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Spectral Sensing Using a Handheld NIR Module Based on a Fully Integrated Sensor Chip

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Rapid Determination of the Peroxide Value of Edible Oil 

An Archaeometric Investigation into the Former Cataract House Hotel via Elemental Analysis

In a follow-up to my February 2020 column, I started a more systematic study of extractables and leachables. Following a suggestion from Mark Witkowski of the FDA, I looked at three sets of centrifuge vials that were exposed to the following liquids in an effort to evaluate the potential of Raman microscopy to identify compounds exiting in polymers under particular conditions: saline, phosphate buffer, water, saline treatment at 100 

C, ethanol, chloroform, pH 5, and pH 9. Although all containers were made of polypropylene (PP), they didn’t behave similarly. Compounds that were extracted from PP vials from different manufacturers were not always the same. Although the number of spectral types that are recorded is large, this article focuses on a few whose interpretation is interesting. The goal was to figure out when it makes sense to employ Raman microscopy for such identification. The characteristics considered were ease of sample preparation, the minimum quantity of material amenable to analysis, and the quality of the identification.

 is the Principal Raman Applications Scientist for Horiba Scientific in Edison, New Jersey.

The selection of solvents for generating extractables and leachables from these polypropylene vials was informed by two USP publications (1,2). Polished stainless steel slides were used as substrates for the analysis because most metals, including stainless steel, do not have a Raman spectrum. These slides virtually provided a background-free substrate for the spectra. For each vial, samples were prepared immediately after receipt and then approximately 10 days after receipt. After the droplets dried, a mosaic micrograph was recorded with a low magnification objective. Then, the regions of interest were selected and imaged at 50x or 100× magnification before spectra were recorded. Interestingly, it was the 638-nm laser line that provided the best results; there was often fluorescence at 532 nm, the signal-to-noise (S/N) using the 785-nm laser was significantly poorer, and the sensitivity in the CH/NH region was compromised. Because of differences in solubility, if there are more than one species in a sample, they tend to separate during precipitation so spectra of the different species are recorded separately. In all cases, spectra were recorded from multiple spots with similar morphology to ensure that a spectrum was representative of the sample. Although multiple spectra can be recorded with the mapping function (that is, in a two-dimensional [2D] area with evenly spaced points), we more often used a function where we could select points in the image. By selecting spots with the same morphology, the analysis time was significantly reduced compared to what would have been required for a map acquisition. To improve the S/N of the spectra, identical multiple spectra from a given data set were averaged together.

We used KnowItAll (KIA) (3) software (Wiley) for spectral interpretation. To optimize the relative intensities in the spectra for matching, spectra were collected with the instrument correction spectrum (ICS) function turned on. If a suitable match did not appear, we used standard rules for spectral interpretation to provide an identification of at least the class of molecule represented by the spectrum.

To demonstrate the functionality of our method, Figure 1 shows the spectrum of one of the vials, as identified as polypropylene (PP) in KIA. However, you may notice that I have not chosen the spectrum with the highest hit quality index (HQI). The first bands near 1620 and 1650 cm

 that are spectrum, with an HQI of 92.1, has extra not in my spectrum. The second spectrum with an HQI of 90.1, is identified as a copolymer of PP and polyacrylic acid (6%). For the moment, I am assuming that these tubes are pure PP, possibly containing some additives, so I have chosen the third entry. The ambiguity in even such a simple query indicates the reasons that some purist spectroscopists warn against solely using spectral data bases for identification.

FIGURE 1: (a) Raman microscope spectrum of a centrifuge vial (bottom black spectrum) with (b) the top three matches in KIA software (see text for discussion). Top matching spectra are shown in both (a) and (b).

Figure 2 shows mosaic images of deposits from centrifuge vials from the three vendors. Many of the deposits indicate the morphology of NaCl crystals, but with material of more disordered morphology adhering to the NaCl cubes. Of course, NaCl has no first-order Raman spectrum, so the spectra that we show were acquired from material clinging to the salt crystals. Figure 3 shows spectra of deposits from vials exposed to saline from each of the 3 suppliers, with the spectrum of PP shown at the bottom for easy reference. The startling observation is that each vial yielded different spectra. When a spectrum indicated the presence of elemental carbon, its two bands had been eliminated manually before plotting in Figure 3 or importing into the KIA software.

FIGURE 2: Low magnification mosaic micrographs of deposits from the saline-exposed centrifuge vials of the three sources studied here. From left to right: first, second, and third vendors.

FIGURE 3: Spectra of deposits from the saline solutions in centrifuge vials of the three vendors. Note that two different species were observed in the vial of the second vendor (second and third spectra from the top). The spectrum of one of the polypropylene vials is shown on the bottom as a reference.

Each spectrum was examined separately in the KIA software, and the results are displayed in Figure 4. None of the matches are as good as that for PP, but there still is ample information present. I should explain how I decide that a match is “good” when a match of the quality of the PP does not appear. The most important thing to understand is that the HQI represents the numerical overlap of the query spectrum with a spectrum from the data base. If there is a baseline, it will tend to overwhelm the HQI without really representing the species present. So the first thing to do is to subtract any baseline in your query spectrum and to enable baseline subtraction in KIA. I have also found that if there are broad bands in my spectrum, for instance from carbon, the searching algorithm finds carbon before anything else. Therefore, it is often helpful to remove the carbon contribution manually from the query spectrum. I will be the first to admit that doing so is not rigorous, but it is effective. That is because looking for a mixture spectrum with sharp bands and the carbon spectrum is not always straightforward because the carbon spectrum is so variable.

FIGURE 4: (a) Spectrum of extract from the first vial (black spectrum) exposed to saline with the “best” match spectrum being 3-hydroxybutanoic acid, found in IDExpert matching software (red spectrum). (bi) Spectrum of extract from the second vial exposed to saline (red spectrum) with the “best” match identified as a block copolymer styrene/isoprene as displayed in SearchIt (blue spectrum). (bii) Spectrum of a second extract species from the second vial (black spectrum) exposed to saline with the “best” match as displayed in ID Expert, a dye called sepisol fast blue (red spectrum). (c) Spectrum of extract from the vial of the third vendor exposed to saline (blue spectrum) with the “best” match being a mixture of poly (N-vinyl pyrollidone) and naphthalene 1,3,6 trisulfonic acid trisodium salt, as found in SearchIt (red composite spectrum).

So if a really good match does not pop up, the question is how to find the “best” match. What I do is to examine the listing in software, which is arranged in decreasing values for HQI while also examining the spectral pattern. I want to find a hit that has prominent bands in the same region as the prominent bands in the query spectrum. Unfortunately, this is not a simple selection of the spectrum with the highest HQI because of the reason explained in the previous paragraph.

So now let’s consider the results of the species found in the saline solutions from the three vendors’ vials. The first is a small molecule of 3-hydroxy butanoic acid. The second had two spectra—the first, a block copolymer of styrene isoprene, and the second, a spectrum of a dye, Sepisol Fast Blue. The spectrum of the deposit from the third vendor’s vial matched best to a mixture of poly (N-vinyl pyrollidone) and naphthalene 1,3,6 trisulfonic acid trisodium salt.

In Figure 4a, the match to 3-hydroxy butanoic acid is fairly good except there is no >C=O band in my spectrum at approximately 1750 cm

. In fact, this spectrum had the presence of the broad carbon bands near 1340 and 1600 cm

 that were interfering with my search so I eliminated them manually, and a small feature at ~1750 cm

 may have been buried in the broad carbon band that I subtracted. Figure 4a is an example that illustrates the hazards of this kind of operation. In addition, my spectrum also shows a broad doublet above 3000 cm

, which indicates the presence of residual water in the deposit.

Figure 4bi shows the best match to a copolymer of styrene isoprene, which was found in SearchIt but not IDExpert. In IDExpert, the sharp band at approximately 1000 cm

 was never picked up, probably because it is sharp and at low intensity and would thus have a low contribution to the HQI. In addition, a sharp band near 1660 cm

 was also missed. However, it is well known that many aromatics have a doublet at 1000–1040 cm

 often indicates the presence of olefinic >C=C<. Note that SearchIt found the copolymer of styrene isoprene on its first hit. In addition, the relative intensities of these bands at 1000 and 1660 cm

 varies between my spectrum and that in the database, presumably because of the relative concentrations of the aromatic to olefinic species.

Figure 4bii shows the second spectrum observed in the deposit of this vial and identified it as Sepisol Fast Blue. It turns out that the vials from this vendor were colored, and a comparison of this spectrum in Figure 3bii to that of the colored vial (not shown), the spectrum of the colored vial matched a combination of that of PP and that of the spectrum of Sepisol Fast Blue.

Figure 4c shows a “best” match to the spectrum of the third vendor to a mixture of poly (N-vinyl pyrollidone) and naphthalene 1,3,6 trisulfonic acid trisodium salt. Although the match is not perfect, the prominent bands in the composite spectrum do overlap with the prominent bands in the query spectrum. Are my bands broadened by disorder in the polymer phase and interactions with the smaller molecule? It is hard to say, but it is certainly a possibility. This example is a case where liquid chromatography–mass spec- trometry (LC–MS) or gas chromatography–MS (GC–MS) could be helpful in determining the origin of this contaminant.

Why Are These Molecules Present in the PP vials?

Additives are added to polymers for many reasons, which include antioxidant functionality, gas barrier, physical flexibility, and other reasons. The selection of the additive is dictated by the desired final properties of the material. I am not a polymer engineer so I cannot say what could be the reason for finding the products that we did in these saline solutions. However, in the future, after we have analyzed the deposits from all of the solutions, we may see a recognizable pattern.

In this article, there is not enough space to review and analyze the total data set. However, I want to show one example that illustrates the identification of a deposit whose presence I do understand. Figure 5 shows the result of a search in the KIA software of a spectrum deposited from a vial of the third vendor that had been exposed to the phosphate buffer at 100 °C. The match is excellent for a silicone oil. In fact, I have a spectrum of a solid silicone (not shown) whose spectral peaks match just as well, but maybe with different relative intensities. I suppose the difference between solid silicone and silicone oil could be simply the molecular weight, and we know that the Raman spectrum is not very sensitive to molecular weight. We also know that in a solid pellet that has presumably been extruded from the melt, there can be molecular orientation that can affect the relative intensities. Can we make sense of the presence of silicone oil in the deposit? Of course! It is often added as a release agent during a molding or extruding operation.

FIGURE 5: Spectrum of a deposit from a vial of the third vendor exposed to phosphate buffer at 100 

C (blue spectrum) overlaid with a match to silicone oil by KIA (red spectrum).

In an effort to evaluate the potential of Raman microscopy to identify extractables and leachables from polymers, I collected spectra from three sets of polypropylene centrifuge vials from three vendors exposed to 10 different solutions designed to tease out material hiding in the polymer matrix. I examined deposits, for the most part, only from saline solutions. I have described how I collected the spectra, and then how I prepared them for analysis in the KIA software. It is not always clear why particular molecules are being detected, but I picked one example where its presence as a release agent was clear.

The real question is, why bother with all of this? The standard analytical method for identifying extractables and leachables is liquid or gas chromatography combined with mass spectroscopy (LC–MS or GC–MS). What Raman can do that those standard techniques cannot is identify minute amounts of material—typically 1–2 μm

 × 1–5 μm deep. And there are no waste products from the analysis that need to be disposed of. Is the information the same? Maybe yes and maybe no. The measurement is done on intact material whereas MS tears the molecule apart. There may be cases where information is lost in tearing the molecule apart.

From my discussions with Mark Witkowski, I understand that the standard LC–MS and GC–MS techniques have difficulty identifying inorganic materials. In contrast, Raman spectroscopy is equally active for inorganic and organic compounds. In my column last November, where I discussed spectral identification, I showed the excellent identification of a particle of WO

For the polymer engineer who is selecting which additives to put in her product, a Raman microscope measurement could be an easy way to determine the safety of the selected additive.

(1) General Chapter <1663> “Assessment of Extractables Associated with Pharmaceutical Packaging /Delivery Systems,” in 

United States Pharmacopeia 43–National Formulary 38

 (United States Pharmacopeial Convention, Rockville, MD, 2020).

(2) General Chapter <1664> “Assessment of Leachables Associated with Pharmaceutical Packaging /Delivery Systems,” in 

 is the Principal Raman Applications Scientist for Horiba Scientific in Edison, New Jersey. Direct correspondence to: 

Articles

Extractables and leachable are typically studied using chromatographic techniques. This study explores what we can learn by using from using Raman microscopy for such studies.

Exploration of the Use of Raman Microscopy to the Identification of Extractables and Leachables from Polymeric Containers

Computerized system validation (CSV) in good practice (GxP)-regulated laboratories is alleged to take ages and generate piles of paper to keep inspectors and auditors quiet. To the rescue is the draft FDA guidance on computer software assurance (CSA). But...is this really the case?

R.C. McDowall is the principle of McDowall Consulting and director of R.D. McDowall Limited, and "Questions of Quality" column editor for LCGC Europe, Spectroscopy's sister magazine.

I wrote about the Food and Drug Administration (FDA)’s computer software assurance (CSA) in an earlier “Focus on Quality” column (1). I was not complimentary of the CSA, because it was “regulation” by presentation and publication, as the FDA had failed to issue the draft guidance, which I likened to waiting for Godot. Well, Godot has arrived! FDA issued the draft guidance on September 12th, with an effective date of September 13th (2). The Center for Devices and Radiological Health (CDRH) is the originator in consultation with Center for Drug Evaluation and Research (CDER), but only medical device good manufacturing practice (GMP) regulations are mentioned in the guidance.

It is good that the guidance is available so that claims of the more outspoken advocates can be verified. In this article, we explore if computerized system validation (CSV) is to be cast into the recycling bin of history with CSA ruling the roost, or whether CSA and CSV live together happily ever after, especially as the aim of both is that computerized systems are fit for their intended use. I discuss this guidance from the perspective of good practice (GXP)-regulated laboratories in the pharmaceutical industry.

In the interests of transparency, and to put my comments into perspective, let me outline my philosophy for implementing and validating laboratory computerized systems in the pharmaceutical industry: 

1. There is no point in implementing and validating a computerized system if you do not gain substantial business benefits, as noted by in Clause 5.6 of the 2016 World Health Organization (WHO) guidance (3).

 Otherwise, why would an organization bother wasting money on implementing a slow and error-prone hybrid approach? Deriving business benefits involves redesigning the business process, and using electronic signatures coupled with eliminating spreadsheet and calculator calculations and paper printouts. Interfacing the system with other informatics applications will leverage even greater process efficiencies. Yes, this takes more time, but the payback on the overall investment is immense.

2. Performed correctly, the first phase of a validation life cycle (selection) is investment protection and the second phase (implementation) is simply good software engineering practice. 

Documenting user requirements ensures you purchase and implement the right instrument and application (4,5). Documenting the configuration and settings provide you with the baseline for change control and ensures data integrity. It is also the ultimate backup in case of a major disaster, as you’ll be able to reinstall and reconfigure the application quickly.

3. Leverage a supplier’s software development to reduce user acceptance testing subject to an on-site or virtual assessment with a report (6).

 Many functions in laboratory systems are parameterized and not configured (including temperature and detector wavelength), and the business process does not change. If the supplier has developed and tested these functions adequately, why would you want to test them as noted by GAMP 5 (7,8)?

GAMP risk assessment is not the only methodology you can use (9). Furthermore, nobody can test any computerized system completely—not even a supplier. Users should focus on demonstrating that the system works as intended rather than finding software errors. See the big picture for the system: will it be connected to another laboratory informatics application in the future, or remain as a standalone system? Conversely, avoid adding “just in case” user requirements that you may never use. Finally, document your assumptions, exclusions, and limitations for your user acceptance testing (10).

5. Flexibility or scalability in your validation approach is essential, as one size does not fit all.

 You need to tailor your validation to the risk posed by each system and the records generated by it. As regulations, citations, guidance, and technology are changing, there are always better ways to perform the next validation. Maintaining a status quo CSV approach is not an option.

This philosophy above is based on process efficiency and investment protection, while regulations are only mentioned in point 5. If you approach CSV correctly, then business efficiencies pay for the system and the validation costs.

Before the release of the draft guidance, CSA was proposed as a better approach to demonstrating fitness for intended use by avoiding the generation of piles of paper to keep auditors and inspectors quiet. The aims were:

Focus on critical thinking rather than documentation and testing formalization;

Define the intended use of the software and determine the impact on product quality and patient safety;

Identify any functions within the software that impact product quality, patient safety, or data integrity, and determine the risk on them;

Leverage supplier development and documentation where possible; and

Define assurance activities through conducting scripted and unscripted testing activities based on risk.

Now that we have the draft guidance available, we can assess how these promises stack up with reality. Note that the maturity of an organization in interpreting intended use, risk assessment, and testing will impact if they streamline their current CSV approaches or not.

The title of the draft guidance is “Computer Software Assurance for Production and Quality System Software (2).“

The draft guidance consists of 25 pages. We find on page 4 that CSA is not a universal panacea, a change to the regulatory paradigm, an approach that turns the world upside down, or a revolution that replaces CSV—only that CSA is a supplement to an existing 20-year-old FDA guidance. What?! Despite of all the trumpeting of how superior CSA will be, the “General Principles of Software Validation (GPSV) (11),” issued in 2002, will be the master, and CSA only a slave. When finalized, the CSA guidance (11) simply replaces three pages of Section 6 with about 20 pages (see Figure 1). Green represents the sections of the GPSV that will remain effective, red represents the three sub-sections of that will be replaced by the CSA guidance, and yellow shows how Section 6 will look when the CSA guidance is finalized. This column will not consider the examples in Appendix A, as there is no direct impact on spectroscopic systems. Five points immediately arise:

1. Far from being a total replacement for CSV, CSV and CSA will coexist. However, the current Section 6 mentions both system validation and software validation—so what term is CSA replacing? CSV is required for medical device software validation under 21 

 11 (13). CSA will only be focused on lower risk production and automation applications. In February 2022, FDA proposed updating 21 

 820 to harmonize with ISO 13485-2016 (14). It will be interesting to see if the CSA guidance is compatible with the new regulation.

2. CSA does not cover the whole gamut of software covered by CDRH, but only the lower risk software involved in production and quality systems. However, production systems are hardly mentioned in this guidance.

3. If the CSA guidance is supposed to be a supplement to the GPSV and replace section 6, why not combine the two into a single guidance? The obvious advantage is that there would be a single guidance to read rather than two, one of which has a section that has been replaced but without notifying readers. Ozun Okutan has provided a detailed rebuttal of CSA in a blog on LinkedIn. There are existing medical device software standards that are used outside the US so why not use them (15)?

4. The focus is on software—a rather amorphous term. Does this mean application, a macro, or just “software?”

5. The FDA doesn’t make it clear whether sections 3–5 of the GPSV do or no not impact section 6, so it’s a dog’s dinner of a situation.

FIGURE 1: Replacement of Section 6 of the general principles of software validation by the CSA guidance.

When I read the guidance for the first time, this was a shock, as some CSA advocates were talking about a paradigm shift. I was expecting a new approach; instead, the wheels appear to come off the CSA bandwagon, and the guidance is a half-baked anticlimax. Still, let’s soldier on, and see what other gems await us.

One confusing element of the CSA guidance is the terminology used. “Computerized system validation” includes elements of the software, computer hardware, IT infrastructure, any connected instrument, and the trained staff. Conversely, without any definition, “computer software assurance” focuses on software alone:

Computer software assurance is a risk-based approach for establishing and maintaining confidence that software is fit for its intended use.

Computer software assurance establishes and maintains that the software used in production or the quality system is in a state of control throughout its lifecycle (2).

Fine words, but meaningless. At least the definition of validation in the GPSV provides understanding (11).

The main body of the draft guidance (section V) is split into four main sections under the heading of CSA Risk Framework, shown in Figure 2.

FIGURE 2: Structure of the CSA risk framework from Section V of the draft CSA Guidance (2).

The first stage is to define the software’s high level intended use, which is not about individual functions within the software. This is essential, because software that is used directly or indirectly as part of production or quality system must be validated under 21 

 820.70(i) (12), whereas the guidance does not apply for software that does not. Direct software is stated to be:

 Automating production processes, inspection, testing, or collection and processing of production data.

 Development tools that test or monitor software system or automated software test tools.

 Automating quality system processes, collection and processing of quality system data, or maintaining a quality record under 21 

Software usually not requiring validation is listed as general business management and supporting infrastructure, such as networking. For the latter, there is a potential clash if applied in pharma as the Principle of EU GMP Annex 11 requires IT infrastructure to be qualified (16).

As the General Principles guidance is still effective, don’t forget the sentence in Section 5.2.2: 

It is not possible to validate software without predetermined and documented software requirements

 (11). Your user requirements specification (URS), that I interpret as “intended use,” is the means to ensure the right system for the right job and protect your investment.

As CSV uses URS, and CSA uses intended use, there is a potential for conflict and misunderstanding. It would help greatly if the CSA guidance provided advice on how to document intended use. Is definition of intended use a user requirements specification, or a replacement for it? It could also include the use of a software engineering tool to manage requirements electronically.

Are these two terms interchangeable or different? It depends. If you are the 

, the two terms have different meanings. “Intended use” is applied to analytical instruments, but “intended purpose” refers to both analytical procedure and software, as they focus on the overall process. This is emerging during the revision of 

In the guidance, the FDA offers a binary choice here between “high process risk” and “not high process risk”—no fence sitting here—and identifies high process risk features as functions or operation automated by software. The five areas identified in the guidance are shown in Figure 3. Item 2 is the one area where laboratory systems may be involved—Process Analytical Technology (PAT), where spectroscopic systems integrated with process systems test drug content in tablets.

FIGURE 3: High process risk software functions (2).

Laboratory-centric examples provided in this section to illustrate the risk-based approach are three enterprise resource planning (ERP) scenarios, a manufacturing execution system (MES), and an automated graphical user interface.

Determining the Appropriate Assurance Activities

Simply put, for high process risk functions, the higher the risk, the more validation, sorry assurance activities are required, and vice versa. Mentioned earlier in the guidance are the requirements to ensure that security, configuration settings, data storage, data transfer, and error trapping features must be set up and documented—interestingly, nothing about data integrity.

A major problem with the CSA guidance is that the word “testing” is mentioned 145 times, versus 106 times for quality. There is a potential danger of falling into the trap of trying to test quality into software rather than ensure requirements are properly defined.

One of the key CSA benefits advocated was streamlined test documentation. There are five types of assurance activity (as shown in Figure 4), which are mostly based on the general concepts of software testing in part 1 of 

FIGURE 4: The types of assurance activities (testing) in the draft CSA Guidance (2).

Dynamic testing in which the tester’s actions are not prescribed by written instructions in a test case, and is outlined in 

 (18); it would be good if the FDA referenced the 2022 version to be current. The guidance sub-divides testing, as shown in Figure 4, into:

Ad-hoc testing unscripted practice that does not rely on large amounts of documentation;

Error-guessing testing based on a tester’s knowledge of past failures; and

Exploratory testing spontaneously designs and executes tests based on the tester’s relevant knowledge and experience of the test item.

As I noted in my earlier article, prototyping is performed without documentation (1), as well as evaluating new software in a development environment.

Dynamic testing in which the tester’s actions are prescribed by written instructions in a test case (18), which are divided into:

 A hybrid of scripted and unscripted testing scaled according to the risk passed by the system; and

 Scripted testing that includes links to traceability to requirements and auditability

These last two scripted testing approaches are not in the 

 29119-1 (18), but have been devised by FDA.

A problem with the guidance is extensively quoting a software and systems engineering standard 

 29119, where testers will have a fuller picture of software and may be testing the same modules or application repeatedly or in depth. What problems can you see from the testing options above?

1. To perform error-guessing and exploratory unscripted testing, you need a knowledge of the application you are testing. If it is new system and you have just received training, how much do you know of past failures or experience of the test item? Zero? Then these two test approaches have just bitten the dust.

 Annex 11 clause 4.4 states, “User requirements should be traceable throughout the life cycle (16).“ Therefore, can only robust scripted testing be applied in a regulated GMP pharmaceutical laboratory? So where is the much-vaunted improvement and benefit in testing claimed by the CSA community? There appears to be none in a pharmaceutical context.

Does this mean that a coach, horses, and a safari of migrating wildebeest have been driven though a key benefit of CSA? The risk is that there is an apparent disconnect between the guidance’s approach to streamlining testing and a user’s knowledge of the application coupled with pharma GMP regulatory requirements for network qualification and requirements traceability (16). Another problem is with the ISO standard quoted for the different testing approaches.

Q: When is a Consensus Standard Not a Consensus Standard?

FDA use some of the definitions for types of software testing from this ISO standard. Developing a consensus is important for ISO standards; however, there is a problem with ISO 29119-1 (18). The standard has been criticized as too simplistic, and does not represent a consensus for software testing (19,20). Wikipedia has a summary of the contro- versy, which has led at least two testing organizations to call for the standard to be withdrawn (21). Some of the reasons given for the withdrawal were it did not represent a consensus, it profited by selling standards, it was deemed too prescriptive, there was no need for testing standardization, and—wait for it—there was a heavy focus on documentation (21)! The draft CSA guidance appears to select some testing methodologies (and ignore others) without reason.

In Table I of the CSA guidance, we can see the level of documentation required. It is important to note that all types of assurance activities require documentation of the work performed, but obviously the extent and detail varies. Its good to see that the old saying “If its not written down, it’s a rumor” survives at the FDA.

 Requires intended use and risk determination to be documented, a summary description of the testing performed, any issues found and resolved, who performed the testing and when, and review and approval (as appropriate!), plus a conclusion statement.

 Requires intended use and risk determination to be documented, as well as the test objectives, expected results, pass/fail criteria, detailed report of testing, any issues found and resolved, who performed the testing and a conclusion statement, and also calls for review by a second person pre- and post-execution of the protocol.

An example of exploratory testing of a spreadsheet for collecting and presenting nonconformance data is discussed. A spreadsheet? You cannot be serious! Readers will know of my high regard for spreadsheets used in a regulated environment (22,23), so I’m surprised to see a spreadsheet example in an FDA guidance document. This raises serious issues of data integrity and governance, such as how to document and test requirements to show that the DI controls work. Perhaps this was a lack of critical thinking?

Completing this section is the following sentence:

As a least burdensome method, FDA recommends the use of electronic records, such as system logs, audit trails, and other data generated by the software, as opposed to paper documentation and screenshots, in establishing the record associated with the assurance activities (2).

This is best part of the whole guidance! I have no problem with this sentence, as I have used an application to self-document user acceptance testing for years (6). Why do more?

In this section, I’ll look at what was promised versus what was delivered, where have we seen this before, and summarize some of the inconsistencies that we’ve seen in the guidance.

Now let us compare the CSA approach promoted in presentations and publications before in the draft guidance was issued, as shown in Table I.

Let us look at the missing approaches in more detail:

 Proposed as a major component that was lacking in CSV. Is it mentioned directly in the CSA guidance? No. Missing in action.

 Even unscripted testing requires documentation as noted in the draft guidance

However, the impression given in some CSA presentations was that unscripted testing equated to undocumented testing.

Is it mentioned directly in the CSA guidance? No. Missing in action.

 discusses leveraging supplier involvement in sections 2.1.5, 7 and 8.3, plus appendix M2 for supplier assessments, as noted in my earlier publication on CSA (1). 21 

 11.10(a) on validation could also imply using suppliers for accuracy and reliability of electronic records (13).

Why supplier involvement was omitted in the guidance is probably due to the emphasis on 

 29119-1. The main emphasis of the FDA draft guidance appears to be on developing and testing software, and not on effective implementation of purchased applications.

 Fine, in principle, but it takes time to learn and use the automation tool, as well as validate and assure the software. Expect the first system to take longer to validate. As many lab systems are standalone how will the automation tool cope with this?

CSA: Much Ado About Nothing?

We continue our survey of the spectra of carbonyl-containing polymers by looking at the spectrum of cellulose acetate. What makes cellulose acetate unique is that it is a carbohydrate molecule that is reacted to obtain pendant ester groups. I will also introduce you to polycarbonates. Carbonates are a carbonyl-containing functional group that contain three oxygen atoms. An example of an economically important polycarbonate is Lexan, which is made into windows and car parts. In this column, we examine its spectrum in detail.

, is the founder and CEO of Big Sur Scientific, a maker of portable mid-infrared cannabis analyzers. He has over 30 years experience as an industrial infrared spectroscopist, has published numerous peer-reviewed papers, and has written three books on spectroscopy. As a trainer, he has helped thousands of people around the world improve their infrared analyses. In addition to writing for Spectroscopy, Dr. Smith writes a regular column for its sister publication Cannabis Science and Technology and sits on its editorial board. He earned his PhD in physical chemistry from Dartmouth College.

I stated in my last column (1) that a polyester is a polymer with an ester group in its backbone. But what about polymers with pendant ester groups? What do we call those, and where should we discuss them? For lack of a better term, I will call polymers with pendant ester groups pendant ester polymers (PEPs). A common PEP is the family of cellulose acetates. These polymers are made when a polymer, cellulose, reacts with acetic acid. Figure 1 shows the synthesis of cellulose diacetate.

FIGURE 1: The synthesis of cellulose diacetate from cellulose and acetic acid.

Acetic anhydride is sometimes used instead of, or in addition to, acetic acid (2). Given that paper is made from cellulose and acetic acid is found in vinegar, you could in theory make cellulose acetate at home, but I wouldn’t recommend it because you typically need a sulfuric acid catalyst. The cellulose monomer has three O-H groups that can react to form an ester. Thus, cellulose acetate, cellulose diacetate, and cellulose triacetate are possible. Cellulose acetate is commonly found in consumer products, including cigarette filters and playing cards.

We have previously discussed the infrared (IR) spectrum of cellulose (3), and we have also discussed the spectra of esters (4) and polyesters (1). Recall that acetate esters have a special high wavenumber C-C-O stretching peak at ~1240 cm

(going forward, assume all peak positions are in cm

 even if not so expressed) caused by a mass effect (5). Normally saturated esters, like those in cellulose acetate, exhibit C-C-O stretches between 1210–1150 cm

 (4). Therefore, the acetate C-C-O peak at 1240 cm

 breaks one of our rules, but it is a useful rule break because it is one we understand and can make use of. Acetate esters are common in the world of polymers and even in the world of controlled substances; for example, heroin contains this functional group. Thus, given the importance of acetate esters, it is just as well they contain a special peak signifying their presence in a sample.

Figure 2 shows the structure of cellulose triacetate and the IR spectra of cellulose acetate and cellulose triacetate.

FIGURE 2: The chemical structure of cellulose triacetate and the IR spectrum of cellulose triacetate (blue) and cellulose acetate (red). The “AcO” in the structure denotes acetate.

Note that the alcohol O-H stretch at 3484 is bigger in cellulose acetate than in the triacetate because in the triacetate more of the O-H groups have reacted to form esters. Otherwise, this spectrum contains the classic saturated ester Rule of Three peaks (4) with the C=O stretch at 1748 cm

, the acetate ester C-C-O stretch at 1237 cm

Organic carbonates are a functional group similar to esters, so it is appropriate to group polycarbonates with PEPs. The chemical term carbonate is a little confusing. In general, the term refers to a functional group with the formula CO

. There exist inorganic carbonates that contain the CO

 ion where each carbon-oxygen bond has the same bond order (6). Although this functional group contains a carbon atom, it acts inorganic because it forms ionic bonds and comprises rocks such as limestone. The literature provides more information on the spectra of inorganic carbonates (6). We will discuss the spectra of inorganic carbonates here soon when we cover inorganic molecules.

Organic carbonates contain a carbon atom with three oxygens bonded to it, but the bonding is covalent and consists of C=O and C-O bonds, which is shown in Figure 3.

FIGURE 3: The molecular framework of an organic carbonate.

Note that organic carbonates contain two alpha carbons. If both alpha carbons are saturated, the carbonate is saturated. If one alpha carbon is aromatic and one is saturated, the carbonate is said to be mixed. If both alpha carbons are aromatic, the carbonate is aromatic.

As you can see in Figure 3, carbonates are symmetrical, and each half can be thought of as an ester, with each half having a carbonyl carbon, ester oxygen, and an alpha carbon. Note that the carbonyl carbon has not one oxygen attached to it as in an ester but two, which means that there are two ester oxygens and two alpha carbons as seen.

Given the structural similarity between esters and organic carbonates we might expect carbonates to follow the Rule of Three as esters do. Carbonates do have three intense peaks like an ester (7), but the vibrations responsible are a little different than in esters because the structures of esters and carbonates are different. Briefly, esters have a C=O stretch around 1700, a C-C-O stretch near 1200 cm

. Carbonates have C=O and O-C-C stretches, but unlike esters have an O-C-O linkage and hence an O-C-O asymmetric stretching peak instead of a C-C-O peak. Table I shows the group wavenumbers for the three varieties of organic carbonates.

Note that the C=O stretch is different for all three types of carbonates, and that for mixed and aromatic carbonates particularly the C=O stretch is much higher than any ester C=O stretch we have studied, making these two types of organic carbonate easy to spot. Table I also shows that the organic carbonate O-C-O stretching peak position goes down as we move from the saturated, mixed, and aromatic versions of the functional group. The O-C-C stretching peak range is the same for all three types of carbonates. The saturated carbonate C=O stretch falls in the same range as that of saturated esters (4). However, saturated carbonates have theirO-C-O peak from 1280 to 1240 cm

, whereas saturated esters have their C-C-O peak lower from 1210 to 1160 cm

. It is the relative position of these two peaks that allows one to distinguish saturated esters and carbonates from each other.

The IR spectrum of an aromatic carbonate and a common polycarbonate, Lexan (poly 2,2-Bis[4-hydroxyphenyl]propane), is seen in Figure 4.

FIGURE 4: The chemical structure and IR spectrum of the aromatic polycarbonate Lexan.

Lexan is an aromatic carbonate because the two alpha carbons are part of benzene rings. The peak labeled A at 1777 cm

is undoubtedly a C=O stretch based on its strength and position. The carbonyl stretch of aromatic carbonates in general falls from 1820 to 1775 cm

. The C=O stretch of mixed carbonates falls from 1790 to 1760 cm

, and for saturated carbonates, this peak is seen at 1740±10. In Figure 4, the O-C-O stretch is labeled B at 1230 cm

. For aromatic carbonates, this peak is typically found from 1230 to 1205 cm

, whereas for mixed carbonates, it falls between 1250–1210 cm

, and for saturated carbonates, it falls from 1280 to 1240 cm

. The O-C-C stretch in Figure 4 is at 1015 cm

, and for all carbonates, this peak falls from 1060 to 1000 cm

Figure 5 shows the spectrum of a mixture of polystyrene and Lexan.

FIGURE 5: The chemical structure and IR spectrum of a mixture of polystyrene and Lexan.

We have studied the spectrum of polystyrene previously (8). In Figure 5, we can see C-H stretches above 3000 cm

 from the aromatic rings in both Lexan and polystyrene, aromatic ring modes at 1601 cm

 from the mono-substituted benzene ring in polystyrene (8). Note that the 698 cm

 peak is the biggest one in this spectrum. The Lexan peaks are the C=O stretch at 1774 cm

 labeled B, and the O-C-C stretch at 1015 cm

 labeled C. These peaks are close to their positions in pure Lexan.

Figure 6 shows the spectrum of a mixture of Lexan and polybutylene terephthalate. We saw the spectrum of pure polybutylene terephthalate (PBT) in the last column (1).

FIGURE 6: The chemical structure and IR spectrum of a mixture of Lexan and polybutylene terephthalate.

The three main peaks from the polycarbonate Lexan are present, with the C=O stretch falling at 1777 cm

. The “Rule of 3” peaks are present for PBT with the C=O stretch at 1719 cm

. As a result of there being carbonate and ester functional groups present, there are two carbonyl stretches. Also, because both these functional groups have multiple C-O stretching vibrations, there are eight peaks between 1300–1000 cm

. The two Lexan peaks in this region were easy to spot, but the PBT peaks at 1280 and 1118 cm

 are barely discernible as shoulders, and I only found them after examining an expanded view of the spectrum. This observation points out one of the problems with mixtures, which is the fact that peaks from one molecule can hide the peaks of other molecules. We have discussed techniques for getting around the mixture analysis problem in previous columns, including spectral subtraction (9) and library searching (10).

We defined PEPs and looked at the synthesis and spectra of a common PEP, cellulose acetate. We found that cellulose acetate follows the ester Rule of Three as expected, and that it clearly exhibits the high wavenumber C-C-O stretch of acetate esters found near 1240 cm

We continue our survey of the spectra of carbonyl-containing polymers by looking at cellulose acetate and the economically important polycarbonate Lexan.

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