Tag Archives: detection

Salmonella

July 15 Virtual Event Targets Challenges and Best Practices in Salmonella Detection, Mitigation and Control

By Food Safety Tech Staff
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Salmonella

Next week, Food Safety Tech is hosting the second event in its Food Safety Hazards Series, “Salmonella Detection, Mitigation, Control & Regulation”.

The event begins at 11:45 am ET on Thursday, July 15.

Presentations are as follows:

  • Get with the Program: Modernization of Poultry Inspections in the United States; A panel discussion with Mitzi Baum, STOP Foodborne Illness;
    Sarah Sorscher, Center for Science in the Public Interest; Martin Weidman, DMV, Ph.D., Cornell University; and Bruce Stewart-Brown, Perdue Foods
  • Detect, Deter, Destroy! A Discussion on Salmonella Detection, Mitigation and Control, with Elise Forward, Forward Food Solutions; Dave Pirrung, DCP Consulting; additional speaker TBA
  • A Case Study on Salmonella, with Rob Mommsen, Sabra Dipping Company
  • Sponsored TechTalks will be provided by Will Eaton of Meritech, Patrick Casey of BestSanitizer, Adam Esser of Sterilex, and Asif Rahman of Weber Scientific.

Register now for the Food Safety Hazards Series: Salmonella Detection, Mitigation, Control & Regulation.

The More The Merrier: A Multi-Hurdle Approach to Detection

Prevention of foreign object contamination is a growing priority for food processors. Multi-hurdle approaches are becoming a more common method to foreign materials detection in processing plants. During this webinar, we’ll look at what a multi-hurdle approach can look like in different environments, along with the emerging set of technologies for automated inspection: Vision systems.

Food Safety Hazards Series: Salmonella Detection, Mitigation, Control and Regulation

Food safety experts will discuss challenges and tangible best practices in Salmonella detection, mitigation and control, along with critical issues that the food industry faces with regards to the pathogen. This includes the journey and progress of petition to USDA on reforming and modernizing poultry inspections to reduce the incidence of Salmonella and Campylobacter; Salmonella detection, mitigation and control; and a case study on the pathogen involving crisis management.

Olga Pawluczyk, P&P Optica
FST Soapbox

Assessing Detection Systems to Make Food Safer

By Olga Pawluczyk
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Olga Pawluczyk, P&P Optica

It is an exciting time to be in the food industry. Consumers are ever more aware of what they are eating and more demanding of quality. And the vital need to reduce global food waste is transforming how we produce and consume food. This is driving innovation all the way along the supply chain, from gate to plate.

One of the biggest areas of opportunity for the industry to increase automation and improve food safety is in the processing plant. The challenges processors have faced in the last 12 months have accelerated the focus on optimizing resources and the drive for more adoption of new technology.

Foreign material contamination is a growing issue in the meat industry and new types of detection systems are emerging to help address this challenge. As Casey Gallimore, director of regulatory and scientific affairs at the North American Meat Institute, highlighted in a recent webinar, 2019 was a record year for the number of recalls related to foreign object contamination, which totaled 27% of all FSIS recalls in that year.

“There are a number of potential reasons why recalls due to foreign object contamination have increased over the years: Greater regulatory focus, more discerning consumers, [and] more automation in plants. But one important reason for this trend is that we have a lot of new technology to help detect more, [but] we are not necessarily using it to its full potential,” said Gallimore. “As an industry, we have a strong track record of working together to provide industry-wide solutions to industry-wide problems. And I believe that education is key to understanding how different detection systems—often used together—can increase the safety and quality of our food.”

Types of Detection Systems

Processors use many different detection systems to find foreign materials in their products. Equipment such as x-rays and metal detectors, which have been used for many years, are not effective against many of today’s contaminants: Plastics, rubber, cardboard and glass. And even the most well trained inspectors are affected by fatigue, distraction, discomfort and many other factors. A multi-hurdle approach is imperative, and new technologies like vision systems need to be considered.

Vision systems, such as cameras, multi-spectral, and hyperspectral imaging systems can find objects, such as low-density plastics, that may have been missed by other detection methods. Yet, depending on the system, their performance and capabilities can vary widely.

Camera-based systems are the most similar to the human eye. These systems are good for distinguishing objects of varying size and shape, albeit in two-dimensions rather than three. But they become less effective in situations with low contrast between the background and the object being detected. Clear plastics are a good example of this.

Multi-spectral systems are able to see more colors, including wavelengths outside of the visible spectrum. However, multispectral systems are set up to use only specific wavelengths, which are selected based on the materials that the system is expected to detect. That means that multispectral systems can identify some chemical as well as visual properties of materials, based on those specific wavelengths. It also means that other materials, which the system has not been designed to find, will likely not be detected by a multispectral system.

Another relatively new type of vision system uses hyperspectral imaging. These systems use chemistry to detect differences in the materials being inspected and therefore recognize a broad range of different contaminants. They are especially good at seeing objects that cameras or human inspectors may miss and at identifying the specific contaminant that’s been detected. The same system can assess quality metrics such as composition and identify product flaws such as woody breast in chicken. Hyperspectral systems also gather tremendous amounts of chemistry data about the products they are monitoring and can use artificial intelligence and machine learning to get a more holistic picture of what is happening in the plant over time, and how to prevent future contamination issues. This might include identifying issues with a specific supplier, training or other process challenges on one line (or in one shift), or machinery in the plant that is causing ongoing contamination problems.

Many processors are considering implementing new inspection systems, and are struggling to understand how to compare the expected performance of different systems. One relatively simple methodology that can be used to evaluate system performance is, despite its simplicity, called a “Confusion Matrix”.

The Confusion Matrix

A confusion matrix is often used in machine learning. It compares the expected outcome of an event with the actual outcome in order to understand the reliability of a test.

Figure 1 shows four possible outcomes for any kind of test.

Actual (True Condition)

Predicted

(Measured Outcome)

Positive (P) Negative (N)
Positive Detection True Positives (TP) False Positives (FP)
Negative Detection False Negatives (FN) True Negatives (TN)
P = TP + FN N = FP + TN
Figure 1. Confusion Matrix

But what does a confusion matrix tell us, and how can it help us assess a detection system?

The matrix shows us that a detection system may incorrectly register a positive or negative detection event—known as a ‘False Positive’ or ‘False Negative’.

As an example, say we are testing for a disease such as COVID-19. We want to know how often our system will give us a True Positive (detecting COVID when it *IS* present) versus a False Positive (detecting COVID when it *IS NOT* present).

Let’s apply this to processing. If you are using an x-ray to detect foreign objects, a small piece of plastic or wood would pass through unnoticed. This is a False Negative. By contrast, a system that uses hyperspectral imaging would easily identify that same piece of plastic or wood, because it has a different chemical signature from the product you’re processing. This is a True Positive.

A high rate of false negatives—failing to identify existing foreign materials—can mean contaminated product ends up in the hands of consumers.

The other side of the coin is false positives, meaning that the detector believes foreign material to be present when in fact it is not. A high rate of False Positives can lead to significant and unnecessary product wastage, or in time lost investigating an incident that didn’t actually occur (see Figure 2).

True Positives and False Positives
Figure 2. Balance of True Positives and False Positives

The secret to a good detection system lies in carefully balancing the rates of true positives and false positives by adjusting the sensitivity of a system.

This is where testing comes in. By adjusting a system and testing under different conditions, and then plotting these outcomes on the confusion matrix, you get an accurate picture of the system’s performance.

Effectiveness of a Detector

Detection is not just the act of seeing. It is the act of making a decision based on what you have seen, by understanding whether something of importance has occurred. Many factors influence the effectiveness of any detection system.

Resolution. This is the smallest size of object that can possibly be detected. For example, when you look at a photograph, the resolution affects how closely you can zoom in on an image before it becomes blurry.

Signal to noise ratio. This measures the electronic “noise” of the detector and compares it with the “background noise” that may interfere with the signals received by the detector. Too much background noise makes it harder to identify a foreign object.

Speed of acquisition. This measures how fast the detector can process the signals it receives. Motion limits what you can see. As line speeds increase, this impacts what detectors are able to pick up.

Material being detected. The type of material being detected and its properties will have a significant impact on the likelihood of detection. As previously mentioned, for example, x-rays are unlikely to detect low-density materials such as cardboard, resulting in a high number of False Negatives.

Presentation or location of material being detected. Materials that are underneath another object, that are presented on an angle, are too similar to the product being inspected, or are partially obstructed may be more difficult for some detectors to find. This also presents a risk of False Negatives.

Complexity of the product under inspection. Product composition and appearance vary. For example, just like the human eye, finding a small object on a uniformly illuminated and uniform color background like a white kitchen floor is much easier than finding the same small object on a complex background like industrial carpet. Coarsely ground meat might be more difficult to detect than uniform back fat layers, for example.

Environment. Conditions such as temperature and humidity will have a significant effect on detection.

Detection Curves

To understand system performance even better, we can use a detection curve. This plots out the likelihood of detection against different variables (e.g., object size) and allows us to objectively compare how these different factors impact the performance of each system.

Figure 3 shows how this looks when plotted as a curve, with object size on the x-axis (horizontal) and the probability of detection (a True Positive from the Confusion Matrix) on the y-axis (vertical). It shows three examples of possible detection curves, depending on the detector being used.

Detection curves
Figure 3. Examples of detection curves for different detectors. Probability of detection of an object increases as the size of the object increases.

A detection curve tells you both the smallest and largest object that a detector will find and the probability that it will be found.

In the example presented by Figure 3, Detector 3 can see essentially 100% of large and very large objects, as can Detector 2. But Detector 3 is also more likely than the other two systems in the example to see microscopic objects. Based on this detection curve it would likely be the best option if the goal were to detect as many foreign objects as possible, of all sizes.

Of course, the performance of a detector is determined by multiple measures, not just size,

Detection capability can be improved for most detection systems, but typically comes at a significant cost: Increasing sensitivity will increase the number of false positives, resulting in increased product rejection. This is why looking at the detection curve together with the false-positive/false-negative rates for any detection system gives us a clear picture of its performance and is invaluable for food processing plants when selecting a system.

Using the confusion matrix and a detection curve, processors can compare different detection systems on an apples-to-apples basis. They can easily see whether a system can identify small, tiny or microscopic objects and, crucially, how often it will identify them.

Every detection method—X -ray, metal detection, vision systems, manual inspection—presents a trade-off between actual (correct) detection, rejection of good product (false positive) and missed detections (false negative). This simple way to compare differences means processors can make the right decision for the specific needs of their plant, based on easily gathered information. For all of us data geeks out there, that sounds like the Holy Grail.

Susanne Kuehne, Decernis
Food Fraud Quick Bites

The Automated Nose of a Master of Wine

By Susanne Kuehne
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Susanne Kuehne, Decernis
Wine fraud
Find records of fraud such as those discussed in this column and more in the Food Fraud Database. Image credit: Susanne Kuehne

Since only 417 Masters of Wine exist globally (and their palates and noses)—and they are amazing in identifying wines by grape varietal or blend, type, vintage and location—it is a good idea to have some automated backup when it comes to wine fraud detection. Aside from other analytical methods, nuclear magnetic resonance (NMR) spectroscopy can be used in the authentication of wine. The new proton measurement 1H NMR Method with easier sample preparation is recommended for the investigation of wine fraud, to detect for example the addition of water or sugar. NMR spectroscopy measures several compounds of a wine at once and therefore is able to detect a fingerprint of a wine, such as the geographic origin or grape varietal.

Resource

  1. Solovyev, P.A., et. al. (January 27, 2021) “NMR spectroscopy in wine authentication: An official control perspective”. Comprehensive Reviews in Food Science and Food Safety. Wiley Online Library.
Food Fraud: A Global Threat with Public Health and Economic Consequences

Fundamentals of Food Fraud Explained, Global Threat Cannot Be Ignored

By Food Safety Tech Staff
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Food Fraud: A Global Threat with Public Health and Economic Consequences

Food fraud is a global problem, the size of which cannot be fully quantified. A new book edited and authored by experts on the topic seeks to comprehensively address food fraud, covering everything from its history and mitigation strategies, to tools and analytical detection methods, to diving into fraud in specific products such as ingredients, meat, poultry and seafood.

“As we point out in the first sentence of the introduction to Food Fraud: A Global Threat with Public Health and Economic Consequences, food fraud prevention and risk mitigation has become a fast-evolving area. So fast, in fact, that some people may question the value of publishing a comprehensive resource focused on these issues for fear that it will be outdated before the ink is dry. The co-editors of the book disagree,” says Steve Sklare, president of The Food Safety Academy, chair of the Food Safety Tech Advisory Board and co-editor of the book. “This book was written with the goal of providing a solid resource that is more than an academic exercise or reference. The discussion of the fundamental principles of food fraud mitigation and real-world application of this knowledge will provide a useful base of knowledge from which new information and new technology can be integrated.”

Sklare co-edited the book with Rosalee Hellberg, Ph.D., associate director of the food science program at Chapman University and Karen Everstine, Ph.D., senior manager of scientific affairs at Decernis and member of the Food Safety Tech Advisory Board. He hopes that offering access to the book’s first chapter will help communicate their message to the folks responsible for addressing food fraud, whether they are members of the food industry, regulators or academics, or professionals at small, medium or large food organizations.

Complimentary access to Chapter 1 of Food Fraud: A Global Threat with Public Health and Economic Consequences is available in the Food Safety Tech Resource Library. The preview also includes the book’s Table of Contents.

Mitzi Baum, Stop Foodborne Illness
Food Safety Culture Club

Partnerships in Promoting Prevention (of Foodborne Illness)

By Mitzi Baum
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Mitzi Baum, Stop Foodborne Illness

At Stop Foodborne Illness, or STOP, we know about collaborative partnerships. For more than 26 years, affiliating with like-minded organizations to prevent foodborne disease is the mainstay of our success and continues to provide beneficial results today.

The mission to prevent illness and death due to contaminated food resonates with our allies and aligns with their goals to coordinate and expand efforts. At any given moment, STOP is working with a diverse spectrum of individuals and industries to move the needle on foodborne illness prevention. Today, STOP’s work is focused on constituent services and food safety policy with the overarching goal of public health. Below are examples of current collaborative projects that are uniquely effective.

Alliance to Stop Foodborne Illness

The Alliance to Stop Foodborne Illness (Alliance) is an initiative of STOP, leading food companies, and other organizations committed to the goal of preventing foodborne illness. For 25 years, Stop Foodborne Illness has communicated the compelling personal stories of people and families who have experienced serious foodborne illness or the death of loved ones. The goals of communicating these personal stories are to make clear why food safety must be a central value of the food system and to help motivate people in both the food industry and government to do their best every day to reduce hazards and prevent illness. Through the Alliance, STOP and leading food companies are collaborating to expand the reach and impact of personal stories to strengthen food safety cultures and prevent foodborne illness.

The Alliance to Stop Foodborne Illness has a mission to:

  • Forge partnerships between STOP and leading food companies to build trust and support strong food safety cultures.
  • Collaboratively design and implement innovative, well-tailored programs that make compelling personal stories an integral motivational element of food safety culture and training programs.
  • Expand the reach and impact of personal stories through outreach to the small- and medium-size companies that are key contributors to modern supply chains.

Current Alliance members: Costco, Cargill, Conagra Brands, Coca-Cola, Yum! Brands, Nestle USA, LGMA, Empirical Foods, Maple Leaf Foods, Mars, Walmart, Wegmans, and Amazon.

Constituent/Advocate Engagement

Working with those who have been impacted by severe foodborne illness is base to our prevention work. We engage our constituent/advocates in many projects and continually seek additional opportunities.

  • STOP’s new website houses a navigational map for anyone who is in crisis, post-crisis or managing the long-term consequences of surviving severe foodborne disease. This structured, informational composition was created by constituent/advocates that are sharing their lived experiences. This incisive work provides incredible insight into the journey that may lie ahead and how to manage the potential labyrinth.
  • With our partner, Center for Science in the Public Interest, we have created a national platform for survivors of salmonellosis and campylobacteriosis to speak about their experiences surviving these diseases.
  • The Alliance has created multiple working partnerships with individual constituent/advocates.
  • STOP’s speaker’s bureau provides opportunities for our constituent/advocates to share their personal stories with large groups in person or virtually.
  • A recent college graduate who is a constituent/advocate is leading the creation of a new program for the organization.

Dave Theno Fellowship

Dave Theno Fellowship is a partnership with Michigan State University (MSU) that provides a recent public health, food science, animal science or political science graduate (undergraduate or graduate degree) the opportunity to conduct two distinct research projects, engage in STOP programming, participate on coalition calls and earn a certificate in food safety from MSU.

STOP is working with MSU to create a new course for its Online Food Safety Program that focuses on food safety failures and the impact of those system breakdowns on consumers.

Early Detection of Foodborne Illness Research

In conjunction with North Carolina State University, Michigan State University, Eastern Carolina University and University of Michigan, STOP is engaging in research to identify gaps in knowledge and application of the 2017 Infectious Disease Society of America Clinical Practice Guidelines of the Diagnosis and Management of Infectious Diarrhea (IDSA) for healthcare workers. Our early findings have identified that most healthcare workers do not know about nor follow the IDSA guidelines, which include reporting of cases of infectious diarrhea and identification of the pathogen for identification and prevention of potential widespread outbreaks.

To support this research, STOP is completing a systematic literature review with the intent to publish.

Recall Modernization Working Group

STOP has been convening a group of experts comprised of individuals from academia, Alliance members, external industry partners, food industry associations, public health organizations, and industry consultants to deep dive into food recalls to define the current landscape, discuss systemic changes necessary for expedient and efficient execution of recalls for both industry and consumers and develop recommendations on how to accomplish those changes.

Everyone is susceptible to foodborne illness; thus, we need a varied, coordinated approach. Each of these partnerships helps our colleagues meet their goals while promoting prevention of foodborne illness by straddling both industry and consumer focused work. Executing our mission takes many forms and that requires diversity in partnerships, a shared vision and tangible, sustainable results.

Food Safety Consortium

2020 FSC Episode 8 Preview: Listeria Detection, Mitigation and Control

By Food Safety Tech Staff
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Food Safety Consortium

This week’s episode of the 2020 Food Safety Consortium Virtual Conference Series focuses on that pesky bug lurking in many food manufacturing and processing facilities: Listeria. The following are highlights for Thursday’s session:

  • Listeria monocytogenes: Advancing Food Safety in the Frozen Food Industry, with Sanjay Gummalla, American Frozen Foods Institute
  • Shifting the Approach to Sanitation Treatments in the Food & Beverage Industry: Microbial Biofilm Monitoring, with Manuel Anselmo, ALVIM Biofilm
  • A Look at Listeria Detection and Elimination, with Angela Anandappa, Ph.D., Alliance for Advanced Sanitation
  • TechTalk on The Importance of Targeting Listeria Where It Lives, presented by Sterilex

The event begins at 12 pm ET on Thursday, October 29. Haven’t registered? Follow this link to the 2020 Food Safety Consortium Virtual Conference Series, which provides access to 14 episodes of critical industry insights from leading subject matter experts! We look forward to your joining us virtually.

Alex Kinne, Thermo Fisher Scientific
FST Soapbox

The Importance of Metal Detection in Preventing Food Contamination

By Alex Kinne
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Alex Kinne, Thermo Fisher Scientific

Foreign object detection is a critical step for food processors, with inspection personnel experiencing increased pressure to ensure food safety regulatory compliance without hindering productivity. This pressure has only increased as food processors are faced with accelerated timelines to meet changing supply chain demands for more at-home meals, including frozen and processed, shelf-stable foods as buying habits have changed during the COVID-19 crisis.

Identifying and Understanding Contaminants

Among foreign objects, metals such as ferrous, nonferrous and stainless-steel shavings or broken pieces from equipment are among the most common foreign objects of concern in food processing plants. As part of their HACCP assessment, food processors must identify where these foreign objects could enter the process and ensure that control, such as a metal detector, is in place to reduce escapes into food products.

Overcoming Detection Challenges

Metal detection has long been used as a tool for finding foreign metal objects in food. However, until recently, metal detection had shortcomings. Mineral-rich foods like fresh salad greens, or high-salt content foods, including meat, cheese and fresh-baked bread, are highly conductive and can mimic metal signals. Metal detectors were also susceptible to environmental conditions like temperature swings and electromagnetic interference from nearby equipment in the processing plant. They also pose an ongoing challenge to avoid excessive false rejects, which increase the potential for costly scrap or rework, impacting operational efficiency.

For bread, there is a further complication from the varying densities, air bubbles and other physical characteristics of each loaf since no two are exactly the same. The variations can “confuse” metal detectors into thinking a contaminant is present when it is not, and consequently rejecting good products.

Recent Advancements in Metal Detection

Recent technological advancements are designed to overcome these challenges. Newer technology enables the operator to quickly and easily fine-tune up to five frequencies to achieve the optimal sensitivity settings to find only the metal and ignore the host product. Advancements in software have enabled the automated set-up of detection parameters, saving time. And tracking features allow the metal detector to adjust on the fly without intervention by an operator. Less-skilled line workers are able to perform these tasks versus highly skilled labor required in the past. What used to take hours can be accomplished in minutes, resulting in maximum food safety and operational efficiency.

One of the new technologies scans up to five user-selectable frequencies at a time from 50 to 1000 kHz. It enables users to identify contaminants that are up to 70% smaller in volume than previous single-frequency technology. It reduces the probability of escapes to near zero.

Providing a high probability of detection, safety and operational efficiency allows for a higher level of food safety and brand protection while meeting user processing demands. Keeping the food supply free from foreign objects is always crucial for consumer safety and brand protection. Current events and accompanying demands on food processors underscore the importance. The right technology solution for a specific application depends on application-specific requirements. Given the many factors that can impact detection results, it is prudent to request a complimentary product test performed by the inspection equipment manufacturer(s) under consideration. A product test provides a real-world performance estimate and any technical recommendations for improving contaminant prevention, helping to set up food processors for success.

Manuel Orozco, AIB International
FST Soapbox

Detecting Foreign Material Will Protect Your Customers and Brand

By Manuel Orozco
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Manuel Orozco, AIB International

During the production process, physical hazards can contaminate food products, making them unfit for human consumption. According to the USDA’s Food Safety and Inspection Service (FSIS), the leading cause of food recalls is foreign material contamination. This includes 20 of the top 50, and three of the top five, largest food recalls issued in 2019.

As methods for detecting foreign materials in food have improved over time, you might think that associated recalls should be declining. To the contrary, USDA FSIS and FDA recalls due to foreign material seem to be increasing. During the entire calendar year of 2018, 28 of the 382 food recalls (7.3%) in the USDA’s recall case archive were for foreign material contamination. Through 2019, this figure increased to approximately 50 of the 337 food recalls (14.8%). Each of these recalls may have had a significant negative impact on those brands and their customers, which makes foreign material detection a crucial component of any food safety system.

The FDA notes, “hard or sharp foreign materials found in food may cause traumatic injury, including laceration and perforation of tissues of the mouth, tongue, throat, stomach and intestine, as well as damage to the teeth and gums”. Metal, plastic and glass are by far the most common types of foreign materials. There are many ways foreign materials can be introduced into a product, including raw materials, employee error, maintenance and cleaning procedures, and equipment malfunction or breakage during the manufacturing and packaging processes.

The increasing use of automation and machinery to perform tasks that were once done by hand are likely driving increases in foreign matter contamination. In addition, improved manufacturer capabilities to detect particles in food could be triggering these recalls, as most of the recalls have been voluntary by the manufacturer.

To prevent foreign material recalls, it is key to first prevent foreign materials in food production facilities. A proper food safety/ HACCP plan should be introduced to prevent these contaminants from ending up in the finished food product through prevention, detection and investigation.
Food manufacturers also have a variety of options when it comes to the detection of foreign objects from entering food on production lines. In addition to metal detectors, x-ray systems, optical sorting and camera-based systems, novel methods such as infrared multi-wavelength imaging and nuclear magnetic resonance are in development to resolve the problem of detection of similar foreign materials in a complex background. Such systems are commonly identified as CCPs (Critical Control Points)/preventive controls within our food safety plans.

But what factors should you focus on when deciding between different inspection systems? Product type, flow characteristics, particle size, density and blended components are important factors in foreign material detection. Typically, food manufacturers use metal and/or x-ray inspection for foreign material detection in food production as their CCP/preventive control. While both technologies are commonly used, there are reasons why x-ray inspection is becoming more popular. Foreign objects can vary in size and material, so a detection method like an x-ray that is based on density often provides the best performance.

Regardless of which detection system you choose, keep in mind that FSMA gives FDA the power to scientifically evaluate food safety programs and preventive controls implemented in a food production facility, so validation and verification are crucial elements of any detection system.

It is also important to remember that a key element of any validation system is the equipment validation process. This process ensures that your equipment operates properly and is appropriate for its intended use. This process consists of three steps: Installation qualification, operational qualification and performance qualification.

Installation qualification is the first step of the equipment validation process, designed to ensure that the instrument is properly installed, in a suitable environment free from interference. This process takes into consideration the necessary electrical requirements such as voltage and frequency ratings, as well as other factors related with the environment, such as temperature and humidity. These requirements are generally established by the manufacturer and can be found within the installation manual.

The second step is operational qualification. This ensures that the equipment will operate according to its technical specification. In order to achieve this, the general functions of the equipment must be tested within the specified range limits. Therefore, this step focuses on the overall functionality of the instrument.

The third and last step is the performance qualification, which is focused on providing documented evidence through specific tests that the instrument will performs according to the routine specifications. These requirements could be established by internal and industry standards.

Following these three steps will allow you to provide documented evidence that the equipment will perform adequately within the work environment and for the intended process. After completion of the equipment validation process, monitoring and verification procedures must be established to guarantee the correct operation of the instrument, as well procedures to address deviations and recordkeeping. This will help you effectively control the hazards identified within our operation.

There can be massive consequences if products contaminated with foreign material are purchased and consumed by the public. That’s why the development and implementation of a strong food safety/ HACCP plan, coupled with the selection and validation of your detection equipment, are so important. These steps are each key elements in protecting your customers and your brand.