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Spectroscopy in Pharmaceutical Analysis

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

A Look at Using Handheld NIR Spectroscopy in Combination with Wavelength Variables Selection and PLS Calibration

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

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 then reviewed the spectra of the three types of carbonates: saturated,mixed, and aromatic. Polycarbonates exhibit three intense peaks from C=O, O-C-O, and O-C-C stretching, similar in size and position to the trio of peaks from esters. We looked at the spectrum of pure Lexan and two spectra where it is found in a mixture. The position of the C=O stretch in carbonates may be used to distinguish the three types of carbonates from each other. The C=O of carbonates is much higher than most esters making carbonates easy to spot. To distinguish between saturated carbonates and saturated esters, use the fact that saturated carbonates have an O-C-O peak from 1280 to 1240 cm

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

https://en.wikipedia.org/wiki/Cellulose_acetate

Infrared Spectral Interpretation, A Systematic Approach

, 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 

, Dr. Smith writes a regular column for its sister publication 

 and sits on its editorial board. He earned his PhD in physical chemistry from Dartmouth College. He can be reached at: 

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

Infrared Spectroscopy of Polymers, IX: Pendant Ester Polymers and Polycarbonates

In this Part 4 survey article describing instrument services and testing, we look into spectroscopy electronics, including printed circuit board (PCB) design and manufacturing, a description of spectroscopy instrument design services, a summary of instrument testing services, and a description of the firmware and software aspects of instrumentation. This is the final installment of our four-part instrument component survey series. As promised, we have published tutorial articles, and posted the 

The Spectroscopy Instrument Components Terminology Guide

. We hope our readers found these articles helpful for our “under the hood” look into spectroscopy instrumentation.

Jerome Workman Jr. serves on the Editorial Advisory Board of Spectroscopy and is currently with Unity Scientific LLC. He is also an adjunct professor at Liberty University and U.S. National University. He can be reached at jworkman04@gsb.columbia.edu.

In this final installment of our four-part spectroscopy components survey article, we take a closer look at four main topics, covered in four tables, with each table containing the following columns of information content—the instrument component service name, a text description of the service offered, basic specifications (information), and references and links. The previous three parts of this series were published in the March, June, and September issues of Spectroscopy (1–3), with additional references given in the literature (4–7). Table V refers to spectroscopy electronics, including printed circuit board (PCB) design and manufacturing. Table VI describes spectroscopy design services, specifically instrument design and manufacturing services. Table VII summarizes instrument testing services, most notably, electrical safety testing, electromagnetic compatibility (EMC), and electromagnetic interference (EMI) testing. Finally, Table VIII delves into the firmware and software aspects of instrumentation, which include instrument driver and control firmware, instrument function driver software, data transfer and user interface software, and, finally, data analysis software. All component service functions are listed in alphabetical order for each table.

The Spectroscopy Instrument Components Terminology Guide 2022

The Concise Handbook of Analytical Spectroscopy: Physical Foundations, Techniques, Instrumentation and Data Analysis, in five volumes, first edition, UV, Vis, NIR, IR, and Raman

 (World Scientific Publishing-Imperial College Press, Hackensack, NJ and Singapore, 2016). ISBN: 978-9814508056.

 (John Wiley & Sons, New York, NY, 1st ed., 2002). ISBN: 978-0-471-98847-2

(7) ASTM (American Society for Testing and Materials) ASTM Volume 03.06, “Molecular Spectroscopy and Separation Science; Surface Analysis” (ASTM International, West Conshohocken, PA, 2017).

. Direct correspondence about this article to 

To conclude this four-part series of spectroscopic instrumentation and components, we examine spectroscopy electronics, including printed circuit board (PCB) design and manufacturing, spectroscopy instrument design services, instrument testing services, and the firmware and software aspects of instrumentation.

A Survey of Basic Instrument Components Used in Spectroscopy, Part 4: Instrument Services and Testing

At the recent Eastern Analytical Symposium (EAS), held November 14–16, Donald Dahlberg taught his 20th, and final, short course on chemometrics, called “Chemometrics Without Equations.” 

Donald Dahlberg first heard about chemometrics in 1998 when he spent his sabbatical year at the University of Washington (UW). One of his former students from Lebanon Valley College, Mary Beth Seasholtz, was a graduate student in Bruce Kowalski’s Laboratory for Chemometrics at UW. When Dahlberg returned to Lebanon Valley College, he started teaching chemometrics to his undergraduate students, and involving them in research, which included collaborative research with a local confectionary company.

A couple of years later, Neal Gallagher of Eigenvector Research contacted Dahlberg about creating a chemometrics workshop that did not involve the parallel presentation of matrix algebra. With that, the “Chemometics without Equations (or hardly any)” course, as it was originally dubbed, was born. It was first presented at EAS in 2001 and has been presented every year since, except the first Covid year of 2020, making a total of 20 appearances. Each time, Dahlberg has been assisted by either Gallagher or Barry M. Wise.

With the 2022 edition of the course at EAS, its run comes to a close, as Dahlberg has decided to retire from teaching it. Gallagher and Wise helped Dahlberg, an avid bourbon connoisseur, commemorate the occasion with a bottle of Blanton’s.

Neal Gallagher (left) and Barry Wise (right) presented Don Dahlberg (center) with a gift to celebrate his 20th and final “Chemometrics without Equations” course at the Eastern Analytical Symposium (2022) on November 14, 2022.

Over these two decades, Dahlberg has gently introduced hundreds to the field of chemometrics. His teaching at EAS also led to his developing special versions of the course tailored to forensic science and cultural heritage. He has presented the course at John Jay College of Criminal Justice, the Forensic Science Department at the University of New Haven, the Museum of Modern Art, the Getty Museum, and the Library of Congress.

“My goal has been to introduce the power of chemometrics to those inside and outside of analytical chemistry,” said Dahlberg. “Even though it is time to end my presentations at EAS, I intend to continue to help those who wish to explore the field of chemometrics.”

At the recent Eastern Analytical Symposium (EAS), held November 14–16, Donald Dahlberg taught his 20th, and final, short course on chemometrics, called “Chemometrics Without Equations.”

Chemometrics without Equations—Don Dahlberg Teaches His 20th and Final Course

In this study, a colorimetric discrimination of Pd

 ions in the solvent and solid film states in one silver nanoparticles (AgNPs) sensing system is presented. First, silver nanoparticles were prepared by reducing AgNO

 with sodium borohydride in the presence of chitosan and different organic acids, including acetic acid, propanedioic acid, and citric acid. The addition of different organic acids allowed for the surface plasmon resonance (SPR) intensity and size distribution of AgNPs to be adjusted. Chitosan acts as a stabilizer and complexing agent, endowing AgNPs excellent film-forming properties. Then, the chitosan-stabilized AgNPs in the solvent and solid-film state are used to detect metal ions. In the presence of Hg

 ions, the color of the AgNP solution changed rapidly from pale yellow to colorless and light brown, respectively. The characteristic SPR peaks of the AgNPs also disappeared completely, and the solid films of AgNPs with a yellowish-brown color also change rapidly to colorless and dark brown with the addition of Hg

 ions, respectively. The discrimination of Hg

 ions can be clearly observed in both the solvent and the solid film state. However, the addition of other metal ions cannot change the color of the AgNPs.

With the rapid expansion of industry, global environmental pollution is becoming more serious. Heavy-metal ion contamination is one of the most concerning types of environmental pollution. These contaminants can cause acute or chronic toxicities to human body, leading to various diseases (1). For example, palladium has the propensity to bind to deoxyribonucleic acid (DNA), thiol-containing amino acids, proteins, and other biomolecules, which disturbs some cellular processes and causes serious physiological disorders (2,3). Mercury exposure can lead to severe damage to the central nervous and endocrine systems, resulting in deleterious effects on several important organs, such as the brain and kidneys (4,5). Thus, the fast and convenient monitoring of palladium and mercury is important for waste management, environmental protection, and public health. Conventional analytical methods for palladium and mercury detection include atomic absorption spectroscopy (AAS) and atomic emission spectroscopy (AES) (6,7), solid phase microextraction–high performance liquid chromatography (SPME–HPLC) (8,9), inductively coupled plasma–mass spectrometry (ICP-MS) (10,11), ICP–atomic emission spectroscopy (ICP-AES) (12,13), electrochemistry (14,15) and adsorption dissolution voltammetry (16,17). These instrumental techniques can provide direct and quantitative information about palladium and mercury concentrations, but these techniques are time-consuming, costly, and not well-suited for the quick detection of palladium and mercury in the field. Fluorimetric and colorimetric methods provide a useful alternative for the detection of mercury and palladium because of their relative simplicity, cost-effectiveness, and nondestructive nature (18). Fluorescent sensors for Hg

 ions reported are mainly based on the coordination mechanism of organic probes with Hg

 ions (19–21), in which the fluorescence signals of the probes are always quenched because of the efficient spin-orbit coupling and non-radiation deactivation of the excited states of Hg

 ions (22–24). However, they suffer from different degrees of interference with other transition metal ions, and most of these sensing systems possess disadvantages, such as requiring high synthetic expertise to prepare multistep and expensive fluorescence probes and laborious purification steps.

Colorimetric methods based on the noble metal nanoparticles, which involve the naked-eye detection of a change in color caused by the distance-dependent surface plasma resonance (SPR) phenomena, have been developed for various heavy metal ions (25). For the detection of Hg

 ions, most of the colorimetric sensors are based on AuNPs (26). The interaction between AuNPs and Hg

 ions results in the aggregation of AuNPs with the color change from red to an aggregation-induced purple or colorless (27,28) or the anti-aggregation of AuNPs with a color change from purple to red (29). Although AuNPs as colorimetric sensors are promising tools for the detection of Hg

, they usually require careful modification of AuNPs with DNA or thiol-containing organic molecules under precise experimental conditions, which makes them time-consuming and costly. Compared with AuNPs, AgNPs can also be Hg

 colorimetric sensors, which are mostly based on the redox reaction between AgNPs and Hg

 (30–32) with a color change from yellow to colorless. For colorimetric sensing of Pd

 ions, Anwar and coworkers reported cationic pyrazinium thioacetate-stabilized AuNPs serving as an effective Pd

 ion naked-eye sensor with color changing from pink to colorless (33). However, AgNPs have never been exploited as Pd

 ion colorimetric sensors (34). Moreover, the colorimetric sensing of Pd

 ions in one noble metal nanoparticles sensing system has never been realized (35).

In this study, we presented a colorimetric discrimination of Pd

 ions in one AgNP sensing system for the first time. By introducing the biopolymer chitosan as the stabilizer and complexing agent, this system was able to distinguish Pd

 ions simultaneously in the solvent and solid-film state. First, silver nanoparticles were prepared by reducing AgNO

 with sodium borohydride in the presence of chitosan and different organic acids, including acetic acid (AA), propanedioic acid (PA), and citric acid (CA). It was demonstrated that the addition of different organic acids could finely adjust the SPR intensity and size distribution of AgNPs. This study also revealed for the first time the effect of organic acid in the synthetic procedure of AgNPs. Chitosan, which is a biocompatible polysaccharide with largely amount of free amino and hydroxyl groups exhibiting unique polycationic, chelating, and film-forming properties, acted as a stabilizer and complexing agent, leading to the formation of composite solid films (36). Then, the chitosan-stabilized AgNPs in the solvent and solid-film states were used to detect metal ions. In the presence of Hg

 ions, the color of the AgNP solution changes rapidly from pale yellow to colorless and light brown, respectively. Correspondingly, the characteristic SPR peaks of the AgNPs completely disappeared. The solid film of AgNPs with its yellowish-brown color also changed rapidly to colorless and dark brown with the addition of the Hg

 ions was clearly observed in both the solvent and the solid-film state. However, the addition of other metal ions did not change the color the AgNPs. It was further revealed that this method showed a linear response range of 50–90 μm and 100–300 μm for Hg

. The limit of detection (LOD) was estimated to be 2.26 x 10

, respectively. The colorimetric discrimination of the Hg

 ions in solvent and solid film provided a new avenue for simultaneous onsite detection metal ions by the naked eye.

Colloidal silver nanoparticles were prepared by reducing AgNO

 using sodium borohydride with chitosan as the stabilizer and complexing agent in the presence of different kinds of organic acids (AA, PA, or CA) at room temperature, and the resulting solutions were labeled as AgAA-CHIT, AgPA-CHIT, and AgCA-CHIT, respectively. The first optical indication of AgNP formation was provided by the color change of the solution from colorless to pale yellow, and it was further monitored by UV-vis spectroscopy. As shown in Figure 1, the absorbance intensity follows the order of AgPA-CHIT > AgCA-CHIT > AgAA-CHIT, suggesting that these organic acids participate in the reaction, and the reduction rate of Ag

is strongly affected by the nature of the organic acid (37). When preparing the three organic acid solution with the same concentration (0.20 mol/L), the pH values were 1.90, 2.26, and 3.01 for PA, CA, and AA, respectively, implying that a pH effect cannot be ruled out in the formation of AgNPs. PA-CHIT with a lower pH and a higher degree of protonation of amino groups of chitosan helped promote the reduction of the silver precursor (38), leading to a higher absorption intensity.

FIGURE 1: UV-vis spectra of chitosan-stabilized AgNP solutions: AgPA-CHIT (blue), AgCA-CHIT (red), and AgAA-CHIT (black).

As displayed in Figure 1, the formation of metallic AgNPs in the AgAA-CHIT and AgCA-CHIT solutions was confirmed by the appearance of the SPR band at 404 nm with the similar intensity in the corresponding UV-vis spectra. According to the SPR peak position and intensity, it was estimated that the average diameter of the AgNPs was 27 nm (39) for both samples (Table SI). When the AgNPs were synthesized in the chitosan-propanedioic acid solution, different behavior was observed. The SPR band showed a slight blue shift to 398 nm, and the absorption intensity dis- played significant enhancement (Figure 1), which is indicative of the smaller and higher concentration of metal nanoparticles. The size and morphology of AgNPs were estimated by transmission electron microscopy (TEM) and are displayed in Figure 2a, which shows the presence of polydispersed spherical nanoparticles in the size range of 11–18 nm. Compared with AgAA-CHIT and AgCA-CHIT (Figure 2b–2c), AgPA-CHIT particles (Figure 2a) have better dispersion, particle size uniformity, and homogeneous morphology.

FIGURE 2: TEM images of (a) AgPA-CHIT, (b) AgCA-CHIT, and (c) AgAA-CHIT.

Interaction of Silver Nanoparticles with Various Metal Ions

Because the silver nanoparticles synthesized in the presence of propanedioic acid have better dispersion, particle size uniformity, and absorption intensity, AgPA-CHIT particles were chosen to evaluate the response to various metal ions. The UV-vis absorption spectra and color change of AgPA-CHIT were taken in the presence of different metal ions, including Al

. Figure 3 shows the effect of various metal ions on the freshly prepared AgPA-CHIT particles. The solutions in contact with Hg

 changed from pale yellow to colorless and light brown, respectively (Figure 3a), and the corresponding SPR band completely disappeared (Figure 3b). However, the effect of other metal ions on the color and SPR band of AgPA-CHIT particles was negligible. The color difference of AgPA-CHIT with Hg

 provided a method to visibly detect these two metal ions simultaneously with the naked eye. Moreover, the introduction of biopolymer chitosan as the stabilizer and complexing agent endowed the AgNPs with good film-forming properties. Therefore, the AgPA-CHIT film was further prepared to evaluate the metal selectivity. As shown in Figure 3c, when different metal ions with the concentration of 10

 M were added to the AgPA-CHIT films, only the films contacting with the Hg

 ions were significantly different with the color changing from yellowish brown to colorless and brownish-black, respectively (the visualization images are taken on white paper, so the colorless film looks white). Color changes in the state of solution and film can be observed within one minute. The results revealed that discrimination of the Hg

 ions can be clearly observed in both the solvent and the solid-film state. Thus, it is suggested that the AgPA-CHIT particles with significant Hg

 differentiation properties could serve as a fast, simple, and convenient onsite naked-eye probe for Hg

FIGURE 3: (a) Photographs and (b) UV-vis spectra of AgPA-CHIT with the addition of different metal ions. (c) Photographs of different metal ions dripping onto the AgPA-CHIT solid film.

To further investigate the recognition mechanism of AgPA-CHIT to the Hg

 ions, the UV-vis titration and TEM images were further tested. As shown in Figure 4 and 5, the characteristic SPR band of AgPA-CHIT was observed at 398 nm, and the color of the solution is pale yellow in the absence of Hg

 to AgPA-CHIT, the color of the solution decreased, accompanying with the blue shifting and broadening of the SPR band. Finally, the solution turned colorless after the addition of 400 μm of Hg

 and the SPR band almost completely disappeared. When different concentrations of Pd

 to AgPA-CHIT was added, the color of the solution deepened gradually, accompanying the broadening and red shifting of the SPR band and finally turning to light brown after the addition of 400 μm of Pd and the SPR band almost completely disappeared.

FIGURE 4: (a) Photographs and (b) UV-vis spectra of AgPA-CHIT with different concentrations of Hg

FIGURE 5: (a) Photographs and (b) UV-vis spectra of AgPA-CHIT with different concentrations of Pd

To obtain further information about the characteristic of AgPA-CHIT in the absence and presence of Hg

, the samples were analyzed using TEM. The corresponding TEM images are displayed in Figures 2a and 6. It can be seen that well dispersed silver nanoparticles are presented in Figure 2a. After the addition of Hg

 to the AgPA-CHIT solution, the nanoparticles completely disappeared, which might be because of the redox reaction between Hg

 and zero-valent silver with the standard potential of 0.85 V (Hg

/Ag), respectively (40). It was proposed that after the addition of Hg

 ions to the freshly prepared AgPA-CHIT, mercury (II) ions bound to the surface of AgPA-CHIT particles to move the biological stabilizer chitosan away from the silver surface, leading to a redox reaction between the silver and mercury ions (Scheme 1). This redox reaction may account for the blue shift of the SPR band of AgNPs. The addition of Pd

 to the AgPA-CHIT solution caused the nanoparticles agglomerated to form larger ones (Scheme 1), which was also in accordance with the red shift of the SPR bands.

FIGURE 6: TEM images of AgPA-CHIT after recognition of (a) Hg

SCHEME 1: Proposed mechanism for AgPA-CHIT to discriminate Hg

 ions was further determined by using the characteristic SPR peak of AgPA-CHIT. Three repeated experiments demonstrated that the concentration of both metal ions <10 μm did not change the color of AgPA-CHIT. As shown in Figure 4b and 5b, the absorption peaks of AgPA-CHIT decreased by increasing the concentration of the Hg

 ions. There is a linear relationship between the absorption intensity changes and the linear response concentration ranges, which are 50–90 μm and 100–300 μm for Hg

, respectively. Thus, it was calculated that the AgPA-CHIT could be used for the colorimetric detection of Hg

 M, respectively (Figure 7). Compared with other sensors and methods for sensing of Hg

 (Table SII and SIII), our proposed methods required neither careful modification of AgNPs nor a high cost of instrumentation. Moreover, the simultaneous discrimination of Hg

By using green synthesized AgNPs modified by chitosan and organic acid, a simple, cost-effective, and highly selective onsite colorimetric detection method for Pd2+ and Hg2+ ions was developed.

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