Grab sampling has been the industry standard for decades. But familiar doesn't always mean optimal.
In food manufacturing, the difference between periodic snapshots and continuous process monitoring isn't just a question of technology, but a financial one too. Hidden labor costs, decisions made on non-representative data, and hours lost waiting for lab results add up to significant, largely invisible operational drag.
This article examines five common myths that keep manufacturers attached to lab-based testing, presenting research and ROI calculations that challenge each one.
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Introduction: The Grab Sampling Limitation
Walk into most manufacturing facilities today, and you'll find a familiar scene: operators interrupting workflows at irregular intervals throughout their shift to collect samples for testing. The specific timing varies by process. Some operations sample every few hours, others test at the beginning, middle, and end of production runs, still others follow per-batch schedules. But the fundamental approach remains the same.
Each sample represents a single moment in time, a snapshot of a process that keeps running. Between samples, you're operating on assumptions about what's happening in your process rather than actual knowledge.
This grab sampling approach has been the industry standard for decades. It's familiar, validated, and what auditors expect to see. But here's the reality: it's also introducing variability, delaying critical decisions, and costing more than most manufacturers realize. In grain milling, for example, grab sampling at hourly intervals means tons of wheat may be processed before a moisture deviation is detected, leaving operators blind to gradual drift that erodes yield, consistency, and profitability long before corrective action is taken. Even with rapid testing methods available, you're still limited to periodic checks rather than continuous visibility.
Process NIR (Near-Infrared) spectroscopy offers a better, more efficient path forward. Instead of periodic snapshots using in-lab analytical and wet chemistry testing methods, you get real-time monitoring. Instead of pausing workflows to collect samples, measurements happen automatically within the process stream. Instead of samples representing tiny fractions of production, you get continuous data across your entire run.
Yet many manufacturers hesitate, held back by five common myths that don't stand up to scrutiny. Let's examine what the research actually tells us about online process monitoring versus traditional lab-based testing, demonstrating with hypothetical ROI calculations.
Myth #1: “Process NIR Has Poor Return on Investment”
The upfront investment in Process NIR equipment can trigger hesitation to implement, but focusing solely on capital costs misses the complete financial picture. Your current grab sampling program utilizing primary techniques such as HPLC, Soxhlet extraction, Kjeldahl methods or even benchtop NIR already carries significant costs: direct labor for sampling personnel and lab technicians, materials including sample containers and lab supplies, and ongoing equipment maintenance and calibration. These expenses are distributed across operational budgets, making them less visible than a single capital purchase.
Research demonstrates that facilities implementing inline NIR monitoring achieve measurable improvements in process efficiency through reduced labor, minimized waste, and earlier deviation detection. Process analytical technology enables real-time adjustments that prevent quality issues from compounding, reducing the need for costly laboratory testing and eliminating delays inherent in traditional sampling approaches. Every batch held for lab clearance ties up working capital. Every quality issue caught hours after it started results in rework or disposal. Every process deviation that goes undetected for an entire shift compounds into larger problems.
In dairy processing, studies show that continuous NIR monitoring provides more reliable data on key quality attributes like fat and protein content compared to periodic grab sampling. Research in cheese production documented how frequency-domain analysis of continuous NIR measurements revealed unknown sources of variation in the manufacturing process, enabling improvements impossible to identify through periodic sampling. This foundation of continuous data enables process optimization and cost reduction that intermittent testing cannot deliver.
The question shouldn't be whether you can afford Process NIR. It should be whether you can continue accepting the ongoing costs and missed optimization opportunities of an approach that samples less than 0.1% of your production.
Myth #2: “Laboratory Testing Is More Accurate Than Online Measurements”
Your lab likely has excellent equipment and skilled technicians. No one is questioning the quality of your analytical work. The question is whether the samples you're testing actually represent what's happening in your process. But here's the uncomfortable truth: sampling error typically overshadows measurement error. Research confirms that heterogeneity is the largest source of errors in sampling and sample preparation. When you pull a few hundred grams from batches weighing tons, you're making enormous assumptions about the entire lot based on a tiny fraction of material.
Studies across multiple industries, including this study utilizing the DA 7440 in cheese manufacturing, demonstrate this limitation clearly. In food manufacturing, continuous NIR monitoring revealed process variation patterns that grab sampling simply could not detect because it measured too few points in time. In one documented cheese production case, frequency-domain analysis of continuous NIR data uncovered unknown sources of variation, enabling process improvements impossible to identify through periodic sampling. Research in grain milling confirms that NIR spectroscopy can accurately monitor moisture, protein, and other critical parameters while providing statistically robust data based on thousands of measurements rather than three or five grab samples.
Process NIR significantly sidesteps errors associated with manual sampling by measuring continuously throughout production, automatically analyzing a far larger proportion of your lot in real time. The real comparison isn't lab precision versus NIR precision. It's the reliability of periodic measurements from questionable samples versus continuous measurements of your actual process stream.
Myth #3: “We lack the internal expertise to implement and maintain Process NIR technology”
The concern about skills gaps is valid. Developing robust chemometric calibration models requires specialized knowledge in multivariate statistics and spectroscopy that most manufacturing teams don't possess in-house. But this reality doesn't preclude successful implementation. Strong suppliers such as Perten provide method development assistance, calibration using your actual materials, validation against your reference methods, and ongoing technical support. Once models are established, they require periodic verification and occasional recalibration, not constant chemometric expertise.
NIR spectroscopy relies on indirect measurement, requiring calibration against reference methods using chemometric techniques to extract analytical information from complex spectral data. However, once these calibration models are validated, daily operation does not require the same level of specialized knowledge. Operators interact with simple interfaces showing the measurements that matter – moisture content, protein levels, fat content, or whatever parameters you're monitoring.
Consider your current situation. You're already investing in expertise to maintain a compliant QC program. You have properly trained sampling personnel, careful sample handling protocols, lab technicians, and validated methods. Process NIR redirects some of that investment from reactive testing to proactive monitoring. The learning curve exists, certainly, but it's comparable to implementing any new quality control protocol.
Myth #4: “Our Manufacturing Process Is Too Unique for NIR Spectroscopy”
Every manufacturer believes their process has unique challenges. Different raw materials, specific formulations, particular operating conditions. This is true and is one of the strengths of process NIR – it’s readily adaptable to your needs and environment. Calibration models are developed specifically for your inputs, your process, and your critical quality attributes. During method development, you create calibrations using your actual products under your actual conditions.
The breadth of successful applications proves the point. Process NIR monitors moisture and protein in grain milling, measures fat and protein in dairy processing, analyzes composition in snack food production, and tracks blend uniformity in ingredient manufacturing. None of these applications use identical approaches because each is tailored to specific needs. High moisture content? Specific wavelength regions and path lengths address it. Pigmented materials? Reflectance measurements and spectral preprocessing handle it. Temperature variability? Compensated models account for it.
Perten addresses this adaptability challenge through our proprietary Honigs’ Regression, a unique calibration method that learns from your specific materials without requiring constant recalibration. Unlike traditional one-size-fits-all methods that struggle with process variations, Honigs’ Regression builds a library of your actual products and adapts as you update the library with new examples of samples or products. A single calibration can handle diverse materials from whole wheat to ground wheat, barley, flour, and bakery mixes, improving its accuracy as you add more examples over time.
Your specialized products aren't a barrier to Process NIR. In fact, they're exactly what the technology is designed to handle. The documented diversity of successful implementations isn't evidence that NIR works everywhere without modification. It's evidence that technical challenges can be addressed through proper method development.
Myth #5: “Process NIR Implementation Disrupts Production”
It’s no myth that change can be disruptive. But the assumption that Process NIR implementation requires lengthy process line shutdowns doesn’t match reality. minimal modification to existing equipment. Many installations happen during scheduled maintenance windows without impacting production schedules. Parallel operation during validation means your existing quality control remains in place while the new system is proven out.
Now consider the ongoing disruptions from grab sampling. Multiple times per shift, someone stops what they're doing to collect samples. Process optimization decisions can wait hours for lab results. As noted previously, no matter how efficient your grab sampling process is, you are still lacking representative results on the entire batch, only a snapshot in time. When issues are discovered, unexpected interventions create real disruptions as you scramble to salvage batches or adjust processes that have drifted significantly from target.
Research across manufacturing environments, from industrial operations to continuous food production, shows successful installations without extended production shutdowns. All required careful planning, proper engineering, and systematic validation, but these are standard requirements for any quality-critical system. The disruption calculus isn't new system installation versus zero disruption. It's planned, one-time implementation effort versus the ongoing operational interruptions that are already part of your daily reality.
The choice is not:
Process NIR with disruption vs grab sampling without disruption
It is:
Planned, one-time implementation vs continuous, invisible disruption every day
Process NIR does not introduce disruption. It replaces chronic disruption with structured control.
Moving From Lab-Based to Process-Based Quality Control
These five myths share a common thread: they focus on perceived risks of new technology while accepting the very real limitations of current approaches. Grab sampling made sense when it was the only practical option. But accepting periodic snapshots as representative, building in delays for decisions, and tolerating sampling variability no longer makes sense when proven alternatives exist.
The manufacturers who have made the transition report the same realization: they wish they'd done it sooner. The benefits extend beyond eliminating sample collection. Better process understanding through continuous data, tighter quality control with real-time monitoring, reduced waste from early deviation detection, and confidence from complete process visibility add up to operational improvements that justify the investment.
Process NIR isn't about replacing all lab testing. In-house quality control labs remain essential for regulatory testing, method validation, and investigating anomalies. Rather, it's about making better, faster decisions during production by seeing what's actually happening rather than inferring it from limited samples.
References and citations:
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.
Grassi, S., Strani, L., Casiraghi, E., & Alamprese, C. (2020). Electric Drive Supervisor for Milling Process 4.0 Automation. Sensors, 20(4), 1147.
Gy, P.M. (1995). Introduction to the Theory of Sampling. Part 1: Heterogeneity of a Population of Uncorrelated Units. Chemometrics and Intelligent Laboratory Systems, 14, 67–76.
Karoui, R., Hammami, M., Kemps, B., & De Baerdemaeker, J. (2019). Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing. International Dairy Journal, 103, 104623.
Lyndgaard, C.B., Rasmussen, M.A., Aru, V., Hansen, P.W., & Engelsen, S.B. (2023). In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation – A Case Study from Commercial Cheese Production. Foods, 12(5), 1026.
PerkinElmer. (2020). 5 NIR Spectroscopy Pre-Calibration Techniques for Food Analysis. Retrieved from https://blog.perkinelmer.com/posts/5-nir-spectroscopy-pre-calibration-techniques-for-food-analysis/
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.
Zareef, M., Chen, Q., Hassan, M.M., Arslan, M., Hashim, M.M., Ahmad, W., Kutsanedzie, F.Y.H., & Agyekum, A.A. (2022). Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review. Comprehensive Reviews in Food Science and Food Safety, 21(4), 3820-3858.