1. NIR Spectrometer Standardization: 10 Key Challenges of Multi-Instrument NIR Networks & Their Solutions for Consistency Across NIR Instruments

Story: Setting the Standard Right from the Start

After publishing my previous article, "Introduction to NIR Spectrometer Standardization: 7 Article Series for Consistency Across NIR Instruments," Jerry, a LinkedIn follower, recently asked me a deep and thought-provoking question: "Will the next instrument have the same optical response as the last one?" This question got to the heart of the NIR spectrometer standardization we are discussing. Even though it seems like an easy question, it shows how hard it is for manufacturers to make sure that all of their tools are the same. As Jerry correctly pointed out, system vendors shouldn't leave it up to users to find problems after the fact; instead, they should focus on making sure that instruments are made the same way every time before they leave the plant.

This question made me think of a common situation that many people have seen in the field. Imagine that you are an engineer who uses NIR to check out how the soil is made in different places. You calculate how much fertilizer to use to get the best crop growth from the test. Now, it can be very bad for crop health and resource management if one spectrometer gets a much higher nitrogen content than another, even though they are both the same model and are said to be set to the same standards. You could use too little or too much fertilizer, which could lower crop yields, raise costs, or even hurt the environment.

At FOSS North America, where I work, we believe in preventing issues rather than fixing them after the fact. As the manufacturer of NIR systems, we take responsibility for standardization before our instruments reach customers. This requires more than just adjusting each device; it also needs a strong, uniform production process that keeps differences to a minimum in the first place.

Let me share a personal story that illustrates the importance of standardization. As a child in an orphanage, I was fascinated by the rotating Christmas tree during our New Year celebrations. One day, curiosity got the better of me, and I decided to investigate the mechanism that made it turn. Crawling underneath, I discovered an intricate system of gears and pinions working in perfect harmony to create a mesmerizing rotation.

However, my eagerness to understand led to a painful lesson when I carelessly stuck my finger into one of the holes, injuring myself. This experience taught me that even a small disruption in a well-designed system can have significant consequences. In the same way, one device that isn't properly calibrated in NIR spectroscopy can skew data and compromise the reliability of the entire network.

Similarly, in NIR spectroscopy, one improperly calibrated device can compromise the reliability of an entire network. At FOSS, we subject our instruments to rigorous quality control tests, ensuring they meet exacting standards for optical response and overall performance. This approach helps prevent the kind of unexpected and potentially harmful outcomes I experienced as a curious child.

When Jerry asked if the next instrument would have the same visual reaction as the last one, I was happy to say, "Yes, of course." For users, we want to take away the need to guess, so they can be sure that a reading taken in Iowa will be the same as one taken in Brazil or anywhere else... We make sure that every instrument works as intended, no matter how many are made—by engaging in strong factory standardization. This way, we can make choices based on data every time, no matter which instrument is the first or the hundredth.

This story shows how important it is to be involved during the manufacturing process in order to make sure that the final product is truly regular. This is a concept that I think will be important to everyone who uses NIR technology to make important decisions, from farmers to drug companies. It also sets the stage for the more in-depth look at problems and answers in multi-instrument NIR networks that we'll do in this series. Stay tuned for more information as we talk about the challenges and ways to keep things the same across different NIR setups.

So, let's get to the main point of the piece, which is about the problems that come up when you try to manage a multi-instrument NIR network and how to solve them.

Introduction to Challenges of Multi-Instrument NIR Networks

Implementing and maintaining a standardized NIR network across multiple instruments can be a daunting task. While standardization promises consistent and reliable data across various devices, achieving this ideal scenario is fraught with complex challenges. Each challenge, if left unresolved, has the potential to significantly undermine the reliability and validity of your NIR data. In industries such as pharmaceuticals, agriculture, and food processing, where precision is paramount, even minor inconsistencies can lead to incorrect conclusions and costly errors. Consequently, a detailed understanding of these obstacles is critical for organizations that rely heavily on NIR spectroscopy.

The need for addressing these challenges is magnified in environments that require precise monitoring of product quality and compliance with stringent regulations. For instance, in pharmaceutical manufacturing, the slightest deviation in NIR readings could mean that a drug batch does not meet its specification for active ingredient content, leading to potential health risks or recalls. Similarly, in agricultural applications, inconsistent NIR data could result in misinformed decisions about crop management or fertilizer application, adversely impacting yield and profitability.

Another factor complicating the standardization process is the diversity of NIR instruments available on the market. Instruments from different manufacturers, and even different models from the same manufacturer, may exhibit varied sensitivities and spectral outputs. This diversity poses a significant challenge when attempting to harmonize data across a network, making a uniform standardization approach difficult to implement. Therefore, a robust strategy is needed to account for these variations.

Given the high stakes involved, it's imperative to dissect each challenge individually and provide concrete strategies to overcome them. Addressing these obstacles requires not only technical expertise but also an understanding of the operational environment in which these instruments are deployed. The following sections delve into the ten most pressing challenges of managing multi-instrument NIR networks, offering insights into how each can be mitigated to maintain data integrity and reliability.

1. The "Identical Twins" Conundrum: Variability Between Instruments

Even if two NIR spectrometers are exactly the same, their performance can vary in ways that are easy to see. This could be because of small changes in optical parts, how the sensors are aligned, or even how the products were made. These small differences may not seem important at first, but they can add up to big changes in spectral readings, especially when comparing results from a group of instruments. For example, because of small differences in how sensitive its detection is, one analyzer might always read a grain batch as having a slightly higher moisture content than another.

In industries like drug manufacturing, where small differences can cause big problems, these differences are even more of a problem. Imagine that the amount of active ingredients in a medicine is measured by a number of different tools. If one instrument regularly reads lower than the others, a batch that this instrument says is fine might not be fine when tested by a unit that is properly adjusted. This could lead to not following the rules set by regulators, which could lead to costly refunds or even legal problems.

The problem gets worse when instruments are spread out in different places, each of which has its own external factors that can affect them. Temperature, humidity, and other environmental factors can all have a small impact on how well a spectrometer works, which can lead to inconsistent results. Because of this, it is very important for businesses to come up with a plan that takes into account both the changes between instruments and the outside conditions.

Setting up a strict cross-validation process between all the tools in the network is a realistic way to deal with this problem. In cross-validation, the results from one instrument are compared to those from a standard or "golden" reference instrument. This helps find tools that aren't performing as expected, so they can be re-calibrated at the right time. Using chemometric methods like Partial Least Squares (PLS) regression can also help fix these differences, bringing all instruments up to the same level.

I explored various statistical models for Near-Infrared (NIR) spectroscopy calibration development, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Principal Components Regression (PCR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Discriminant Analysis in my article titled: "From MLR to ANN: Navigating Through These 6 NIR Calibration Methods for Food Analysis."

In conclusion, differences in NIR devices will always be a problem. But, they can be handled well with the right calibration procedures, regular cross-validation, and advanced data harmonization techniques. The goal is to make sure that every instrument in the network, no matter what brand or type it is, gives the same accurate data, which helps everyone in the company make smart decisions.

2. The “Every Sample’s a Snowflake” Dilemma: Sample Variability

Beyond instrument variability, sample variability poses a significant challenge in maintaining data consistency. Different samples naturally exhibit variations in their physical and chemical properties, such as moisture content, particle size, and matrix effects. This variability complicates the standardization process, as even small differences in sample composition can lead to significant discrepancies in spectral readings. For instance, a small change in the particle size of a flour sample can alter the light scattering properties, impacting the NIR spectra and, consequently, the accuracy of the analysis.

Industries like food processing and agriculture are particularly prone to sample variability due to the inherent heterogeneity of raw materials. Consider a scenario where a food company analyzes protein content in wheat flour. If the particle size distribution varies from batch to batch, the NIR readings for protein content may fluctuate even if the actual protein content remains constant. This can lead to confusion and potentially misinformed decisions about product quality and consistency.

Inconsistent sample handling practices across different facilities further compound the problem. For example, if one site grinds samples more finely than another, the resulting spectra may not be directly comparable, even when using the same calibration model. Such inconsistencies can obscure true trends in the data and hinder the ability to make accurate comparisons across the network. To mitigate these issues, strict protocols for sample preparation and handling must be established and adhered to by all facilities.

Moreover, sample variability can also impact the robustness of calibration models. If the calibration set does not adequately represent the range of variability seen in production samples, the predictive power of the model will be compromised. This can lead to poor performance when analyzing new samples, making it crucial to build comprehensive calibration models that encompass a wide range of sample characteristics.

In summary, while sample variability is a natural and unavoidable challenge, its impact can be minimized through standardized sample preparation protocols, comprehensive calibration models, and rigorous validation procedures. By controlling for sample variability, organizations can ensure that NIR spectrometers deliver accurate and reliable results, supporting robust quality assurance processes.

3. The Puzzle Pieces That Just Won’t Fit: Data Comparison and Aggregation

Taking care of a group of NIR spectrometers can be like putting together a tricky puzzle: if the pieces don't fit, it's hard to see the whole picture. Comparing data from different places or instruments isn't possible without uniform tools, which makes it hard to come to useful conclusions. When tools aren't synchronized, putting together a lot of data can lead to wrong conclusions, which could cost a lot of money.

Multinational pharmaceutical companies and large-scale farming operations are two examples of businesses that depend on networks of NIR spectrometers. Inconsistent data can make it harder to find trends and improve processes so that quality standards are met easily. For example, a dairy business that uses NIR spectroscopy in various facilities might have trouble combining data on moisture content because the standards for calibrating the instruments aren't always the same. This could make it hard for them to make sure that the standard of their products is the same all over their world distribution network.

To get around these problems, you need to set up a centralized data management system that lets you combine and compare data from different sources. This system should be able to match up calibration models and use statistical methods to make data from different tools more similar. Principal Component Analysis (PCA), for instance, can be used to find trends and outliers in the data, which makes it easier to keep an eye on and manage quality characteristics.

Setting a uniform set of performance standards for all tools in the network is another good idea. These standards should come from a "golden" standard instrument, and all other devices should be checked and set against it. By making sure that every device meets these set standards, organizations can lower variation and get more accurate results when they compare and combine data.

The main problem with NIR spectroscopy is that it is not very useful without a uniform way to compare and group data. To get the most out of NIR technology in quality assurance processes, you need a strong data management plan backed by advanced statistical tools and regular performance targets.

4. The Slow Fade: NIR Spectrometer Drift Over Time

NIR spectrometers can shift over time, just like any other precise tool. Drift is when an instrument's reaction slowly changes over time because of things in the surroundings, worn-out parts, or changes in the optical position inside the instrument. Long-term use of a spectrometer will always include drift, even if it is well taken care of. This shift could be due to changes in temperature or humidity, or it could be due to dust building up in the optics of the device. Because of this, the readings from a single analyzer may change slightly over time, which could lead to wrong data if they are not regularly fixed.

One clear example of this is in farming, where spectrometers are often subject to changing weather conditions, like changes in temperature with the seasons. Imagine that there is a NIR detector in a grain bin that checks the amount of moisture in stored wheat. If the device hasn't been re-calibrated after a hot summer, internal drift could cause it to show higher moisture levels than they really are. This might cause workers to do things that aren't necessary, like lowering the air or adding drying steps, which will hurt the quality of the product and cost more.

What happens when drift happens in the drug industry can be even worse. Take a look at an NIR analyzer that is used to check how much of an active ingredient is in a drug mixture. If the instrument has drifted and hasn't been fixed, the wrong dose could be used in a whole batch, putting patients at risk and causing expensive refunds. This means that in controlled industries, it is necessary to regularly check and adjust spectrometers that are prone to shift.

Setting up a regular plan for recalibration is one way to stop drift. This includes calibration checks that happen every day, every week, or every month, based on the setting and how important the data being made is. To set a baseline for each device, reference standard readings should also be taken on a regular basis. Anyone who notices changes from this standard should immediately fix the problem by recalibrating the device.

There are also software options that can be used to track and fix drift automatically. These solutions use machine learning techniques and real-time tracking to find early signs of drift and suggest the best ways to calibrate again. To keep their NIR networks accurate and reliable over time, businesses can use these technologies to reduce the effects of drift whenever possible.

5. The Moody Spectrometer: Environmental and Operational Variability

NIR spectrometers are very sensitive to changes in their surroundings, which can make them work less well or produce less accurate data. Different places can have different spectral outputs because of things like weather, humidity, and lighting. This makes it hard to get consistent results everywhere. Even small changes in how samples are treated, like differences in sample thickness, packing density, or direction, can cause variability that makes it harder to understand the data.

In a grain holding center, NIR spectrometers are placed in different bins to check the amount of moisture in the grain. If one silo is in an area where the humidity changes a lot and another is in a more stable area, the two tools may give very different numbers for the same type of grain when it comes to moisture. This can lead to wrong estimates of the moisture level, which can lead to bad storage choices and the possibility of food going bad.

NIR instruments are also used to check the amount of fat in dairy goods in food preparation plants. Spectral data can change when the room temperature or lighting changes, which makes it hard to keep quality uniform across production lines. If these environmental factors aren't managed, they could cause problems with measuring fat levels, which could lead to poor product quality and possibly not following labeling rules.

To deal with these problems, environmental control methods need to be put in place at all NIR survey sites. This means keeping the temperature and humidity steady, reducing the amount of light disturbance from outside sources, and protecting the instruments from vibrations and other things that could cause problems. Also, operators should be taught to handle samples in the same way and follow standard processes. This will reduce operating variability as much as possible.

Some of the time, environmental adjustment models can help lessen the effects of changes in the environment. These models change the spectrum data to take into account changes in temperature and humidity that are known to happen. This gives a more accurate picture of the sample's real features. By using these models in their calibration procedures, businesses can lessen the effect of changes in the surroundings and how things are run, which keeps NIR readings accurate and reliable.

6. The “Two Chefs in the Kitchen” Syndrome: Operator Variability

Human factors can make data very different from collecting and analyzing NIR data. Different workers may have different levels of skill and may handle samples or set up tools in slightly different ways. Small changes like these can add up and make the spectral readings less consistent. One expert might put a sample in a sample holder more tightly than another, which could change the path length and, in turn, the spectral readings.

Let's say that a food processing plant hires several workers to work different shifts and run NIR spectrometers. The data that is collected may be very different if each worker takes a slightly different set of steps for preparing samples or calibrating instruments. This can hide real changes in sample properties and make it hard to keep the final quality uniform. Variability caused by operators can also make it harder to make accurate calibration models since the models have to take into account how different operators have changed the data.

Operator variability has an effect on more than just preparing samples. It also has an effect on analyzing data and fixing problems. When an instrument gives strange results, workers with less experience might make the wrong diagnosis, which could mean that the instrument needs to be recalibrated or that the wrong conclusions are drawn about the quality of the product. It can be hard to trust the data that the NIR network produces when this happens. It also messes up processes.

To cut down on operator variation, companies should give all NIR techs thorough training programs. These classes should teach how to prepare samples, how to calibrate instruments, and how to read data. Standard operating procedures (SOPs) should be made and strictly followed by all workers to make sure that everything runs smoothly. Also, technology can be used to keep people from having to do important tasks, which lowers the chance of operator-induced variability.

Another good idea is to test all workers' skills on a regular basis. By looking at how well each worker handles samples and generates data, companies can find places to improve and make sure that everyone is working at the same level. This makes the data more reliable and encourages a mindset of always getting better and operational success.

7. The Timing Troubles: Calibration Frequency and Protocols

Calibration is the most important part of getting correct NIR readings, but different facilities have very different rules about how often and how to do it. Some companies may adjust their devices every day, while others may only do it once a week or once a month. This difference can cause instruments to lose their accuracy, which causes data errors that make the NIR network less reliable.

Imagine two places using the same kind of NIR spectrometer but with different plans for calibrating them. One factory re-calibrates every morning before work starts, but the other only does it once a week. If the weather at the second site changes a lot during the week, the spectrometer might not work as well, which would mean that the readings are wrong. This can lead to two similar instruments giving different results, which makes it harder to standardize measures across the network.

To make sure that everyone follows the same calibration procedure, businesses should come up with a standard calibration schedule that takes into account both the unique needs of each device and the situations in which it works. Instruments that are used in places with a lot of change should be adjusted more often, while instruments that are used in places with less change may need to be recalibrated less often. A method based on risk can help figure out the best timing for calibrating each instrument, making sure that all of them are in sync without needless downtime.

Setting a regular plan for calibration is important, but it's also important to carefully record each calibration event. This means writing down the date, time, and conditions of the area where the testing took place, as well as the reference standards that were used. This kind of writing leaves a clear audit trail that can be looked at to find possible sources of variation or shift.

Standardizing calibration procedures and keeping thorough records can help businesses keep their NIR data accurate and reduce the amount of variation caused by calibration. This proactive method makes sure that all instruments stay in great shape, which supports measures that are accurate and reliable across the network.

8. The “Bad Neighborhood” Effect: Environmental Control Measures

Because spectrometers are very sensitive to changes in their surroundings, environmental control methods are very important for making sure that NIR readings are accurate. Temperature, humidity, light exposure, and even motion can change how the device reads the sample's spectral data, which can cause readings to be different. If these things aren't handled well, they can lead to big differences in readings between instruments and measurement places, which will eventually make the data less accurate and reliable.

For instance, let's say that two similar NIR spectrometers are used in two different production facilities, one in a lab that keeps the temperature stable and the other in a building where the temperature and humidity change a lot during the day. Even if both devices are exactly calibrated, differences in the surroundings can cause spectral readings to be off, which makes it hard to compare data from two different places. These differences in the surroundings can lead to wrong conclusions about the sample's properties, which could affect choices about product quality or process control.

One realistic way to deal with this problem is to make sure that each measurement spot follows strict rules for controlling the environment. It is important to carefully control and keep an eye on temperature and humidity, especially in places where these things change throughout the day. Taking sensitive measures in climate-controlled rooms or cages can lessen the effect of changes in the environment by a large amount. Putting in shaking dampers and protecting instruments from direct light sources can also help keep results stable and reduce interference from outside sources.

Using real-time environmental adjustment methods that change the spectral data based on the recorded environmental conditions is another good idea. These methods use sensor data to account for known changes in the environment, like temperature or humidity changes. This makes sure that the adjusted spectral data accurately shows the properties of the sample. By adding these kinds of algorithms to the calibration routines for the NIR network, companies can get more stable results in a wider range of settings.

Lastly, it's important to make sure that all places that use NIR spectrometers follow the same rules for controlling the climate. In order to do this, a standard set of environmental conditions must be maintained during the measures, and all sites must follow these rules. Cross-validation studies and regular checks can help make sure that rules are being followed and find places that might need extra environmental control measures. Organizations can lessen the effects of changing environments and keep the accuracy of their NIR readings by being mindful of managing the environment.

9. The Silent Saboteur: Data Management Systems

Spectral data, calibration records, and results—think оf these аs the treasure chest in а NIR network. Yet, every company, even different departments within one, may have its own secret recipe for how they gather, store, and look at this data. It's wild sometimes. Different systems can make things like comparing apples to oranges. And it messes with how data gets used for decisions. Bam! Chaotic, right? One team might have all the cool gadgets with LIMS, while another's stuck juggling spreadsheets. Weird! Inconsistent methods lead to mix-ups, and who wants that? Things can get messy and blurry. Oops! It's not a good look when data gets lost or copied twice. Messes up the flow big time.

Now, here's the kicker—companies should nab a centralized tool. Get all NIR gadgets talking the same language. Bingo! It should zip data automatically from each spectrometer. Zap! So long, manual entry! Errors? Nah. Less mess. It should do a top-notch job at checking and tracking data. Get it right, folks! It's gotta be bang on. While everyone's busy fixing issues, they should follow the same data-handling rules. Be clear about how to gather, name, store, and get to data. Everyone's on the same bus! Training from time to time will drum these rules into every corner. Woohoo, sync in action! It's important.

And hey, what about protection? Gotta keep the data safe and sound. Use encryption and access controls. Backup? You bet! Prevent accidental losses and keep the sneaky peepers away. Companies can totally fast-track their NIR business. Good quality data and quicker decisions. Plain and simple! They should really give it a go—centralizing data management and setting it straight with sturdy protocols. What a game-changer, folks! It's true.

10. The Compliance Maze: Regulatory Challenges

For businesses that use NIR spectrometers in a lot of different places, it can be hard to keep up with all the rules and regulations that apply to them. There may be different rules for NIR readings in different businesses and countries, which makes it harder to keep a standard network. For instance, companies that make medicines have to follow the rules for Good Manufacturing Practice (GMP), and companies that make food have to follow strict rules for food safety. If these rules aren't followed, the company could face fines, product refunds, and damage to its image.

Think about a company that processes food and does business in more than one country. Each country has its own rules about how much moisture is allowed in food. Because of local rules, the business might have to adjust its NIR instruments in a different way. This means that the same product is measured to different standards in each country where it is made. This can make things very hard for the company in terms of logistics, as it becomes harder to make sure that products are all the same and harder to collect data from production sites around the world.

To deal with these problems, businesses need to come up with a full compliance plan that takes into account the unique rules of each area and field where they work. This includes putting together a central compliance team whose job it is to keep up with the rules and make sure that all tools and processes meet these requirements. The expert staff and the compliance team should work together to make any changes that need to be made to calibration methods, paperwork procedures, or data management systems.

Also, businesses should think about using compliance management tools to automatically keep track of and report actions related to compliance. These systems can send out automatic reminders when calibrations are due, keep full records of all readings and calibrations, and make compliance reports that can be given to regulatory authorities. By using technology to make compliance management easier, businesses can make sure that all legal requirements are always met while also making their teams' jobs easier.

Lastly, businesses should do regular internal checks to make sure they are following all the rules and find places where they can do better. As part of these checks, records of instrument calibration, data management, and staff training programs should be looked over. Organizations can lower the risk of not following the rules and make sure their NIR networks work within the limits of all applicable standards and laws by being proactive about regulatory compliance.

Taming the Chaos: Mastering Multi-Instrument NIR Networks

When trying to figure out how to use a multi-instrument NIR network, it can sometimes feel like collecting cats, which comes with its own set of problems! However, there are a number of good things you can do to make sure the data is correct and to make it easier for everyone in your network to make reliable decisions. These plans should be put into action. Performing a study into the underlying causes of fluctuations is important to start this method. These causes could include changes in samples, differences in the tools used, or changes in the conditions of the surrounding surroundings.

The amount of variation in your network can be greatly reduced by putting in place control measures such as regular calibrations, active management of external conditions, and the adoption of rules for careful sample handling. These techniques will help cut down on the amount of variation that occurs. This will greatly improve the accuracy and dependability of your readings if you pay attention to these important things at the right times. If you follow these well-thought-out plans, you should be able to standardize your NIR network. You can turn a system that could be chaotic and disorganized into one that works well, produces regular results, and is in harmony with itself. This way of doing things not only gives you more confidence in the data you have but also helps you make choices that are well-informed and based on facts you can trust.

Wrapping It Up: Turning NIR Challenges into Wins

Taking care of a network of many NIR spectrometers is not easy, and each problem is unique and hard to solve. To keep all the instruments in sync, you need to know a lot about all the things that can affect the quality of the data. These include differences between instruments, inconsistent samples, problems comparing data, and environmental drift. By tackling these problems head-on and putting in place uniform processes, businesses can make sure that their data is accurate and consistent, which helps them make better decisions and achieve better results.

The next piece, which is due in two weeks, will talk about practical ways to deal with these problems, such as step-by-step instructions for putting standards and calibration methods into action. This topic can be broken down into several parts to give a more complete picture and more specific instructions, making up for the message that was missed last month.

About the Author: Navigating NIR, One Challenge at a Time

How are you, fellow food business and data admirers? I'm Vitaly Kirkpatrick , and I'm your expert on all things NIR. I combine science with real-world uses in the farming and food businesses. I've been working with Near-Infrared (NIR) spectroscopy for more than 8 years, and I've seen it all, from the surprising wins to the hard problems that come with running NIR networks.

Allow me to tell you a short story that shows how I solve problems. A few years ago, I worked with a dairy business that was having trouble getting all of their NIR results to be the same. Their quality control team had a lot of trouble because the readings from each analyzer were slightly different. It seemed like every instrument was playing a different tune. It would be like trying to bake a cake with materials that were measured on different scales. To make a long story short, I jumped right in, put in place tighter calibration rules, and helped them get their tools to work together. Not only did their readings start to match up, but they also got much better at making things.

This hands-on experience taught me one thing: the devil's in the details when it comes to NIR standardization. Whether it's tweaking calibration or adjusting environmental controls, every small factor plays a part in achieving reliable data.

Through my newsletter, "NIR in Food and Agriculture," I break down these complex challenges into practical steps that you can apply in your operations. With over 1,000 subscribers, we’re a growing community of professionals who love to geek out on NIR spectroscopy and share solutions that keep our industries running smoothly.

As an Industry Sales Manager at FOSS North America , I have firsthand access to cutting-edge NIR technology tailored for agriculture and food production. Let’s turn NIR challenges into wins, ensuring that your data is always as accurate and actionable as possible.

Let's keep the conversation going! Connect with me on LinkedIn or subscribe to my newsletter to stay ahead of the latest trends in NIR technology.

Vitaly Kirkpatrick

NIR Enthusiast and Problem Solver