From Raw Material Identification to Fine and Specialty Chemicals Analysis

The Global Chemical Industries Summit 

Trace Element Detection Limits: What Every Spectroscopist Should Know

A Look at Vibrational Nanoscopy and Infrared Nanoscopy

Advancing Spectroscopy into the Nanoworld of Materials Science

Not Too Hot: The Importance of Optimizing Laser Power for SERS Measurements

A Look at Inline NIR Spectroscopy and Offline THz Raman Imaging

Solid Physical State Transformation in Hot Metal Extrusion 

Five Reasons Why Not Every Peak Shift in Infrared (IR) Spectra Indicates a Chemical Structure Change

Stonehenge is a prehistoric monument that is considered a British cultural icon and one of the most famous landmarks in Europe. The origin of the sarsen megaliths that comprise Stonehenge have interested archaeologists for decades. What can spectroscopic techniques, such as portable X-ray fluorescence spectrometry (PXRF), reveal about the sarsen megaliths and where they originated? David Nash, a professor of physical geography at the University of Brighton in Brighton, United Kingdom, is exploring this question. We recently spoke with Professor Nash about his team’s discovery and what it means for the future of archaeology.

The major conclusion that was drawn from your research is that the sarsen megaliths that form the primary architecture of Stonehenge most likely originated from the West Woods, Wiltshire, approximately 25 km north of the Stonehenge site (1). How did your team arrive at this inference and how certain are you of this result?

We arrived at the inference that West Woods was the likely source for the Stonehenge megaliths in our 

 via two stages of investigation (1). First, we analyzed all the remaining 52 sarsen stones at Stonehenge using PXRF. Statistical analysis of the resulting data set showed that most of the stones share a consistent chemistry—therefore, they can be considered to come from the same source area. Only two stones were different. The second stage of the research involved comparing higher resolution inductively coupled plasma–mass spectrometry (ICP-MS) data from one sarsen stone at the monument (Stone 58) with equivalent data for sarsen stones from 20 areas across the southern United Kingdom. Stone 58 falls in the centre of the statistical cluster from our PXRF analysis and can therefore be considered chemically representative of the sarsens at Stonehenge. The comparison of ICP-MS data for Stone 58 showed a direct match with the chemistry of sarsen stones in West Wood, and with these samples only. We can be 100% confident of the chemical match between Stone 58 and West Woods—all immobile trace elements that we compared overlapped within instrumental uncertainty. We cannot yet, however, state with certainty the precise area of West Woods from which the stones were sourced.

In your study, your team used PXRF to provide an initial chemical characterization of the sarsen uprights and lintel stones. Why was PXRF the best technique to use for this analysis as compared to other field or laboratory methods?

Stonehenge is a highly protected site, and it is not possible to sample stone from the monument today. Therefore, we had to use a portable technique to ascertain the chemical variability at the monument. Personally, I would not use a technique such as PXRF to compare the chemistry of sarsen stones at Stonehenge and those in other areas. However, PXRF works well for intercomparison of stones at Stonehenge because we know that the stones were erected at roughly the same time and have been exposed to the same degree of natural weathering over the last 4500 years.

Numerous studies in the past have explored plausible explanations for where the stones that comprise Stonehenge came from. What did you do in this study that is unique from what other researchers have done?

Previous studies that have sought to identify where the sarsen stones at Stonehenge came from have been largely speculative. Archaeological studies, for example, have tried to identify hollows that could be quarry sites. Our research was the first ever to utilize chemical data to provenance the stones. This kind of work is widespread in other branches of archaeology, particularly those concerned with the analysis of stone tools, but had never been applied in the context of the British Neolithic.

Your research provided an in-depth characterization of the sedimentology, mineralogy, and geochemistry of a known sarsen megalith at Stonehenge (2). What challenges did you face in using ICP-MS and ICP–atomic emission spectrometry (AES) to generate precise whole-rock geochemical data?

 studies make use of ICP-MS analyses of immobile trace element data. The only real issue was obtaining a reliable sample size from Stonehenge. We were given permission to sample a core that had been drilled from Stone 58 as part of restoration work at the monument in the 1950s. Our sample was approximately 7 cm long, and we needed to be able to capture material for ICP-MS, ICP-AES, and isotope analyses and to prepare geological thin sections.

You and your team used a various number of analytical techniques besides PXRF, ICP-MS, and ICP-AES, including X-ray computed tomography (CT) imaging and high-resolution scanning electron microscopy (SEM) with cathodoluminescence (CL) and energy-dispersive (EDS), and automated SEM-EDS mineral analysis (QEMSCAN). Why were so many techniques used and what data were most valuable to infer origin of the samples?

Our 2021 study provides the first detailed characterization of the petrography, mineralogy, and geochemistry of a sarsen at Stonehenge. Given that the core from Stone 58 was of national and international importance, we wanted to make sure that we applied every state-of-the-art analytical technique to it as possible. We had already published the geochemical data linking the Stonehenge sarsens to West Woods. However, the use of other techniques allowed us to obtain much more detail about the stone. For example, CT imaging allowed us to explore the sedimentary structure of the rock, and thin section analysis enabled us to investigate the distribution of different types of minerals and quartz cements within the stone. We also conducted cathodoluminescence analysis under an SEM to explore the different phases of quartz cement development within the rock. This analysis led to one of the most interesting sets of results from the study in my opinion because it showed that the quartz cements had developed over at least 16 cycles of precipitation. Finally, we used isotope analyses to identify the potential sources of the mineral grains within the stone. The legacy of using this battery of different techniques is that we now have a suite of data that we can use, in addition to geochemical analysis, to refine the potential source areas for the sarsen stones.

Are there still further mysteries surrounding Stonehenge that your team is interested in exploring in a future study?

There is still a lot of work to be done. Most obviously, we would like to refine the source area for the sarsen stones and see if we can identify where the two “unknown” stones originated.

What are your next steps in this work? How have your findings impacted archaeological and geographical research going forward?

The main impact of our research is finally putting a dot on the map for the main source of sarsens at Stonehenge. This now allows us to draw more informed inferences about the routes that the stones took on their way to Stonehenge. Before the publication of our results in 2020, all proposed route maps were purely supposition. Now we have a starting point for the journey of the stones. We also have a geochemical technique that we can apply to other monuments made of sarsen within the wider Stonehenge landscape. It would be fascinating to see if the people who constructed the sarsen circle and avenue at Avebury, for example, obtained their stone from the same areas as the Neolithic peoples who built Stonehenge.

(1) D.J. Nash, T.J.R. Ciborowski, J.S. Ullyott, M.P. Pearson, T. Darvill, S. Greaney, G. Maniatis, and K.A. Whitaker, 

(2) D.J. Nash, T.J.R. Ciborowski, T. Darvill, M.P. Pearson, J.S. Ullyott, M. Damaschke, J.A. Evans, S. Goderis, S. Greaney, J.M. Huggett, R.A. Ixer, D. Pirrie, M.R. Power, T. Saige, and N. Wilkinson, 

 is a Professor of Physical Geography at the University of Brighton, United Kingdom. He has published three books and more than 110 journal articles during a 30-year career exploring the origins of silcrete and calcrete duricrusts in southern Africa and Europe using a range of spectroscopic techniques. In the last 10 years, he has turned his attention to the analysis of silcrete in archaeological contexts, including investigations of the sarsen stones (silcretes) at Stonehenge. Direct correspondence to: 

Articles

What can spectroscopic techniques, such as portable X-ray fluorescence spectrometry (PXRF), reveal about the sarsen megaliths of Stonehenge and where they originated? David Nash, a professor of physical geography at the University of Brighton in Brighton, United Kingdom, is exploring this question. We recently spoke with Professor Nash about his team’s discovery and what it means for the future of archaeology.

Using Portable X-ray Fluorescence Spectrometry (PXRF) to Explore the Origins of the Sarsen Megaliths at Stonehenge

As science advances and the ability to study smaller and smaller entities becomes a necessity, laboratory professionals have had a need for an optical fiber-based ultrasonic imaging tool capable of resolving biological cell-sized objects—a need that, until recently, has gone unfulfilled. Salvatore La Cavera III and his colleagues in the Faculty of Engineering at the University of Nottingham, in Nottingham, United Kingdom have developed a novel measurement system consisting of two ultrafast lasers that excite and detect high-frequency ultrasound from a nano-transducer fabricated onto the tip of a single-mode optical fiber. The ability to extrapolate the single fiber to tens of thousands of fibers in an imaging bundle effectively demonstrates the endoscopic potential for this device, as it illustrates the ability to catalyze future phonon endomicroscopy technology, bringing the prospect of label-free in vivo histology within reach. La Cavera spoke to Spectroscopy about research using this tool.

Your paper (1) shows, for the first time, that a single ultrasonic imaging fiber is capable of simultaneously accessing 3D spatial information and mechanical properties from microscopic objects. What is the significance of this achievement?

Most endoscopes provide access to one human sense: sight. However, history has shown that clinical diagnostics and pathology are optimized through a wide range of sensory information, such as listening with a stethoscope, seeing with an endoscope, and even feeling with the clinician’s hand. Our technology provides a pathway for combining these latter two senses in an endoscopic context—

viewing objects in 3D and feeling for their stiffness. The fact that this can be carried out on microscopic length scales is a tremendous opportunity. Imagine being able to perform biopsies or histopathology inside the human body, but with the added modality of being able to “see” the stiffness of a cancer cell relative to healthy cells.

What prompted you to address this subject in this way?

Mechanobiology, the study of how mechanical properties influence the building blocks of life and disease, is an exciting new field of study that is growing rapidly. One of the central themes in this young field is that the mechanical properties of cells play a vital role in their development, under both healthy and diseased conditions. Therefore, there is great interest in developing endoscopic and probe-based technology to expand the toolbox for mechanobiology and more quickly bring it into clinical practice.

Were there any previous research efforts, by yourself or others, that you found especially useful in advancing your work to this level?

The work presented in our latest publication is inspired by technology our laboratory has been developing for over a decade, but also by the works of many incredible researchers across several fields. The foundation of our technology was developed for the purpose of cell microscopy, which I repurposed in 2018-2019 for Brillouin spectroscopy at the tip of an optical fiber cable (which then led to the imaging capabilities presented here). Aside from the work of our group, the mechanisms used by our technology draw from the early theoretical and experimental work of J. Maris, J. Tauc, C. Thomsen, V. Gusev, O. Wright, O. Matsuda, G. Scarcelli, S.H. Yun, and others; there is also a great wealth of experience laid before us by the early development of photoacoustic endoscopy and all-optical laser ultrasound, through the works of L. Wang, P. Beard, A. Desjardins, and many others.

Briefly describe your discovery process, and how this differed from what was previously done.

The field of high-frequency acoustics has been under development for nearly half a century. Our approach has taken opto-acoustic sensor technology that has been widely reported since the 1980’s and has devised a way to make it an order of magnitude thinner, which means it can be deployed on the tip of a standard optical fiber (and therefore compatible with endoscopes). This overcomes many of the shortcomings of high-frequency acoustics–complex electrical systems and signals that quickly die– and provides access to its unique strengths such as high resolution and sensitivity to stiffness information.

Were there any particular challenges you encountered in your work?

Aside from the usual—instruments not working, exhaustion, hunger—the main challenges for the project were to do with the fabrication of the nanometer-sized sensors onto the tips of optical fiber (with good reproducibility), the engineering of the motion of the fiber-tip with nanometer-precision, and especially the signal processing of the acoustic signals to extract 3D information.

What sort of feedback did you receive regarding this work?

Young technologies, such as ours, tend to receive a broad spectrum of feedback. Some people get it right away and are very enthusiastic and complimentary; others are skeptical and may not resonate with the work or see the vision. Both sides have been equally integral for the development of our technology, and in shaping how we tell the story of our device. Especially feedback and encouragement from colleagues who have helped drive its development.

What are the next steps in this research?

In order to keep driving the technology towards maturity and future industrial and clinical applications, the acquisition speed of the system will need to be increased. There are a number of pathways toward achieving this which will be exciting to pursue. Additionally, the compatibility of the technology and signal processing with various biological tissues will be a key next step. Application to biology will be greatly enabled by increasing how deeply into tissue the device can probe. Ultimately, we are aiming to introduce our optical fibersystem into the human body and inspect stiffness in diseased cell/tissue towards in vivo biopsies.

Are there any specific references to your work that would be helpful for our readers in understanding your research?

Our initial development of the optical fiber Brillouin spectrometer can be found at 

, which then culminated in the 3D imaging-themed publication we’ve discussed in our paper. Additional information regarding the underlying technology and mechanisms (for imaging) can be found in the following publications from our group (3-5), in addition to the many helpful publications written by the authors listed above.

S. La Cavera, F. Pérez-Cota, R.J. Smith, et al., 

https://doi.org/10.1038/s41377-021-00532-7

S. La Cavera, F. Pérez-Cota, R. Fuentes-Domínguez, R. J. Smith, and M.Clark, 

F. Pérez-Cota, R. Fuentes-Domínguez, S. La Cavera, et al., 

R.J. Smith, F. Pérez-Cota, L.Marques, et al.,

https://doi.org/10.1038/s41598-021-82639-w

F. Pérez-Cota, R.Smith, E. Moradi, et al.

 is an EPSRC Doctoral Prize Fellow in the Optics and Photonics Research Group at the University of Nottingham, in Nottingham, United Kingdom, where he is developing phonon-based endoscopes for high resolution and high contrast imaging and tissue characterization. He holds a bachelor's degree in Physics, a master's in Photonic and Optical Engineering, and a doctorate in Electrical and Electronic Engineering from the University of Nottingham.

Salvatore La Cavera III and his colleagues in the Faculty of Engineering at the University of Nottingham, in Nottingham, United Kingdom have developed a novel measurement system consisting of two ultrafast lasers that excite and detect high-frequency ultrasound from a nano-transducer fabricated onto the tip of a single-mode optical fiber. 

Phonon Imaging in 3D with a Fiber Probe

R.D. McDowall is principal of McDowall Consulting and director of R.D. McDowall Limited, and "Questions of Quality" column editor for 

The U.S. Food and Drug Administration (FDA) has a new approach to computerized system validation (CSV) called computer system assurance (CSA). Without a draft guidance issued, are we entering an era of regulation by presentation and publication? As a result, does CSA risk becoming “complete stupidity assured?”

Computerized system validation (CSV) has an uninspiring reputation for being a slow, no-value-added activity that only wastes time and delays the implementation of new software.Is that an accurate portrayal?

 As somebody who has been involved with CSV for over 35 years, I would say it depends. Here are two CSV examplesone in which using CSV is sublime and another in which using CSV is ridiculous:

Map and improve your processes from which your intended use requirements are written. Then, apply effective risk management and scientifically sound logic to leverage supplier development and application configuration to focus testing. The rationale for why you don’t test one function is just as important as why you should test another. This approach is an example of managing the cost of compliance versus the cost of noncompliance, as discussed in a recent “Focus on Quality” column (1).

An organization has a one-size-fits-all approach to CSV. You know that you are wasting effort when the corporate CSV standard operating procedure (SOP) states that you must write three specification documents (user requirements specification, functional specification, and a design specification), but the intended use for a UV spectrometer is measuring absorbance of samples at one or two wavelengths. A detailed risk assessment adds fuel to the no added value fire, which is followed by a demand for detailed test instructions accompanied by copious screen shots to demonstrate that each step of each test had been executed.

A one-size-fits-all validation approach lacks the flexibility to tailor each validation based on intended use and condemns any regulated laboratory to mountains of paper. You can see why CSV gets a bad reputation; instead of applying common sense that then results in business benefit generated by the system, CSV is an inflexible approach, and coupled with the ultraconservative nature of the pharmaceutical industry, consigns it as an old-fashioned, outdated process. You should have an accurate assessment of a supplier’s development and testing, the application software category, and the impact of the records created by the system to focus the CSV effort where it is most needed. Flexibility is the name of the game.

Approximately 10 years ago, the FDA’s Center for Devices and Radiological Health (CDRH) started the “Case for Quality” initiative that was aimed at reviewing the problems medical device companies had with regulatory compliance. By 2015, one of the areas identified was CSV for the following reasons:

Instead of demonstrating its intended use, CSV was aimed at producing documentation to keep auditors and inspectors quiet.

CSV was synonymous with delay, and as a result, it ended up being a necessary evil instead of a value-added activity.

Risk assessments were complex, burdensome, and expensive.

80% of test problems were because of errors by a tester or in the test instructions.

Industries used regulatory burden as an excuse for not using technological advances to improve CSV.

As a result, the FDA set up a joint CDRH and industry team to develop a new validation approach to computerized systems used in the medical device industry with the aim of following the least burdensome approach, called 

An CDRH guidance for industry is the General Principles of Software Validation issued in 2002. In section 2.3, it states:

We believe we should consider the least burdensome approach in all areas of medical device regulation. This guidance reflects our careful review of the relevant scientific and legal requirements and what we believe is the least burdensome way for you to comply with those requirements

This section concludes with an invitation that if a company knows of a better way to validate software, talk to the Agency. The guidance goes further in section 4.8 on validation coverage which is quoted verbatim:

Validation coverage should be based on the software’s complexity and safety risk, not on firm size or resource constraints. The selection of validation activities, tasks, and work items should be commensurate with the complexity of the software design and the risk associated with the use of the software for the specified intended use. For lower risk devices, only baseline validation activities may be conducted. As the risk increases additional validation activities should be added to cover the additional risk. Validation documentation should be sufficient to demonstrate that all software validation plans and procedures have been completed successfully 

The key takeaway here is to focus on intended use, risk and the nature of the software used. The 20-year-old FDA guidance sends a clear message that paraphrased says it is important to not kill yourself with compliance. For too long, the pharmaceutical industry has not evolved from a risk-averse to a risk-managed industry.

The FDA is divided into several centers. There are two centers that are discussed at length in this column:

Center for Drug Evaluation and Research (CDER):

 This center is responsible for the pharmaceutical industry. The applicable Good Manufacturing Practice (GMP) regulations are 21 

 211, which were first issued in 1978 with a minor update in 2008 (3,4). Because of the age of these regulations, there is no explicit requirement to perform validation of computerized systems.

Center for Devices and Radiological Health (CDRH): 

This center is responsible for medical devices, including scanners. The GMP regulations CDRH uses are 21 

 820 (5) that are based on a 1990s version of ISO 13485 (6). GMP has specific requirements to validate software used in a medical device (21 

 820.30) and used for process control and the quality management system (QMS) (21 

Software that is used to control or operate a medical device is already validated when you purchase it, which contrasts radically with software used in the pharmaceutical industry that is not validated when you buy it (although many suppliers would like you to believe it is), and the laboratory must undertake CSV to demonstrate that it is fit for intended use, which is based on business needs and the process being automated. Bear in mind that CSA is aimed primarily at medical devices and not the pharmaceutical industry.

The joint industry–CDRH team developed the principles of CSA as:

Focus on the intended use of an application, usually a medical device

Shift from documentation to critical thinking

The evidence should add value to the testing process, which is to reduce the number software bugs as well as demonstrate intended use

Use automation to help the process such as requirements management and testing

Pilot projects were used to verify and refine the CSA approach. Since 2017, there have been a number of presentations and publications from both FDA staff members and members of the various pilot projects. So far, so good.

However, despite a draft guidance for industry for CSA being on CDRH’s list of documents to be issued since 2018, nothing has appeared from the Agency, which is a problem.

This is the regulatory equivalent of “Waiting for Godot” where the two main characters of the play are on stage for over two hours spouting rubbish and Godot never turns up.

It is interesting to contrast the differences in approach to regulations between the USA and Europe. Since 2011, the European Union (EU) GMP has updated eight of the nine chapters of Part I, including several Annexes such as 11 and 15. Indeed, chapter 4 and Annex 11 are being revised again to reinforce data integrity principles. In contrast, the U.S. GMP (21 

 211) published in 1978 has only been updated once in 2008 with the addition of one clause impacting manufacturing: 211.68(c) (4).

It is my opinion that the FDA, specifically CDRH, is inept and unprofessional in failing to issue a draft guidance on CSA.

Instead of updating regulations, FDA issues advice as either a Level 1 Guidance for industry documents or a Level 2 Question and Answers section published on the FDA website. Let us focus on the Level 1 guidance, which are usually issued as a draft for industry comment and after a prolonged reflection once a final version is issued. Relatively fast track examples of guidance
issuance are:

The Part 11 Scope and Application guidance, which was issued in February 2003 and finalized in August the
same year (7).

The Data Integrity and Compliance with cGMP guidance moved more slowly in progressing from a draft in April 2016 to a final version in December 2018 (8).

However, the usual guidance issuance within the FDA is less stellar and more glacial:

The Out of Specification guidance draft was completed in September 1998, but the final version did not arrive until October 2006 (9).

The GMP method validation guidance draft guidance issued in 2000. The final guidance was issued in July 2015 a mere 15 years after the draft. (10).

It could be argued that guidance documents are regulation by the back door, but all have the phrase “contains nonbinding recommendations” emblazoned on each page, which could mean that content could be difficult to enforce.

In the absence of a draft guidance for industry, there are presentations from CDRH officials and industry members from the pilot programs as well as articles, white papers, and industry guidances published. The situation is that we are putting the industry interpretation cart before the regulatory horse. Typically, the reverse is true: FDA issue a draft guidance for industry comment and then presentations and publications follow with industry implementing after citations in Warning Letters. Not this time, as the genie is already out of the bottle. Houston, we have a bigger problem.

Regulations and Level 1 regulatory guidance documents must go through due process. This process involves issuing a draft for industry comment, revision where appropriate, followed by the final version. For regulations, the final version published in the 

 contains a precis of industry comments together with the review and response by the Agency that either rejects or acts upon them. You can see this for 21 

: The regulation is three pages and the preamble comments are 35 pages (11).

However, with CSA, the situation is different. The FDA list guidance documents that they will issue each year and one for CSA has been on the list for at least three years. Covid is not an excuse for inaction by the Agency as the guidance was promised before the pandemic and working from home should enable a guidance to be issued. Instead, presentations and publications outlining how to undertake CSA are being thrown out like garbage. However, it is important to note that:

This is regulation by presentation and publication.

Without a draft guidance for industry, we cannot see if the FDA aims for CSA are being filtered, enhanced, or subverted. In other words, there is a concern over the regulatory integrity of the perspectives being presented. As a regulated industry, we cannot change direction based solely on rumor: We need a draft guidance. But...

With all of the issues surrounding CSA, an important question emerges: Do we need it at all? Have we got regulations and guidance for the pharmaceutical industry in place now that give us the flexibility to do what is purported to be in the CSA guidance? In my view, the answer is yes, and I’ll explain in the following sections. Of course, this is my interpretation, but because there is no guidance for industry, there may be gaps in my discussion.

Below are two quotes from “General Principles of Software Validation”. Section 2.1 simply states:

This document is based on generally recognized software validation principles and, therefore, can be applied to any software (2).


The guidance scope outlined in section 2 notes:

This guidance recommends an integration of software life cycle management and risk management activities. Based on the intended use and the safety risk associated with the software to be developed, the software developer should determine the specific approach, the combination of techniques to be used, and the level of effort to be applied” (2).

A flexible risk-based CSV approach for all software is mirrored in EU GMP Annexes 11 and 15. Clause 1 of Annex 11 focuses on risk management:

Risk management should be applied throughout the lifecycle of the computerized system taking into account patient safety, data integrity and product quality. As part of a risk management system,

decisions on the extent of validation and data integrity controls should be based on a justified and documented risk assessment of the computerized system (12).

The regulation explicitly states that the extent of validation and data integrity controls should be based on the risk posed by a system to a product and patient. A product for a laboratory can be interpreted as the data for a submission or the medicinal product for patients. Clause 1 implicitly means that a one-size-fits-all validation approach is inappropriate. It is important to fit the validation to the system, not the other way around.

A system level risk assessment for analytical instruments and systems was published in this column in 2013 by Chris Burgess and myself (13). This approach can be used to classify each into the updated USP <1058> Groups B and C subtypes (14)
to determine the extent of qualification and validation required. Next, we have Annex 15 on Qualification and Validation clause 2.5:

Qualification documents may be combined together, where appropriate, e.g. installation qualification (IQ) and operational qualification (OQ) (15).


This makes sense when an engineer installs and qualifies your next spectrometer,
you could have an single installation qualification (IQ) or operational qualification (OQ) document that combines the two activities into one document. A single document for pre-execution review and post-execution approval is appealing. This approach is mirrored in USP <1058> that allows, where appropriate, qualification activities and associated documentation to be combined together (IQ and OQ) (14). But why stop there?

Remember the UV spectrometer we discussed in the introduction where all that the system did was measure absorbance at a few wavelengths? Why not take Annex 15 clause 2.5 to a logical conclusion and combine all validation elements into one? The document should include:

The intended use requirements only (that is, the operating range, accuracy, linearity, expected performance, and compliance).

Requirements traceability within the document is easy to establish with test references and links to SOPs for system use.

Either cross reference to a supplier’s installation and commissioning documents or, if not available, the same elements can be written within the integrated document.

Test preparation and any documented assumptions, exclusions, and limitations of the test approach.

Test cases to demonstrate the system meets intended its requirements

Test execution notes to record and
resolve any testing problems

Validation summary report and operational release statement

This sounds like a long list, but with the focus on intended use requirements only, this can be a relatively short document. Control of the process would be via an SOP or validation master plan. I have practiced and published such an approach for systems based on GAMP software category 3 and simple category 4 even if the data generated were used in batch release or submitted to regulatory agencies (16). The key is documented risk management as required by clause 1 of Annex 11 (12).

I know what you are thinking, General Principles of Software Validation (2) recommends risk assessments to focus work and EU GMP Annex 11 says your need risk management should be applied throughout the lifecycle of the computerized system (12). However, this does not mean you must always perform a qualitative failure mode effect analysis (FMEA) described in GAMP 5 (17).

Let me give you an example of stupidity of a risk assessment for remediation of a data integrity audit finding: All users sharing the same user account. Sharp intake of breath: no attribution of action! What happened next? The laboratory then assessed the risk and impact of a shared user account with an FMEA risk assessment with a numeric assessment of all elements. At the end of the assessment, a single number is produced and compared against a scale to determine if it is critical, major, minor, or low. Unsurprisingly, the number indicates that this is a critical issue (the same as the auditor’s finding!)—and only now a remedial action is triggered. Guess what the resolution is? Yep, give each user their own account. Least burdensome approach? I don’t think so.

How stupid is this? It is death by compliance. Once identified in the audit, you know you have a critical vulnerability. You are out of compliance. Fix it. Don’t perform a risk assessment when you know what the only possible outcome will be. Just fix it. Annex 11 does not require a risk assessment; it requires that risk management be applied. The audit has identified the risk; the remediation is to give each user their own account that is faster, easier, and compliant with GMP. As Audny Stenbråten, a retired Norwegian GMP inspector, stated, “Using risk assessment does not provide an excuse to be out of compliance.” There is an interesting article by O’Donnell and others entitled “The Concept of Formality in Quality Risk Management“ that is recommended for further reading on this topic (18).

Rather than just apply a single risk assessment methodology as described in the GAMP Guide (17), there are methodologies that could be applied to implement a scalable risk assessment approach to both application software and IT infrastructure (19).

Advocates of CSA mention trusted software suppliers. Let’s go back to 2008 and the publication of GAMP 5, often cited by the FDA, which discusses leveraging supplier involvement in sections 2.1.5, 7, and 8.3 plus Appendix M2 for supplier assessments (17). There are comments about leveraging supplier testing into your validation. To leverage supplier testing and reduce your validation effort, you must do more than just sending out a questionnaire for the supplier to fill out and QA to stick in a filing cabinet or document management system. This process requires a proactive assessment that reviews the procedures and practice of software development for software category 4 applications, such as:

What is the software development life cycle is used?

What software development tools are used?

How are requirements for the system specified and understood?

How extensive and accurate are software code reviews and are they acted upon?

How are software builds and configuration managed?

Is regression testing systematically
performed and accurately reported?

How are software errors identified,
classified and resolved?

The greater the investment in understanding the suppliers QMS and software development, the more you can rely on supplier decisions and processes. This type of assessment is not suitable for a questionnaire, but either an onsite or remote audit. It will require at least a day to perform. You are looking for a robust software development process. Identify two or three requirements and trace them through the supplier’s development process: How extensive is the work and does this give you confidence in the supplier? As part of the evaluation, include questions that a supplier must answer about collaboration and sharing information about instrument and software issues and updates. You want a supplier you can trust. This assessment must be documented in a report as it is the foundation on which you leverage the supplier’s development into your validation project to reduce the amount of work.

The application assessed is a configurable software product (GAMP category 4).

Although classified as category 4 at the application level, there are many functions that are category 3 that are simply parameterized, such as selecting a wavelength to measure a sample absorbance.

Assess the requirements in the URS and compare to the application then classify each one as either category 4 (configured) or 3 (parameterized)

Computerized system validation (CSV) has an uninspiring reputation for
being a slow, no-value-added activity that only wastes time and delays the
implementation of new software. Is that an accurate portrayal? And is it a
necessary evil?

Does CSA Mean  “Complete Stupidity Assured?”

Because antibiotics are regularly used for chicken, food safety is of utmost importance, and health experts pay attention to the effect antibiotics could have on human health. This study examines how surface-enhanced Raman spectroscopy (SERS) was used to identify two antibiotic residues in chicken, doxycycline hydrochloride (DCH) and tylosin (TYL). A single-factor experiment method was adopted to optimize the SERS detection conditions. Results show that the SERS intensities of the chicken samples containing DCH and TYL had greater effectiveness in the peaks of 672 and 771 cm

 under gold nanoparticles (Au NPs) as the enhancement substrate at 10 min of the optimal adsorption time. The original SERS spectra were pretreated using the method of adaptive iterative penalty least square (air-PLS) and the second derivative, where the feature vectors were extracted by principal component analysis (PCA). The first four principal component scoring was selected as the input values of linear discriminant analysis (LDA) with an overall classification accuracy of 100% for the test set. The experimental results show that SERS technology can identify DCH and TYL in chicken.

With the rapid development of social industrialization and economization, meat, eggs, milk, and dairy products have gradually become an essential part of the contemporary diet because of the continuous improvement in the modern standard of living (1). Meanwhile, an increasing number of problems related to animal food safety have emerged around the world (2). Recently, antibiotics have been widely used in poultry to treat infectious diseases and accelerate growth (3). Tylosin (TYL), a 16-membered macrolide compound produced by the culture solution of Streptomyces fradiae, has been used as a special veterinary drug, promoting growth and treating mycoplasma in chicken. Furthermore, doxycycline hydrochloride (DCH), a semi-synthetic broad-spectrum antibiotic derived from oxytetracycline, was frequently considered as a feed additive in chicken farms. However, if chickens were overfed DCH and TYL, the two antibiotic residues may not be completely metabolized in chicken. As a result, they can cause allergic or toxic reactions, posing a potential threat to human health. 

The common detection methods used for antibiotics were microbiological inhibition methods (4), high performance liquid chromatography–tandem mass spectrometry (HPLC–MS) (5), and immunoassay (6). Although these methods were simple to operate and inexpensive, they had stricter requirements for sample pretreatment and a time-consuming detection process, which made it difficult to meet the requirements of rapid detection of DCH and TYL residues in chicken. Therefore, a rapid, nondestructive, and sensitive detection method of DCH and TYL residues in chicken needed to be implemented.

Surface-enhanced Raman spectroscopy (SERS), a simple and ultrasensitive analysis technique, was characterized by simple sample preparation and the nondestructive and fast spectral collection (7,8). Some metals, including silver or gold nanoparticles (NPs), were used as SERS enhancement substrates (9). With the advantages of SERS technology, SERS has been widely used in food safety detection and has been utilized to detect pesticide residues on fruit surface (10), veterinary drug residues in animal (11), and food-borne pathogenic microorganisms in food (12). Peng and others used SERS technology to detect TYL residues in water, establishing a good linear relationship between TYL concentrations and Raman intensities to conduct quantitative analysis for TYL in water (13). Lee and others adopted a SERS method for the qualitative and quantitative analysis of chlortetracycline and oxytetracycline in animal feed, resulting in the real-time monitoring and screening of tetracycline drugs in animal feed (14). Premasiri and others identified 12 bacterial strains of urinary tract infections (UTI) with a sensitivity of more than 95% and a specificity of more than 99% by using the partial least-squares discriminant analysis (PLS-DA) classification method (15). However, there is not yet research on identifying DCH and TYL residues in chicken. Hence, there is a need for a rapid method of detection of identifying DCH and TYL residues in chickens using SERS technology.

In this study, TYL and DCH residues in chicken could be rapidly identified using SERS combined with multivariable technology. The four spectral pretreatments— adaptive iteratively reweighted penalized least squares (air-PLS) and normalization, air-PLS and standard normal variate (SNV), air-PLS and first derivative, and air-PLS and second derivative—were performed on the raw SERS spectra. The principal component scoring was selected as the inputs for the support vector machine (SVM) model, and the linear discriminant analysis (LDA) model established the corresponding prediction models and compared their classification effects. The experiment results provided a theoretical basis for identifying antibiotic residues in animal-derived food.

DCH (99%) and sodium chloride (NaCl) were purchased from Nanchang Jingke Scientific Instrument Co. Ltd. TYL (98%) was provided by Dr. Ehrenstorfer from Germany. Gold (III) chloride trihydrate (HAuCl

O) with a gold content of no less than 49% was obtained from Sigma-Aldrich. Trisodium citrate was analytical grade (Xilong Chemical Co., Ltd.). Ultrapure water used throughout the experiments was extracted by an automatic reverse osmosis (RO) pure water machine (Hunan Colton Water Co., Ltd.). The fresh chicken breasts bought from Hualian supermarket in Nanchang, China, were placed in the sterile vacuum bag and kept in the laboratory refrigerator at −20 °C before use.

We obtained 0.04 g/L DCH and TYL standard solutions by dissolving 0.02 g DCH and TYL standards with ultrapure water, respectively. Next, these different concentration gradients of DCH solution, TYL solution, and DCH and TYL solution (0.005~0.035 g/L) were obtained by diluting by DCH or TYL standard solutions with ultrapure water.

The chicken breasts were cut promptly into slices after they were taken out from the refrigerator, and they were dried in an FD-1A-50 vacuum freeze dryer (Beijing Medical Laboratory Instrument Co. Ltd.) for 24 h. The dried chicken slices were sheared into equal square pieces. Subsequently, they were dipped in DCH solution, TYL solution, and the DCH and TYL solution to obtain three groups of the spiked chicken samples. The untreated fresh chicken samples were the chicken samples without DCH and TYL (the blank group).

Gold NPs were synthesized by a seed growth method using sodium citrate that is mentioned in the literature (16). All glassware was soaked by freshly prepared aqua regia for 24 h before using and then washed completely by ultrapure water. Then, 150 mL of 2.2 mM sodium citrate solution was placed in a three-neck round bottom flask to a rolling boil for 15 min (ZNCL-T intelligent constant temperature agitator, Yarong Instrument Co. Ltd.). Next, 1 mL of 25 mM HAuCl

 solution was added after the solution boiled. In the meantime, the color gradually changes in the solution could be observed from yellow to blue-gray and to light pink within 10 min. When the above solution cooled to 90 °C, 1 mL sodium citrate solution (60 mM) was added at once, and then 1 mL of 25 mM HAuCl

 solution was added approximately 2 min later. The reaction solution was removed in 2 mL aliquots when stirring for 30 min. By that repeating growth process 14 times, gold NPs with progressively larger sizes were obtained. Eventually, the gold NPs colloid was cooled and stored at 4 °C for further use.

For the next part of the study, 30 μL of gold NPs was dripped onto the chicken samples, and then their spectra were measured using a DXR micro-Raman spectrometer (Seymour Fisher Technology Co., Ltd.) with an adsorption time of 10 min. Here, the instrument parameters were set as follows: 18.9 mW of the laser energy, 5 s of collect exposure time, 10 s of preview collection time, 10 times of sample exposures, and 16 times of background exposures. For identifying the four groups of chicken samples, the SERS spectrum of each sample was collected only once, and a total of 400 samples were obtained. Among them, two-thirds (268) of the samples were randomly selected as a training set to establish the discrimination model, and the rest (132) was used as a test set to verify the classification accuracy.

Adsorption time was one of the important factors affecting SERS intensity. To enhance SERS signal intensities of DCH and TYL in chicken, the adsorption time was optimized in this paper. According to the single factor experiment method, the SERS spectra of samples were measured at 0, 2, 4, 6, 8, 10, 12, and 14 min of adsorption time, respectively, after 30 μL gold NPs were dropped on chicken samples containing DCH and TYL. The SERS spectra of five parallel samples, of which the average spectrum was taken as the SERS spectrum at each adsorption time, were measured for each adsorption time point. The optimal adsorption time was determined by comparing the SERS intensities of the characteristic peaks at 672 and 771 cm

Also, different SERS enhancement substrates could produce different enhancement effects on the SERS signal of chicken samples. In this regard, 30 μL gold NPs and 30 μL gold NPs mixed with 50 μL NaCl (0.1 g/L) were added onto chicken samples containing DCH and TYL, and then their SERS spectra were measured at 10 min of adsorption time. Five parallel samples were processed for each enhancement method (gold NPs and gold NPs mixed with NaCl), and their average spectrum was taken as a SERS spectrum of each enhancement substrate. The SERS enhancement substrate was determined by comparing the SERS intensities of the characteristic peaks at 672 and 771 cm

The SERS spectral data in the range of 400 to 1800 cm

 were selected to make processing analysis. After some useless background noises in the original, SERS spectra were deducted through the air-PLS algorithm carried out in Matlab 2014a software (The Mathworks, Inc.). The second derivative was processed using the Unscrambler X10.4 (Camo). Next, feature vectors were extracted using the PCA method on the Unscrambler X10.4. The numbers of principal component scoring were chosen by analyzing the PCA scoring chart, and they were used as the input values of classification recognition models (SVM and LDA). The best classification recognition model was determined by comparing the results of discrimination for four groups of chicken samples. To improve the classification accuracy, the best spectral preprocessing method was selected based on the total classification accuracy of the test set by comparing four kinds of preprocessing methods (air-PLS and normalization, air-PLS and SNV, air-PLS and first derivative, and air-PLS and second derivative). The sensitivities and specificities of the four groups of samples were calculated to examine the ability of correct classification for each group of samples.

The SERS spectra of the DCH and TYL standards from 400 to 1800 cm

 were presented in Figure 1. The SERS characteristic peaks of TYL standard at 673, 811, 931, 1238, and 1591 cm

, which didn’t overlap with those of the DCH standard, could be observed. These peaks could be used as the characteristic peaks to identify TYL from these two standards. Similarly, 493, 610, 703, 771, 884, and 1355 cm

 were regarded as the SERS characteristic peaks to distinguish DCH from these two standards. However, whether these peaks could be used as SERS characteristic peaks to identify DCH and TYL in chicken still needed further analysis.

FIGURE 1: The SERS spectra of (a) TYL and (b) DCH standards.

To confirm whether DCH and TYL in chicken could be identified, the average spectra of four groups of samples (blank group in curve a, DCH group in curve b, TYL group in curve c, and DCH+TYL group in curve d) can be seen in Figure 2. The SERS characteristic peaks of the DCH and TYL group at 484, 561, 646, 672, 771, 820, 893, 1285, and 1372 cm

 were not repeated with those of the blank group. Thus, these peaks were preliminarily determined to distinguish DCH or TYL residues in chicken. Further comparing and analyzing the SERS spectra of the DCH group and the TYL group, the palpable SERS peaks of the DCH group were at 646, 771, 893, 1285, and 1372 cm

, and the peaks of the TYL group at 561, 672, 820, 1285 and 1372 cm

 could be observed. The SERS peaks of these two groups of DCH and TYL were not overlapped at 561, 672, 771, 820, and 893 cm

, and the SERS peaks at 646, 1285, and 1372 cm

 concurrently belonging to these two groups could not be considered SERS-identified peaks of DCH or TYL in chicken. The results illustrated that at 561, 672, 771, 820, and 893 cm

 could be used as the SERS characteristic peaks of DCH and TYL residues in chickens. Considering whether these peaks truly belong to DCH and TYL, 672 and 771 cm

 could be used as the characteristic peaks of DCH and TYL by analyzing the characteristic peaks of standard in Figure 1. The Raman peak of DCH in chicken was blue-shifted to 672 cm

 compared with that of DCH standard at 673 cm

 could be regarded as SERS characteristic peaks to identify DCH and TYL in chicken.

FIGURE 2: The average SERS spectra of samples for four groups: (a) Blank group, (b) DCH group, (c) TYL group, and (d) DCH and TYL group.

Effect of Optimization Conditions on SERS Intensities

When the molecules of DCH and TYL aggregate on the rough surfaces of gold NPs with adsorption capacity, the Raman intensities of for both can be enhanced (17). When NaCl, magnesium sulfate, and other electrolytes are selected as emulsifying agents, their Raman intensities may be further increased or decreased (18). Therefore, the enhancement effect of NaCl on SERS intensities for chicken samples containing DCH and TYL was investigated in this paper. As observed from the illustration in Figure 3, SERS intensities at 672 and 771 cm

 in the curves A were lower than those in curve B. The addition of Cl

 can promote the polymerization of gold NPs, and plenty of hotspots would be generated to further enhance the SERS enhancement effect in this process. However, the SERS enhancement effect can be also inhibited in the case of a large amount of Cl

, because of the decline of the surface area ratio of gold NPs to the measured molecules following the growing amount of Cl

 (19). From this, gold NPs were chosen as SERS enhancement substrate in the follow-up experiments.

FIGURE 3: The representative spectra of DCH and TYL group: (a) 30 μL gold NPs mixed with 50 μL NaCl (0.1 g/L) solution, (b) 30 μL of gold NPs. The inset was the mean SERS intensities at 672 and 771 cm

The adsorption time of molecules on the rough surface of gold NPs had a vital impact on the enhancement of SERS intensities. The influence of diverse adsorption time on SERS intensities of chicken samples containing DCH and TYL are displayed in Figure 4. In the range of 0 to 14 min, a large SERS intensity was found at 672 cm

 when the adsorption time reached 6 min. If 6 min was selected as the best adsorption time, the detection limit of DCH in chicken would be affected because SERS intensity at 771 cm

 was relatively weaker than that at 672 cm

. When the adsorption time was extended to 10 min, SERS intensity at 771 cm

 gradually reached the topical maximum, and the peak intensity at 662 cm

. Hereby, 10 min was used as the best adsorption time.

FIGURE 4: The effect of the different adsorption times on SERS intensities.

Classification Results of SERS Spectral Data

An increasing number of antibiotic residue problems in food have emerged
around the world. We examine how SERS is used to identify antibiotic residues in chicken, focusing on doxycycline hydrochloride and tylosin.