Mid-infrared spectrum, peppermint oil
Figure 1. Mid-infrared spectrum of peppermint oil. The spectrum represents a “chemical snapshot” of the oil, as different peaks are produced as a result of different chemical bonds in the oil.

Once the infrared spectrum of an ingredient is measured, it is then compared to a reference set of known good ingredients. It is important that the reference spectrum or spectra are measured with ingredients or materials that are known to be good (or pure)—otherwise the measurements will only represent lot-to-lot variation. The comparative analysis can assist lab personnel in gaining valuable information—such as whether the correct ingredient was received, whether the ingredient was adulterated or replaced for dishonest gain, or whether the product is of acceptable quality for use. The use of comparative algorithms for ingredient identification also decreases subjectivity by reducing the need for visual inspection and interpretation of the measured spectrum.

Correlation is perhaps the most widely used algorithm for material identification with infrared spectroscopy and has been utilized with infrared spectra for identification purposes at least as early as the 1970s.2 When using this approach, the correlation coefficient is calculated between the spectrum of the test sample and each spectrum of the known good set. Calculated values will range from 0, which represents absolutely no match (wrong or unexpected material), to 1, representing a perfect match. These values are typically sorted from highest to lowest, and the material is accepted or rejected based on whether the calculated correlation lies above or below an identified threshold. Due to the one-to-one nature of this comparison, it is best suited to identification of materials that have little or no expected variability. For example, Figure 2 shows an overlay of a mid-infrared spectrum of an ingredient compared to a spectrum of sucrose. The correlation calculated between the two spectra is 0.998, so the incoming ingredient is determined to be sucrose. Figure 3 shows an overlay of the same mid-infrared spectrum of sucrose with a spectrum of citric acid. Notable differences are observed between the two spectra, and a significant change in the correlation is observed, with a coefficient of 0.040 calculated between the two spectra. The citric acid sample would not pass as sucrose with the measurement and algorithm settings used in this example.

Mid-infrared spectrum, sucrose
Figure 2. An overlay of the mid-infrared spectrum of sucrose and a spectrum of a different sample of sucrose.
Mid-infrared spectrium, sucrose, citric acid
Figure 3: An overlay of the mid-infrared spectrum of sucrose and a spectrum of citric acid.

When testing samples with modest or high natural variability, acceptable materials can produce a wider range of infrared spectral features, which result in a correspondingly broad range of calculated correlation values. The spread in correlation values could be of concern as it may lead to modification of algorithm parameters or procedures to “work around” this variation. Resulting compromises can increase the potential for false positives, meaning the incorrect ingredient or adulterated material might be judged as passing. Multivariate algorithms provide a robust means for evaluating ingredient identity for samples with high natural variability.

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Alec Senese, Bayer Crop Science, Digital Pest Management
FST Soapbox

Do You Embrace Technology at Home, But Not at Work?

By Alec Senese
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Alec Senese, Bayer Crop Science, Digital Pest Management

Many have seen some variation of the bell curve used to visualize the distribution of the five personality types of technology adopters (see Figure 1). These personality types were first ideated by Beal and Bohlen to highlight personality types that were more or less likely to adopt new technology in agriculture. This model has been expanded to include many other types of technology and is still used today.

Innovation Adoption Lifecycle
Figure 1. The Innovation Adoption Lifecycle.

Which type are you? While it can be fun dinner conversation to compare and contrast your tech enthusiast friends who always have the latest iPhone with laggard pals who insist on using a flip phone, is it possible that self-awareness of your product adoption personality could be vital to your personal and professional success?

Are you an early adopter who is excited and interested by how new technology offerings can change how you live and work? Or are you perhaps a member of the late majority that prefers to play it safe? More importantly, does your product adoption personality serve you in your career? Or does your resistance to change impede your company’s ability to thrive in a competitive marketplace where embracing innovation is key to protecting your product and brand? If the answer is yes, it may be worth keeping that propensity in mind as you make technology decisions at work.

We are each complex human beings who unintentionally bring our unique biases and habits to work with us. Rather than letting those biases and habits control our decisions, we can choose to be aware of our tendencies towards important issues like choosing whether to invest in new technology and approach problems through a less biased lens. When it comes to something as important as food safety and brand equity, we can’t afford to let our biases be in control. It is important to know that any technology provider worth their salt will happily answer questions and even let you try their solution. This firsthand experience is invaluable when choosing to invest in new solutions. Knowledge is powerful and you may be surprised at where it leads.

John McPherson, rfxcel
FST Soapbox

End-to-End Supply Chain Traceability Starts with High-Quality Data

By John McPherson
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John McPherson, rfxcel

End-to-end traceability technology across the food and beverage (F&B) supply chain has many benefits for companies at all nodes of the chain, not least of which is the ability to act to prevent problems such as irreversible damage, loss, and theft. For these technologies to best deliver on their promise, however, they need standardized and quality-assured data. F&B supply chain stakeholders need to take steps to achieve effective data management to truly take advantage of the benefits of traceability and real-time monitoring technologies.

Since FSMA was introduced in 2011, actors across the F&B supply chain have had to change their behavior. Prior to FSMA, companies tended to react to events; today, proactive and preemptive measures are the norm. This is in line with what the legislation was designed to do: Encourage the prevention of foodborne illness instead of responding after their occurrance.

F&B manufacturers and distributors rely on technology to help predict potential obstacles and mitigate issues along their supply chains. But expressing a desire to embrace technologies such as real-time monitoring solutions and predictive analytics isn’t enough to achieve ultimate supply chain efficiency. Only by taking the necessary steps can companies get on track to ensure results.

Any company that is thinking about deploying a traceability solution has a lot to consider. Foremost, data must be digitized and standardized. This might seem challenging, especially if you’re starting from scratch, but it can be done with appropriate planning.

Let’s examine what F&B companies stand to gain by adopting new, innovative technologies and how they can successfully maximize data to achieve end-to-end supply chain traceability.

New Technologies Hold Huge Potential for F&B Supply Chains

The advantages of adopting new technologies far outweigh the time and effort it takes to get up and running. To smooth the process, F&B companies should work with solution providers that offer advisory services and full-service implementation. The right provider will help define your user requirements and create a template for the solution that will help ensure product safety and compliance. Furthermore, the right provider will help you consider the immediate and long-term implications of implementation; they’ll show you how new technologies “future-proof” your operations because they can be designed to perform and adapt for decades to come.

Burgeoning technologies such as the Internet of Things (IoT), artificial intelligence (AI) and blockchain are driving end-to-end traceability solutions, bridging the gap between different systems and allowing information to move seamlessly through them.

For example, real-time tracking performed by IoT-enabled, item-level sensors allows companies to detect potential damage or negative events such as theft. These devices monitor and send updates about a product’s condition (e.g., temperature, humidity, pressure, motion and location) while it is in transit. They alert you as soon as something has gone wrong and give you the power to take action to mitigate further damage.

This is just one example of how data from a fully implemented real-time, end-to-end traceability platform can yield returns almost immediately by eliminating blind spots, identifying bottlenecks and threats, and validating sourcing requirements. Such rich data can also change outcomes by, for example, empowering you to respond to alerts, intercept suspect products, extend shelf life, and drive continuous improvement.

As for AI technologies, they use data to learn and predict outcomes without human intervention. Global supply chains are packed with diverse types of data (e.g., from shippers and suppliers, information about regulatory requirements and outcomes, and public data); when combined with a company’s internal data, the results can be very powerful. AI is able to identify patterns through self-learning and natural language, and contextualize a single incident to determine if a larger threat can be anticipated or to make decisions that increase potential. For example, AI can help automate common supply chain processes such as demand forecasting, determine optimal delivery routes, or eliminate unforeseeable threats.

Blockchain has garnered a lot of buzz this year. As a decentralized and distributed data network, it’s a technology that might help with “unknowns” in your supply chain. For example, raw materials and products pass through multiple trading partners, including suppliers, manufacturers, distributors, carriers and retailers, before they reach consumers, so it can be difficult to truly know—and trust—every partner involved in your supply chain. The immutable nature of blockchain data can build trust and secure your operations.

To date, many F&B companies have been hesitant to start a blockchain initiative because of the capital risks, complexity and time-to-value cost. However, you don’t have to dive in head-first. You can start with small pilot programs, working with just a few stakeholders and clearly defining pilot processes. If you choose the right solution provider, you can develop the right cultural shift, defining governance and business models to meet future demands.

To summarize, new technologies are not disruptive to the F&B industry. If you work with an experienced solution provider, they will be constructive for the future. Ultimately, it’s worth the investment.

So how can the F&B industry start acting now?

How to Achieve End-to-End Traceability

Digitize Your Supply Chain. We live in a digital world. The modern supply chain is a digitized supply chain. To achieve end-to-end traceability, every stakeholder’s data must be digitized. It doesn’t matter how big your company is—a small operation or a global processor—if your data isn’t digitized, your supply chain will never reach peak performance.

If you haven’t begun transitioning to a digitalized supply chain, you should start now. Even though transforming processes can be a long journey, it’s worth the effort. You’ll have peace of mind knowing that your data is timely and accurate, and that you can utilize it to remain compliant with regulations, meet your customer’s demands, interact seamlessly with your trading partners, and be proactive about every aspect of your operations. And, of course, you’ll achieve true end-to-end supply chain traceability.

Standardize Your Data. As the needs of global F&B supply chains continue to expand and become more complex, the operations involved in managing relevant logistics also become more complicated. Companies are dealing with huge amounts of non-standardized data that must be standardized to yield transparency and security across all nodes of the supply chain.

Many things can cause inconsistencies with data. Data are often siloed or limited. Internal teams have their own initiatives and unique data needs; without a holistic approach, data can be missing, incomplete or exist in different systems. For example, a quality team may use one software solution to customize quality inspections and manage and monitor remediation or investigations, while a food safety team may look to a vendor management platform and a supply chain or operations team may pull reports from an enterprise resource planning (ERP) system to try and drive continuous improvement. Such conflict between data sources is problematic—even more so when it’s in a paper-based system.

Insights into your supply chain are only as good as the data that have informed them. If data (e.g., critical tracking events) aren’t standardized and quality-assured, companies cannot achieve the level and quality of information they need. Data standards coming from actors such as GS1 US, an organization that standardizes frameworks for easy adoption within food supply chains, can help with this.

There are many solutions to ensure data are standardized and can be shared among different supply chain stakeholders. With recent increases in recalls and contamination issues in the United States, the need for this level of supply chain visibility and information is even more critical.

Data Security. Data security is crucial for a successful digital supply chain with end-to-end traceability, so you must plan accordingly—and strategically. You must ensure that your data is safe 24/7. You must be certain you share your data with only people/organizations who you know and trust. You must be protected against hacks and disruptions. Working with the right solution provider is the best way to achieve data security.

Incentive Structures. Incentives to digitize and standardize data are still lacking across some parts of the F&B supply chain, increasing the chances for problems because all stakeholders are not on the same page.

Companies that continue to regard adopting traceability as a cost, not an investment in operations and brand security, will most likely do the minimum from both fiscal and regulatory standpoints. This is a strategic mistake, because the benefits of traceability are almost immediate and will only get bigger as consumers continue to demand more transparency and accuracy. Indeed, we should recognize that consumers are the driving force behind these needs.

Being able to gather rich, actionable data is the key to the future. Industry leaders that recognize this and act decisively will gain a competitive advantage; those that wait will find themselves playing catch-up, and they may never regain the positions they’ve lost. We can’t overstate the value of high-quality digitized and standardized data and the end-to-end traceability it fuels. If companies want to achieve full visibility and maximize their access to information across all nodes of their supply chains, they must embrace the available technologies and modernize their data capabilities. By doing so, they will reap the benefits of a proactive and predictive approach to the F&B supply chain.

Alec Senese, Bayer Crop Science, Digital Pest Management
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If You Think Plague Is a Thing of the Past, Think Again

By Alec Senese
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Alec Senese, Bayer Crop Science, Digital Pest Management

Rodents are vectors of more than 50 pathogens, including plague.1 While plague may be considered a problem of the past, according to the World Health Organization, between 2010 and 2015, there were 3,248 cases of reported plague worldwide and 584 deaths. While it is clearly not the 1300’s when the plague killed millions, the CDC confirms, “plague occurs in rural and semi-rural areas of the western United States, primarily in semi-arid upland forests and grasslands where many types of rodent species can be involved.” While the fact that plague is still lurking is a bit surprising, it should be no surprise that rodents can spread more than 50 diseases. Not the least of these diseases is Salmonella braenderup, the cause of recall of approximately 206,749,248 eggs in 2018. The good news: In the age of IoT, new technology can enable an immediate response to help prevent infestations from growing out of control.

With rodent populations on the rise due to climate change and the resultant public health issues in major cities across the United States, public health officials and pest managers face unimaginable challenges in staying ahead of rapidly growing and spreading rodent infestations. Earlier this year, Los Angeles had a typhus outbreak that resulted from a rat infestation near an encampment for those experiencing homelessness. The unsanitary conditions created a harborage for rats that spread the flea-borne illness. Cases of typhoid have doubled in the area since 2012. When and where will the next pathogen outbreak from rodent activity hit?

If that’s not frightening enough, it is important to highlight that once an infected, flea-carrying rodent enters a facility, eliminating the rodent does not always necessarily mean eliminating the presence of plague pathogens. The World Health Organization explains that once vectors have been introduced through rodents and their fleas, it is not enough to eliminate rodents. Vector control must take place before rodent control because “killing rodents before vectors will cause the fleas to jump to new hosts.”

Controlling the spread of pathogens via rodents is becoming increasingly important, particularly in sensitive environments like food processing and manufacturing facilities. Effective management begins with early and accurate detection and sustained through continuous monitoring. However, the traditional method of manual rodent inspection by its very nature cannot provide facility and pest managers with either early detection or continuous monitoring.

Thanks to IoT, monitoring systems can now be used in a wide variety of rodent monitoring devices inside and outside a facility. The systems transmit messages in real time over wireless networks and provide pest managers, facility management and public health officials with 24/7 visibility of rodent activity in a monitored location, which will enable more timely responses and help improve the effectiveness of mitigation efforts. Digital IoT technologies are rapidly becoming the modern proactive tool used to help predict and control rodent issues before they occur in an age when traditional, reactive methods are insufficient.

Reference

  1. Meerburg, B.G., Singleton, G.R., and Kijlstra, A. (2009). “Rodent-borne Diseases and their Risk for Public Health”. Crit Rev Microbiol.