What It Really Takes to Build and Validate a Reliable IR/NIR Model
Infrared spectroscopy—both NIR (Near Infrared) and MidIR (Mid Infrared)—is a powerful analytical tool for rapid, nondestructive measurement. But despite its advantages, IR often develops a reputation for being unreliable or not accurate as primary methods. The issue isn’t the technology itself. The challenge lies in the calibration and validation process, which requires a deep understanding of chemometrics, sample variability, application environment and instrument behavior.
This article outlines the technical considerations behind building and maintaining a high-performing IR/NIR model, and why expertise, not instrumentation, is the determining factor in long-term success.
Why IR/NIR Calibrations Are Inherently Challenging
IR models are multivariate by nature. They rely on subtle spectral features that correlate with chemical or physical properties. Several factors make calibration complex:
- Spectral overlaps are common, especially in NIR, where broad overtone bands require careful preprocessing.
- Matrix effects can obscure or shift spectral features.
- Sample heterogeneity introduces variability that must be captured in the calibration set.
- Instrument to instrument differences can affect transferability.
These challenges mean that IR models cannot be treated as an out of the box functionality. They require thoughtful design and continuous evaluation.
Just like AI - Calibration Models Require Training
Modern discussions around AI provide a useful analogy: a model only performs well when it has been trained on representative, high-quality data.
IR/NIR calibrations work the same way:
- They must be trained on sufficiently diverse samples.
- They must include expected sources of variability (raw materials, suppliers, seasonal changes).
- They must be updated when the underlying chemistry or supply chain evolves.
A good example of this challenge is seen in agricultural materials such as canola. Canola seeds produced today are not chemically identical to those produced a decade ago, even though they carry the same name. Breeding programs, shifts in growing regions, climate variability, fertilizer practices, and processing methods all influence the underlying chemical composition. These changes alter the relative concentrations of proteins, oils, moisture, and minor constituents—each of which affects the IR spectrum.
Because IR and NIR models rely on subtle spectral–property relationships, even small shifts in composition can move new samples outside the original calibration space. When this happens, the model begins to show bias, increased error, or unexpected behavior. This is what we refer to as model drift.
If the calibration does not incorporate updated samples that reflect the current chemistry, the model will continue to diverge from the reference method. This is not a failure of the instrument; it is a natural consequence of evolving raw materials. Maintaining accuracy requires periodically adding new data, reassessing the calibration domain, and confirming that the model still represents the population it is intended to predict.
Validation: The Step That Determines Whether a Model Will Succeed
Validation is often misunderstood as a simple accuracy check. It is a structured process that answers a fundamental question:
Does this rapid technique produce results that are fit for purpose and maintain the performance in accuracy and precision?
Effective validation requires:
- Selecting representative samples, not just many samples.
- Understanding why certain samples fall outside the calibration space.
- Evaluating bias, precision, and robustness across expected ranges.
- Comparing results to reference wet chemistry to confirm performance.
A model may appear inaccurate when the real issue is that the sample lies outside the calibration domain, bad chemistry data, or sample matrix change. Recognizing this distinction requires experience.
Maintaining Accuracy Over Time
Even a well-built model is not static. IR/NIR systems require ongoing monitoring because:
- Raw materials change
- Suppliers shift
- New product variants are introduced
- Environmental conditions affect spectra
Longterm performance depends on:
- Routine checks against reference methods
- Monitoring for drift
- Adding new calibration data when needed
- Periodic recalibration or refinement
A model should be a reliable workhorse, but only if it is maintained with the same rigor used to build it.
Why Expertise Matters More Than the Instrument
Instrument selection is important, but it is not the limiting factor. The critical expertise lies in:
- Understanding spectral behavior
- Choosing appropriate preprocessing techniques
- Designing calibration sets that capture real-world variability
- Applying chemometric methods correctly
- Diagnosing unexpected results
- Knowing when and how to update a model
For example:
- A soybean model must consider geographic and seasonal variability.
- A marine oil model must include algal oils if they appear in the supply chain.
- A biodiesel model may require continuous updates as feedstocks evolve.
These decisions cannot be automated or solved by software alone or so-called universal calibrations. They require domain knowledge, chemometric skills, and experience with real-world samples.
Addressing Unique Products and Complex Matrices
Some products simply do not fit standard calibration templates. Specialty ingredients, evolving supply chains, and novel formulations require:
- Customized calibration strategies
- Iterative refinement
- Alternative sample preparation approaches
- Targeted data collection to fill gaps
This is where technical expertise becomes essential. The ability to diagnose issues, adjust methods, and refine models is what ensures long-term reliability.
Conclusion
Building a robust IR/NIR model is not about the instrument or the software. It is about understanding the chemistry, variability, spectral behavior, and statistical methods required to translate spectra into meaningful results.
A successful model requires:
- Thoughtful calibration design
- Rigorous validation
- Ongoing monitoring
- Deep chemometric expertise
When these elements come together, IR/NIR becomes an exceptionally powerful analytical tool that is fast, reliable, and capable of supporting complex workflows across industries.
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