Introduction
For decades, food processors have had mixed feelings about lab testing equipment. On one hand, there’s excitement—data to monitor processes, assess raw materials, and ensure consistency. On the other, there’s hesitation—new tools and methods that feel outside core competencies. To help bridge that gap, instrument manufacturers developed fast, easy-to-use near-infrared spectroscopy (NIR) solutions built on proven analytical methods, but without the need for a chemistry degree or a full lab team to run them.
As the adoption of NIR grew, so too did the options available to food processors for how to implement this technology into their operations. In addition to the in-lab or at-line benchtop versions of the instruments, opportunities arose for food processors to supplement in-lab testing with real-time testing on the processing line. In this article, we will review how NIR technology can be utilized in food processing operations, as well as offer suggestions for food processors to consider when adopting NIR.
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NIR Spectroscopy – A High-Level Overview
NIR technology allows for rapid, non-destructive measurement of components like moisture, fat, protein, sugar, colour, and ash. Shining light through a sample, NIR creates spectra depicting how materials absorb or reflect light that is passed from the instrument to the sample. Each component has a distinct absorption pattern—based on its molecular structure—within the NIR range (roughly 950 to 1,650 nanometers). This pattern can be represented graphically, similar to the spectra shown in Figure 1.
Calibration Models
Once you have spectra, you can build a calibration model to identify and quantify components. A calibration model establishes mathematical relationships between previously mentioned analytical laboratory methods, or primary reference data, and the spectra.
To create a calibration model, reference data must be obtained, utilizing analytical methods such as solvent extraction for fat, protein by the Kjeldahl or Dumas methods, or oven drying and Karl Fisher titration methods to measure moisture. The number of samples you need for a strong model depends on the material, the variability you're expecting, and how you’ll use the model. As Dr. David Honigs (#MrNIRPerson on social media), puts it: “Start with as many samples as you can reasonably collect, keep them representative, and plan for updates as you learn more. Building a good calibration isn’t just a numbers game—it’s about understanding your data’s story and where it’s headed next.”
There are different calibration methods available—Partial Least Squares (PLS) for simple, linear datasets; Artificial Neural Networks (ANNs) for more complex patterns; and Honigs Regression (HR), Perten’s proprietary method for non-linear data. Regardless of the method, ongoing updates are key to keeping your calibration accurate and trustworthy.
Choosing the Right NIR for your Operations
Food processors have a number of NIR options available to meet their needs, including in-lab or at-line benchtop NIR analyzers, as well as process NIR analyzers that can be integrated directly into the processing line. The choice between benchtop and process NIR analyzers should involve the consideration of a number of factors, including:
- Are you using the data for spot checks, production control, or R&D?
- Do you have the lab space—and the staff—to support benchtop systems?
- How much calibration support or data interpretation will your team need?
There is, unfortunately, no “one-size-fits-all” solution for implementing NIR technologies into your operations. That being said, with the combination of technology, calibrations, and support, there is likely a solution to meet the needs of most food processing organizations. And, to future-proof your investment, ensure that the instrument provider you are working with has calibrations that can be utilized across both benchtop and process NIR platforms, so your data stays consistent and scalable across the whole operation.
Benchtop NIR Analyzers
Often utilized in a laboratory or near a key decision point of a process, benchtop NIR analyzers are quick and easy to use. Samples often require little to no preparation, enabling a fast analysis, in as little as 10 seconds. Although the workflow for each benchtop NIR analyzer can vary slightly, most involve pouring a solid or semi-solid sample into a reusable or disposable open-faced dish, leveling the sample off to create a relatively flat surface, and placing the dish in the appropriate location under the light source. Sample profiles (i.e. grain, ground meat, yoghurt, etc.) and associated calibrations should be pre-loaded onto the instrument, and chosen on the instrument’s touch screen. To achieve an accurate and representative result, the sample dish will often be rotated by the instrument during the analysis. An example workflow for a benchtop NIR analyzer is included in Figure 2.
As mentioned previously, benchtop NIR instruments can be utilized either in a lab, or in close proximity to the processing line. The robust nature of a benchtop NIR enables it to be utilized in the lab for R&D, formulation, and QA. Laboratories tend to be controlled environments, with laboratory staff trained in complex matrices and methods, helping to ensure consistency of measurements, and sample preparation, if required. Placement in a laboratory could, however, limit the ability of the instrument to aid in production control.
When decisions need to be made in real time, at-line placement makes a big difference. In this scenario, the NIR is placed at-line, and grab samples can be pulled from the line and analyzed immediately on the instrument. Samples will typically need to be analyzed as-is with no preparation to achieve the optimal benefit of placement at-line. For at-line use, consider disposable dishes for quick cleanup, IP ratings for dust or moisture, and hygienic design features for food safety compliance. Regardless of where the benchtop NIR is placed in an operation, it does still require some, albeit limited, manual intervention to achieve results. For users seeking a more automated approach to NIR analyses, NIR sensors installed directly in or on the processing line are a good option, with the automation potentially stretching beyond the analysis itself.
Process NIR
Process NIR utilizes the same NIR technology as benchtop versions, but installation in-line on a pipe or chute, or on-line over a moving belt, enables real-time data collection from analyses of in-process products. The benefits of implementing process NIR technology can have a near immediate impact on a food processing operation in one or more of the following ways:
- Better Process Control Collection of real time data allows for real time changes to processing conditions, such as drying times and blending, to achieve true precision throughout the food production process. Improved control can also have a direct impact on the bottom line, with reduced energy expenditures, and optimal use and blending of raw materials.
- Increased Yield In highly competitive markets, food processors must do more with the same or fewer inputs. With the data generated from process NIR sensors, safety margins can be reduced, leading to less waste of key components, such as protein and fat, and the production of more finished product with the same raw materials.
- Maximized Margins and Profits Reduced indirect costs tied to labor and energy savings, coupled with less waste and tighter control specifications, ensure that your margins and profits are maximized, and that you begin to see a return on your investment quickly.
Placement of process NIR sensors will typically depend upon the type of material being measured, and the physical production environment. For products such as grains, granules, powders, pellets, pastes, liquids and slurries that are flowing through a pipe or chute, an NIR sensor installed directly in the pipe or chute is ideal.
Examples of Perten’s DA 7350 in-line sensors are shown in Figure 3. In addition to NIR measurements of moisture, protein, fat, oil, and ash, the DA 7350 also includes an integrated camera for colour measurements, speck detection, and real-time images of product and materials during manufacturing to enable visual monitoring of flowing materials. This product view offers operators a unique insight into products from the control room, providing a better understanding of the process and whether there are any obvious nonconformities or outliers, such as foreign objects or materials, broken kernels, granulation/particle size, impurities, or flows/blockages.
In production environments that utilize moving conveyor belts, on-line NIR sensors can be installed directly over the belt, as shown in Figure 4, to perform analyses of product flowing below. The sensor can automatically detect whether product is present below, allowing it to easily handle process changes, including varying product height, colour, and temperature, without any undue effort or effect on measurement results.
Regardless of the type of NIR instrument utilized in food processing applications, whether benchtop or process, it is clear to see that there are considerable benefits to implementing this technology in your operations.
Learn more about NIR offerings from Perten, visit us at www.perten.com