The Process NIR Readiness Checklist

1: The Business Case

The question to answer: Is the problem you're solving specific enough to measure against?

Process NIR performs best when it's anchored to a well-defined quality challenge – a parameter that matters, a point in the process where measurement changes a decision, a gap in your current data with a real cost attached. Vague objectives produce vague outcomes.

Questions to ask:

✔ Which quality parameters do you currently monitor – moisture, protein, fat, starch – and which of those drive the most significant quality or yield decisions on your line?

✔ Where does your current QC approach create lag? Are there moments in production where a result returns after the window for intervention has closed?

✔ Have you quantified what that lag costs? Out-of-spec batch rates, rework volume, and over-formulation are all measurable, and they form the baseline against which process NIR performance will eventually be judged.

✔ If process NIR delivered exactly what you hoped for, what would be different about your operation six months from now?

2. The Data Foundation

The question to answer: Does the lab data you already have reflect the process you're actually running?

Process NIR calibration is built on reference data, and the existing lab measurements that teach an instrument what to look for. The quality and coverage of that data has a direct bearing on how quickly a calibration can be developed and how well it will perform across real production conditions.

This is where manufacturers most often discover surprises. Lab data that looks comprehensive can have significant gaps: it may not cover the full range of raw material variability, seasonal shifts in ingredient composition, or the extremes of normal production variation.

 Questions to ask:

✔ How consistent is your current lab methodology? If the same sample were measured twice, under the same conditions, would the results agree closely enough to be useful as reference data?

✔ Does your historical data cover the real variation your process sees, such as different suppliers, seasonal ingredient changes, and formulation adjustments?

✔ How much reference data do you have, and across what time period? A broader, more representative dataset produces a more robust calibration.

✔ Are there production scenarios – new raw material sources, peak season formulations, atypical batches – that your current dataset doesn't represent?

3. The Process Picture

The question to answer: Is your production environment stable enough for NIR to anchor to?

Near-infrared measurement works by detecting variation in a sample against a known reference point. For it to do that reliably, the process itself needs to operate within a consistent enough range for the calibration to remain valid. This doesn't mean your process needs to be identical every day, but it does mean the variation needs to be understood and accounted for.

Questions to ask:

✔ Do you have documented control limits for the parameters you want to measure? If the process drifts beyond those limits routinely, NIR will flag variation it was never calibrated to handle.

✔ How much does your raw material composition vary between suppliers, seasons, or crop years? High variability is manageable, but it needs to be represented in the calibration.

✔ Are there recurring process conditions – cleaning cycles, startup periods, or temperature fluctuations – that affect product composition and would need to be accounted for in the calibration model?

✔ If your process changed significantly – a new supplier, reformulation, new product line – do you have a process for updating your calibration to reflect that?

4. The Deployment Fit

The question to answer: Is your facility physically ready for the instrument type you're considering?

The three main process NIR deployment configurations – at-line, in-line, and on-line – have different physical requirements. Perten's DA 7250, DA 7350, and DA 7440 are purpose-built for each one of these environments, but understanding which configuration fits your measurement objectives, and what it will require from your facility, avoids late-stage surprises in the planning process.

Questions to ask:

✔ Where in your production line would measurement add the most value? Is that a point where a technician can present a sample (at-line), where an in-line probe could sit directly in the flow (in-line), or where a conveyor or chute-based system is more appropriate (on-line)?

✔ What are the environmental conditions at your intended measurement point in terms of temperature range, dust levels, moisture exposure, and vibration? Robust NIR instruments such as Perten’s are designed for extreme manufacturing environments, but specific conditions still affect installation planning.

✔ Are there electrical supply, communication cabling, or physical space constraints at the measurement point that would affect installation?

✔ If integration with your existing line controls or data systems is expected, has that requirement been scoped with your engineering or IT teams?

5. The Team Setup

The question to answer: Do you have the internal conditions for a sustainable deployment?

Technology rarely fails on its own terms. Where process NIR implementations quietly lose momentum, the cause is usually organisational – unclear ownership, misaligned expectations between teams, or a production environment that wasn't prepared for what parallel testing actually involves.

Questions to ask:

✔ Is there a named instrument owner? Someone with clear accountability for day-to-day operation, calibration maintenance, and performance monitoring? Ownership that's assumed rather than assigned tends to surface at the worst possible moment.

✔ Are your lab and production teams aligned on their respective roles? The lab team's involvement in calibration validation and ongoing reference measurement is central to how process NIR maintains accuracy over time.

✔ Have you had a conversation with your production operators about what will change in their workflow during parallel testing? Buy-in at that level matters more than most pre-implementation planning accounts for.

✔ Is there internal technical capability or an external support relationship to handle calibration queries, instrument maintenance, and routine performance checks?

6. The Success Criteria

The question to answer: Do you know what good looks like at 12 months, and have the right people agreed on it?

This is the stage most manufacturers skip, and the one that causes the most frustration later. Not because implementations fail to deliver, but because success was never defined in terms specific enough to measure. Process NIR generates data and that data needs to be compared against something meaningful.

Questions to ask:

✔ What KPIs would tell you clearly whether the deployment has delivered? Reduction in out-of-spec incidents, decrease in rework volume, reduction in lab turnaround dependency, improvement in yield consistency – any of these can serve as measurable targets if the baseline is established in advance.

✔ Have those KPIs been agreed with the stakeholders who will be reviewing performance? Different stakeholders need different things from the data, and knowing that in advance shapes how you report on it.

✔ Do the stakeholders who approved the investment understand what the parallel testing period involves and that calibration refinement in the first few months is expected, not a sign that something is wrong?

✔ Is there a defined review point – 90 days, six months, 12 months – at which performance will be formally assessed against the agreed KPIs? 

What Comes Next

If you've worked through these six sections honestly, you now have a clear picture of where you stand and, more usefully, where to focus before implementation begins. That's useful regardless of where you are in the decision process.

The manufacturers who navigate process NIR implementation well tend to share one characteristic: they treated the preparation as seriously as the technology itself. The instrument is the straightforward part. The groundwork is where the outcomes are determined.

 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 and Technology, 46(2), 211–230.
2. 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.
3. Workman, J. & Weyer, L. (2012). Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy (2nd ed.). CRC Press.