Measuring Heavy Metals and Other Elements in Alternative Proteins

Simulation of an Algorithm for Space Target Materials Identification 

Data Transforms in Chemometric Calibrations

Analysis of Serum from Acute Leukemia Patients Using Surface-Enhanced Raman Spectroscopy (SERS)

Robin F.B. Turner and James M. Piret are the winners of the 2022 William F. Meggers Award, to be presented this fall at the SciX conference, which will be held October 2–7 at the Northern Kentucky Convention Center in Greater Cincinnati. The Meggers Award, from the Society for Applied Spectroscopy, is given to the authors of the outstanding paper appearing in the journal 

. It is presented at the SciX conference in the year following the calendar year of publication.

Turner and Piret are receiving the award for their paper, “Augmented Two-Dimensional Correlation Spectroscopy for the Joint Analysis of Correlated Changes in Spectroscopic and Disparate Sources,” published in the May 2021 issue of the journal (

(5), 520-530, 2021), along with their co-authors, H. Georg Schultze, Shreyas Rangan, Martha Vardaki, Timothy Kieffer, Michael Blades, and Diepiriye Iworima. The paper presents an augmented form of two-dimensional correlation spectroscopy that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. It demonstrates the approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells.

Turner is a professor at The University of British Columbia with joint appointments in the Michael Smith Laboratories and Department of Electrical & Computer Engineering, and an Associate Member of the Department of Chemistry. He earned his PhD in electrical engineering from the University of Alberta in 1990. His current research activities focus on applications of Raman spectroscopy to analytical problems in biochemistry, biotechnology, and biomedical engineering.

Piret is a professor at the University of British Columbia in the Department of Chemical & Biological Engineering and the Michael Smith Laboratories. He received a bachelor’s degree from Harvard University in Applied Mathematics to Biochemistry, and a chemical engineering doctoral degree from MIT in 1989. His research focus is on innovative process and device technology development for mammalian cell culture therapeutic protein or cell manufacturing.

Turner and Piret discussed this work, and other work being done with their team, in a recent 

Articles

Robin F.B. Turner and James M. Piret are the winners of the 2022 William F. Meggers Award, to be presented this fall at the SciX conference.

Robin Turner and James Piret Receive 2022 William F. Meggers Award

Lu Wei, an assistant professor of chemistry at the California Institute of Technology, will be presented with the Emerging Leader in Molecular Spectroscopy Award at the SciX 2022 conference, which takes place October 2–7 at the Northern Kentucky Convention Center in Greater Cincinnati. The award, presented by 

magazine, recognizes the achievements and aspirations of a talented young molecular spectroscopist, selected by an independent scientific committee.

Wei’s work focuses on the development and application of stimulated Raman scattering (SRS) microscopy for bioanalysis, spectroscopy-informed design of vibrational imaging probes, and sample-engineering strategies. She has made several ground-breaking contributions in the field of nonlinear Raman spectromicroscopy, to push the next frontier of nonlinear biological Raman imaging.

Wei received her PhD in 2015 from Columbia University. During her PhD work, she pioneered a bio-orthogonal chemical imaging platform that coupled stimulated Raman scattering (SRS) with small bio-orthogonal probes and allowed noninvasive imaging of metabolic dynamics in live cells and tissues with high spatial and temporal resolution, for investigations of cellular metabolism. Since starting her independent career at the California Institute of Technology in 2018, Wei has been working to develop next-generation Raman microscopy to achieve unprecedented resolution, sensitivity, and multiplexity with improved molecular precision and quantification for complex bioanalysis.

Lu Wei, an assistant professor of chemistry at the California Institute of Technology, will be presented with the Emerging Leader in Molecular Spectroscopy Award at the SciX 2022 conference.

Lu Wei to Receive Spectroscopy’s Emerging Leader in Molecular Spectroscopy Award at SciX 2022

Martin Zanni, who is the Meloche-Bascom professor of chemistry at the University of Wisconsin-Madison, is the winner of the 2022 Ellis R. Lippincott Award. 

The award, jointly established in 1975 by the Optical Society (OSA), the Coblentz Society, and the Society for Applied Spectroscopy, recognizes an individual who has made significant contributions to the field of vibrational spectroscopy and honors the unique contributions of Professor Ellis R. Lippincott, one of the developers of the diamond anvil cell, widely used in high pressure research. It will be presented to Zanni at the SciX conference, which takes place October 2–7 at the Northern Kentucky Convention Center in Greater Cincinnati.

Zanni received his PhD from the University of California-Berkeley, working with Dan Neumark, and was an NIH Postdoctoral Fellow at the University of Pennsylvania with Robin Hochstrasser. He is one of the early pioneers of two-dimensional (2D) infrared (2D IR) spectroscopy and has made many technological innovations that have broadened the capabilities and scope of a wide range of multidimensional spectroscopies and microscopies. He uses these new techniques to study topics in biophysics, chemical physics, photovoltaics, and surfaces.

Zanni has received many national and international accolades for his research and is the only person to have received the ACS Nobel Laureate Signature Award as both a student and a mentor. He founded PhaseTech Spectroscopy Inc., the first company to commercialize 2D IR and 2D electronic spectroscopies and is the first person to receive the Craver, Coblentz, and Lippincott Awards.

Ellis R. Lippincott Award to Be Presented to Martin Zanni at SciX 2022

The 2022 Craver Award will be presented to Wei Min at SciX, taking place October 2–7 at the Northern Kentucky Convention Center in Greater Cincinnati. Min is a professor of chemistry at Columbia University. He also is affiliated with the Department of Biomedical Engineering, the Kavli Institute for Brain Science, and the NeuroTechnology Center, all at Columbia University.

Min has made important contributions to the development of stimulated Raman scattering (SRS) microscopy and used it to open up a broad range of applications. He has co-invented modern SRS microscopy and pushed its sensitivity down to the single-molecule level through stimulated Raman-excited fluorescence (SREF) spectroscopy. His team also has designed and synthesized Raman-active probes exhibiting rainbow-like spectral “colors”. In addition, his group has opened up applications including bio-orthogonal chemical imaging of small biomolecules (such as lipids, amino acids, glucose, and drugs), metabolic activity imaging in animals, and super-multiplexed vibrational imaging.

The Craver Award is conferred by the Coblentz Society, which created the award to recognize the efforts of young professional spectroscopists who have made significant contributions in applied analytical vibrational spectroscopy. The Coblentz Society was named after Dr. William Coblentz (1873–1962), who devoted most of his life to investigating the infrared spectra of “pure” compounds. The society was founded more than 65 years ago with the purpose of fostering the understanding and application of vibrational spectroscopy.

The 2022 Craver Award will be presented to Wei Min at SciX, taking place October 2–7 at the Northern Kentucky Convention Center in Greater Cincinnati. 

Wei Min to Receive 2022 Craver Award at SciX 2022

Inductively coupled plasma mass spectrometry (ICP-MS) instruments can perform low-level elemental analysis in a wide range of sample types, from high-purity chemicals to high matrix digests. But achieving consistently low detection limits requires good control of elemental contamination, as well as spectral interferences. A clean working area, careful selection of reagents, and good sample handling techniques are key to successful trace and ultratrace elemental analysis. In this article, we provide five practical tips for controlling contaminants and minimizing detection limits.

Inductively coupled plasma-mass spectrometry (ICP-MS) instruments offer high sensitivity and low random background, which enables the technique to provide ng/L (ppt) level trace element analysis. But routinely achieving low detection limits requires more than just a sensitive instrument. In industries such as semiconductor manufacturing, experienced analysts develop working practices that minimize the potential for contamination to affect results. The following practical suggestions can be implemented in any laboratory, and will help ensure data quality is not compromised by contamination that could have easily been prevented.

Consider Whether the Laboratory Environment is Suitable for Your Analysis

Analytical laboratories doing trace element analysis in industries such as environmental monitoring, food safety, pharmaceutical manufacturing, and clinical research do not need a special clean laboratory environment to achieve the method detection limits (DLs). But even a normal trace element laboratory should be designed and constructed in a way that minimizes sources of elemental contamination.

In the semiconductor manufacturing and process chemical industries, trace elements are routinely measured at ppt and sub-ppt levels, so laboratories in these industries often install a dedicated cleanroom. Cleanrooms are classified according to the number of particles of various sizes between 0.1 and 5 microns per unit volume of laboratory air. For example, an International Organization for Standardization (ISO) Class 3 laboratory (equivalent to the earlier US Federal Standard 209E [FS209E] Class 1) can contain a maximum of 8 particles of 1 micron and 1000 particles of 0.1 micron per cubic meter of air. An ISO Class 7 laboratory (FS209E Class 10,000) may contain up to 83,200 particles of 1 micron per cubic meter.

However, ISO Class 1 to 4 cleanrooms are expensive to build and maintain, and only retain their rated classification through strict control of access and working practices. A lower cost alternative is to install the ICP-MS in a small Class 10 enclosure located inside a Class 10,000 laboratory or normal trace element laboratory. Another option is to place just the autosampler in a clean enclosure, such as a high efficiency particulate air (HEPA) filtered laminar flow hood. Preparation of samples and standards can also be performed in a similar hood to avoid contamination of vials and solutions while they are open on the bench.

Limit Sources of Particulate Contamination

In a typical laboratory, one of the most problematic sources of sample contamination is airborne particulate material. Consider where contamination may come from and try to eliminate particulate sources from the laboratory.

Common sources of particles in the laboratory include air conditioning units (especially those with overhead vents), corroded bare metal surfaces, printers and personal computers (PCs), and recirculating water chillers, particularly where a fan is used in an air-cooled heat exchanger. Contamination can also arise from dirt and dust brought into the laboratory on people’s shoes, clothing, and personal belongings. Simple steps can remove or reduce the contribution from these sources; for example, by placing the water recirculator in an adjacent service room, or printing to a remote device in a neighboring office. Sticky mats by entrance doors can significantly reduce the amount of dust brought into the laboratory on people’s shoes.

Using appropriate materials during laboratory-based activities can also help to reduce contamination. Gloves are routinely used for safety reasons when handling potentially corrosive acids, and powder free nitrile gloves will help minimize particle contamination.

With a little thought and attention to the way laboratory services, equipment, and personnel are managed, particulate contamination in the laboratory can be minimized without the high costs of installing and operating a dedicated cleanroom.

Avoid Glass! Use Plastic Labware to Minimize Elemental Backgrounds

The following recommendations are appropriate for laboratories that analyze typical ICP-MS sample types prepared and stabilized in dilute acid solutions. If other solvents are used, the details of the cleaning and rinsing protocols will need to be adjusted as appropriate.

Sample vials, pipette tips, and other labware that comes into contact with the sample solution can be a significant source of contamination. Acidic solutions should not be prepared or stored in glassware, even if it has been precleaned, as the acid will extract metal contaminants from the glass. Alkaline solvents such as ammonium hydroxide and tetramethylammonium hydroxide (TMAH) will also extract metals from glass containers. Plastic labware is much cleaner than glass, but brands that contain pigments made with metal additives should be avoided where possible. Clear plasticware made of materials such as polypropylene (PP), low density polyethylene (LDPE), polyethylene terephthalate (PET), or fluoropolymers (PTFE, FEP and PFA) are recommended for the best chemical resistance and lowest levels of contamination. New labware should be acid rinsed to remove surface contamination and manufacturing residues prior to use.

Polypropylene sample vials and centrifuge tubes (15 mL or 50 mL) from brands such as DigiTUBE, Corning, or Nalgene are inexpensive, largely free from metal contamination and are Class A graduated so can be used for volumetric sample preparation. Many laboratories prepare standards and do sample dilution by weight. Conical base DigiTUBEs are available with a skirt to allow them to stand upright on the balance or bench, without needing to be held in an autosampler rack.

Implement Effective Procedures for Cleaning, Storage, and Use of Labware

If the laboratory’s workflow and sample throughput allow, it is good practice to soak sample vials and tubes before use in a covered, clear plastic tank containing ultrapure water (UPW) or dilute acid (for example 0.1% HNO3), as shown in Figure 1. These plastic containers can be purchased at relatively low cost from a normal hardware store. This sort of precleaning approach will help remove manufacturing residues such as mold release agents, which can contain metals, including Al and Zn. More aggressive cleaning may be required to achieve the lowest background levels for ultratrace analysis. Vials and containers should be rinsed three times in UPW prior to use.

Figure 1: Clear plastic containers of various sizes used for cleaning and storage of sample tubes and other consumables.

The same sort of precleaning approach can also be used for soaking pipette tips before use, although clear tips supplied in a covered container will be dust free and do not usually require precleaning except for when ultralow level analysis is required. The rinse baths should not become significantly contaminated, and can usually be used for a year or more before the UPW or acid needs to be changed.

Similar clear plastic storage containers can be used to clean and store ICP-MS sample introduction components, ideally with separate containers for different components, such as standard and inert kits (Figure 2). Sample introduction parts such as the quartz spray chamber and torch can be soaked in an acid bath, then dried before use. Interface cones can be sonicated in UPW or a dilute laboratory cleaning agent such as Citranox, then dried and stored in a sealed container. For very contaminated cones, a fine abrasive polishing powder and a pointed Q-tip can be used, followed by sonication in UPW.

Figure 2: ICP-MS sample introduction components are cleaned and stored in separate containers for different kits, for example standard quartz and PFA inert kit components.

Use Reagents of Appropriate Quality for the Analysis

Good quality deionized water (18 MΩ.cm) is essential to maintain the low background levels required for trace level analysis, particularly for common contaminants such as Na, Al, and Fe. The elements B and Si are more difficult for ion exchange systems to remove, so pay particular attention to the background levels for these elements. An increase in B or Si backgrounds may indicate that the ion exchange cartridge of your ultrapure water system needs to be replaced.

The quality of reagents such as acids should be appropriate for the levels of trace elements being measured but, in general, using better quality acids will reduce the time you have to spend dealing with errors due to contamination. A viable alternative to high purity acids is to purchase reagent grade acids and purify them in the laboratory using sub-boiling distillation.

When using concentrated high purity acids to prepare samples, calibration standards, and quality control standards (QCS), a small volume of acid should be decanted into a micro beaker or the acid bottle lid before pipetting into the sample or standard vials. This will help avoid contaminating the acid in the bottle.

Once empty, the perfluoroalkoxy alkanes (PFA) bottles that high purity acids are supplied in can be rinsed and dried and will then be excellent metal-free containers for subsequent use. These bottles can be used to prepare and store diluents, and can even be used for low-level standard addition analysis with the spikes being added directly into the sample in the reused acid bottle.

These practical tips from experienced ICP-MS analysts have been demonstrated to reduce errors due to contamination. By implementing a few simple procedures and ensuring reagents and consumables are suitable for the intended analysis, ICP-MS users can minimize the possibility of data quality being compromised by trace element contamination.

Bert Woods and Ed McCurdy are with Agilent Technologies, Inc. Direct correspondence to: 

Inductively coupled plasma mass spectrometry (ICP-MS) instruments can perform low-level elemental analysis in a wide range of sample types, from high-purity chemicals to high matrix digests. But achieving consistently low detection limits requires good control of elemental contamination, as well as spectral interferences. A clean working area, careful selection of reagents, and good sample handling techniques are key to successful trace and ultratrace elemental analysis. In this article, we provide five practical tips for controlling contaminants and minimizing detection limits. 

ICP-MS: Key Steps to Control Contamination and  Achieve Low Detection Limits

, 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.

We continue our survey of the infrared (IR) spectra of polymers with a look at the spectra of polymers that contain carbonyl or C=O bonds. Our long-term goal is to examine the spectra of polymers that contain ketone, carboxylic acid, ester, and carbonate linkages. Studying these spectra is vital, because these molecules are important economically and are ubiquitous in society.

We begin this installmentwith a discussion of the unique bonding of the carbonyl functional group and how that impacts their infrared (IR) spectra. We then review the structures and spectra of ketones, and examine an example spectrum of a polymer with a ketone linkage in it.

A carbon–oxygen double bond is referred to as a carbonyl bond. The structure of this bond is seen in Figure 1. As shown in Figure 1, the carbon in this bond is called the “carbonyl carbon.”

Figure 1: The chemical structure and charge distribution in a carbonyl bond.

Carbonyl bonds are highly polar because of the large electronegativity difference between carbon and oxygen. As a result, the carbonyl carbon has a large partial positive charge, and the oxygen has a large partial negative charge as denoted by the small Greek letter deltas in Figure 1. Recall that when in a chemical bond there are two charges separated by a distance, it forms what is called a dipole moment (1). Additionally, remember that the change in a dipole moment with respect to bond length, dµ/dx, during a molecular vibration is one of the things that determines the IR intensity (1).

Because the carbonyl group has a large dipole moment, when this group stretches and contracts, the value of dµ/dx is large, which results in an intense peak. The C=O stretching vibration is illustrated in Figure 2.

Figure 2: The stretching vibration of a carbonyl bond.

Carbonyl stretching peaks generally fall between 1900 and 1600 cm

(assume all peak positions hereafter are in wavenumber units), a relatively unique part of the IR spectrum. This area is sometimes referred to as the “carbonyl stretching region.” The carbonyl stretching peak is perhaps the perfect example of a 

, which is a peak that shows up intensely in a unique and relatively narrow wavenumber range. This makes C=O stretches one of the easiest IR peaks to spot and assign. Therefore, an IR spectrometer is, in some respects, the perfect carbonyl detector.

Carbonyl bonds can be divided into two classes depending on what type of carbons are attached to the carbonyl carbon. A saturated carbonyl group has two saturated carbons attached to the carbonyl carbon. An aromatic carbonyl group has one or more aromatic carbons attached to the carbonyl carbon. Examples of an aromatic carbonyl group and its bonding is seen in Figure 3 (please pardon the diagram; it is hard to depict three-dimensional [3D] molecular orbitals in two dimensions).

Figure 3: (a) The p-type orbitals and (b) conjugation within an aromatic carbonyl group.

Recall that when we introduced aromatic rings (2), we showed that some of the electrons in a benzene ring delocalize to form a cloud of electron density called a pi cloud, and the electrons in this cloud are called pi electrons, which are illustrated in Figure 4.

Figure 4: (a) The p orbitals on each of the six carbon atoms in benzene that each contribute an electron to the ring. (b) The collection of delocalized P orbital electrons that form a pi cloud of pi electron density above and below the plane of the benzene ring.

Aromatic rings, such as benzene, contain p-type orbitals with electron density in them, and these orbitals stick up out of the plane of the molecule as illustrated at the top of Figure 4. The carbonyl group also contains a p-type orbital that points through space towards the orbitals on the aromatic ring as illustrated in Figure 3. The second carbon-oxygen bond in a carbonyl group has significant electron density perpendicular to the C=O bond axis forming what is also called a pi bond with pi electrons in it.

On the left side of Figure 3, you can see that the pi bond of the C=O group points in space towards the pi electron cloud of the benzene ring. Because of the proximity of these two pi electron clouds, there is some orbital overlap and, as a result, some of the electron density is siphoned off from the C=O bond into the benzene ring, which is illustrated by the dashed lines to the right in Figure 3. This phenomenon, whereby electrons are pulled from part of a molecule, is called 

. Conjugation weakens the C=O bond, lowers its force constant, and lowers its peak position compared to if there were only saturated carbons attached to the benzene ring.

Figure 5 shows the spectrum of acetone. Acetone has two saturated carbons bonded to the carbonyl carbon; hence, no conjugation takes place. Note that its C=O stretching peak falls at 1716 and is labeled A. It is also important to note how intense this peak is; carbonyl stretches are some of the strongest IR features you will ever see.

Figure 6 shows the spectrum of acetophenone, which has an aromatic ring bonded to the carbonyl carbon; therefore, its C=O bond is conjugated. As a result, its C=O stretch is at 1686, 30 cm

Figure 6: The IR spectrum of acetophenone (phenyl methyl ketone), C6H8O.

In general, we find that conjugated C=O groups have carbonyl stretching vibrations about 20–30 cm

 lower than non-conjugated carbonyl groups. That is why going forward we will be quoting two peak position ranges for each carbonyl-containing functional group we study, one for the saturated version of the functional group and one for the aromatic version.

Perhaps one of the most common chemical structures to contain a carbonyl group are ketone molecules. Ketones consist of a C=O with two carbons attached, one to the left and one to the right as illustrated in Figure 7.

Figure 7: The structural framework of the ketone functional group.

Note that the carbons attached to the carbonyl carbon are denoted “alpha carbons.” If both alpha carbons are saturated, we speak of saturated ketones, and if one or both alpha carbons are saturated, it is called an aromatic ketone.

A common example of a ketone-containing molecule is acetone (dimethyl ketone), whose IR spectrum we have already seen in Figure 5. The carbonyl stretch of acetone falls at 1716, and it is labeled A in Figure 5. In general, for saturated ketones, this peak appears at 1715±10. Note how large this peak is, and how it is easily the biggest in the spectrum.

It is tempting to assume that the carbonyl stretching peak for ketones uniquely identifies them, but this is not the case. Polymers containing saturated esters, aldehydes, and carboxylic acids among others have their C=O stretching peaks at approximately 1715 as we will see in future columns. Thus, the C=O stretching peak is diagnostic for carbonyl groups but not for anything else. We are dependent upon other peaks in IR spectra besides the C=O stretch to distinguish the different types of carbonyl-containing functional groups from each other.

For ketones, the peak that can help distinguish them from other functional groups is the C-C-C stretching vibration, which is illustrated in Figure 8.

Figure 8: The C-C-C stretching vibration of ketones.

Note that this vibration involves the two alpha carbons stretching asymmetrically about the carbonyl carbon. This vibration gives rise to an intense peak between 1230 and 1100 for saturated ketones. This peak appears in the spectrum of acetone in Figure 5 at 1222 and is labeled B. Note that it is the second largest peak in the spectrum.

Normally, a C-C stretching vibration peak is small because the electronegativity difference between the carbon atoms is often negligible, giving non-polar bonds and small values of dµ/dx (1). However, the carbonyl carbon in the C=O bond has a large positive partial charge on it as seen in Figure 1. This large positive partial charge polarizes the bonds to the two alpha carbons, and when these C-C bonds stretch and the dµ/dx is large, it results in a large ketone C-C-C stretching peak seen in Figure 5.

Acetophenone is an example of an aromatic ketone, and its spectrum is seen in Figure 6. Although there is an aromatic and a saturated carbon attached to the carbonyl carbon in acetophenone, it is considered an aromatic ketone, which is because it only takes the presence of a single aromatic substituent to engage in conjugation and lower the carbonyl stretching peak position. Note in Figure 6 that the C-C-C stretch is at 1266 and is labeled B. The general range for the C-C-C stretch of aromatic ketones is 1300 to 1230.

Table I summarizes the group wavenumbers for keytones.

Note in Table I that both the C=O stretch and C-C-C stretching peak positions are sensitive as to whether the ketone is saturated or aromatic. The C=O stretch of saturated ketones falls above 1705, whereas for aromatic ketones it falls below 1700. Additionally, the C-C-C stretch of saturated ketones falls below 1230, whereas for aromatic ketones it falls above 1230. Thus, either one of these peaks can be used to distinguish saturated from aromatic ketones.

The fact that the name of this polymer contains the word ether twice just might make you think that it contains ether linkages only, but it also has a ketone in it per the last part of its name.

Polyetheretherketone is a copolymer made from monomers that form ketone and ether linkages upon the formation of the polymer. We discussed the ether linkages of PEEK in the last column (4); here, we focus on its ketone functional group. The structure and spectrum of PEEK are shown in Figure 9.

Figure 9: The IR spectrum of polyetheretherketone (PEEK).

The spectrum of PEEK is a little strange in that the ketone peaks are not the biggest in the spectrum. This honor goes to the C-O stretching peak of the aromatic ether linkages and ring modes from the benzene rings present. These peaks are so large because the repeat unit contains two ether linkages and three benzene rings respectively.

The C=O stretch of PEEK is at 1653, agreeing with the position of aromatic ketone C=O stretches stated in Table I. The C-C-C stretch of PEEK is seen at 1279, which also agrees with Table I. PEEK is one of the few polymers that contains a ketone linkage.

In this introduction to the spectra of carbonyl containing polymers, we reviewed the structure and bonding of the C=O bond. We found that the C=O bond has a large dipole moment and gives strong carbonyl stretching peaks in the 1900 to 1600 range. We also saw how conjugation can alter the force constant of the C=O bond, lowering its stretching peak position.

We then reviewed the spectra of saturated and aromatic ketones, and discussed how the diagnostic peak pattern for ketones is a C=O stretch approximately 1700 and a C-C-C stretch around 1200. Both of these peaks are sensitive as to whether the ketone is saturated or aromatic, so either can be used to determine what type of ketone is present in a sample. We then analyzed the spectrum of polyetheretherketone, a ketone containing polymer.

Brian C. Smith, PhD, 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 infrared  (IR) spectra of polymers with a look at the spectra of polymers that contain carbonyl or C=O bonds. Our long-term goal is to examine the spectra of polymers that contain ketone, carboxylic acid, ester, and carbonate linkages. Studying these spectra is vital, because these molecules are important economically and are ubiquitous in society. 

The Infrared Spectra of  Polymers, VII: Polymers with  Carbonyl (C=O) Bonds 

James Piret, and Robin Turner, of Michael Smith Laboratories (Vancouver, BC Canada) and the University of British Columbia (UBC), have been exploring the benefits of extracting and displaying correlated spectrometric and non-spectrometric variables with a proposed method called 

Their work has uncovered several advantages of using Raman spectroscopy for these applications. Here, they discuss their efforts to develop an approach that permitted the integration of diverse biochemical information with measured spectra for co-analysis to characterize the spectra and take advantage of the available spectral information.

Piret and Turner are recipients of the 2022 Society for Applied Spectroscopy (SAS) William F. Meggers Award, presented annually at FACSS SciX. This award is given to the author(s) of the outstanding paper appearing in 

. More details on the award are available from the 

In a recent paper that was selected for the Society for Applied Spectroscopy 2022 William F. Meggers Award (1), you examined the benefits of extracting and displaying correlated spectrometric and non-spectrometric variables. Can you briefly describe this concept, the benefits, and how this method compares to the well-known two-dimensional correlation spectroscopy (2D-COS)?

Conventional 2D-COS analysis can be very useful for evaluating correlated and anti-correlated changes in different parts of a spectrum as a function of some perturbation to the system being investigated. It can thus provide useful insights into how the system responds to that perturbation. However, the interpretation of 2D-COS data can be quite challenging in the case of chemically complex systems such as living cells for which the spectra exhibit overlapping features that may have contributions from multiple components at all wavenumbers. For example, nucleic acids have several vibrational bands in the fingerprint region and some overlap with protein bands, some with lipid bands, some with carbohydrate bands, and many overlap with abundant small molecules that have chemical moieties in common with nucleic acids.

Independent (non-spectroscopic) measurements that yield relevant monovariate data can often aid the interpretation of spectral data by providing a direct measurement of some components represented in the spectra. For example, total protein or total nucleic acid biochemical assays can help gauge the amount of overlapping signal at bands where they both contribute. Likewise, highly selective assays for specific proteins or nucleic acids can help interpret spectral data. In general, any independent measurements can aid interpretation and link biological variables with observed changes in overlapped spectral peaks. However, these inferred links can normally be made only between the measured data and features in the spectra that are known to correspond to the measurand, typically specific peaks. It would be even more useful if one could identify and evaluate correlated spectral features that may be distributed throughout the spectrum. This would be feasible if the non-spectrometric data could be co-analyzed with the spectral data, perhaps in an appropriately modified form of 2D-COS analysis.

Our lead author, Dr. Georg Schulze, proposed a method that he dubbed "multisource correlation analysis" (MuSCA) whereby any available non-spectroscopic data from the same sample could be encoded along with the measured spectra for each perturbation point. The non-spectrometric data is encoded using evenly spaced artificial peaks with uniform widths and with amplitudes appropriately scaled relative to the value of the non-spectrometric (for example, biochemical assay) amplitudes at each perturbation point. These artificial peaks are appended to the measured spectra at each perturbation point. The resulting augmented hyperspectral data set is then analyzed using 2D-COS to yield maps (such as covariance and correlation coefficient maps) that directly provide correlations between the spectral and non-spectral data throughout the entire spectrum. It is essentially an adaptation or extension of the concept used in perturbation domain decomposition (PDD).

What inspired you to work on this method and approach? Was there a need for an improvement over the currently used methods?

We are exploring the ability of Raman spectroscopy to monitor cellular changes during the manufacturing of therapeutic cells. Cell changes during these processes are typically manifested by changes in their biochemical composition which can be very informative in terms of the state and quality of the cells (for example,increased or decreased levels of specific proteins as well as other changes in macromolecular composition). These changes can be directly measured by biochemical assays, but those measurements normally depend on costly and destructive assays that require samples of the cells to be removed from the system. Frequent sampling can significantly decrease the number of valuable cells to treat patients or even cause a microbial contamination, putting at risk the ability to perform life-saving therapies. Furthermore, the time delay before cell assay results are available can compromise the ability to respond effectively. So, there is considerable incentive to reduce the need for sampling and offline analyses.

Spectroscopic measurements offer the potential for non-destructive, 

 measurements that obviate the need for sampling and return results much more rapidly (even nearly in real time). Infrared (IR) and Raman spectroscopy have been shown to provide information-rich data about cells and tissues, but the data can be difficult to interpret as outlined above. This is especially true if the biochemical measurands have no clear relationship to the spectral data. For example, measurements of transcription factors in cells can signal the expression of developmentally important genes. Those measurements may correlate with certain features in measured spectra, though it may not be clear 

 what spectral features are relevant or why.

Multivariate analytical methods such as principal component analysis (PCA) can often reveal qualitative differences in spectral data that are useful, but it is usually difficult to infer the biological origin for the observed separation in PCs. We were drawn to the prospect of using analytical approaches like 2D-COS that give quantifiable statistics relating spectral variations. We hoped to be able to characterize the spectroscopic data sufficiently well during method development that product sampling during manufacturing could be significantly reduced. Thus, we sought to develop an approach that permitted the integration of diverse biochemical information with measured spectra for co-analysis in order to characterize the spectra and make use of all of the spectral information available. This supplements our use of specific peak amplitudes that are known to represent relevant chemical moieties in some cellular components, though does not consider unassigned peaks, peak shapes, or features between peaks. MuSCA permits co-analysis of the spectral data and biochemical data at every wavenumber, and thus emphasizes spectral changes that can be linked to their biological origin. Thus, the benefit of understanding how spectra relate to information that is obtained from sampling and offline analyses is that spectral measurements can then substantially reduce or altogether replace such procedures.

One might also want to include an array of non-spectrometric measurements to examine all the relationships between spectrometric and non-spectrometric ones and even mutually between non-spectrometric ones. A benefit that would derive from such an approach is that one might uncover unexpected and potentially useful relationships.

Another recent paper details applications of Raman spectroscopy in the development of cell human therapies (3), offering insight into the current state of emerging therapies. What advantage does the use of Raman spectroscopy provide for the analysis of single cells or tissues?

Many different types of therapeutic cells being developed with the hope of offering much improved treatments or even cures for a number of human diseases. Some cell therapies are already approved and many more are currently being tested in clinical trials. The specific role and value of Raman spectroscopy depends completely on the specifics of the cells and the manufacturing process. Raman can be useful during development of therapeutic cells by providing information about the metabolic or developmental state of the cells under particular experimental conditions, or to assess the quality or functionality of the cells as a final product. For example, we are collaborating with our co-author Prof. Timothy Kieffer, who is working on an improved protocol for directing the differentiation of human pluripotent stem cells into pancreatic cells that produce insulin for treating Type I diabetes. The current protocol involves seven stages of culture in defined media where cells are stepwise induced to develop toward the desired pancreatic cells. Each stage of the process involves the costly culture medium, and for patient safety it is extremely important to be able to monitor the cells and determine early if there are signs of abnormal (off-target) cells developing. It is also important to determine what type of off-target cells may be emerging. One aspect of our work in this collaborative project is to develop Raman spectroscopic methods to address both of these objectives.

Post-development, Raman may also be useful as a process analytical technology (PAT) for quality assurance/quality control (QA/QC) monitoring the manufacturing process. Raman spectroscopy is already used as a PAT for manufacturing other biologics (such as therapeutic antibodies) and small-molecule therapeutics. The complexity of therapeutic cells makes the development of PAT for their manufacture far more challenging such that we (and others) are working to develop innovative methods based on Raman spectroscopy for these processes. Dr. Piret will speak about this in his plenary lecture at SciX in October.

In general, the main advantage of Raman spectroscopy for these applications is, as mentioned above, that it offers a rapid, information-rich, non-perturbing, method of assessing changes in the chemical composition of the cells, which in turn signal the expected (or unexpected) response of the cells. The challenge of course is extracting the relevant information from the spectra that is convolved with much irrelevant information. Another advantage is that Raman spectroscopy can be implemented in a variety of ways depending on where in the process or how the measurements need to be made or what analytes are of most interest. For example, we rely heavily on the implementation of near-infrared Raman spectroscopy interfaced with an optical microscopy platform (Raman microspectroscopy); other applications may be able to exploit fiber-optic probes that can be inserted within a bioreactor to, for example, monitor changes in the composition of the culture medium. Another related advantage is that Raman measurements can be carried out using any optical frequency from infrared to ultra-violet, and there can be advantages to choosing a particular wavelength. There are many possibilities discussed in our review (3).

Please describe some of the challenges you faced in working with collecting and analyzing Raman spectra of therapeutic cells and how these challenges may have affected useful clinical applications.

The main challenge encountered in collecting and analyzing Raman spectra for any application involving cells, not only therapeutic cells, is that the sensitivity of spontaneous Raman is quite low. This requires collecting the Raman scattered light for appreciable times (minutes) for each spectrum in order to achieve satisfactory signal-to-noise performance. This can be problematic for measuring live cells, especially mobile live cells. It is a serious limitation if spatially resolved measurements are needed, for example to produce so-called "chemical images" where spectra need to be measured at each pixel of a chemical image. As a result, we have mainly been working with fixed cells for method development using Raman micro-spectroscopy. This allows cells to be isolated and immobilized in the absence of interfering medium components, and also allows cell samples to be preserved at low temperature for future re-examination, but it is clearly not the conditions that would be needed in a PAT application.

 measurements are difficult to implement. We have explored the use of spatially offset Raman spectroscopy (SORS) that permits measurements to be made through interfering barriers such as the plastic or glass walls of culture vessels, and fiber-optic probes that can be inserted into the vessels. However, the development of improved exposure systems remains a technical challenge that needs to be overcome to advance clinical applications.

Of great promise are emerging methods based on coherent (

. spontaneous) Raman spectroscopy such as coherent anti-stokes Raman spectroscopy (CARS) and stimulated Raman spectroscopy (SRS). These techniques can potentially address the sensitivity limitations, allowing near real-time spectral acquisition, and facilitate 

 measurement capability due to their high confocality. These techniques are now commercially available in turn-key instruments that enable measurements within flow-cells or micro-fluidic systems for rapid atline analyses. The analytical methods that we are developing should be transferable or at least adaptable to SRS data acquisition.

In what ways may other researchers become involved in cell therapy research and what other analytical techniques show promise?

There are many dimensions to cell therapy research and analytical measurements are critically important in all of them. There are open questions that could be addressable by almost any analytical science researcher. To suggest just a few areas, these include engineering of appropriate exposure systems for effective data acquisition, development of online-compatible biochemical analysis systems, or advanced imaging systems with analytical capabilities. That said, for analytical scientists to work effectively in this area it is important to have a suitable biomedical research collaborator to elucidate the specific problems that need to be addressed and, importantly, provide access to appropriate cells and biochemical data. Fortunately, there are now biomedical researchers at most institutions that are working on some aspect of therapeutic cell research or development, and they are often networked with biotechnology industrial collaborators. However, those experts rarely approach the analytical scientists, so analytical scientists usually would need to approach them.

In addition to the spontaneous Raman that is our specialty, we mentioned above some other non-linear techniques based on Raman scattering (CARS, SRS). There are also some multi-modal techniques that combine Raman with other analytical modalities such as fluorescence, elastic light scattering, optical coherence tomography, phase-contrast microscopy, and mass spectrometry. Other promising non-spectroscopic methods include new non-linear imaging techniques such as two-photon fluorescence lifetime imaging (FLIM) for cell cluster and organoid analyses, second and third harmonic generation (SHG, THG) for high-order structure analysis. In terms of data analytics, there are also many efforts to implement machine learning techniques for all kinds of data analysis as well as bioprocess monitoring and control. We have worked on some new multivariate data analysis techniques as discussed here, but there are many established methods that are also applicable to cellular data analysis that we and others have used in analytical method development such as PCA (mentioned above, cluster analysis, partial least-squares discriminant analysis, and non-negative matrix factorization. There are also some promising new transducers based on biosensors and electrochemical methods. Basically, almost any new chemical analysis or data analysis technique may be useful in some aspect of therapeutic cell development or manufacturing research. There is a lot of room for analytical scientists and engineers to contribute to this relatively new field and new innovations could have a huge impact.

What are your next steps in working with using Raman spectroscopy of cells for therapeutic purposes?

We are continuing our Raman spectroscopy and other analyses of stem cell processes that produce cells for treating diabetes, as well as investigating the application of our methods to T cells. This includes laboratory benchtop work by examining the many still remaining cellular variations that can be discerned using Raman spectroscopy. We are also pursuing a number of interests in developing improved data analytic methods to further exploit the rich information content of Raman spectra. Overall, we are working towards the ultimate goal to translate our knowledge and experience from the benchtop to manufacturing settings, thus implementing Raman spectroscopy with the associated analytical techniques as a fully-fledged PAT.

H.G. Schulze, S. Rangan, M.Z. Vardaki, D. G. Iworima, T.J. Kieffer, M.W. Blades, R.F.B. Turner, and J.M. Piret, 

(2) H.G. Schulze, A. Jirasek, M.W. Blades, and R.F.B. Turner, 

https://doi.org/10.1177%2F0003702820979331

S. Rangan, H.G. Schulze, M.Z. Vardaki, M.W. Blades, J. M. Piret, and R.F.B Turner, 

James Piret has a Bachelor’s degree from Harvard  in Applied Mathematics to Biochemistry, and a Chemical Engineering doctoral degree from MIT in 1989. He is a professor at the University of British Columbia in the Department of Chemical & Biological Engineering and the Michael Smith Laboratories; he is also an associate member of the School of Biomedical Engineering. His research focus is on innovative process and device technology development for mammalian cell culture therapeutic protein or cell manufacturing.

Robin Turner earned a PhD degree in electrical engineering from the University of Alberta in 1990. He is now a Professor at The University of British Columbia with joint appointments in the Michael Smith Laboratories and Department of Electrical & Computer Engineering; he is also an Associate Member of the Department of Chemistry. His current research activities focus on applications of Raman spectroscopy to analytical problems in biochemistry, biotechnology, and biomedical engineering.

James Piret, and Robin Turner, of Michael Smith Laboratories (Vancouver, BC Canada) and the University of British Columbia (UBC) have been exploring the benefits of extracting and displaying correlated spectrometric and non-spectrometric variables with a proposed method called multisource correlation analysis (MuSCA). Their work has uncovered several advantages of using Raman spectroscopy for these applications. Here, they discuss their efforts to develop an approach that permitted the integration of diverse biochemical information with measured spectra for co-analysis to characterize the spectra and take advantage of the available spectral information. 

Multisource Correlation Analysis (MuSCA) Applied to Raman Spectroscopy for Biochemical Analysis

Although single-cell multiparameter measurement has been increasingly recognized as a key technology toward systematic understandings of complex molecular and cellular functions in biological systems, it is still generally challenging for existing methods to decipher a large number of phenotypes in a single living cell, despite extensive efforts in analytical techniques. Wei Min, of the Department of Chemistry at Columbia University in New York City, and his associates recently published a paper outlining their devising a set of multiplexed Raman molecular probes with sharp and mutually resolvable Raman peaks to simultaneously quantify cell surface proteins, endocytosis activities, and metabolic dynamics of an individual live cell. Min, who recently spoke to us about this work, is the 2022 recipient of the Craver Award, presented annually at FACSS SciX to recognize the efforts of young professional spectroscopists that have made significant contributions in applied analytical vibrational spectroscopy. This interview is part of an ongoing series of interviews with the winners of awards that are presented at the annual SciX conference, which will be held this year from October 2 through October 7, in Covington, Kentucky.

What makes single-cell multiparameter measurement recognized as a key technology toward systematic understandings of complex molecular and cellular functions in biological systems?

Multiparameter measurement of single cells is important because of the immense structural and functional complexity of living systems. A recurring theme is that a large number of players (molecules) are interacting at every length scale, ranging from protein networks (10~100 nm), to organelles (100 nm – 1 µm), to various cell types within tissues (1~100 µm), to synergistic tissues within functional organs (100 µm – 1 cm), and reaching maximum complexity in nervous systems (~80 billion neurons, each interacting > 1000s of other neurons).

Your paper (1) describes the exploitation of combining ultra-bright Raman dots (Rdots) with simulated Raman scattering (SRS) microscopy as an alternative measurement technique for super-multiplex live-cell profiling. What allows Rdots combined with SRS to be an enhanced imaging technique over the use of fluorescent dyes?

Rdots are specially designed so that their spectral response are much sharper with higher resolution (50 times) than the standard florescent dyes. This allows us to employ many different “colors” of Rdots to target a large number of biological targets simultaneously.

Briefly discuss your overall findings and their implications. Were you surprised by the results?

After developing the technique, we used it for multiplexed live-cell profiling and phenotyping under various drug perturbations. We found that our single-cell multiparameter measurement can readily enable powerful clustering, correlation, and network analysis with biological insights. The results are surprisingly robust and hence we believe the technique can be applicable to a wide range of live cell assays that require multiplexed reading.

Are there any challenges or limitation involved with utilizing your technique? What options or alternative developments are available to overcome these challenges or to improve your approach?

Bioconjugation of Rdots to target specific biological species or pathways can be a potential challenge. The good news is that many protocols are available in the community, as people have accumulated enough experience working with other nanoparticle-based imaging reagents such as quantum dots and gold nanoparticles.

What sort of response has your paper received from the research community?

Many people in our field were surprised by the simplicity of the Raman microscope technique and the strong size of the signal brought by Rdots.

What are your next steps regarding this research?

In addition to multiplexed detection, which essentially uses one color for each target, we are also exploring a barcoded detection strategy which utilizes a combinatorial scheme to encode and then decode a large pool of similar objects (molecular mapping).

The Craver Award is presented annually to recognize the efforts of young professional spectroscopists that have made significant contributions in applied analytical vibrational spectroscopy. Please comment on the meaning of being 2022’s recipient.

I feel much honored to be recognized by the Craver award. In the field of Raman microscopy, label-free imaging has been the prevalent paradigm for decades. It is pleasing to see that our efforts of developing Raman-active imaging probes (including small tags, organic dyes and nanoparticles) are being accepted by the community. More and more researchers are now incorporating Raman probes into their research strategy, opening up more applications of vibrational spectroscopy in biomedical science and technology.

https://doi.org/10.1038/s41467-021-23700-0

Wei Min is currently a Professor of Chemistry at Columbia University. He is also affiliated with the Department of Biomedical Engineering, the Kavli Institute for Brain Science and NeuroTechnology Center at Columbia University. After graduating from Peking University in 2003, he received his PhD from Harvard University in 2008 studying single-molecule biophysics with Prof. Sunney Xie. After continuing his postdoctoral work in Xie group, Min joined the faculty at Columbia University in 2010, and was promoted to Full Professor there in 2017.

Min has made important contributions to the development of stimulated Raman scattering (SRS) microscopy and employed it to open up a broad range of applications. He has co-invented modern SRS microscopy and pushed its sensitivity down to single molecule level through stimulated Raman excited fluorescence (SREF) spectroscopy; his team has designed and synthesized Raman-active probes exhibiting rainbow-like spectral “colors”; his group has opened up applications including bioorthogonal chemical imaging of small biomolecules (such as lipids, amino acids, glucose, and drugs), metabolic activity imaging in animals, and super-multiplexed vibrational imaging.

Wei Min, of the Department of Chemistry at Columbia University in New York City, and his associates recently published a paper outlining their devising a set of multiplexed Raman molecular probes with sharp and mutually resolvable Raman peaks to simultaneously quantify cell surface proteins, endocytosis activities, and metabolic dynamics of an individual live cell. Min, who recently spoke to us about this work, is the 2022 recipient of the Craver Award, presented annually at FACSS SciX to recognize the efforts of young professional spectroscopists that have made significant contributions in applied analytical vibrational spectroscopy.

Multiplexed Live-Cell Profiling with Raman Probes

The use of proficiency tests (PTs) in an analytical inorganics laboratory is an essential part of assuring clients the laboratory is producing accurate results. In the first part of this two-part series, we examined the ways in which contaminants can alter the results in a detrimental fashion in regard to proficiency testing and laboratory audits. In Part II, we examine the actual planning, running, and reporting of a PT scheme and the possible impact of error and contamination.

A proficiency test (PT) scheme does not start only upon opening the ampule or bottle to test the sample. The steps that lead up to the actual testing can play an important role in the success or failure of a testing scheme. As was discussed in the previous column, standards, calibrations, and maintenance are all important components in reducing error and contamination even before the first sample is placed on the instrument. Once the laboratory and all its contributing standards and equipment are ready, it is time to turn the attention to the actual PT sample.

The proper handing and processing of a PT sample can be critical in passing a PT test. For calendar-based PTs, one needs to check for shipping dates and delays, which could affect the quality of the PT. Upon receipt of the PT sample, open the package and check its condition. Report any damage to samples or if they have been thermally compromised. The first step after receiving and accepting the PT shipment is to check all of the handling and safety instructions to ensure that the samples are kept at their optimal conditions.

Some materials may require temperature control whereas others may have instructions on how to handle the sample during preparation or give information regarding the possible range of values for the PT. Once the PT is in-house, fresh chemicals and standards should be prepared and all calculations should be rechecked. Check PT instructions for methods and report results (that is, online, by phone, or by mail), and the correct reporting formats that are acceptable (that is, correct weight or concentration notations).

Take special note of any sample preparation procedures or method notes that are to be employed with that PT scheme. Any differences between your normal sample preparation and the PT preparation and analysis should be discussed with the quality manager. Otherwise, all differences in sample preparation because of the matrix or material should reveal the deviations or additions to prepare the sample that differ from routine preparation.

Once a PT has been analyzed, the data is reported back to the PT provider for analyses and final acceptance. The PT provider often has multiple methods for evaluating the data based on the type of PT that is being analyzed and the governing bodies under which the PT scheme is intended. Some accreditation or standards organizations offer documentation and tables for the reporting of PT results, whereas other PT results are entered into programs to create results from programmed algorithms and statistics.

ISO guidelines (ISO 13528) have two basic methods for the statistical evaluation of proficiency tests: the 

-value is most commonly used in interlaboratory comparisons where the laboratories report their uncertainty calculations (equation 3).

-value between 1 and -1 are considered successful and in agreement with the reference values. Results with 

-values greater than one or less than -1 are considered not successful and disagree with the reference values, which should trigger a corrective action.

-score can also be conducted within interlaboratory comparisons without an uncertainty calculation, but the 

-score assumes all samples reported are from the same population, batch, or sampling; therefore, all samples have the same uncertainty. The most practical of the statistical functions for chemical and biological analyses is the 

The results are calculated after the outliers are removed or are obtained from robust statistics. 

-scores less than two are considered successful and in agreement with accepted values. Scores between two and three are considered suspect whereas scores greater than three are unsuccessful and disagree with the accepted results.

Once scores are compared against the reference values and are assigned passing or failing scores, those scores are reported to the laboratories. Scores can either reflect individual scores for individual analysts or they can reflect the overall laboratory proficiency. In many cases, one unsuccessful score does not necessarily mean that the entire laboratory fails the PT scheme. One or more assessed failures in key practices or tests can cause a laboratory performance to be deemed unsatisfactory or multiple failures submitted in one PT round. Multiple consecutive failures for an analysis can also result an unsatisfactory rating. Results of a particular analysis may be deemed suspect if a clear bias or trend towards unsatisfactory results is apparent, including consistent bias over one standard deviation above or below the assigned values. Even if the results themselves are passing, the PT results can be considered unsatisfactory if there is a clear and measured bias. All failures or unsatisfactory results should be directed towards root cause analysis and corrective actions as dictated by that laboratories’ quality management system (QMS).

In the event that an analyst or laboratory fails a PT test, there should be a procedure for a comprehensive review of the processes, preparation, and analyses involved, along with a plan of action to correct the problems found. First, a root cause analyses is conducted to identify and document the problem. Then, a decision is made whether the problem was because of an error or a systematic defect that needs to be corrected. In cases of error, a full corrective action may not be required, but instead, a retraining for that analyst or laboratory. If a root cause analysis finds deficiencies within a system, then a corrective action is devised to remedy the issue. A retesting of a corrected system must be conducted to determine if the correction was successful.

Some of the points of review should include the laboratory processes and all internal quality control data. Points to reexamine during a system and procedure review for a PT failure include:

Sample storage and handling—were all requirements met?

Preparation—did the preparation process differ between normal samples and the PT samples?

Instrumentation and equipment—does the deficiency lie within the system, and can it be corrected with replacement, repair, or recalibration?

Environment—were all materials and equipment kept at the correct temperature?

Controls—were all the standards, QC samples, and controls within expiration? Were samples and standards prepared fresh or were new standards opened?

Calibrations—are all the dilutions correct and within the correct dynamic range for the system? Were all the calculations correct and documented including unit conversions?

Are there sources of contamination that may have caused the deviation in results?

Sources of Laboratory Contamination and Error

It is hard to imagine that such tiny amounts of contamination can dramatically change laboratory values. In most cases, the root of that contamination can be traced to a common source. The most common sources of contamination, in addition to the previously discussed volumetric labware, are water, reagents, laboratory environment, and personnel.

Water is one of the most basic yet most essential laboratory components. There are many types, grades, and intended uses for water. Inferior quality water can cause a host of problems from creating deposits in labware or inadvertently increasing a target element or element concentration in solution.

The actual type of water produced by a commercial laboratory water filtration system can vary in pH, solutes, and soluble silica. Critical analytical processes should always require a minimum quality of American Society for Testing and Materials International (ASTM) Type I water. All trace analysis should be conducted with the highest purity water. Analysts who use certified reference materials (CRMs) and perform quantitative analysis need to use quality water to prevent contaminating their CRMs, standards, PTs, and samples with inferior quality water.

Another source of contamination are all the chemicals used in sample processing. Just as in the case of water, there are several types or grades of chemicals, reagents, acids, and solvents. Some designations are set forth by standards set by national or international organizations other types or grades of material are designated by individual manufacturers based on intended use. Some grades are general laboratory grades with intended use for noncritical applications, whereas other grades high in purity and low in contaminants are designated for more critical analyses.

For example, consider acids, which by their very nature are oxidizers and many of the strongest acids are used in the processing of samples for inorganic analysis. Common acids in digestion and dissolution, such as perchloric acid, hydrofluoric acid, sulfuric acid, hydrochloric acid, and nitric acid, are commercially available in several grades from general laboratory or reagent grade to high purity trace metal grade. Acid grades often reflect the number of distillations the acid undergoes for purification before bottling. The more an acid is distilled, the higher the purity of the acid. These high purity acids have the lowest amount of elemental contamination but can become very costly.

To reduce contamination, it is recommended that high purity acids be used to dilute and prepare standards and samples when possible. In addition to using pure acid, it is important that the chemist check the certificate of the acid under analysis to identify the elemental contamination levels present in the acid.

All laboratories believe they observe a respectable level of laboratory cleanliness. Most chemists recognize there are inherent levels of contamination present in all laboratories. A common belief is that the tiny amounts of environmental and laboratory contamination cannot profoundly change the analytical results. Spex conducted a test where nitric acid was distilled in both a clean room and a normal preparation laboratory. The nitric acid distilled in the regular laboratory had considerable amounts of aluminum, calcium, iron, sodium, and magnesium contamination not found to the same degree as in the clean room.

Laboratory air also can contribute to the contamination of samples and standards. Common sources of air and particulate matter contamination are from surfaces and building materials. Surface contaminants can be found in dust and rust on shelves, equipment, and furniture. Dust contains many different earth elements such as sodium, calcium, magnesium, manganese, silicon, aluminum, and titanium. Dust can also contain elements from human activities (Ni, Pb, Zn, Cu, and As).

Laboratory personnel can add their own contamination from laboratory coats, makeup, perfume, and jewelry. Many other common elements can be brought in as contamination from personal care products. Even sweat and hair can cause elevated levels of sodium, calcium, potassium, lead, magnesium, and other ions. If a laboratory is seeing an unusually elevated level of cadmium or other heavy metals in the samples, it could be from cigarettes, pigments, or batteries. If the levels of lead are out of range, contamination can be from paint, cosmetics, and hair dyes (Table II).

Even with clean laboratory practices in place, erroneous results can often find their way into sample analysis. To eliminate some of these spurious results, replication of blanks and sample dilutions can be employed. The blank results should be averaged, and the sample run or PT values can either be minimally selected or averaged. The difference between the two values can then be plotted against a curve established against two more standards. A minimum of two standard points can be used if the chance of contamination is minimal, such as in the case of rare or uncommon elements. Additional standard points should be considered if the potential for contamination is high with common elements, such as aluminum, sodium, and magnesium. Multiple aliquots of blanks and dilutions can also be employed to further minimize analytical uncertainty.

As we have discussed, PTs are a necessary and helpful tool in the QMS system arsenal of a laboratory; but they do have limitations and misuses. In PT schemes where interlaboratory tests are used to establish values, there is often a retrospective aspect to the tests. These tests take time and have very time-critical periods for analyses in order for the results to be included in the data set. In many cases, the PTs available only include a small subset of analytes or matrices that a laboratory will analyze on a routine basis, meaning only those types of samples or target analytes are covered by the PT, whereas the others remain unmonitored.

Understanding the proper ways to plan, run, and report proficiency tests will help you avoid errors and contamination.

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