Spectroscopy Tech Innovations & Applications

A Look at Inorganic Analyses, Proficiency Tests, and Contamination and Error, Part I

What Does Your Proficiency Testing (PT) Prove? 

Applications of Raman Spectroscopy in Solvent Distillation and Exchange 

Is Traceability the Glue for ALCOA, ALCOA+, or ALCOA++?

2022 Review of Spectroscopic Instrumentation

 magazine’s much-anticipated new product review is back, providing spectroscopists, researchers, and educators with detailed listings and reviews of the latest spectroscopy instruments, accessories, components, and software. All of these new products launched within the past 12 months. Here, we offer highlights of the 2022 Review of Spectroscopic Instrumentation, which appeared in our May issue.

Agilent Technologies introduced an environmentally friendly coolant fluid, and Applied Rigaku Technologies, Inc. presented a new X-ray fluorescence (XRF) instrument. Hübner introduced a new laser source for laser-induced breakdown spectroscopy (LIBS). A new inductively coupled plasma (ICP)–optical emission spectroscopy (OES) instrument, introduced by Radom Corporation, is argon-free, operating with microwave-sustained atmospheric plasma. Advion Interchim Scientific introduced the laboratory-based Solation ICP-mass spectrometry (MS) system for complex samples, and Applied Rigaku Technologies, Inc. introduced a new X-ray fluorescence (XRF) instrument.

IS-Instruments introduced a deep ultraviolet (UV) Raman system with excitation at 228 nm, and WITedc GmbH has a new cryogenic Raman imaging microscope. Elodiz, Ltd. and Ocean Insight introduced devices that are more geared to field and process applications.

REFLEX Analytical Corp. and Nanosens AS have both introduced new cells and cuvettes. S.T. Japan-Europe GmbH has introduced an improved MicroVice while Specac now provides a heated puck for the Quest. An interesting new product from Specac is the Arrow, which is a disposable silicon ATR accessory.

Armadillo SIA has introduced two new optical fiber products while Trace Matters Scientific has a new flexible ion guide. Sources are one type of component where there have been several new entries, including products from Pyroistech S.L, Micro X-Ray, Inc., and Edinburgh Instruments.

Both Ocean Insight and Optiwave Systems introduced driver and instrument control software for their products. Here, we also include spectral libraries in the software category. S.T. Japan-Europe GmbH has introduced three new libraries: a microplastics infrared (IR) library, a Raman microplastics database, and a Raman database for psychoactive and illicit drugs.

See the details of all new spectroscopy instrumentation 

Articles

Here, we offer highlights of the 2022 Review of Spectroscopic Instrumentation, which appeared in our May issue.

Highlights of Our 2022 Review of Spectroscopic Instrumentation

First defined by the Centers for Disease Control and Prevention (CDC) following the 1990–1991 Gulf War, established medical diagnoses, laboratory tests, and hypothesis-driven research have failed to explain the multifacted symptomatology of Gulf War illness (GWI), a chronic illness with no known validated biomarkers. Noureddine Melikechi of the Department of Physics and Applied Physics at the University of Massachusetts (Lowell, MA) saw an urgent need for the development of an untargeted and unbiased method to distinguish GWI patients from non-GWI patients; he and his associates utilized laser-induced breakdown spectroscopy (LIBS) in their efforts to meet that need. Melikechi spoke with 

 about this work, and his research paper that has resulted.

In one of your recent papers (1), you focus on the diagnosis of Gulf War illness (GWI). In that paper you used laser-induced breakdown spectroscopy (LIBS) to measure blood samples in order to differentiate between GWI patients and non-GWI patients. Would you briefly describe the results of this research and what you have learned?

Gulf War Illnessaffects a quarter of the nearly 700,000 veterans who served in the 1990–1991 Gulf War. It is a chronic illness with multiple symptoms that include both physical and emotional disorders. Using a LIBS-liquid biopsyapproach similar to that of our previous studies, we searched for spectral signatures that can differentiate blood plasma of cancerous and non-cancerous mice. We have also shown that this technique can be used to differentiate blood plasma of healthy patients from patients known to suffer from Alzheimer’s disease. For our GWI work, we examined the LIBS signatures of blood plasma acquired from GWI patients and non-GWI patients. We used various machine learning techniques for the analysis of the spectra obtained. By comparing the LIBS spectra obtained from blood plasma of GWI patients and non GWI patients, we generated a classification model which was used to conduct a blind test. This test yielded a classification accuracy of the unknown cases of 90% with 100% sensitivity, and 83% specificity. These are encouraging results, but it is important to note that this initial study was performed with a smaller number of samples than we would have liked. We plan to examine samples from a larger number of patients in the next phase of our work.

You also have used LIBS to research the diagnosis of melanoma in biomedical fluids (2), Alzheimer's disease (3), and epithelial ovarian cancer (EOC) (4). Which application looks the most promising, and why?

My research group has been working on the development of minimally invasive, multidisciplinary techniques for the detection, screening, and diagnosis of cancer with special emphasis on EOC and melanoma, as well as on early signs of Alzheimer’s disease using (principally) blood plasmas. For these diseases, we have obtained discrimination accuracies between diseased and healthy controls that approach, and in some cases surpass, 80%. Other researchers have also been able to apply similar approaches and obtain encouraging results, for example, on the detection of multiple myeloma in humans (

). It is too early to know which, if any, application is more promising as much more work needs to be done. Can LIBS alone or integrated with other analytical techniques contribute to the screening and possibly diagnosis of cancers and neurological diseases? Much work needs to be done before this question can be answered with more certainty.

What benefits have you seen using LIBS spectroscopy over other spectroscopic techniques in diagnosing various types of illness?


There are several spectroscopic techniques that can be envisaged, such as Raman spectroscopy, Fourier transform infrared spectroscopy–attenuated total reflectance (FTIR-ATR), and inductively coupled plasma mass spectrometry(ICP-MS). We have selected to use LIBS because it is a multi-elemental analysis technique that can be performed with very limited sample preparation and can provide qualitative and quantitative information for a great variety of samples, including biomedical fluids. Few other spectroscopic techniques present such advantages. However, LIBS also has limitations, for example in sensitivity. It may be that integrating atomic and ionic information obtained using LIBS with molecular information obtained using Raman or FTIR-ATR or preparing samples prior to making measurements, such as by using Tag-LIBS, will provide better results.

What are the biggest challenges that you have encountered in developing LIBS methods for diagnostic applications? What options or alternative developments are available to overcome these challenges or to improve your approach?

The main challenges are not specific to developing LIBS methods. First, this work requires the commitment and engagement of an interdisciplinary team dedicated to examining, what may often be an old problem, with new perspectives. Only then, can the interdisciplinary team of researchers synergistically take advantage of the knowledge and experience of each. This is a challenge as we are, typically, not trained to work in interdisciplinary teams. Second, it is critical to have access to biomedical samples of “good quality” and in sufficient quantity to be able to acquire “enough” spectroscopic data. As LIBS is essentially sensitive to practically all chemical elements, significant attention must be given to ensure that all biomedical samples are treated, during and post collection, the same way. Failure to do this can result in the introduction of external chemical elements in one or more of the samples that can influence the measurements and hence the final analysis and conclusions. Third, it is important to keep an open mind and learn from what the data tells us and not be blinded by pre-conceived and possibly hidden assumptions.

The abundance of biomedical data, such as spectroscopic and imaging data, and the development of powerful new computational resources may allow for early detection of diseases that were previously impossible to attain. A key step is to clearly and unambiguously identify the atomic and molecular features that are behind the differences observed in the spectra and to understand at the fundamental level the reasons for the existence of such differential spectroscopic signatures. We are in the process of examining our data to seek such information.

What sort of feedback did you receive from these papers and your reported results?

The fact that, as interdisciplinary teams, we are looking at cancers, Alzheimer’s disease, and gulf war illness with researchers from different institutions is a sign that there is an interest in this approach.

Are there any additional analytical approaches or methods that you are planning to explore? Would you describe your use of specialized chemometrics and machine learning and how these techniques are an advantage in data analysis?

We are refining our analytical approaches by investigating the use of powerful, novel, as well as established, machine learning tools. Many of these tools have not been investigated fully yet in this area of research. We are also looking into extracting spectral features from the LIBS data that contribute significantly to the differentiation and classification of the blood plasma samples. Such features will be used to train our models and to identify the associated chemical elements.

What are your next steps regarding your LIBS research?

The studies we have conducted thus far are focused on distinguishing the LIBS spectra of the blood plasma of healthy patients with patients identified as having only one disease. Reality is more complex. A richer, more comprehensive clinical study that involves a larger number of patients with multiple conditions is needed. Such a study will help evaluate the specificity and sensitivity accuracies of the approach in scenarios that are closer to clinical settings. It may be useful to consider combining atomic and ionic information with molecular information of blood plasma samples and possibly proteomics. Together these tools may provide clues that help identify new biomarkers for cancers or neurological diseases. As useful as the analysis of spectral data with machine learning techniques is at distinguishing diseased and healthy biomedical samples, much work is needed to better understand the onset of diseases at the atomic and molecular levels prior to the appearance of clinical symptoms.

(1) R. Gaudiuso, S. Chen, E. Kokkotou, L. Conboy, E. Jacobson, E.B. Hanlon, and N. Melikechi, 

 (2021) https://doi.org/10.1177/00037028211042049

(2) R. Gaudiuso, E. Ewusi-Annan, N. Melikechi, X. Sun, B. Liu, L.F. Campesato, and T. Merghoub, 

(3) R. Gaudiuso, E. Ewusi-Annan, W. Xia, and N. Melikechi, 

(4) N. Melikechi, Y. Markushin, D.C. Connolly, J. Lasue, E. Ewusi-Annan, and S. Makrogiannis, 

(5) Y. Markushin, P. Sivakumar, D.C. Connolly, and N. Melikechi, 

Noureddine Melikechi is a Professor of Physics and serves as the Dean of the Kennedy College of Sciences at the University of Massachusetts Lowell. In recent years, Noureddine’s research group has been working on the development of approaches for the early detection of laser spectroscopic signatures of cancers and Alzheimer’s disease. In addition, as a member of NASA’s Mars missions, Noureddine contributes to the analysis of LIBS data collected by the ChemCam and the SuperCam instruments on board the Curiosity and Perseverance rovers. A native of Algeria, Noureddine received his Diplôme d'Études Supérieures in Physics from the University of Sciences and Technology Houari Boumediene, Algeria, and his M.Sc. and D.Phil., both in Physics, from the University of Sussex. Noureddine is a Fellow of Optica, the America Physical Society and the American Association for the Advancement of Science.

Noureddine Melikechi of the Department of Physics and Applied Physics at the University of Massachusetts (Lowell, MA) saw an urgent need for the development of an untargeted and unbiased method to distinguish Gulf War illness (GWI) patients from non-GWI patients; he and his associates utilized laser-induced breakdown spectroscopy (LIBS) in their efforts to meet that need.

Diagnosis of Gulf War Illness Using Laser-Induced Spectra Acquired from Blood Samples

The Society for Applied Spectroscopy (SAS) encourages undergraduate students doing research in spectroscopy to apply for one of a limited number of travel awards of up to $300 per student to attend the SAS National Meeting at SciX 2022, taking place October 2–7 at the Northern Kentucky Convention Center in Greater Cincinnati. Although the grant is unlikely to cover all the student’s travel costs, reduced hotel rates and registration fees for students are typically available for the conference.

Undergraduate students in degree programs at two- and four-year colleges are eligible for this competitive grant, which is based on merit and financial need. An applicant’s research advisor must be an SAS member in good standing at the time of application. Students are required to present their research at the SciX meeting as a poster during the Sunday night SAS student poster session on October 2.

Applications for the grant program are due to the SAS by June 1, 2022 and must be submitted by the student's research advisor. Decisions on grant recipients will be made in a timely manner and communicated to the student and advisor. Applicants can receive this grant only once during their undergraduate academic career.

The application materials consist of an application form; letter(s) of recommendation addressing student's research abilities and merit completed by advisor; a financial need statement and approximate travel budget completed by advisor and student; a copy of the student’s abstract as submitted to SciX.

 can be downloaded from the SAS website and must be submitted to:

The Society for Applied Spectroscopy (SAS) encourages undergraduate students doing research in spectroscopy to apply for one of a limited number of travel awards of up to $300 per student to attend the SAS National Meeting at SciX 2022, taking place October 2–7 at the Northern Kentucky Convention Center in Greater Cincinnati. 

SAS Undergraduate Student Travel Grant Applications for SAS National Meeting at SciX 2022 Due June 1

Lu Wei, PhD, an assistant professor of chemistry at the California Institute of Technology, has won the 2022 Emerging Leader in Molecular Spectroscopy Award, which is presented by 

 magazine. This annual award recognizes the achievements and aspirations of a talented young molecular spectroscopist, selected by an independent scientific committee. The award will be presented to Wei at the SciX 2022 conference this fall, where she will give a plenary lecture and be honored in an award symposium.

Lu Wei, California Institute of Technology

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. Wei has already made several seminal 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, Wei 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.

She further developed a super-multiplexed optical imaging strategy that exploited a unique pre-resonance SRS excitation region and achieved remarkable sensitivity with up to 24-plex imaging.

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. One of her key contributions is the development of super-multiplexed and super-resolution vibrational microscopy for subcellular structure–function phenotyping in a complex cellular and tissue environment. Toward this end, she devised a sample-engineering label-free super-resolution SRS imaging strategy and obtained the first multiplexed imaging to within a spatial resolution of 100 nm. In parallel, she also pioneered the development of advanced vibrational probes (such as a photoactivatable and photoswitchable Raman probe), which is a significant component for achieving super-multiplexed and super-resolution vibrational microscopy.

Wei has also pioneered a selective deuterium strategy that allowed the first in situ live-cell quantification and spectral analysis of aggregate composition from native polyQ aggregates, the key hallmark for Huntington’s disease. In addition, she further developed and applied subcellular Raman spectromicroscopy with data mining cross-correlation to transcriptomics and lipidomics for Raman-guided subcellular pharmacometabolomics for metastatic melanoma.

Wei has published 28 papers with 1913 citations and has given 30 oral presentations at scientific conferences. She is a reviewer for multiple journals, including, among others

Proceedings of the National Academy of Sciences of the United States of America (PNAS), the Journal of the American Chemical Society, Chemical Science, Angewandte Chromatographie, ChemComm, the Journal of Physical Chemistry Letters, the Journal of Physical Chemistry B, Small, Langmuir, ACS Omega, Theranostics, Optics Express, Optics Letters, 

She is also the organizer of a symposium planned for the 2023 Fall ACS conference on optical spectroscopy and microscopy across biological scales.

Wei has also won several other honors and awards. She was named a Sloan Research Fellow in Chemistry in 2022 and a Heritage Medical Research Institute Investigator at Caltech in 2021. She was twice named a Scialog Fellow: in 2021, for advanced bioimaging, and in 2018 for chemical machinery of the cell. She also received the NIH Director’s New Innovator Award, the Amgen Early Innovator Award, a Blavatnik Regional Awards for Young Scientists, in chemistry, from the New York Academy of Sciences, and an American Chemical Society PHYS Division Young Investigator Award.

For information about how to nominate a candidate for the 2023 award, please see the 

Lu Wei, PhD, an assistant professor of chemistry at the California Institute of Technology, has won the 2022 Emerging Leader in Molecular Spectroscopy Award, which is presented by Spectroscopy magazine. 

Spectroscopy Magazine Announces the 2022 Emerging Leader in Molecular Spectroscopy

Trace inorganic analysis is an essential part of analytical testing. In many cases, contamination can alter or skew elemental analysis, which leads to inaccurate results. Another way in which contaminants can alter results in a detrimental fashion is in regard to proficiency testing and laboratory audits. Proficiency testing (PT) is a series or schemes of samples that are intended to assess the performance of individuals or laboratories in specific areas or for specific analytical tests. PT is also designed to be used to improve processes and methods. If contamination and error enter into the analysis process, then the results can be altered, causing the analyst or laboratory to perform poorly in a PT scheme. In this two-part article, we examine the development, rationale, and best practices for PT, including how contamination can enter inorganic samples and how to eliminate or control those sources.

Proficiency testing (PT) is a phrase that often solicits groans or resigned sighs. Most of us on the receiving end of a proficiency test dislike even the thought of being “evaluated” despite the fact that testing is our “business” in many cases. The truth is that PT is not a tool to trick analysts or put them on the spot. Laboratories use PT to comply with their accreditation requirements and evaluate analyst performance. PT is an integral part of a quality management system (QMS) under quality assurance and control (QA/QC).

A QMS contains the practices and processes a business follows to meet customer requirements. The International Organization for Standardization (ISO) has become one of the world’s largest developers of voluntary international standards for all manufactured, agricultural, and technological products and services. The standards issued by ISO are the framework for the quality management systems of ISO accredited companies and laboratories (1).

In the 1990s, ISO began creating standards for laboratories to standardize procedures and ensure competency and accuracy. Throughout the years, laboratories, reference materials, and PT providers have pursued ISO accreditation for their facilities as a mark of quality and reliability (2).

A QMS includes components such as a mission statement, goals, and objectives through all the processes for organizational structure, data management, manufacturing processes, and QA/QC. Many laboratory practices fall under the heading of QA/QC while still being part of the overall quality system. Laboratory QA/QC has may processes routinely included in standard operating procedures (SOP). Some of the key processes for analytical laboratories are the use of certified reference materials (CRMs) or standards, the use of validated or verified methods, and the use of PT to ensure quality.

In PT, one uses characterized samples that are created to represent the types of samples, matrices, and targets being analyzed in laboratories. Most of the time, these samples contain measured values that are not disclosed to the PT participants and are treated like blind samples—samples in which the nature and quantities are unknown. Some proficiency tests may give information in regard to the target identity, quantitative range, and other information, which directs the analyst in how to perform sample preparation or analyses. PT samples can come in many different forms, from solid or liquid native matrices to extracted oils, liquids, and solids. The analyst is expected to prepare and treat the PT sample in the same way similar types of samples would be routinely processed.

PT participants confidentially share their results with the PT provider for final evaluation and grading. In this way, PT serves as an indicator for the competency of an individual laboratory staff and its analytical performance. Results reported to the PT provider are compared to the reference values established for that PT, which are either established by a reference laboratory or determined by averaging the values reported by the PT participants. Participating PT laboratories can report their values either as individual values or as a composite average value determined by the results obtained by all their individually tested analysts.

PT participants that achieve passing PT results are ensuring the validity and reliability of their laboratory test results. PT is an external quality assessment tool. PT should be an integral part of the testing laboratory’s quality system. In the evaluation of a PT program, the laboratory must consider some critical points: the qualifications of the PT administrator; the quality and qualifications of the PT provider; and the accessibility of data. Within most QMS systems, such as those based on the ISO guidelines, laboratories are required to use certified providers for their analytical standards, methods, and PT. For ISO 17025 laboratories, PT providers must be accredited to ISO 17043 and CRM providers to ISO 17034.

PT is not a means of method validation. Methods used for PT should have previously been validated by the laboratories or standards organizations that have issued those methodologies—that is, the United States Pharmacopeial Convention (USP) (3) and the American Society for Testing and Materials (ASTM) International. PT serves to measure the ongoing proficiency of independent laboratories through interlaboratory comparison of test results for the same sample. Some regulatory bodies stipulate the frequency of PT participation, requiring laboratories to participate in PT on an annual basis. Other laboratories participate in accordance with their accreditation audit schedule. Some accreditation audits only occur once every other year. Thus, the laboratories would participate in PT once every two years.

Understanding the key components of the QMS a laboratory operates under is an important part of passing any PT test. Some of the issues with PT results are not the result itself but the way the statistics, standards, and methods of the QMS have been applied, leading to error.

Evaluating Methods for Regular Use and Proficiency Testing 

Analytical methods intended for use in an accredited laboratory (for regular use or in PT) must either be validated or verified under the laboratory’s QMS. Method validation is the process in which a new method undergoes testing and statistical analysis to determine if it is fit for the intended purpose. Method validation occurs either when a new method is created, or when an existing method is changed so significantly that all the statistical parameters need to be reestablished. Method verification occurs when a method is transferred from one type of process to another similar type of process, such as transferring an atomic absorption (AA) method to an inductively coupled plasma (ICP) method.

Passing a proficiency test can serve as method verification because PT is used to check an already validated method. For example, standard methods and compendial methods are verified, whereas in-house–developed methods are validated. After an in-house method has been validated, it can be verified by successfully passing a proficiency test from an accredited third-party provider. In this example, PT is integral to the QMS of the participant laboratory (4).

All methods must undergo statistical evaluations and must establish the method dynamic range as part of the quality management program, including the statistical concepts of true value, error, bias, uncertainty, accuracy, and precision, which are all interrelated concepts.

Understanding the Basis of Laboratory Statistics

The goal for all analytical analyses is to have measured values close to the true value. 

 is a measurement of how close measured values get to the true value; precision indicates how close a group of results are to one another.

Factors that move the measured value away from the true value are called 

 is the partiality or systematic deviation from the true value that results in over or underestimation. 

 is the difference between the measurement and the true value. Error causes values to differ when measurement is repeated. It is impossible to completely eliminate error, but it can be controlled and documented.

Errors are characterized as random or systematic. Random errors are fluctuations and variances in even the most tightly controlled systems that produce anomalies. Random errors are often impossible to eliminate. Precision is often associated with the statistical measurement of random error. The second type of error is systematic error, which appears from a traceable source, such as instrument malfunction, record-keeping errors, or measurable repeated variants, that affects result outcomes. Systematic errors can often be eliminated by routine maintenance, procedure changes, or record keeping (Figure 1).

FIGURE 1: Graphic representation of accuracy, bias, and precision.

The final important concept for laboratory analyses is uncertainty. Uncertainty is a statistical estimate attached to a value that characterizes the range of values where the true value lies within a stated confidence interval. Uncertainty estimates the effect of short-term fluctuations, variables in the performance of an analyst or piece of instrumentation, or accounts for bias or drift that can be corrected or calculated (Figure 2).

FIGURE 2: Graphical representation of uncertainty and error.

Uncertainty can be calculated in many different ways depending on the type of instrument, equipment, or processes being evaluated. However, all the possible factors that can influence or change the range of expected values must be included in an uncertainty calculation. A suitable QC-known certified reference material or reference standard should be employed for calculating statistics and validating an analytical method.

Standards are a critical key in all analytical analyses. In the case of conducting a PT study or scheme, standards play a critical role in obtaining passing results. The first step in using standards as a part of an analytical process is to understand what standards or reference materials are and how they are used. Standards can be used for qualitative analysis (identity), quantitative analysis (numerical results), or both. They can also aid in identifying or eliminating errors, or be used to determine statistical variables, such as uncertainty, accuracy, and precision.

As a result, questions often arise regarding which standards are used and at what point standards are introduced into a sample preparation and analysis process. One of the most important considerations for choosing a standard (for both general analyses and PT schemes) is whether the standards fit for the purpose in which they will be used (Figure 3). Some questions to ask are: Does the standard reflect the identity or type of sample that is going to be tested? Are the forms (oil, power, solid, among others) of the standards or samples significantly different in a way that will affect the outcome of testing? In the same vein, is the matrix of all the samples, standards, and PT schemes the same or different, and will that change the results or processing? Are the standards amenable to the type of instrumentation being used? Will the samples and standards fall into the analytical range of those instruments? Will changes have to be made to the methods or instruments? The choice of standards can also be difficult to navigate because there are often many choices between reference materials, CRMs, standards, and QC samples.

FIGURE 3: The role of standards and fit for purpose.

Often, the highest level of commercially produced reference materials are certified standards or CRMs produced by primary or secondary standards providers under a QMS such as ISO. These suppliers produce CRMs that have one or more certified values with uncertainty established using validated methods and are accompanied by a certificate of analysis. The uncertainty characterizes the range of the dispersion of values that occur through the determinate variation of all the components that are part of the process for creating the standard.

CRMs have a number of uses, including validation of methods, standardization or calibration of instrument or materials, and for use in QA/QC procedures. A calibration procedure establishes the relationship between a concentration of an analyte and the instrumental or procedural response to that analyte.

 is the plotting of multiple points within a dynamic range to establish the elemental response within a system during the collection of data points. The matrix effect can be responsible for either elemental suppression or enhancement. In analyses where a matrix can influence the response of target, it is common to match the matrix of analytical standards or reference materials to the matrix of the target sample to compensate for matrix effects.

Calibration curves are the practical application or function of standards in analyses. Standards are diluted to points on the curve that reflect the range of target concentrations and are often affected by the limitations of the instrumentation. Data can become biased by calibration points biased by instrument limits of detection, quantitation, and linearity and by the response of the system versus its baseline (signal-to-noise).

The limit of detection (LOD) is the lower limit of a method or system at which the target can be detected as different from a blank with a high confidence level (usually over three standard deviations from the blank response). The limit of quantitation (LOQ) is another lower limit of a method or system in which the target can be calculated, where two distinct values between the target and blank can be observed, usually over 10 standard deviations from the blank response (Figure 1). The signal-to-noise ratio (S/N) is a measurement of the ratio between response of an analyte to the baseline variation (noise) of the system. LOD values are often recognized as target responses that have three times the response of baseline noise or S/N ≥ 3. LOQs are recognized as target responses that have 10 times the response of baseline noise or S/N ≥ 10. Limits of linearity (LOL) are the upper limits of a system or calibration curve where the linearity of the calibration curve starts to be skewed, creating a loss of linearity. This loss of linearity can be a sign that the instrumental detection source is approaching saturation. Finally, dynamic range is the array of values between the LOQ and the LOL where the greatest potential for accurate measurements will occur. Dynamic range can be established for instruments and analytical methodologies. Effective calibrations are created within the system‘s and method’s dynamic range.

The establishment of an instrument’s dynamic range is most often designated by the type of instrumentation. The dynamic range for a method is most commonly established during the method validation or verification process (Table I and Figure 4).

FIGURE 4: Calibration curve limits and range.

Changes to methods or policies should all be completed and validated prior to any PT.

Updated copies of those policies and procedures should be reviewed with all staff members participating in the PT.

Train staff for proper handling of PT samples, results, and reports.

Make sure all maintenance and repairs of systems are completed well in advance of a PT round.

Calibration and linearity checks should be performed by analysts on equipment and instruments prior to starting PT.

Replace all consumable chemicals with fresh materials.

Complete any statistical evaluations of precision and accuracy needed for the intended method or systems before PT.

Check calibration and cleanliness of equipment, volumetrics, and labware.

Derailing PT with Contamination and Error

The fastest way to significantly deviate from a result’s true value is by error and contamination. As we discussed earlier, there are some forms of error (random error) that are difficult to eliminate entirely. Then, there is systematic error, in which some mistakes or deviations in systems or procedures can be corrected. First, let us look at common sources of error in the laboratory: calculation, measurement and dilution mistakes, errors, and contamination.

Calibration curves are a key element of analytical procedures. These curves are created by diluting standards into several target points along the dynamic range to cover the possible target results. Proper dilution of standards and samples is based on the understanding of basic dilution and volumetric procedures and dilution factors. Volumetric measurement is a common repeated daily activity in most analytical laboratories. Many processes in the laboratory, from sample preparation to standards calculation, depend on accurate and contamination-free volumetric measurements. Unfortunately, laboratory volumetric labware, syringes, and pipettes are common sources of misuse, contamination, carryover, and error in the laboratory.

The root of these errors is based on the four “I” errors of volumetrics: incorrect choice, inadequate cleaning, infrequent calibration, and improper use. These four “I”s can lead to error and contamination, which negates all intent of careful measurement processes.

Incorrect choice can be seen in the choice of grades of labware or the selection of pipettes and syringes for analytical measurements. Many errors can be avoided by understanding the markings displayed on the volumetric containers and choosing the proper tool for the job. There is a lot of information displayed on volumetric labware. Most labware, especially glassware, is designated as either Class A (analytical or quantitative) or Class B (general use) labware. If a critical measurement process is needed, then only Class A glassware should be used for measurement.

As for syringes and pipettes, many manufacturers recommend a minimum dispensing volume of approximately 10% of the total volume of the syringe or pipette. Spex studies showed that dispensing such a small percentage of the syringe’s total volume created a large amount of error (5). Dispensing 20% of a 10 μL syringe created over 20% error. The error only dropped down below 5% once the volume dispensed approached 100%.

Larger syringes were able to get closer to the 10% manufacturer’s dispensing minimum without a large amount of error, but the error did drop as the dispensed volume approached 100%. The better choice for measuring materials using a syringe or pipette is to select a pipette closest to the targeted amount to be dispensed to reduce error.

Inadequate cleaning of syringes and pipettes can lead to crossover and contamination, which can skew your results. Many volumetric containers can be subject to memory effects and carryover. In critical laboratory experiments, labware sometimes needs to be separated by purpose and use. Labware subject to elevated levels of organic compounds or persistent inorganic compounds can develop chemical interactions and memory effects. It is sometimes difficult to eliminate carryover from labware and syringes even when using a manufacturer’s stated instructions. For example, many syringes are cleaned by several repeated solvent rinses prior to use. A study of syringe carryover by Spex showed that some syringes are subject to elevated levels of chemical carryover despite repeated rinses (5).

Infrequent calibration happens when a laboratory does not follow a quality plan or is lacking in its routine maintenance. Many laboratories have schedules of maintenance for equipment such as balances and automatic pipettes, but often overlook calibration of reusable burettes, pipettes, syringes, and labware. Under most normal use, labware often does not need frequent calibration, but there are some instances where a schedule of recalibration should be employed. Any glassware or labware in continuous use for years should be checked for calibration. Glass manufacturers suggest that any glassware used or cleaned at high temperatures, used for corrosive chemicals, or autoclaved, should be recalibrated more frequently. It is also suggested that under normal conditions, soda-lime glass should be checked or recalibrated every five years, and borosilicate glass should be checked after it has been in use for 10 years.

Inorganic analysts know that glassware is a source of contamination. Even clean glassware can contaminate samples with elements such as boron, silicon, and sodium. If glassware, such as pipettes and beakers, are reused, the potential for contamination escalates. At Spex, a study was conducted to measure the residual contamination of pipettes after being manually and automatically cleaned using a pipette washer (5). An aliquot of 5% nitric acid was drawn through a 5 mL pipette after the pipette was manually cleaned according to standard procedures. The aliquots were analyzed by ICP-mass spectrometry (MS). The results showed significant residual contamination still persisted in the pipettes despite a thorough manual cleaning procedure but was reduced when an automated washer was employed.

Finally, improper use can apply to the actual way in which the labware is to be used. If a volumetric is designed to contain liquid, it will be marked by either the letters TC or IN. Labware that is designated to deliver liquid would be marked by either the letters TD or EX. Sometimes, there are additional designations, such as wait time or delivery time, inscribed on the labware. The delivery time refers to a period of time required for the meniscus to flow from the upper volume mark to the lower volume mark. The wait time refers to the time needed for the meniscus to come to rest after the residual liquid has finished flowing down from the wall of the pipette or vessel. An analyst should wait for the liquid to stabilize before attempting to dispense for other dilutions.

The first dilution many laboratories make is a stock solution or starting solution. This type of working standard is made to create a higher concentration stock from raw materials or concentrated material from which other standards will be made.

For this calculation, one needs the concentration of the target stock, the final weight or volume of the total stock and the purity of the raw material (or concentration of the concentrate being used).

Another type of dilution is a simple dilution using a dilution factor as seen in the below equations:

To make calibration curves, most people employ a mixture of simple dilutions separate from each other or use a serial dilution which is a series of dilutions, where each dilution is cumulative. Although this type of dilution is typical in some situations, if an error occurs during early dilutions, then subsequent dilutions can also be in error; therefore, bias could be created in the calibration curve. In preparing for a PT scheme, it is best to create a checklist of all the steps, equipment, supplies, and materials that could be needed for the testing. It is important to check equipment calibration and cleanliness before starting the PT (6,7).

Best Practices for Planning for Proficiency Testing

Use only calibrated volumetrics to create dilutions and allow sufficient time for equilibration of the volumetrics as noted by the equipment.

Check the quality of all volumetric labware and chemical materials to reduce contamination.

Plan ahead for ordering standards, chemicals, and consumable supplies needed for the study.

Check and recheck all calculations for calibration curves and dilutions.

Check the study dates and plan ahead for sufficient time to complete the PT study.

Make sure to order PT schemes well ahead of study start date to ensure enough time.

The function and reputation of a laboratory is based on producing accurate results that are above reproach and often can be legally defensible. A QMS dictates within its policies a set of checks and balances to provide validation that the results are valid. A part of this checks and balance system is the use of PT to document the ability of the laboratory and its employees to produce quality results. In some laboratories, such as in the case of a trace inorganic laboratory, routine issues, such as contamination and error, can severely alter results and cause bias. These biases can lead to erroneous results or PT failures. By recognizing all the possible sources of error and contamination in the inorganic laboratory environment, one can actively try to eliminate contamination and error, thereby producing more accurate results. Part 2 of this article, which will be published in the July “Atomic Perspectives” column, will examine the planning, running, and reporting of a PT scheme and the possible impact of error and contamination.

(1) International Organization for Standardization, 

Conformity Assessment – General Requirements for Proficiency

 (ISO/IEC Standard No. 17043) (2010) Retrieved from: 

(2) International Organization for Standardization, 

General Requirements For the Competence of Testing and Calibration Laboratories

 (ISO/IEC: Standard No. 17025) (2017). Retrieved from: 

https://www.iso.org/publication/PUB100424.html

(3) United States Pharmacopeial Convention, USP Proficiency Testing Program. 

(4) NSI Laboratory Solutions, Proficiency Testing. 

www.nsilabsolutions.com/proficiency-testing/

(5) SPEX, CertiPrep Webinar Clean Laboratory Techniques. 

https://www.spexcertiprep.com/webinar/clean-laboratory-techniques 

(6) American Society for Testing and Materials, ASTM D1193-06(2018), Standard Specification for Reagent Water. 

(7) Spex, Knowledge Base for White Papers on Dilutions, Calibrations, and Standards. 

www.spex.com/Knowledge-Base/AppNotesWhitepaper

 is a Senior Applications Scientist with Spex, an Antylia Company who specializes in scientific content and education for the analytical community.

 is the Global Product Manager for NSI Lab Solutions Inc. (a Spex/Antylia Scientific company), leading NSI’s product and business development efforts.

 is the Sr. Manager of Proficiency Testing Programs at USP, directing the development of the USP PT program and coordinating the PT studies with NSI Lab Solutions. ●

, the editor of the “Atomic Perspectives” column, is the principal of Scientific Solutions, a consulting company that serves the educational and writing needs of the trace element analysis user community. Rob has worked in the field of atomic spectroscopy and mass spectrometry for more than 45 years, including 24 years for a manufacturer of atomic spectroscopic instrumentation. He has authored more than 100 scientific publications, including a 15-part tutorial series, “A Beginners Guide to ICP-MS.” In addition, he has authored five textbooks on the fundamentals and applications of ICP-MS. His most recent book, a paperback edition of Measuring Heavy Metal Contaminants in Cannabis and Hemp was published in December, 2021. Rob has an advanced degree in analytical chemistry from the University of Wales, UK, and is a Fellow of the Royal Society of Chemistry (FRSC) and a Chartered Chemist (CChem). Direct correspondence to 

Proficiency testing (PT) can assess the performance of individuals or laboratories, but it’s important to understand how to do it properly.

What Does Your Proficiency Testing (PT) Prove? A Look at Inorganic Analyses, Proficiency Tests, and Contamination and Error, Part I

This column series has been organized into sections based on the infrared (IR) spectroscopy of specific chemical bonds. We have discussed functional groups containing C-H, C-O, and C=O bonds. Here, in the sixth installment of our survey of the IR spectra of polymers, we discuss polymers that contain C-O bonds. The spectra we will examine include polyvinyl alcohol, cellulose, starch, and copolymers that contain C-O bonds such as polyethetherketone, polyurethanes, and polyimides. The unifying theme of all these spectra is that they all contain one or more large C-O stretching peaks between 1300–1000 cm

We covered the spectroscopy of the C-O bond in earlier columns (1–4). In those installments, the functional groups we studied were alcohols and ethers. Recall that the overriding spectral feature of C-O bonds are one or more large C-O stretching peaks between 1300–1000 cm

 (1) (going forward, assume all peak positions are in cm

 units unless otherwise noted). Usually, the C-O stretch is the most intense peak in this wavenumber region.

Recall that alcohols hydrogen bond strongly to each other similar to water (1,2). An example of alcohols hydrogen bonding is seen in Figure 1.

FIGURE 1: An example of two alcohol molecules hydrogen bonding to each other.

The effect of hydrogen bonding on the spectra of alcohols is that any peaks from O-H stretching and bending motions are unusually broadened, which is illustrated in the spectrum of ethanol seen in Figure 2.

FIGURE 2: The mid-infrared (mid-IR) spectrum of ethanol (ethyl alcohol). Note that peaks A and E are broadened compared to the rest of the peaks in the spectrum.

Note in the figure that the O-H stretching peak labeled A at 3492 is approximately 1000 cm

 wide at its base, and the O-H wagging peak labeled E at 667 is approximately 500 cm

 wide at its base. These peaks are significantly broader than the peaks from other functional groups seen in this spectrum. In general, the O-H stretching peaks of alcohols fall at 3350±50, whereas the O-H wag is normally seen at 650±50. The C-O stretching feature of alcohols falls from 1260 to 1000, with the range being determined as to whether the alcohol is primary, secondary, tertiary, or a phenol. The C-O stretching peak of ethyl alcohol, a primary alcohol, is labeled C at 1050, which is in the range expected. A summary of the group wavenumbers for alcohols is seen in Table I.

In addition to alcohols, another functional group containing C-O bonds found in polymers is ethers. The chemical structure of ethers is characterized by two C-O bonds, which is why two ether carbons can be seen in Figure 3.

FIGURE 3: The molecular structure of the ether functional group.

If both ether carbons are saturated, the ether is said to be saturated. If both ether carbons are aromatic, the ether is aromatic. And finally, if one ether carbon is saturated and the other is aromatic, we have a mixed ether. Note that ethers do not contain O-H bonds. Therefore, they do not hydrogen bond, do not contain broadened peaks, and do not have O-H stretching and bending vibrations like alcohols. In fact, the only useful group wavenumber that ethers have is a C-O stretch, which is typically one or two peaks that fall from 1300 to 1070. A summary of the group wavenumbers for ethers is seen in Table II.

Note in Table II that saturated ethers have one C-O stretching peak from 1140–1070, aromatic ethers have one peak from 1300–1200, and mixed ethers have two C-O stretching peaks—one at 1050–1010 and the other at 1300–1200. The reason mixed ethers have two C-O stretching peaks is that the two ether carbons are bonded to different types of carbon so are chemically different, have different C-O stretching force constants, and hence have different peak positions (5). For mixed ethers then, the aromatic C-O stretching vibration gives the peak from 1300 to 1200, whereas the saturated ether C-O stretching peak falls from 1050 to 1010. Note that for both mixed and aromatic ethers, the aromatic C-O stretch has the same range of 1300–1200. As a general rule, if an ether exhibits a C-O stretching peak from 1300 to 1200, then at least one of the ether carbons is aromatic.

The Infrared (IR) Spectra of O-H Containing Polymers

The way in which polyvinyl alcohol is synthesized is unusual in that rather than being made from an O-H containing monomer, one of the reactants is a polymer. This reaction is shown in Figure 4.

FIGURE 4: The reaction via which polyvinyl alcohol is made from polyvinyl acetate.

The reactant is polyvinyl acetate, which contains a pendant acetate moiety, which is an ester. Polyvinyl acetate is hydrolyzed with aqueous acid to give polyvinyl alcohol while splitting off a molecule of acetic acid. The IR spectrum of polyvinyl alcohol is seen in Figure 5.

FIGURE 5: The IR spectrum of polyvinyl alcohol.

We have the classic broad, intense O-H stretch at 3353, and the C-O stretch of a secondary alcohol at 1094. This particular spectrum cuts off at approximately 700, so the expected O-H wag at ~650 is not visible. Another thing to notice in Figure 5 is the presence of peaks from unreacted acetate, giving us the classic ester “Rule of 3” peaks (6) at 1736, 1251, and 1094. Note how the peak at 1094 is broadened. My interpretation of this peak is that it is an overlap of the alcohol and ester C-O stretching peaks. Despite the ester peaks, polyvinyl alcohol is easily identifiable because it contains an alcohol group, courtesy of the broadened peaks at 3353 and 1094.

The IR Spectra of Polymers Containing Ether Linkages

The polyacetal molecule is made by polymerizing formaldehyde, which has the formula H

-C=O. The C=O bond in formaldehyde is opened up, forming polyacetal, whose structure and spectrum are seen in Figure 6. Note the C-O stretching peak of polyacetal at 1090, as expected for a saturated ether.

FIGURE 6: The structure and spectrum of polyacetal. Note the saturated ether C-O stretching peak at 1090.

The fact that the name of this polymer contains the word ether twice just might make you think that it contains ether linkages, which it does. This material is, in fact, a copolymer made from monomers that form ketone and ether linkages upon the polymer’s formation. We will talk about polymers containing ketones in a future column. The structure and spectrum of PEEK are shown in Figure 7.

FIGURE 7: The IR spectrum of polyetheretherketone (PEEK). Note the aromatic C-O stretch at 1227.

This structure contains aromatic ether linkages only, and the aromatic C-O stretching peak falls at 1227 as expected.

Foam rubber, the bouncy stuff that we often sit and jump on, contains a urethane linkage whose structure is seen in Figure 8 (we will cover polyurethanes in a future column). However, the urethane is often copolymerized with a monomer that gives a saturated ether linkage. We know this because the spectrum of a foam rubber sample seen in Figure 8 has a C-O stretching peak at 1050, consistent with the presence of a saturated ether linkage.

FIGURE 8: The IR spectrum of a sample of foam rubber. Although the urethane peaks dominate the spectrum, there is a C-O stretching peak from ether linkages at 1050.

Kapton is the trade name for an engineering polymer that contains the imide functional group, which we will cover in a future column. However, note from the structure of Kapton in Figure 9 that it contains aromatic ether linkages as well.

FIGURE 9: The IR spectrum of Kapton, an imide-ether copolymer. Note the C-O stretching peak at 1241.

Note that the spectrum of Kapton contains a peak at 1241 assigned as the aromatic C-O stretching peak of its aromatic ether linkages. This peak is in the middle of the range where it should fall, which is from 1300 to 1200 cm

Polymers that Contain Both Alcohol and Ether Groups 

Carbohydrates are biological molecules that contain both alcohol and ether link- ages, which means their C-O stretching regions are complex. The building blocks of carbohydrates are sugar units or monosaccharides. When we build a polymer out of these sugar units, we get a polysaccharide. We covered the spectra of carbohydrates in a previous column (4).

A common polysaccharide is cellulose, which makes up the cell walls of plants and is otherwise known as wood, paper, or cotton. Cellulose is a high polymer of the monosaccharide glucose—the product of photosynthesis. The structure and IR spectrum of cellulose are seen in Figure 10.

FIGURE 10: The infrared spectrum of cellulose, a polysaccharide.

The glucose units in cellulose are stitched together by saturated ether linkages. There is so much hydrogen bonding in cellulose that the complex -O-H stretching peak is a broad blob. The C-H stretches are similarly blobby, and the C-O stretching region is best characterized as an envelope with any number of shoulder peaks on top of it.

Another important and well-known polysaccharide is starch. Starch is also a high polymer of glucose, but the saturated ether linkages that connect the glucose molecules in starch have a different orientation than in cellulose, which can be seen by comparing the structure of cellulose in Figure 10 to the structure of starch in Figure 11. Figure 11 also shows the IR spectrum of starch.

One of the maxims about IR spectroscopy is that if two molecules have different chemical structures, they will have different IR spectra (5). Cellulose and starch are similar but not identical molecules; therefore, they have similar but not identical IR spectra. For example, note that the O-H stretching envelopes of cellulose and starch top out at 3350 and 3258, respectively, and that their complex C-O stretching envelopes peak out at 1027 and 998, respectively. Additionally, the shoulders on the C-O stretching envelope in both spectra are at distinct positions, which means that one can readily distinguish between cellulose and starch via IR spectroscopy.

This article was devoted to polymers containing C-O bonds that are not epoxies. We reviewed the spectroscopy and group wavenumbers of alcohols and ethers, two significant functional groups that contain C-O bonds. We examined the spectrum of polyvinyl alcohol, found that its O-H stretching and bending peaks are broadened because of hydrogen bonding, and that its C-O stretching peaks falls as expected. Then, we looked at the spectra of polymers containing ether linkages, and examined the spectra of polyacetal, polyetheretherketone, urethane foam, and Kapton. Lastly, we examined the spectra of polysaccharides that contain both alcohol and ether groups and found that we can use IR spectroscopy to distinguish cellulose from starch.

, 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. He can be reached at: 

An examination of polymers with C-O bonds, focusing on the spectra of polyvinyl alcohol, cellulose, starch, and copolymers.

The Infrared Spectra of Polymers, VI: Polymers With C-O Bonds

Custom flow through capillary cells, micro cavity cells, and microfluidic cuvettes with channel sizes as small as 100 microns from REFLEX
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Custom flow through capillary cells, micro cavity cells, and microfluidic cuvettes with channel sizes as small as 100 microns from REFLEX
are made from ultraviolet quartz, infrared quartz, and glass.

Micro cavity flow cells

We present our annual review of products introduced at Pittcon or during the previous year, broken down by the following categories: atomic spectroscopy, ultraviolet and visible (UV-vis), near-infrared (NIR), infrared (mid-IR), Raman, imaging, nuclear magnetic resonance (NMR), mass spectrometry (MS), accessories, and components.

Between the online Pittcon event of 2021 and the time this review was written, a lot has changed. We all anticipated an in-person Pittcon for 2022; unfortunately, that did not happen because of a surge in Covid cases in the southeastern United States where the event was going to be held. As a result, Pittcon 2022 was converted into a virtual conference. But the fact that one of the “introduction points” for new spectroscopy instruments did not happen live did not stop the launch of exciting new spectroscopic instruments in the past year.

Our annual review of new products for atomic and molecular spectroscopy, including details by category and highlights of overarching trends.

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