Paul Lopolito is a technical services specialist at STERIS, Mentor, OH.

Paul Lopolito

Stainless steel pipe fittings, clamps, and connectors arranged in industrial. Precision manufacturing, mechanical assembly, and hygienic processing systems for food and pharmaceutical industries. | Image Source: ©Artinun -stock.adobe.com

Type III or Class III rouge (magnetite) appears as a black oxide in water systems and production equipment exposed to sanitization, pasteurization, or steam sterilization conditions. Remediation of type III rouge often requires electropolishing or mechanical polishing of the stainless-steel surface, resulting in significant labour cost, safety risk to operators with confined space entry, downtime of the equipment or system, and a reduction in thickness of the stainless steel. Raman spectroscopy is a non-invasive analytical method that can quickly identify the presence of magnetite (Fe

) in rouge. Based on the identification of magnetite, a detergent additive can be combined with a formulated citric/oxalic or a phosphoric/citric acid blend to transform insoluble magnetite to a more soluble form of iron oxide. This approach to type III rouge remediation utilizing a detergent blend, spray device, and recirculating pump reduces downtime, safety risk, material loss, and overall cost of maintenance.

A common surface in production equipment and piping is 300 series stainless steel. This surface is compatible with most pharmaceutical and biopharmaceutical processes; however, certain conditions can lead to discoloration and/or rouge on the surface. Rouge is a corrosion product on or in the surface of the iron alloy. Rouge is generally classified as Type I–III or Class I–III.

 Type I rouge (migratory rouge) is an iron oxide that is loosely attached to the surface and can be wiped off the surface. The origin of this rouge is often from an upstream source. Type II rouge (in situ rouge) is difficult to wipe off and often contains underlying damage to the surface. Examples of Type II rouge include galvanic, intergranular, pitting and stress corrosion. Common acid blends, such as citric/oxalic or phosphoric/citric solutions, can be effective at removing Type I and II rouge.

 These treatments do not restore original surface roughness. Electro- or mechanical polishing may be required to correct surface damage due to Type II rouge from pitting corrosion. Type III rouge is often associated with high temperature and purity water systems. Type III rouge is challenging to remediate due to the presence of magnetite (Fe

). Magnetite removal often requires aggressive chemicals or electro- or mechanical polishing. An acid detergent with phosphonate can be used alone or in combination with a citric/oxalic or phosphoric/citric acid blend to improve the solubility of magnetite and the removal of Type III rouge.

Understanding when and which acid detergent (or detergent additive) to use in rouge remediation can be difficult. The selection of the acid detergent should consider effectiveness to remove the rouge, substrate compatibility, rinse profile, stability and shelf life, analyzability, toxicity, disposal constraints, personnel safety, toxicity, foaming characteristics, microbial effectiveness, quality of manufacture, long-term availability, quality of manufacture and overall cost.

 Raman spectroscopy can be a powerful tool in identifying the oxide compound(s) present and provide direction to successful remediation. Alternative spectroscopy and microscopy techniques may be applicable for surface characterization and corrosion monitoring, such as X-ray diffraction (XRD) and scanning electrochemical microscopy (SECM).

 Raman uses inelastic light scattering, where the incident light (from a high-intensity laser light source) transfers energy to molecular vibrations, and the scattered light collected at the detector provides structural information relating to the molecule. Each molecule/compound will have a unique spectrum. Magnetite displays a primary peak with Raman spectroscopy between 662 and 670 cm

 A secondary peak is often observed at 532 and 550 cm

. A common roadblock in successful rouge remediation is the presence of magnetite. A quick identification of magnetite in a rouge surface using Raman spectroscopy can be helpful in selecting the right chemical treatment and cleaning parameters.

This article reviews the use of Raman spectroscopy in a series of case studies to quickly identify the iron oxide compound(s), including magnetite and hematite, on the surface of representative endcaps or piping spool pieces. The article also provides a strategy using a detergent additive to remove magnetite without performing costly electro- or mechanical polishing.

A Raman microscope with a 532 nm laser (DXR3, Thermo Scientific) was used to analyze selected sample areas. Areas that visually exhibited the greatest amount of residue were selected and focused on using the confocal microscope prior to analysis. The samples were analyzed as is (without cleaning, modification, etc.) using the following conditions in 

After analysis of the selected sample areas, a spectral library search (Wiley Science Solutions KnowItAll Spectral Library) was performed for each Raman spectrum.

For reference and comparison throughout the following case studies, the Raman spectra of hematite (Fe

Hematite exhibits peaks at approximately 220, 290, 400, 600, and 1300 cm

Figure 2. Raman Spectrum of Magnetite (Fe

A large multinational biopharmaceutical drug manufacturer indicated that visible rouge was unable to be removed from a tank using 10% v/v citric/oxalic acid detergent at 70 to 80 

C. Citric/oxalic and phosphoric/citric acid blends are commonly recommended for rouge removal.

 The nature of the rouge was investigated using Raman spectroscopy to identify the oxide composition and determine the reason for the difficulty in the chemical removal. Raman spectroscopy is an analytical tool that can be used in the laboratory and modified for use on the manufacturing floor.



. The following swab samples of the rouge were analyzed using Raman spectroscopy (See 

TA-9620 Pre-Derouge Swab Bottom Visible Ring

TA-9640 Rouge Observation Top Spot Near Sight Glass

Figure 3. Raman Spectra of Swab Samples. Figure courtesy of the authors.

. A spectral library search (Wiley KnowItAll Spectral Library) was performed for each Raman spectrum. All the residue samples exhibited a best match to a mixture of iron oxide compounds—hematite (Fe

). A representative search result is shown in 

Figure 4. Raman Spectra of Iron Oxide Mixture Reference (Mixture of Fe

) and TA-9720 Bottom of the Tank Outlet. Figure courtesy of the authors.

. All the Raman spectra from the selected swabs were similar to each other and exhibited peaks around 200, 300, 400, 700, and 1300 cm

, which can be attributed to an iron oxide mixture of Fe

. The general recommendation based on the swab analysis by Raman spectroscopy was to increase the concentration of the citric/oxalic agent detergent to 15% v/v and add a detergent additive at 15% v/v with recirculation through a device at 60 to 80

C for 1 to 3 hours to solubilize and remove the rouge containing magnetite (Fe

A large multinational biopharmaceutical blood product drug manufacturer submitted two end caps (one smaller and one larger) for Raman spectroscopy analysis. Both end caps exhibited brown discoloration consistent with rouge (

Figure 5. Small End Cap. Figure courtesy of the authors.

Figure 6. Large End Cap. Figure courtesy of the authors.

With 10x magnification, the small end cap exhibited orange, blue and purple/brown discolored areas. Areas of each colour were selected for Raman analysis to determine if there were any differences in the Raman spectra. Representative areas of analysis for the three different colours observed on the end cap’s surface are marked by a red square in the images below (

Figure 7. Small End Cap – Orange Area (10x mag). Figure courtesy of the authors.

Figure 8. Small End Cap – Blue Area (10x mag). Figure courtesy of the authors.

Figure 9. Small End Cap – Purple/Brown Area (10x mag). Figure courtesy of the authors. 

The three different colored areas of the end cap were analyzed, and the Raman spectra were found to be comparable to each other. A comparison of the spectra is shown in 

Figure 10. Raman Spectra of Analyzed Areas of Small End Cap. Figure courtesy of the authors.

. A magnified image of the residue present on the large end cap is shown in 

. The small area marked by a red square is representative of the area selected for Raman analysis.

Figure 11. Large End Cap – Brown Area (50x mag). Figure courtesy of the authors.

. The Raman spectrum of the brown area from the large end cap is shown in 

Figure 12. Raman Spectrum of Large End Cap. Figure courtesy of the authors.

. The three Raman spectra of the small end cap and the Raman spectrum of the large end cap were comparable to each other, with peaks at approximately 660, 600, 496, 408, 292, and 223 cm

.

When a spectral library search was performed (Wiley Spectroscopy KnowItAll Spectral Library), the residue from the end caps appeared to be a mixture of hematite (Fe

). A comparison of the Raman spectra of the residue from the small and large end caps to the hematite and magnetite Raman spectra is displayed in 

Figure 13. Raman Spectra of End Caps, Magnetite, and Hematite. Figure courtesy of the authors.



The end cap spectra matched well to the hematite spectrum; however, the peak around 665 cm

 had a greater intensity than the hematite reference spectrum, suggesting the presence of magnetite, which has a high intensity peak around 665 cm

 The residue was effectively removed from stainless steel by agitated immersion using a solution of 15% v/v formulated phosphoric/citric detergent plus 15% v/v detergent additive at 80 °C for 24 hours (

Figure 14. Derouge and Passivate End Cap Samples. Figure courtesy of the authors.

A large multinational biopharmaceutical drug product manufacturer submitted pipe samples with suspected rouge for Raman spectroscopy analysis. The pipe samples exhibited a brown discoloration consistent with rouge throughout its outer surface.

. Three different areas on the pipe were analyzed using Raman spectroscopy. The spectra of the three areas were found to be comparable to each other. A comparison is shown below in 

Figure 15. Raman Spectra of Analyzed Areas of Pipe. Figure courtesy of the authors.

. A spectral library search was performed (Wiley Spectroscopy KnowItAll Spectral Library), and the Raman spectrum of the brown discoloration from the pipe had a best match to the spectrum of a mixture of hematite (Fe

). A comparison of the Raman spectrum of the hematite and magnetite mixture and the Raman spectrum of the brown residue from the pipe is shown in 

Figure 16. Raman Spectra of Iron Oxide Mixture and Brown Residue From Pipe. Figure courtesy of the authors.

 The brown discoloration was effectively removed compared to untreated controls from stainless steel by agitated immersion using a solution of 15% v/v formulated phosphoric/citric acid detergent plus 15% v/v formulated detergent additive or a 15% v/v formulated citric/oxalic acid detergent plus 15% v/v formulated detergent additive at 80 °C for 4 hours. The surface was tested with an electrical pen test kit to confirm a passive surface following treatment. Testing with 15% v/v formulated phosphoric/citric acid or the formulated citric/oxalic acid detergent at 80 °C for 24 hours was not successful in removing the discoloration shown in 

 alongside untreated controls (left side) and successfully derouged and passivated pipe samples (right side).

Figure 17. Case Study 3 Heat Exchanger Pipe Samples, Left to Right:

Sample 3, Cleaned with 15% formulated phosphoric/citric acid detergent

Sample 4, Cleaned with 15% formulated citric/oxalic acid detergent

Sample 5, Cleaned with 15% formulated phosphoric/citric acid detergent plus 15% detergent additive

Sample 6, Cleaned with 15% formulated citric/oxalic acid detergent plus 15% detergent additive

A general requirement of pharmaceutical drug manufacturers is that equipment shall be constructed so that surfaces that contact components, in-process materials, or drug products shall not be reactive, additive or absorptive so as not to alter the strength, quality, integrity, identity and purity of the drug product.

 The presence of discoloration or rouge on direct and indirect product surfaces has been cited by regulatory authorities.

Guidelines for Good Manufacturing Practice for Medical Products for Human and Veterinary Use 

(Annex 1) state in section 2.5 that process equipment, utilities, raw materials and premises are potential sources for microbial and particulate contamination. Stainless steel maintenance and cleaning of direct, indirect, and non-direct surface must be considered within any contamination control strategy.

 A risk-based strategy to stainless-steel maintenance utilizing International Council for Harmonisation (ICH) Q9 

 has two primary principles: the evaluation of risk to quality from rouge should be based on scientific knowledge and ultimately linked to the protection of the patient and product, and the level of effort, formality and documentation should be commensurate with the level of risk.

 Stainless steel commonly used in pharmaceutical and biopharmaceutical manufacturing can be discoloured or rouged based on several factors, including the nature of the in-process solution, product, medias, buffers, reagents and water quality.

Through a series of case studies, the authors used Raman spectroscopy to identify the presence of magnetite on various process and utility system samples (swabs, end caps and spool pieces). Type III rouge (magnetite) presents considerable challenges to chemical remediation and stainless steel maintenance programs. Based on the identification of magnetite, a readily available detergent additive can be combined with a formulated citric/oxalic or phosphoric/citric acid detergent commonly used for derouging and passivating stainless steel. The two-detergent approach transforms the insoluble magnetite to a more soluble form of iron oxide for removal without polishing or a secondary passivation step. This science-based approach to type III rouge remediation incorporates a detergent additive, spray device and recirculating pump to reduce equipment downtime, operator safety risk, material loss from polishing and overall cost of maintenance and compliance to current good manufacturing practices.

 Rouging of stainless steel in WFI and high purity water systems. In: 

 Stainless steel tubing in the biotechnology industry. 

Biotechnology/Pharmaceutical Facilities Design

 Rouging in stainless steel equipment for hygienic services. 

 Addressing rouge in biopharmaceutical manufacturing equipment. 

American Standard of Testing Materials (ASTM).

A 967 – 99: Standard Specification for Chemical Treatments for Stainless Steel Parts

 Cleaning agents for biopharma manufacturing. 

 Cleaning agents and cleaning chemistry. In: Pluta P, ed. 

 Cleaning products: their chemistry, effects on indoor air quality, and implications for human health. 

. 2024;190:108836. doi:10.1016/j.envint.2024.108836.

 Principles and theories of green chemistry for corrosion science and engineering: design and application. 

 2024;26:4270-4357. doi:10.1039/D3GC05207A

 Surface characterization for corrosion monitoring. In: 

Electrochemical and Analytical Techniques for Sustainable Corrosion Monitoring

. Elsevier Inc; 2023:103-120. doi:10.1016/B978-0-443-15783-7.00016-5

 Characterization of iron oxides commonly formed as corrosion products on steel. 

 Raman spectroscopy of iron oxides and (oxy)hydroxides at low laser power and possible applications in environmental magnetic studies. 

. 2009;177(3):941-948. doi:10.1111/j.1265-246x.2009.04122.x

American Society for Testing and Materials.

 ASTM A380–96: Standard Practice for Cleaning, Descaling, and Passivation of Stainless Steel Parts, Equipment, and Systems. West Conshohocken, PA: ASTM International; 1996.

American Society of Mechanical Engineers.

Bioprocessing Equipment (BPE): Non-mandatory Appendix E Passivation Procedure Qualification

21 CFR Part 211: Current Good Manufacturing Practices for Finished Pharmaceuticals

Guideline for Good Manufacturing Practices for Medicinal Products for Human and Veterinary Use: Annex 15, Section 10.10

Code of Federal Regulations (CFR): 21 CFR 211, Part D, Section 211.65

 Published December 15, 2013. Accessed February 27, 2026. 

http://www.fda.gov/ICECI/EnforcementActions/WarningLetters/2009/ucm148998.htm

 August 10, 2022. Accessed February 27, 2026. 

 April 15, 2014. Accessed February 27, 2026. 

 May 1, 2024. Accessed February 27, 2026. 

Guideline for Good Manufacturing Practices for Medicinal Products for Human and Veterinary Use: Annex 1, Section 2.5

International Conference for Harmonisation of Technical Requirements for Registration of Pharmaceutical for Human Use (ICH).

ICH-Q9 Harmonized Tripartite Guideline: Quality Risk Management

 A risk-based approach to stainless steel equipment maintenance. 

Emily Null, MS, scientist; Mildred R. Bernardo, lead scientist; Si Myra Tyson, MS, technical services lab specialist; Dijana Hadziselimovic, technical services laboratory manager; Paul Lopolito, technical services director; all with STERIS Corp.

Articles

Identification and Remediation of Type III or Class III (Magnetite) Rouge in Manufacturing Equipment and Water Systems

automatic stainless steel pharmaceutical fermenter | Image Credit: ©gen_A - stock.adobe.com

Biopharmaceutical manufacturers rely heavily on non-specific, at-line analytical testing to confirm cleanliness of equipment surfaces through final rinse water analysis, potentially leading to false positives and problematic on-line or off-line testing. Spectroscopy methods, such as ultraviolet (UV) spectroscopy at 220 nm, can be used for continuous, in-line monitoring of the cleaning process. The sensitivity of the method can be improved by adjusting the sanitary flow path length. The authors performed interference and enhancement testing using a formulated alkaline and acid cleaner, as well as common biopharmaceutical process residues (such as monoclonal antibody drug substances, insulin drug product, and bovine serum albumin, which displays a cumulative effect like in total organic carbon analysis). Results showed that in-line monitoring capability of UV spectroscopy enables continuous monitoring of the entire cleaning cycle and applicability to quality by design, process analytical technology, process digitalization, and sustainability goals of a Pharma 4.0 manufacturing facility.

Submitted: March 27, 2025
Accepted: May 20, 2025

Demonstration of equipment cleanliness through analytical testing is a critical part of cleaning validation and contamination control strategies. Health authorities require that equipment is visually clean and contaminant residues are reduced to scientifically justified limits (1–4). Contaminants can include residual product or intermediates, cleaning residues, excipients, particulates, and endotoxins. The scientifically justified limits are based on toxicological evaluation and health-based exposure limits (5–9). The European Commission’s Annex 15, Section 10.6.1 states that biologics are “known to degrade and denature when exposed to pH extremes and/or heat;” these are common conditions in a cleaning process, and Section 10.6.2 supports non-specific methods, such as total organic carbon (TOC) and conductivity, when it is not feasible to test for specific product residues, such as when they are degraded (4). The incorporation of a scientifically justified (real-time, in-line) non-specific method such as conductivity or ultraviolet (UV) spectroscopy can aid in ensuring a state of control of a validated cleaning process by providing continuous process verification (10).

Equipment cleanliness requires that the validated cleaning steps have been followed and acceptable cleanliness has been demonstrated by analysis of samples through various techniques, including direct surface, swab, and rinse sampling (4,11). Each technique has advantages and disadvantages and can fit within the scope of a cleaning validation program. As a cleaning process matures into the continued monitoring phase, on-line rinse sampling (at-line or in-line) is advantageous, as it removes the requirement for collection and subsequent analysis of grab samples, decreases the turnaround time of the results, and improves the equipment release process. At-line sampling diverts an aliquot of the sample, often the final rinse water, to the instrument probe (12,13). In-line sampling involves having an instrument probe in the flow path of the fluid stream, providing continuous analysis (14,15).

Analytical methods for in-line and at-line rinse sampling include conductivity; TOC; chromatography methods, such as high-performance liquid chromatography (HPLC) or ultra-high-performance liquid chromatography (UHPLC); and spectroscopic methods, such as UV and fluorescence. The selection and eventual validation of the analytical method depends on the soils being cleaned, including the cleaning agent, the range of the analytical method, and analyte interferences with other components in the soil (16). The International Council for Harmonisation’s (ICH) Q2B document provides guidance on analytical method validation, which requires a specified analytical range, linearity, ruggedness, precision, accuracy, limit of quantitation (LOQ), limit of detection (LOD), and specificity for the analyte in the presence of expected components (17–19).

Conductivity is commonly used to demonstrate removal of cleaning agents but can have interferences with ionic species in buffers and some products. TOC is also a commonly used technique to show removal of product and cleaning agent and can have interferences with organic compounds, which are common in biopharmaceutical manufacturing. Elevated conductivity or TOC results require additional off-line testing to investigate the result and confirm the source of the contamination and risk to next batch or product, and patient.

UV spectroscopy is a common analytical technique used for monitoring chromatography and filtration steps in biopharma manufacturing, in cleaning processes of APIs, and in quality control for testing of raw materials, intermediates, and final product (15, 20–22). UV provides a semi-specific technique for detection of residual product and cleaning agent in cleaning applications.

UV analyses are performed by measuring transmittance of radiation through a sample and comparing against a blank solution. Absorbance of a sample is proportional to its concentration, and the distance the light must travel through the sample based on the Beer-Lambert law, A = εlc, where A is the absorbance, ε is the extinction coefficient, which is a sample specific constant describing how much the sample is absorbing at a given wavelength, l is the pathlength, or the distance traveled by light through the sample, and c is the concentration of the solution. The pathlength of the sample, which is typically a fixed parameter in an analysis, can be modified to change the sensitivity of the method. A 1 cm pathlength is common in laboratory testing. Increasing the pathlength to 10 cm increases the absorbance 10-fold and can consequently decrease the LOD and LOQ.

Every compound has a different spectrum based on the structure. Selecting a wavelength for detection and monitoring can vary based on the highest overall peak in the spectrum or localized maximum. Previous studies have explored detection of cleaning agents by coupling HPLC or UHPLC with a UV detector (23). In these studies, a wavelength of 224 nm was selected based on the alkaline and acidic cleaner; these patented formulations include a chromophore to help with UV detection, either with a standalone detector or coupled with liquid chromatography. These cleaning agents have higher absorbance at the lower range—190–200 nm. However, because many organic molecules also absorb at this lower range, the inevitable interference outweighs the benefits of a higher absorbance. The localized maximum at 220 nm provides greater specificity compared with other compounds expected in the cleaning process.

These formulated alkaline and acid cleaners are composed of multiple components. To leverage one assay, such as in-line UV analysis, for the removal of the formulated cleaner, the following information should be available:

concentration of the analyte in the formulation

rinse profile studies using specific and non-specific methods to demonstrate that all components rinse at the same rate

rinse recovery of the analyte from the surface materials

spray device coverage testing of the equipment and piping circuits

toxicity evaluation of the formulated cleaner.

These studies have been discussed in detail in literature supporting cleaning validation and surface analysis (11,17).

Cleaning processes can degrade therapeutic macromolecules with pH extremes and high temperatures (4). This degradation can render the product biologically inactive. Biologically inactive products may no longer pose a toxicological challenge, but the degraded product needs to be removed from the equipment surface. HPLC, coupled with UV or mass spectrometry (MS), can provide information on whether or not the product is degraded. This approach requires at-line or grab sampling analysis, in addition to the time needed to perform the testing. Non-specific methods, such as TOC and conductivity, are not capable of differentiating intact from degraded products. For example, the TOC of a biologic API will detect native and degraded compounds, unless the product is degraded to an inorganic carbon. Additional studies are needed to demonstrate that product is degraded at those cleaning conditions (24–27). When analyzing protein degradation, sodium dodecyl sulfate polyacrylamide gel electrophoresis provides evidence of degradation by measuring the molecular weight of the sample. A sample is treated with a cleaning agent under the specified concentration, temperature, and time. This sample is analyzed alongside an untreated sample; the responses are compared to verify degradation.

A series of experiments were conducted by the authors to determine the feasibility of using in-line UV spectroscopy for the detection of residual biopharmaceutical drug substance or product and cleaning agent using in-line and standalone instruments. To use in-line UV for cleaning applications, several studies were performed to demonstrate that measurement of the cleaning agent and product can be validated with UV spectroscopy; the combination of cleaning agent and product do not enhance or depress the response; both intact and degraded product, in the presence and absence of cleaning agent, can be detected; comparable responses are observed between in-line and standalone UV detectors; and the real-time, in-line monitoring of product and cleaner is possible with UV spectroscopy.

 Initial studies to determine the optimal wavelength and linear concentration range were carried out using a spectrophotometer (ThermoScientific GENESYS 50 UV-Vis) and 10 mm quartz cuvettes (Fisher Part # 14-958-112). Spectra were collected from 190–400 nm for ~1000 ppm solutions of each cleaner diluted in type 1 water.

The range of the formulated cleaners was qualified from 25–1000 ppm (alkaline cleaner) and 10–1000 ppm (acidic cleaner) by characterizing the linearity and precision (repeatability) of the UV response. Accuracy of the analysis for concentrations calculated from measured UV absorbances was studied by quantitation of prepared samples via an external standards method. Linearity and precision studies were carried out by triplicate preparation and analysis of calibration curves across the concentration range of interest. Separate sample preparations, either alternate lots or concentrations within the linear range, were then prepared, analyzed, and quantitated using these calibration curves to assess the accuracy of the method. The LOQ, LOD, and specificity of the method was inferred based on the linearity, accuracy, and precision studies.

 Model process soils were selected to encompass several product families in biopharmaceuticals that may use clean-in-place cleaning. A sample of bovine serum albumin (BSA), a standard for proteins in analytical methods, was purchased (EMD Millipore); samples previously studied for protein degradation (e.g., monoclonal antibody [mAb] and insulin) were also studied.

Each soil was diluted to concentrations across the analytical range studied for the alkaline cleaner and the UV response characterized. Additional preparations were made that contained 1:1 mixtures of the model soils and the alkaline cleaner. Spectra were collected for each solution, and absorbance at
220 nm monitored.

Effect of the degradation of the mAb on the UV measurement was studied by treatment of the mAb drug product with solutions of the alkaline cleaner at 1% and 3% concentration. Stock solutions were prepared and preheated to 60 oC. Once pre-heated, the mAb drug product was diluted with the degradation solutions 1:10 and temperature maintained for five minutes. To quench the reaction, the degradation solution was diluted to 100 mL with ambient temperature type 1 water.

For analysis via UV, solutions were diluted to cleaning agent concentrations ranging from 5–1000 ppm, and measurements made as described in the method development section.

In-line sensor–static measurement. A PX2 Photometer configured to monitor absorbance at 220 nm and a pathlength of 100 mm (10 cm) (EZ Cal Flow Cell) was provided for testing by Custom Sensors and Technology (Fenton, MO). Solutions of the alkaline and acid cleaner were prepared at concentrations ranging from 5–100 ppm. The measurement cell was configured with a fill port above the sensor and a drain port below to allow filling and draining of the chamber.

The sensor was blanked with type 1 water prior to sample solution absorbance measurement. UV absorbance was measured for approximately two minutes. The cell was rinsed with type 1 water between each measurement.

After data were collected with the PX2 photometer, the same solutions were analyzed on a spectrophotometer as previously described for comparison.

In-line sensor–dynamic measurement. The PX2 photometer was configured in the same manner as used during static measurement studies and was fitted on the drain port of a 60 L mixing vessel. Prior to testing, the sensor was blanked on deionized (DI) water from the source used to prepare cleaning and standard solutions.

The vessel was flushed with 10 ppm solutions of alkaline and acid cleaners for approximately two minutes via static spray ball, and the UV absorbance at 220 nm was monitored and compared with the established DI water baseline.

To study cleaning of the vessel, the interior surface was manually soiled with portions of the BSA and insulin samples characterized during the spectrophotometer studies. The vessel was then cleaned via a one-minute rinse with 1% (v/v) alkaline cleaner, immediately followed by a one-minute rinse with 1% (v/v) acid cleaner. The vessel was then rinsed with DI water. The real-time UV absorption at 220 nm was monitored throughout.

Spectroscopic method development and qualification.

 Example spectra used for wavelength selection are provided in

. While neither spectrum exhibits a true peak, a local maximum is observed at 220 nm for each cleaner. As most organic compounds have some UV absorption character at and below 200 nm, selecting a longer wavelength improves the potential specificity of the method. Additionally, each cleaning agent used contained an analyzable surfactant with a chromophore having a characteristic absorbance at 224 nm. For method development and qualification, cleaner concentrations were monitored via absorbance at 220 nm.

Figure 1. Spectra for wavelength selection. (Figure courtesy of the authors)

Using the collected linearity, precision, and accuracy data, the LOD and LOQ were established. The upper limit of the analytical range for each cleaner was
1000 ppm. A summary of the method qualification data is provided in 

 For the alkaline cleaner, an LOD of 5 ppm and an LOQ of 25 ppm were established. For the acid cleaner, the LOD and LOQ were 5 and 10 ppm, respectively. UV spectroscopy is a non-specific method, and other components can contribute a response. Specificity studies are needed for process residues as described in the following section. The data support that both cleaners can be validated per ICH Q2 parameters (18).

Table I. Summary of method qualification.

 The UV response at 220 nm for each soil was observed to be linear across a similar concentration range as the alkaline cleaner. For the cleaner and soil mixtures, an additive effect was observed for absorbance at 220 nm. Spectra demonstrating the additive effect are provided in 

. This additive effect has been previously observed and characterized during UHPLC studies for analytes in different formulated cleaners (23). Another localized maximum specific for downstream protein determination was observed at 280 and 310 nm by Westwood et al. and Rathmore, et al., respectively (15,20).

Figure 2. Alkaline and drug product cleaner spectra. (Figure courtesy of the author)

Linearity of the UV response was retained when the cleaners and process soils were characterized in mixed solutions and the absorbance of the cleaner/soil mixture remained within the dynamic range of the spectrophotometer used. Absorbance curves for an alkaline cleaner, an insulin drug product, and a mixture of both components are provided in 

Figure 3. Drug product and cleaner linearity curves. (Figure courtesy of the authors)

The ratio of absorbance of the model process soil to cleaners ranged from 0.60 to 0.96, as shown in 

 The combination of the cleaner and process soil is additive; the combination of the components does not enhance nor depress the response. As a non-specific method, all the signal needs to be attributed to the single worst-case component. While this attribution may lead to a bias to failing, the additive interaction of the process soil and cleaner supports the use of UV spectroscopy for the detection of residual amounts.

Table II. Absorbance ratios of process soils against alkaline cleaner.

Product degradation. An example spectrum of the combined cleaner and degraded mAb drug product at an approximate ratio of 3.4:1 is provided in 

. The additive effect on measured absorbance previously observed was maintained for the degraded mAb. Additionally, the linearity of the response was preserved, as the absorbance measured remained within the dynamic range of the detector, despite the significantly greater drug product concentration relative to cleaner concentration. A plot of absorbance versus degraded mAb and cleaner solution is provided in 

Figure 4. Spectrometer vs. in-line sensor linearity curves. (Figure courtesy of the authors)

In-line sensor–static measurement. The mean absorbance result was calculated for each two-minute measurement and then plotted as a function of cleaner concentration. A linear regression was performed and compared with a linear regression analysis for absorbance results for the same solutions characterized via spectrophotometer. Linearity was achieved for each cleaner across the range investigated with significantly greater absorbance observed for the longer pathlength cell compared with the spectrophotometer/cuvette measurements. Plots are provided in

Figure 5. Cleaner and degraded monoclonal spectra overlay. (Figures courtesy of the authors)

In-line sensor–dynamic measurement. The absorbance measured for the 10 ppm cleaner solutions were significantly greater than the DI water baseline, indicating that it should be possible to detect cleaner residues at a typical limit concentration. Absorbance results when measured on the spectrometer were 0.112 and 0.138 for the alkaline and acidic cleaners, respectively. When measured on the PX2 photometer, absorbances were 0.306 and 1.432 for the same solutions. The response for the acidic cleaner in the PX2 photometer is as expected given the 10x pathlength compared with the cuvette. However, the alkaline cleaner is not proportional, potentially due to being below the proposed LOQ. Additionally, because the process has not been fully optimized, foam in the line could reduce the effective pathlength.

Figure 6. Cleaner and denatured monoclonal antibody linearity plots. (Figure courtesy of the authors)

For the insulin drug product testing, the process can be clearly visualized in the UV absorbance data. As the 1% cleaning solutions were flushed through the system removing the soil, absorbance values greater than 4.0 were observed for both cleaners. During the rinse immediately following the cleaning process, UV absorbance was observed to rapidly return to baseline (<0.02 absorbance) in under one minute, indicating clearance of both the drug product soil as well as any cleaning agent residue. The same process profile was observed for the BSA cleaning study. 

 shows the absorbance during the cleaning process steps.

Figure 7. Ultraviolet absorbance profile–insulin drug product cleaning. (Figures courtesy of the authors)

Guidance for Industry: PAT–A Framework for Innovative Pharmaceutical Development

 was issued in 2004, and several theoretical and applied articles were published over the years utilizing current cleaning agents and on-line analytical technology (28–34). As the pharmaceutical industry strives toward Pharma 4.0 manufacturing and a digitalized cleaning process, the requirements for innovative formulated cleaners and in-line analytical sensors is paramount (35).

This article revisits the use of in-line UV spectroscopy with a sanitary flow cell at a specific wavelength to demonstrate cleaner removal. Interference and enhancement testing was performed using common biopharmaceutical process residues, such as native and denatured mAb drug substances, insulin drug product, and BSA, which displays a cumulative effect like TOC analysis. The in-line monitoring capability of UV enables real-time continuous monitoring of the entire cleaning cycle and applicability to quality by design, process analytical technology process digitalization, and sustainability goals of a Pharma 4.0 manufacturing facility.

The authors would like to thank John Gleeson and Frank Westwood of Custom Sensors & Technology (Fenton, MO, USA) for providing the prototypes and assisting with in-line testing using the sanitary flow cell with UV detector.

Guide to Inspections Validation of Cleaning Processes

Guide-0028 Cleaning Validation Guidelines.

http://www.Canada.ca/content/dam/hc-sc/documents/services/drugs-healthproducts/compliance-enforcement/good-manufacturingpractices/validation/cleaning-validation-guidelines-guide-0028/document/pub1- eng.pdf

Validation Master Plan Installation and Operational Qualification Non-Sterile Process Validation Cleaning Validation

EC. Annex 15, Qualification and Validation. 

Good Manufacturing Practice Medicinal Products for Human and Veterinary Use

ISPE Baseline Guide: Risk-Based Manufacture of Pharmaceutical Products (Risk-MaPP)

Guideline on Setting Health-Based Exposure Limits (HBELs) for Use in Risk Identification in the Manufacture of Different Medicinal Products in Shared Facilities

 (EMA, November 2014 and effective date: June 2015).

WHO. Working Document QAS/20.849, Draft Working Document–Points to Consider on Different Approaches–Including HBEL–To Establish Carryover Limits in Cleaning Validation for Identification of Contamination Risks when Manufacturing in Shared Facilities. May 2020.

Inspection of Health Based Exposure Limit (HBEL) Assessments and Use in Quality Risk Management (PI 052-1) 

SEI/ANVISA 1254417 Technical Note HBELs, December 2020

Current Step 4 version dated August 2009.

Rivera, E. and Lopolito, P. Evaluating Surface Cleanliness Using a Risk-based Approach. 

Dion, M.; Van Houte, O.; and Verghese, G. On-line TOC Monitoring in GMP Parts Washers. 

Bader, K.; Hyde, J.; Watler, P.; and Lane, A. On-line Total Organic Carbon (TOC) as a Process Analytical Technology for Cleaning Risk Management. 

Aramouni, N.A.K.; Steiner-Browne, M.; and Mouras, R., Application of Process Analytical Technology (PAT) in Real-Time Monitoring of Pharmaceutical Cleaning Process: Unveiling the Cleaning Mechanisms Governing the Cleaning-in-Place (CIP).

 Process Safety and Environmental Protection

 177, 212-222 DOI: 10.1016/j.psep.2023.07.010

Westwood, F.; Ponstingl, M.; and Dickens, J.E., Analytical Figures of Merit of a Dual-Wavelength Absorbance Approach for Real-Time Broad Protein Content Monitoring for Biomanufacturing. 

Journal of Pharmaceutical and Biomedical Analysis

Kaiser, H.J.; and Minowitz, M. Analyzing Cleaning Validation Samples: What Method? 

Kaiser, H.J. and Ritts, B. Validation of Analytical Methods Used in Cleaning Validation. 

 Cleaning Validation: Process Life Cycle Approach. Contamination Control,

In-line UV spectroscopy offers a promising solution for continuous monitoring of cleaning processes in biopharmaceutical manufacturing. By utilizing a specific wavelength, it enables real-time detection of residual cleaning agents and biopharmaceutical products, including both intact and degraded forms. This method supports the goals of Pharma 4.0 by enhancing process analytical technology, quality by design, and sustainability. The study demonstrated that UV spectroscopy can be validated for cleaning applications, providing a non-specific yet effective approach to ensure equipment cleanliness and compliance with regulatory standards.

The authors performed interference and enhancement testing using a formulated alkaline and acid cleaner, as well as common biopharmaceutical process residues.