Tom on NIR, data analysis and the new calibration line for oat hulls

06 May 2026

Tom recently started as a Data Analyst at NutriControl. In his role, he combines his background in chemistry with his interest in data analysis and NIR technology. This places him exactly at the intersection where laboratory knowledge, measurement data and practical quality control come together.

In this interview, Tom talks about his first period at NutriControl, what appeals to him about working with data and why NIR is a valuable technology for many companies. We also take a closer look at the new NIR calibration line for oat hulls. This calibration line makes it possible to gain fast and reliable insight into important quality parameters such as crude fibre, moisture, crude protein, crude fat, starch and crude ash. Especially for companies using oat hulls as a fibre-rich raw material in animal feed, this offers new opportunities for faster control, better process management and greater grip on raw material quality.

You recently started as a Data Analyst at NutriControl. What appealed to you about this role, and what made you choose a laboratory environment?

Because of my background in chemistry (Food & Pharma) and my growing interest in data, I was looking for a role in which I could combine the two. That combination of a laboratory environment and a data role was exactly what I was looking for. In this Data Analyst role at NutriControl, those two areas come together very well.

What specifically fascinates you about NIR technology, and how do you see the role of this technique within modern quality control and data analysis?

What makes NIR so special is that it is an accessible technique: a measurement takes little time, you do not need any chemicals and it is not expensive. At the same time, it can really help companies gain control over the quality of their products. For me as a Data Analyst, it is even more interesting because the technique also generates a lot of data, and there is always something new to discover in that.

You have experience with tools such as Power BI and data analysis. What do you find interesting about working with data, and how do you translate raw measurement data into useful insights for customers?

What I enjoy about working with data is that you can turn it into something that is genuinely useful to people. After all, every company has data, but the real value lies in what you do with it. The team I am part of is very actively involved in this, not only for our own department but also for the rest of the company. As a result, you work with many different types of data, which makes it even more enjoyable.

What makes the new NIR calibration model for oat hulls unique compared to existing analysis methods?

With NIR, you can obtain an extensive profile of the composition of your sample within just a few minutes, including the amount of crude fibre and moisture in the sample. By further developing our calibration model for oat by-products into a specific calibration model for oat hulls, we can make the predictions of these values even more accurate.

Which parameters can we reliably predict with this calibration model, and how do they compare to traditional laboratory analyses?

Crude fibre, moisture, crude protein, crude fat, starch (Ewers) and crude ash. These are parameters that take hours to days to determine using traditional wet chemical analysis. That type of analysis will often be more accurate, but NIR quickly provides a broad picture of your sample.

For which practical applications is this calibration model most relevant?

Oat hulls are used as a raw material for animal feed, as a source of fibre. With an NIR measurement, the quality of the oat hulls can be checked and it can be determined in what ratio the oat hulls should be mixed into the feed.

How quickly can customers obtain NIR results, and what does this mean for their process or decision-making?

You have your result within one minute of starting the measurement. This means you can use it immediately to determine how the product should be processed further, instead of first having to wait days for the analysis result.

What were the biggest challenges in developing this calibration model for oat hulls?

Oat hulls are a relatively new animal feed raw material and are therefore still used on a relatively small scale. Collecting enough samples to train our calibration model took a little longer as a result, but we have now succeeded. We will continue collecting samples to further train the calibration model and make it more robust.

How does this calibration model contribute to better control over raw materials and end products?

Because the measurement takes so little time, you can not only get started with the result quickly, but also measure frequently. In addition, you perform the measurement yourself and do not first have to send the samples to the laboratory. This allows you to continuously monitor the composition of raw materials and end products and quickly detect deviating trends.

To what extent can this calibration model be integrated into existing processes, for example for on-site or inline measurements?

Because you can use the calibration model on your own NIR device, you can perform the measurement on-site and process the result immediately. This could be a device in a fixed location in the factory, but the calibration model is also available for mobile NIR. For example, you can also take a measurement on the farmyard.

Looking beyond oat hulls, which other applications do you see where NIR can really make a difference?

We have calibration models for animal feed raw materials and animal feeds, but also for dairy and pet food. In all these sectors, NIR is a useful tool for quickly estimating the composition and quality of the relevant products on location.

Tom Huijzers R&D Analist

What I enjoy about working with data is that you can turn it into something that is genuinely useful to people.