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Category: Process Analytical Technology (PAT) | PAT | Measurement Technology | Process Control
Author: Dusko Kadijevic, Publisher Analytic Journal
Last updated: May 2026
Reading time: approx. 10 minutes
Target audience: Process engineers, instrumentation engineers, PAT specialists, quality managers in the process industries
PAT (Process Analytical Technology) is the systematic application of measurement, analytical and control technology to understand, monitor and purposefully control industrial processes in real time or near real time. The goal is not end-of-line quality testing, but quality built into the process itself — based on valid measurement data and verified process understanding.
PAT is most often introduced in the context of regulatory requirements — particularly in the pharmaceutical industry. That framing is too narrow. In industrial practice, PAT is primarily a tool for economic optimisation and better control of complex processes.
The benefits appear simultaneously in multiple places: processes run more stably, deviations are detected earlier, release decisions can be accelerated, and costly rework is avoided. Those who treat PAT solely as a documentation or audit topic miss the actual lever — actively improving value creation.
Regulatory note (US/UK): In the United States, PAT has been an explicit FDA policy since the 2004 Guidance for Industry. In the UK, the MHRA has adopted a broadly aligned position post-Brexit, referencing ICH Q8/Q10/Q11 within its own framework. Both agencies encourage the use of PAT as part of a Quality by Design (QbD) approach — and neither limits its applicability to pharmaceuticals alone.
PAT rests on three core elements that must work together: measure, understand, control.
First, suitable process data must be obtained — concentrations, moisture, temperature, particle size, chemical sum parameters or other relevant process variables. The priority is not to measure as much as possible, but to capture the variables that genuinely describe the process.
Measurement values alone do not create process improvement. Only by linking data with process knowledge, model understanding and operational experience does a reliable picture emerge of why a process behaves as it does.
The actual value of PAT is realised when measurement data feed into active process decisions — either as manual adjustments or automatic control loops. The better this feedback pathway functions, the more robust and reproducible production becomes.
One of the most underappreciated aspects of PAT is the timing of its introduction. Those who introduce PAT only at the production stage are solving a monitoring problem. Those who deploy PAT during process development gain process understanding — which they can later use to justify simpler, more robust and more cost-effective measurement technology.
The principle works as follows: during development, high-capability analytical techniques are used — typically NIR or Raman spectroscopy, gas chromatography or mass spectrometry. These techniques provide deep insight into the process chemistry, identify critical process variables, and reveal which measurements genuinely describe the process state.
This understanding enables a deliberate shift to simpler measurement principles in production: a process characterised by NIR spectroscopy during development may be reliably controlled in production by photometry, conductivity or refractive index — because it is known which variable adequately describes the process and where the limits of that simplified measurement lie.
| Phase | Typical Instrumentation | Objective |
|---|---|---|
| Process development / scale-up | NIR, Raman, GC, MS | Build process understanding, identify critical variables |
| Pilot plant / transition | Combination of development and production instruments | Validate correlations, calibrate simplified measurement |
| Production | Photometry, conductivity, refractive index, moisture | Robust, low-maintenance control based on verified process knowledge |
The economic benefit is significant: simpler sensors have lower capital cost, reduced maintenance requirements, higher availability and greater robustness in harsh process environments. Total Cost of Ownership (TCO) falls substantially — without compromising process safety, because the necessary process understanding has already been established.
This approach requires treating PAT not as a purely production-stage activity, but as a continuous strategy from the first laboratory experiment through to full-scale operation.
The same logic applies to retrofitting existing plants. When a capable analytical technique — such as a process spectrometer — is deployed in a running production environment to characterise an established process, this creates the basis for evaluating whether a simpler measurement technology can achieve the same control effect. The shift from spectrometer to photometer is not a step backwards — it is the result of acquired process understanding. For existing plants, this means lower operating costs, reduced maintenance burden and higher plant availability at equivalent process safety.
The PAT business case can be summarised around four levers:
| Lever | Mechanism | Practical Example |
|---|---|---|
| Lower energy consumption | Process is controlled more tightly and corrected earlier | In continuous drying processes, tighter moisture control can reduce energy consumption by 10–20% by avoiding over-drying |
| Lower raw material consumption | Deviations become visible early — not only at batch end | Reaction endpoints determined analytically rather than by time: reagent excess typically reducible by 5–15% |
| Less waste and rework | Quality issues identified in-process | Batch failures in pharmaceutical granulation can be detected early via inline moisture measurement — before the entire batch is compromised |
| Faster processes | Real-time measurement reduces waiting times and safety margins | Drying times with 20–30% safety margins can be reduced to actual residual moisture endpoints — time savings depend on product and process |
Values are indicative figures from industrial practice, not guaranteed outcomes. Achievable improvements depend on the specific process, product and existing operating conditions.
In processes with high energy or raw material costs, even a moderate improvement in process control can be economically significant.
Not every measurement needs to be highly complex. The best solution is not the most sophisticated one, but the one that most reliably answers the relevant process question. PAT in practice often follows a KISS logic: as simple as possible, but as complex as necessary.
| Measurement approach | Typical benefit | When appropriate |
|---|---|---|
| Spectroscopy (NIR, Raman, MIR) | High specificity, multicomponent information, deep process understanding | When the process needs to be characterised in detail initially |
| Gas chromatography (GC), mass spectrometry (MS) | Component-specific identification and quantification | When individual compounds in complex gas mixtures need to be distinguished |
| Photometry, simple gas measurement | Robust online or in-situ monitoring of a single target variable | When a clearly defined process variable needs to be measured reliably and economically |
| Conductivity, pH, refractive index | Simple, stable process indicators | When process state can be adequately described by a single robust variable |
| TOC, moisture, sum parameters | Release testing, quality control, process monitoring | When an established sum parameter sufficiently describes the process |
Selection without claim to completeness. Analytic Journal evaluates measurement principles editorially independently.
The terms inline, online and at-line describe the degree of measurement integration into the process — a central element of any PAT strategy.
| Strategy | Description | Typical application |
|---|---|---|
| Inline | Direct measurement in the process stream, without sample extraction | Fast control loops, reactive processes, continuous monitoring |
| Online | Continuous measurement on an extracted and conditioned sample | Complex analysers, sensitive matrices, samples requiring conditioning |
| At-line | Discrete sample extraction, measurement close to the process | Quality control, release decisions, less time-critical variables |
| Off-line | Sample extraction and measurement in the laboratory | Reference analysis, method development, calibration |
The appropriate strategy depends on process dynamics, the measurement variable, the sample matrix and economic requirements. For fast control loops, inline is often the first choice; for complex or sensitive analysers, online is frequently more appropriate.
PAT is a central prerequisite for Continuous Manufacturing (CM) — particularly in pharma and chemicals. Continuous processes can only be operated safely when critical process variables are available in real time and feed directly into the control system. Without a robust PAT infrastructure, CM is not a stable operating state but an uncontrolled continuous run.
The difference from batch processing is fundamental: in a batch, release is performed at the end of each lot. In continuous operation, there is no lot — quality must be generated and demonstrated permanently within the process. This fundamentally shifts the demands on PAT systems: robustness, model stability over long operating periods and fast control loops become prerequisites, not options.
Regulatorily, the FDA recognised this connection early: the PAT Guidance (2004) and subsequent CM-specific documents explicitly address the need for Real-Time Release Testing (RTRT) strategies — which cannot be implemented without PAT.
US/UK context: In the US, FDA has published specific guidance on Continuous Manufacturing for pharmaceutical solid dosage forms (2019). In the UK, the MHRA issued its own Continuous Manufacturing guidance in 2021, acknowledging CM as an established approach and setting expectations for PAT-supported RTRT. Both frameworks treat PAT as structurally integral to CM — not optional.
The specific requirements for PAT systems in Continuous Manufacturing — control loop design, model validation under sustained operation, RTRT concepts — will be covered in a separate article: "PAT in Continuous Manufacturing" (forthcoming).
PAT is present in virtually every industrial sector with measurement-critical process steps. The common denominator: process states should be visible during operation, not only in the laboratory afterwards.
| Sector | Typical PAT applications |
|---|---|
| Pharmaceutical manufacturing | Drying, granulation, blending, solvent monitoring, Real-Time Release Testing (RTRT) |
| Chemical industry | Reaction monitoring, endpoint determination, stream composition control, synthesis gas analytics |
| Biotechnology | Online monitoring of fermentations, O₂/CO₂ control, substrate monitoring |
| Food and beverage | Moisture, Brix, fermentation processes, product consistency, oil quality, authenticity testing |
| Dairy industry | Real-time fat, protein and lactose content, pasteurisation control, CIP monitoring |
| Meat processing and livestock | Meat quality parameters (fat, moisture, protein), feed analytics, hygiene monitoring |
| Agriculture and crop production | Soil analytics, irrigation control, nutrient monitoring, crop quality, drying processes |
| Gas and energy processes | Fuel gas quality, emissions monitoring, inerting, dew point control |
| Semiconductor and electronics | Ultrapure water control, process gas purity, etch process monitoring, cleanroom particle control |
| Mining and raw material processing | Ore grade determination, flotation monitoring, beneficiation processes, tailings analytics |
| Environmental technology / water | Wastewater treatment, water surveillance, emissions monitoring, regulatory compliance |
| Waste management and recycling | Sorting processes (NIR-based), recyclate quality control, landfill gas monitoring |
| Research and development (cross-sector) | Process characterisation, scale-up support, method development, feasibility studies |
| Standard / Guidance | Issuing body | Content (paraphrased) | PAT relevance |
|---|---|---|---|
| FDA Guidance for Industry: PAT (2004) | U.S. FDA | Framework for PAT in pharmaceutical manufacturing; defines Critical Quality Attributes (CQA) and Critical Process Parameters (CPP) | Foundational regulatory document for pharmaceutical PAT |
| ICH Q8 (Pharmaceutical Development) | ICH | Describes Quality by Design (QbD) as the basis for systematic product development | Directly linked to PAT concepts in pharmaceutical development |
| ICH Q10 (Pharmaceutical Quality System) | ICH | Framework for a pharmaceutical quality system across the product lifecycle | Provides the management context for PAT-supported process monitoring |
| EN ISO 15839 | ISO/CEN | Requirements for online water measurement instruments (sensors and analysers) | Relevant for PAT in water treatment and environmental monitoring |
| IEC 62628 | IEC | Guidance on integrating process analysis systems into automation environments | Covers PAT integration into process control systems |
| Standard / Organisation | Scope | Relevance |
|---|---|---|
| FDA Guidance: Continuous Manufacturing (2019) | U.S. FDA | Specific expectations for CM in solid oral dosage forms, explicitly requiring PAT-supported RTRT |
| USP ‹1058› Analytical Instrument Qualification | USP | Qualification framework for analytical instruments used in US pharmaceutical manufacturing |
| EPA Methods (40 CFR Part 60/63/75) | U.S. EPA | Federal requirements for Continuous Emission Monitoring Systems (CEMS) in industrial facilities |
| ASTM International (various) | ASTM | Numerous test methods relevant to PAT, including ASTM E1655 (NIR calibration), E2412 (used oil monitoring by IR) |
| ISA standards (S5.1, S88, S95) | ISA | Instrumentation, batch control and enterprise integration standards widely adopted in North American process industry |
| Standard / Organisation | Scope | Relevance |
|---|---|---|
| MHRA Guidance: Continuous Manufacturing (2021) | MHRA | UK-specific framework for CM in pharmaceutical manufacturing; aligns with ICH but post-Brexit independent |
| BS EN ISO standards | BSI | UK adoption of EN ISO standards; largely aligned with EU standards post-Brexit, with UKCA marking replacing CE for certain regulated equipment |
| EA (Environment Agency) — Industrial Emissions | EA | UK regulatory framework for industrial emissions monitoring under the Environmental Permitting Regulations (EPR); parallel to EU IED but now an independent UK regime |
| HSE (Health and Safety Executive) | HSE | UK workplace and process safety authority; ATEX/UKCA distinction critical for PAT instruments in potentially explosive atmospheres |
| UKAS accreditation | UKAS | UK accreditation body for calibration and testing laboratories — relevant for reference methods underpinning PAT calibration |
ATEX / UKCA note: For PAT instruments installed in potentially explosive atmospheres (Ex zones), ATEX certification (EU Directive 2014/34/EU) and UKCA certification (UK Statutory Instrument 2016/1107) are now separate requirements. Equipment certified under ATEX for the EU market requires additional UKCA certification for use in Great Britain. This distinction must be considered from the instrument selection stage.
All regulatory information is provided to the best of knowledge at time of publication. Standards and guidance documents are subject to revision — verify current versions with the relevant issuing bodies.
PAT and PAT are often used interchangeably, but they are not the same.
PAT describes the measurement side: which analytical techniques, sensors and systems are deployed, and how.
PAT is the overarching framework that embeds measurement into process understanding and process control. PAT provides the data — PAT converts it into a production-relevant decision.
A process analyser alone is not a PAT solution. It is the combination of measurement, data interpretation and process response that makes the approach complete.
Specific measurement principles in detail
NIR spectroscopy, Raman spectroscopy, gas chromatography and other techniques are referenced here only as components within the PAT framework. Measurement physics, instrument categories, strengths and limitations, and supplier overviews are covered in dedicated articles.
Further reading: "NIR vs. Raman — Which Method for Which Application?" and "Inline Spectroscopy in Pharmaceutical Manufacturing" (forthcoming)
Validation and calibration of PAT systems
The methodological validation of chemometric models and calibration strategies for spectroscopic PAT applications is a specialist field that cannot be covered in this overview article.
Further reading: "Calibration and Validation in PAT" (forthcoming)
Feasibility studies and process access
Before measurement technology can be integrated into a process, the question must be answered: is it actually feasible — technically, geometrically, from a safety perspective? PAT specialists are often the first people to literally look inside a running process. How measurement access points, installation situations and sampling concepts are evaluated, and how a feasibility study is structured, is a topic in its own right.
Further reading: "Feasibility Studies in PAT — Process Access, Installation and Measurement Realisability" (forthcoming)
Inline, Online and At-line — detailed analysis
The decision between measurement strategies, including sample conditioning, sampling systems and maintenance considerations, is covered in a dedicated article.
Further reading: "Inline vs. Online vs. At-line — The Right Measurement Strategy for Your Process" — available on Analytic Journal
What is PAT (Process Analytical Technology)?
PAT is a systematic approach that combines measurement, analytical and control technology to understand, monitor and control industrial processes in real time. The goal is not end-of-line quality testing, but quality built into the process — based on valid measurement data and verified process understanding.
What is the difference between PAT and PAT?
PAT describes the measurement side — which sensors, analysers and techniques are used. PAT is the overarching framework that combines measurement, process understanding and process control into an integrated system. PAT provides the data; PAT converts it into process decisions.
Is PAT only relevant for the pharmaceutical industry?
No. Pharma was the early regulatory driver, but PAT concepts are applicable across many sectors — including chemicals, biotechnology, food production, gas analytics, environmental technology and energy processes. Wherever process stability, energy efficiency and quality are economically relevant, PAT can be applied.
Does PAT always require inline measurement?
No. PAT can be based on inline, online or at-line data. What matters is not the measurement location alone, but the connection between measurement data, process understanding and control. The appropriate strategy depends on process dynamics, the measurement variable and the sample matrix.
Which standards govern PAT?
Key reference documents include the FDA Guidance "PAT — A Framework for Innovative Pharmaceutical Development" (2004), ICH Q8, ICH Q10, and USP ‹1058› for instrument qualification in US-regulated environments. In the UK, the MHRA Continuous Manufacturing Guidance (2021) sets post-Brexit expectations. For emissions monitoring, EPA 40 CFR Part 60/63/75 applies in the US; the Environment Agency's Environmental Permitting Regulations govern equivalent UK applications. For process analyser integration, IEC 62628 and ISA standards are relevant across both markets.
When does PAT deliver an economic return?
PAT delivers the strongest returns where processes are sensitive to small changes, raw materials or energy are cost-intensive, or where high reject rates, long waiting times or frequent rework exist. Even reducing the safety margin on a process variable can generate significant savings in continuous operations.
What is the difference between PAT and Quality by Design (QbD)?
QbD is a product development approach that systematically defines product quality through Design Spaces. PAT is the operational instrument used to monitor and control critical process variables in running production. The two concepts are complementary — QbD sets the framework, PAT implements it at the measurement level.
Why is early PAT deployment in process development so important?
Those who introduce PAT only at the production stage are solving a monitoring problem. Those who deploy it during development build process understanding — which they can later use to justify simpler, more robust instrumentation. A process characterised by spectroscopy during development can often be controlled in production via conductivity or refractive index alone. This substantially reduces Total Cost of Ownership.
What are typical mistakes when implementing PAT?
A common mistake is selecting a measurement principle that is too complex without sufficient prior process understanding — resulting in models that are not robust under production conditions. Another is equating data volume with process understanding: more sensors do not solve a process problem if the underlying process model is absent. PAT implementations also fail when the feedback pathway into process control is not planned from the outset.
PAT is the framework within which modern PAT becomes economically effective. The goal is not simply to measure more — but to understand production processes better and control them purposefully.
The real value lies in generating quality within the process, not testing for it at the end. Those who treat PAT purely as a compliance requirement forfeit most of its potential.
As process complexity increases, efficiency pressure grows, and digital tools become more widely available, PAT is transitioning from a nice-to-have to a strategic production instrument — across all sectors and in all major regulatory environments.
| Reference | Issuing body | Content (paraphrased) |
|---|---|---|
| FDA Guidance: PAT (2004) | FDA | Framework for PAT in pharmaceutical manufacturing |
| FDA Guidance: Continuous Manufacturing (2019) | FDA | US expectations for CM with integrated PAT/RTRT |
| MHRA Guidance: Continuous Manufacturing (2021) | MHRA | UK post-Brexit framework for CM in pharmaceutical manufacturing |
| ICH Q8 | ICH | Quality by Design as the basis for systematic product development |
| ICH Q10 | ICH | Pharmaceutical quality management system across the product lifecycle |
| USP ‹1058› | USP | Analytical instrument qualification in US-regulated pharmaceutical environments |
| ASTM E1655 | ASTM | Standard practice for NIR multivariate calibration — widely used in US PAT method validation |
| EN ISO 15839 | ISO/CEN | Requirements for online water measurement instruments |
| IEC 62628 | IEC | Integration of process analysis systems into automation environments |
| EPA 40 CFR Part 60/63/75 | U.S. EPA | US federal requirements for continuous emissions monitoring (CEMS) |
NAMUR recommendations (NE series) on PAT, particularly regarding sampling systems and analyser maintenance — widely applied in European process industry; frequently referenced as good engineering practice in international projects. ISA standards (S88, S95) are the North American equivalents for batch control and system integration.
Specific literature references will be provided in topic-specific follow-up articles — particularly on calibration, chemometric model development and Continuous Manufacturing. For this overview article, the standards and guidance documents listed above form the primary reference basis.
© 2026 Analytic Journal / Dusko Kadijevic. All rights reserved.
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Editorial Note
This article has been written to the best of our knowledge based on publicly available technical information, standards, and manufacturer data. Specifications may change — for binding information, consult current manufacturer datasheets and relevant standards bodies. Analytic Journal produces editorial content independently of manufacturers and advertising partners.
Author: Dusko Kadijevic — 20 years of experience as a PAT practitioner in the process industry
Dusko Kadijevic | Publisher Analytic Journal | analyticjournal.com
Written May 2026. All content formulated in our own words, created with AI assistance.
First published on 8 June 2026 on analyticjournal.com