Process NIR has a reputation problem. Not because the technology doesn't work but because for many manufacturers, an earlier experience left a lasting impression.
Instruments that drifted. Calibrations that demanded expertise most teams didn't have. High-changeover lines requiring manual bias adjustments multiple times a day. The technology was capable in principle, but the conditions for using it reliably weren't consistently in place.
That was then. This article examines what has changed across hardware, calibration, deployment, and data integration over the past decade – and what the evidence for those changes actually shows.
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Introduction
Ask a food manufacturer who trialed process NIR a decade ago what they thought of it, and the answer is often the same. The idea was sound. The execution was harder than expected. Calibrations drifted. Instruments struggled in industrial conditions. Getting consistent, reliable results meant having expertise most plant production sites didn’t have. Eventually, it got shelved.
That experience is more common than people tend to admit, and it has shaped the way a significant portion of the industry thinks about NIR today. The hesitation isn't irrational. It was earned.
But technology moves. What process NIR could deliver in 2015 and what it can deliver now are meaningfully different things – not because the underlying science changed, but because the practical barriers that caused those early frustrations have been systematically addressed. We look at where those barriers were, what has changed, and what the evidence for those changes actually shows.
The Source of the Reliability Problem
Reliability in process NIR, historically, had three weak points: the hardware, the calibration, and the expertise required to manage both.
On hardware, the issue was straightforward. Early NIR instruments were largely developed for laboratory environments and then deployed in food manufacturing settings they were never designed for. Lab conditions are controlled. Production floors are not. Vibration, temperature fluctuation, humidity, and dust all take a toll on instruments that weren’t built to withstand them. The result was measurement drift, inconsistent readings, and maintenance demands that added cost and eroded confidence in the data.
On calibration, the problem ran deeper than expertise alone. Building an accurate NIR calibration model requires reference data, chemometric software, and the knowledge to use both correctly – skills most food manufacturing operations didn't have in-house. But even where expertise was available, the models themselves were often constrained by a lack of reference data. A reliable food calibration typically requires thousands of samples to account for the natural variability in ingredients and formulations. Most manufacturers couldn't generate that volume independently, which meant early models were too narrow, held up poorly under real production conditions, and needed frequent updates to stay accurate.
On high-changeover lines, the problem compounded further. Older filter-based instruments often required a manual bias adjustment each time the product formulation changed. On a snack food line cycling through multiple formulations in a single shift, that meant repeated recalibration work throughout the day – a significant operational burden that eroded confidence in the technology and, in many cases, led teams to abandon it altogether.
These are the gaps the past decade has closed.
Hardware That Belongs on the Production Floor
The first and most visible change is in how modern process NIR instruments are built. The shift, well documented in research reviewing advances in NIR spectroscopy,[1] is from instruments adapted for industrial use to instruments designed for it from the outset. That distinction has real consequences. Diode array technology, for example, removes moving parts from the optical system entirely – which means vibration, a constant feature of food production environments, no longer compromises measurement stability. Temperature-stabilized spectrometers maintain consistent performance across the range of conditions found on a working production floor, rather than requiring the controlled environment of a laboratory.
Dust and moisture protection has also become a baseline expectation rather than an optional extra. IP65 certification, the international standard for full dust-tightness and water resistance, is now a standard feature of production-grade NIR instruments.
Perten’s DA 7250, DA 7350, and DA 7440 are built on this foundation. The DA 7250 is an at-line benchtop instrument designed for lab and production-adjacent use, delivering multi-component analysis in six seconds with minimal sample preparation. The DA 7350 brings in-line measurement directly into the process stream. The DA 7440 is an on-line analyzer suited to continuous measurement on conveyor belts or in product flows. All three share the same diode array platform, which means calibrations developed on one instrument transfer seamlessly to the others. You’re not starting over when you scale.
Calibration Without the Expertise Overhead
The calibration barrier has also shifted significantly, and this is arguably where the reliability improvement has been most felt.
Modern instruments come with factory calibrations built from global databases of hundreds of thousands of samples.[2] For the majority of standard food manufacturing applications – grain milling, dairy, snack foods, pet food, oilseed processing – a working calibration is available from day one. The need to build models from scratch, which was one of the most significant sources of implementation friction, has been largely removed for common use cases. Calibration transfer has also improved. Earlier deployments were often tied to a single instrument: replace it or add a second one, and the calibration work started again. Research has noted that improved transfer methodologies now allow models to be applied consistently across instruments,[3] which matters considerably for manufacturers with multiple sites or those planning to expand NIR coverage over time.
For applications that do require custom calibration development or ongoing maintenance, approaches like Perten’s unique Honigs Regression method have made the process more manageable for teams without specialist chemometrics expertise.
More Ways to Deploy, More Realistic Starting Points
Where manufacturers once faced a binary choice between the lab and full in-line integration, the options today are considerably more varied. The at-line approach in particular gives manufacturers real-time access to production data without requiring the full infrastructure investment of in-line integration. An instrument placed adjacent to the production line, analyzing in-process samples in seconds with no sample preparation and minimal clean-up, provides a meaningfully different quality picture than periodic lab testing – and it’s a considerably more accessible first step.
For manufacturers whose primary concern is getting reliable, fast data without committing to a large-scale installation, it’s worth understanding that this middle ground now exists and performs well.
What the Accuracy Evidence Actually Shows
Accuracy remains the question most plant managers return to, and it deserves a direct answer.
Modern NIR calibrations, built from large, representative sample sets and validated against reference methods, deliver analytical performance that is fit for routine food manufacturing quality control across a broad range of parameters: moisture, protein, fat, starch, fiber, ash, and more. In dairy process monitoring specifically, research using NIR for milk coagulation control found that models explained process variance at rates exceeding 99.9%, with standard deviations of residuals below 0.007 [4] – a level of precision that reflects how far calibration science has come.
It is also worth being clear about what accuracy in process NIR means in context. NIR is a secondary analytical technique: its outputs are predictions derived from calibration models, which are themselves built against reference method data. The accuracy of NIR cannot exceed the accuracy of the reference measurements it was calibrated against. That is not a limitation unique to NIR. It applies to any secondary analytical method. But understanding it is part of using the technology reliably rather than being disappointed by an unrealistic expectation . A good NIR calibration will generally have an accuracy of 1-1.5 times the standard deviation of the reference method.
What has changed over the past decade is the quality, breadth, and robustness of the underlying calibration data. The DA 7250’s factory calibrations are built from a global database encompassing hundreds of thousands of samples [2] – far beyond what any single manufacturer could build independently. The practical effect is that manufacturers are starting from a much stronger baseline than they were in earlier NIR deployments.
Advanced chemometric techniques, including the application of machine learning approaches in model development, have further improved predictive performance for complex and inhomogeneous samples.[5]
Data That Does Something
Where earlier installations generated data that rarely left the instrument, contemporary process NIR instruments are designed with data integration as a core function. Results can be configured to export automatically, feed into quality management systems, or be accessed remotely through web-based reporting platforms. A production manager can track key parameters over time without being at the instrument. A lab manager can monitor NIR performance against reference methods from a desk. A purchasing team can pull composition data on incoming raw materials without waiting for lab results – even from multiple production plants.
Looking further ahead, some manufacturers are beginning to move beyond traditional calibration models entirely. Digital twin technology – using AI to process full raw NIR spectra alongside other plant sensor data and process telemetry – can predict broader outcomes such as yield, quality scores, or profitability, rather than individual parameters in isolation. It is still emerging, but it points toward a direction of travel that makes the data integration question considerably more significant than it might first appear.
A More Considered Second Look
Process NIR implementation is not without real considerations. Calibration still needs to be appropriate for your specific products and process conditions. Deployment still requires planning. The relationship between NIR data and reference methods still needs to be understood by the people making decisions based on it.
But the barriers that defined the early adoption experience for many manufacturers – hardware unsuited to industrial conditions, calibrations that were too narrow and too fragile, deployment choices that forced a binary decision, and data that sat in isolation – have been substantially addressed. The technology that food manufacturers can evaluate today is not the same as the one that built a cautious reputation a decade ago. For those who wrote off an earlier experience, or who have been watching without acting, the reliability picture has changed enough to justify looking again with fresh information.
Explore Perten’s range of process NIR solutions here
References
[1] Porep, J.U., Kammerer, D.R., & Carle, R. (2015). On-line application of near infrared (NIR) spectroscopy in food production. Trends in Food Science & Technology, 46(2), 211–230. Cited: hardware advancement; deployment configurations.
[2] PerkinElmer / Perten Instruments. DA 7250 product documentation. perten.com Cited: calibration database; accuracy baseline.
[3] Drawell Analytical (2025). How Does NIR Spectroscopy Work For Rapid Food Quality Testing. drawellanalytical.com Cited: calibration transfer across instruments.
[4] Grassi, S. & Alamprese, C. (2018). Advances in NIR spectroscopy applied to process analytical technology in food industries. Current Opinion in Food Science, 22, 17–21. Cited: dairy coagulation accuracy data; process variance >99.9%.
[5] Beć, K.B., et al. (2022). Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives. Foods, 11(10), 1465. Cited: advanced calibration and machine learning improvements.
[Internal] PerkinElmer article: Why Honigs Regression Changes the Way We Think About NIR Calibration. Cited: calibration maintenance accessibility.