Howard Mark serves on the Editorial Advisory Board of 

 and runs a consulting service, Mark Electronics that provides assistance, training, and consultation in near-IR spectroscopy as well as custom hardware and software design and development.

Howard Mark

In our previous column, we introduced a set of data suitable for more extensive examination than was permitted in our previous discussion of the classical least squares (CLS) algorithm. In this column, we start to analyze the data using two methods (CLS and principal component analysis [PCA]) that do not involve the concentrations of the components of the mixtures. We begin our expansion of the analysis by examining the data from the 70-sample data set the same way we did for the previous 15-sample data set. First, we examine the spectral results, and then apply the CLS algorithm to verify the previous findings. Neither of these types of analysis requires the use of external constituent information. We then further expand the analysis by calculating principal components. 

	Although the goal of this column is to examine the effects of using volume fractions vs. weight fractions as the constituent concentrations for principal component regression (PCR) and partial least squares (PLS) calibrations, there is much to be learned from the spectroscopic data itself, without reference to any constituent compositions. Therefore, our initial inspection of the data parallels the initial examination of the smaller, 15-sample set of mixtures, to verify that those computed values did not require concentration values, or involve the application of advanced chemometric calibration algorithms. Instead, our initial foray into looking at the results from the statistical experimental design described in the previous column (1) was to examine the behavior of the spectral data alone, without involving the use of chemometrics. The application of chemometrics to the data will be described later, and for reasons that will become clear, may not be described fully here. The advantage of analyzing the spectral data without bringing in the constituent values is that the expectations of their behavior are better known, and more easily described. This allows us to compare our results with the theoretical expectations, so that when we introduce the constituent values, we will have some foreknowledge of how we might expect the data to behave. Some of these behaviors have already been listed qualitatively in Tables I and II (see the previous column [1]), where we compared current near-infrared (NIR) spectroscopy practice to theoretical expectations.

	While we have spectra of the pure materials that comprise the mixtures, they were not taken as part of the calibration set. Therefore, for comparison and demonstration purposes, we use the five mixtures containing each of those materials at 60% (the maximum value they can attain in these samples) with each of the other mixture components at their lowest value (10%) as surrogates for the pure materials. The plots in Figure 1 present these spectra, for reference.

	In keeping with our desire for initial analysis to not use the reference values, our next activity is to present the results of performing classical least squares (CLS) analysis on the mixture data. One aspect of CLS gives us the ability to reconstruct the spectrum of a sample from the spectra of the pure components. Thus the validity of the data analysis, as well as the quality of the data, can be captured by CLS analysis by the agreement between a pure-component spectrum computed from the mixture spectra and the measured spectrum of that chemical component. These plots are shown in Figure 2, for the mixtures containing the maximum amount (60 wt%) of each of the ingredients respectively, and for each mixture the minimum amount (10 wt%) of all the other ingredients. It is clear that the agreement between the theoretical reconstructed spectrum and the actual spectrum of each ingredient is almost, although not quite, as good as the agreement between duplicate spectra of the same material. This an important observation, since later on we will be comparing spectra to each other, and knowing the amount of deviation expected between actual measurements and computed approximations will be important in evaluating the computed results.

	In Figure 3, we have plotted, for each of the five ingredients, the values of the CLS-calculated concentrations vs. the known weight percent and volume percent of the ingredients. Not surprisingly, we note the following characteristics of the plots in Figure 3:

	For the plots where the abscissa variable represents the weight fraction, the points corresponding to a given weight fraction of the ingredient lies on a vertical line. The explanation is simple-the samples were made up gravimetrically to prescribed values, thus the actual and the nominal values of weight fraction are all the same (within experimental error) for a given fraction of the specified ingredient. The spectral values plotted on the ordinate direction varied due to the actual changing composition, reflected in the spectral response, hence the data points corresponding to samples of the nominal composition are spread out in only the 

	For the plots where the abscissa variable represents the volume fraction, the points corresponding to a given volume fraction of the ingredient lie along a line essentially parallel to the direction of the line through all the samples of varying composition. This reflects the improvement of the performance attainable through the use of volume fractions. As we’ve seen previously with the more limited sample set (2), when volume fractions are used as the measure of concentration, the change in value of the constituent concentration is mirrored by an equivalent change in the value of the concentration as determined by the CLS algorithm (2). The calculated values of the concentration measure within each group of data points then follows the trend of the data very nicely, instead of them all lying vertically as in the weight percent values of Figure 3.

	The difference between the two cases is that when weight fraction is used for the constituent concentration, the value used to represent concentration of the sample is constant, because the samples were made up that way-to contain a pre-specified amount of the ingredient. In this case, only the spectroscopically calculated value changes (due to the variations in the other components), and therefore the resulting data points fall on a vertical line. When the volume fraction is used as the constituent value, then both the spectroscopically calculated value and known reference value change, and change concurrently, so that the data points for each sample lie on a sloping line that reflects the corresponding changes in the two values.

	Another characteristic of the data can also be seen in some (although not all) of the plots in Figure 3. Some of the clusters of data points for the plots of spectral values versus volume fractions show an additional effect. This effect is the spreading of the data points away from the line defining the main direction of the points in each cluster, in this case perpendicular to the line. In some cases (and this is particularly noteworthy for the butanol plots, methanol plots, and to a lesser extent, for dichloropropane plots), the clusters of points are not randomly spaced away from the line defining the direction of the data, but form a pattern. Albeit imperfect, the pattern shows lines of data points within the cluster, each line containing a different number of data points: first one isolated point, then two on a line, then three, then four. The net effect is that for each cluster, the points appear to be contained in a triangular envelope, mimicking, to some extent, the experimental design from which the plots were created.

	The cause of this phenomenon is not entirely clear at this time, although suspicion falls on the potential residual interactions between the various chemical components of the samples. The possibility of hydrogen bonding between the two alcohols and the highly polar -C-Cl bonds in the chlorine-containing molecules also seem likely sources of this sort of interaction.

	CLS is not the only type of data analysis that can be applied to the spectral data without needing constituent values. Principal component analysis (PCA) is a data analysis method that can be applied to the spectra alone, without needing auxiliary constituent information. We have shown in earlier columns (3–8) that a principal component is a least-square estimator of the underlying data, and that, at each stage, it accounts for the maximum amount of variance remaining in the data after all previous components have been used to explain their respective contributions to the measured data values.

	This being the case, if the theoretical assumptions underlying the data are valid, it should not be necessary to compute more principal components than there are independent sources of variation affecting the data set being examined. This is effectively the same as saying this reflects the number of degrees of freedom in the data, ignoring the innumerable degrees of freedom that represent the noise content of the spectroscopic data.

	In the case of five-component mixtures such as we are dealing with here, you might think that there would be five degrees of freedom in the spectral data. However, since the mixture components must add up to 100% (called 

 of the dataset), the concentration of any four components determines the concentration of the fifth; therefore it is not “free” to vary independently. The restriction that the mixture ingredients must add up to 100% removes a degree of freedom from the data. Therefore the spectral data should contain 5 - 1 = 4 degrees of freedom. Ideally, therefore, a five-component mixture should be completely represented by four principal components, ignoring the noise. What we should observe then, upon performing PCA, is that the variance remaining in the spectral data should decrease as each new principal component is computed and the effect of that component is removed from the data set. Furthermore, the early components will account for more variance than later components; the first principal component that is calculated will account for the most variance of the data, while each of the later components will remove less and less. The effect is cumulative, and successive components will remove more and more variance from the data until the first four components are found, at which point the remaining variance should represent the random noise remaining in the data, and that noise should not change by any appreciable amount if more principal components are computed. The remaining variance will then remain almost constant as successive principal components are calculated. Plotting this residual variance as a function of the number of components should therefore approach a very small almost-constant value, once all the non-noise variations of the data are removed by this process. The actual behavior of this data set is shown in Figure 4.

	What we actually find in Figure 4, distinct from the theoretical expectation, however, is that there is a continual drop-off of the remaining variance in the data up to at least the sixth principal component. In conformance with theory, the earlier principal components do indeed account for more variance than later ones. Nevertheless, the number of principal components needed to account for or “explain” the spectral data is more than the four degrees of freedom, due to the five individual chemical components that we know are present in the data set (while accounting for the closure of the dataset). The reason for this is also not entirely clear, although it is consistent with the finding, from the examination of the CLS results, of possible interactions between some of the ingredients in the mixtures.

Conclusions (From This and the Previous Column)

	CLS analysis of this new data set, which is appreciably larger than the previous data set we were able to use, confirms the results we obtained from the previous data set. The CLS algorithm enabled the reconstruction of the spectra of mixtures from the spectra of the components of each mixture. For several of the constituents (methanol is a good example here), the plot of calculated vs. measured concentration is a straight line when the measured concentration is expressed as volume fraction, but exhibits visible curvature when the concentration is expressed as weight fraction.

	Principal component analysis shows that, contrary to theoretical expectation, it requires the calculation and use of more (>6) principal components to account for all the variance in the spectra than theoretical evaluations (four principal components) of the data would suggest (see Figure 4 for the actual amounts of variance explained). Initial explanations for this behavior, based on previous understanding, cast suspicion on more of the “usual suspects”-mainly interactions between ingredients in the mixtures list.

	Further, and closer, examination of the results revealed that, while interactions cannot be completely exonerated as a cause of the observed behavior, another unexpected phenomenon showed up, which changes our understanding of how apportionment of variance among several competing effects influences the final results obtained. This phenomenon will be described in a subsequent column.

		H. Mark, R. Rubinovitz, D. Heaps, P. Gemperline, D. Dahm, and K. Dahm, 

Jerome Workman Jr. serves on the Editorial Advisory Board of 

. He is also a Certified Core Adjunct Professor at U.S. National University in La Jolla, California. He was formerly the Executive Vice President of Research and Engineering for Unity Scientific and Process Sensors Corporation.

, and runs a consulting service, Mark Electronics, in Suffern, New York. Direct correspondence to: 

Articles

A previous analysis of data is compared to the results achieved using classical least squares and principal component analysis. What did we learn?

More About CLS, Part 2: Spectral Results (Not Needing Constituent Values) and CLS

Our annual review of products introduced at Pittcon, or during the previous year.

	As in the past, this year's edition of our annual review of new spectroscopy products includes products launched at Pittcon 2019 or elsewhere during the past twelve months (Table I). Given the extensive scope of this review, we are only able to include products for which the manufacturer submits a completed form, in response to the survey sent out by 

 or our additional, and rather intensive, efforts to encourage submissions. One of the points of interest in the instruments featured here is a definition of those instruments that are of most interest, or are considered the most innovative, or are the most important to the field. This is, of necessity, a subjective evaluation, and involves a selection process from all the products submitted.

	In this annual review, we like to highlight the product awards given out by the Pittcon organization. Starting two years ago, the selection methods and criteria were changed from what were then called the 

awards, as well as the nature of the awards. The awards are now called the 

. A selection committee, consisting of a blue-ribbon panel of experts, evaluates the entries based on ingenuity, creativity, implementation, and outcomes. Also, companies are segregated into small, medium, and large categories, based on sales figures. This division allows companies in each class to compete with others within that same class. Therefore, there are three awards of each type (gold, silver, and bronze) in each company category, for a total of nine awards. We are pleased to note that some spectroscopy companies were winners in this competition. These include:

		In the small company category: (<$10,00,000), Pendar Technologies won bronze for its system for standoff Raman and Raman measurements of dark materials.

		In the medium-sized company category ($10,000,000 to $100,000,000), Raptor Photonics won gold for its high-definition indium gallium arsenide (InGaAs)–based infrared (IR) camera.

		In the large company category (>$100,000,000), Bruker won bronze for a new XRF analyzer that is fully automated and faster than previous XRF instruments.

	There is another first among these Total Excellence Awards: Previously, no company or product won twice in two successive years, or for the same product. Last year, Gate Scientific won an Excellence award for its temperature-sensing stir bar; this year, it also won for it pH-sensing stir bar, making it the first time a company has won two excellence awards for substantially similar products. Although this product does not involve spectroscopy, the phenomenon of a double-winner caught our attention.

	The review that follows is organized alphabetically, and categorized by wavelength region or type of spectroscopy (mid-IR, X-ray, Raman, and so on), except that we put the two noninstrument categories (accessories and components) at the end. We arrange our review to allow readers to compare instruments from different manufacturers, although this process sometimes classifies handhelds with high-end research tools. The categories used to classify the products are:

	Atomic spectroscopy
	Fluorescence
	Imaging
	Mass spectrometry (MS)
	Mid-infrared (IR) spectroscopy
	Near-infrared (NIR) spectroscopy
	Nuclear magnetic resonance (NMR)
	Raman spectroscopy
	Software
	UV-visible spectroscopy
	X-ray
	Accessories
	Components

	Our included categories fluctuate year to year, depending upon trends in the submissions. The core spectroscopy headings are consistent, but imaging, for example, has been transient. This year, we did receive products classified as imaging, which warranted that being a category in itself, rather than having us only include imaging versions of various technologies under the parent instrument type. Another category that had been absent for a long time, but is showing up again, is fluorescence. As in the past, the accessories section includes tools used with an instrument (such as sample preparation or attenuated total reflection [ATR] devices), whereas the components section covers parts used within instruments (such as lasers or detectors). The software category covers instrument control and data processing, storage, and transmission, as well as spectral databases (libraries) and other collections of specialized information, including programs that analyze the data.

	We've always tried to identify early indicators of future tendencies. In the past, we could sometimes isolate some spectroscopy-specific trends that were noteworthy. This year, some of the major trends that are likely to affect the universe of spectroscopy are not so specific, but rather are more nearly side effects of large-scale trends that affect the greater digital universe we inhabit. The continuing growth and development of computer technology, and the concomitant growth in the associated technology of the Internet, superimpose their own trends on our own, somewhat smaller, universe. Items that have been showing up in the larger digital world for a while are now starting to appear in ours. These may become trends in the future, and therefore may bear watching for their influence on our activities, and include these not-entirely-independent categories:

	Networking
	Internet of Things (IoT)
	Security
	Artificial Intelligence (AI)
	"The Cloud"

	Some current (and even past) practices may become future trends, if and when they become more widespread and generalized. In the meantime, sporadic mentions of these practices appear in advertisements, in scientific literature, and even in this review. For example, some instrument manufacturers have created a capability of remotely controlling and collecting data from their own instruments (networking) for a long time. I myself was involved in a project like that as long ago as 1986. When that capability expands to include more than one type of instrument, then it would be fair to rename that localized networking as belonging to the "Internet of Things" (IoT), but, in my opinion, I think not until then. Similarly, can the current capabilities of the application of chemometric analysis to instrument data, to perform quantitative and qualitative analysis, be called 

 (AI), or is that a different type of activity? Only the future will tell.

	As we've noted almost since first trying to detect instrument trends, the trend toward hyphenated techniques has been coming along slowly, but surely, over the years. A recent twist on that concept, broadening the idea of applying multiple technologies to an analysis, is the packaging of dissimilar, and indeed sometimes even orthogonal, technologies in a single instrument package, without the two technologies necessarily interacting (as hyphenated methods do). In this review, for example, we have at least one instrument giving the capability of applying infrared and Raman spectroscopy to the same sample, and one that incorporates laser induced breakdown spectroscopy (LIBS) with Raman.

	Your reviewers are human, and also finite, and, try as we might, cannot know how the future will develop. We therefore solicited some assistance from the people who are on the front lines of the future impact: the companies that will be affected by it, those reported on in this review. While not advanced enough to be called trends yet, sporadic mention of those activities in scientific papers, advertisements, and other discussions and descriptions of instrument performance potentially point in directions that indicate where the future of instrumentation is going.

	Table II describes the instruments included in the atomic spectroscopy category.

	Advion offers a 90° quadrupole deflector, which has lower interference and improved signal-to-noise ratio, preventing contamination, and thereby produces reliable results quickly and easily.

	Agilent's water analyzers reduce the time needed to run water testing methods according to government standards.

	LTB Lasertechnik announced has a combination LIBS atomic emission spectrometer and a Raman spectrometer in a single unit.

	Milestone's 7th generation Mercury Analyzer completes an analysis in just 5 min, resulting in lower running costs.

	PS Analytical provides several dedicated variations of atomic fluorescence mercury monitoring systems to suit specific sampling and measurement situations.

	Spectro now has a radial dual side-on interface (DSOI) that is said to provide twice the sensitivity of conventional radial plasma-view instruments and equal the sensitivity of newer vertical-torch dual-view systems, with less complexity. This technology is designed to provide advantages in determining trace element concentrations and handling samples with challenging matrices, including certain wastewaters, soils, and sludges, as well as petrochemical, chemical, high-salts, and metal samples.

	Looking at these offerings in the atomic spectroscopy category, a potential emerging trend is for companies to offer specialized products to address occupational health and safety (OHS) concerns and stricter environmental requirements.

	There is just one new fluorescence spectrometer this year (Table III). The PerkinElmer FL 8500 is designed to be an easy-to-use instrument with flexibility to analyze a broad range of sample types and volumes while maintaining sample integrity, with pulse and continuous wave options. The improved signal-to-noise performance allows lower level analyte detection. The device also enables many different experimental setups. Features include phase range, wider wavelength coverage, intuitive software, and interchangeable accessories.

	Agilent is showing an IR imaging instrument that is said to offer high definition, spatially-resolved images of the chemical components on a flat surface, showing which chemicals are present, and their spatial location.

	Malvern Panalytical's Morphologi 4 series of Raman imaging instruments offers sharp and fast measurement of particle size, shape, and chemical identification.

	Middleton introduced a multi-mode hyperspectral imaging system for high-volume sampling and "big data" output. The system supports two spectral cameras for sequential hyperspectral imaging of a sample.

	Videometer utilizes laser light scattering to measure transmission and reflection spectra. Several models are available, each designed for a different specific measurement situation.

	Advion offers an ICP-MS instrument that has a 90° quadrupole deflector, to provide lower interference and an improved signal-to-noise ratio and prevent contamination, thereby producing reliable results quickly and easily.

	Agilent's ICP-MS water analyzers reduce the time needed to run water testing methods when applying government analysis standards.

	Excellims has a high-resolving-power ion-mobility mass spectrometer optimized for cleaning validation and similar applications.

	Ohio Lumex combines the selectivity of GC with the sensitivity of an ion mobility spectrometer for the analysis of volatiles in the headspace of liquid and solid samples without any sample pretreatment.

	The Waters DART QDa combines direct analysis in real time (DART) ionization with their QdA mass detector to create a direct-from-sample analytical system to perform real-time sample recognition. It is aimed at applications in the food market, such as verifying sample authenticity or for detecting adulteration.

	Table V presents the mass spectrometry related products for this year's review.

	The 700 series FT-IR instruments from California Analytical Instruments are designed for fast and easy measurement of multiple hot wet gases that absorb in the IR spectral region. Levels can be detected from ppb to the percent range.

	Keit shows a rugged process FT-IR spectrometer for in situ monitoring of chemical concentrations in liquids and slurries at pH levels from 0 to 14 in various environments. This instrument also comes in a high-temperature version that can operate with samples at temperatures up to 200 °C.

	Ocean Optics' MZ5 is a compact, fully integrated ATR-mid IR spectrometer that measures liquid samples from 1818–909 cm

 (5.5–11 µm). The MZ5 comprises a sample interface, light source, detector, and software, and provides a fast, convenient mid-IR alternative to traditional FT-IR spectroscopy for laboratory research.

	Photothermal Spectroscopy Corp. has a dual-function spectrometer. Infrared and Raman spectra and images can be measured and collected simultaneously from the same measurement point of a sample.

	Thermo has two new FT-IR spectrometers based on their IS-5, equipped with a multicolored LED scan bar, displaying instrument status, which allows users to swiftly execute workflows for improved productivity. The second unit is based on the Nicolet Summit spectrometer, and is a compact and rugged unit for busy, multi-user quality assurance (QA) and quality control (QC) laboratories in a variety of industries, such as polymer-chemical, pharmaceutical, food and beverage, forensics, environmental, electronics, and geology.

	Tiger Optics offers an instrument claiming the ultimate low-detection of moisture and speed of response.

	Verder Scientific incorporates an infrared cell to enable measurement of carbon and sulfur (C/S) for inorganic samples in difficult matrices.

	Table VI presents this year's new mid-infrared products.

	The NIR sector is seeing the pharmaceutical industry, a major customer, moving to lower cost, smaller, more efficient in-line continuous processing spectrophotometers, requiring a new generation of monitoring instruments, and thus the manufacturers are releasing products to fill that need.

	Brimrose provides a wireless, free-space communicating spectrometer, designed to fit into smaller, compact spaces. It is ruggedized, with an Ingress Protection (IP) rating of 65.

	Si-Ware Systems offers a miniature and low-cost spectral sensor based on FT-NIR technology. The sensor operates from 1350 to 2500 nm, and is designed for high-volume production with minimal external components. The sensor is intended for applications in farming, food, healthcare, and industry, as well as in consumer-facing applications.

	Spectral Evolution has a family of instruments operating in the NIR spectral region. They share a core capability of high resolution with high sensitivity field spectroradiometric operation, and are intended for high-altitude or satellite-based measurements. One member of the family extends to the capability of operation via the IoT, and another member includes an integrating sphere for absolute radiometric calibration.

	Table VII presents the near-infrared (NIR) instruments.

	Future trends within the NMR marketplace involve the pharmaceutical industry, a major source of revenue within that market, seeking lower cost, smaller, more efficient in-line continuous monitoring instruments. To meet that need, Nanalysis offers a high-performance benchtop NMR spectrometer (Table VIII).

	Acutech provides a pocket-size, lightweight, handheld Raman analyzer for both laboratory and field detection, providing identification of chemicals without sample preparation or contact. Specialized, dedicated versions are available for process monitoring, and for gemstone identification and analysis.

	B&W Tek is announcing several models of handheld Raman instruments, each designed for a specialized set of applications.

	Cloudminds announced a smartphone based Raman spectrometer, the XI, claimed to be the world's first cloud AI handheld Raman spectrometer. Connecting to the cloud via cellular, WiFi, or bluetooth, this platform allows the user to share data and synchronize instant updates on all devices. Operation of XI is fully automated with an intuitive interface. The device adopts AI deep learning algorithms, enabling faster data analysis with high accuracy.

	LTB Lasertechnik launched an instrument that combines a LIBS atomic emission spectrometer and a Raman spectrometer into a single unit.

	Photothermal Spectroscopy Corp. has a dual-function spectrometer. Infrared and Raman spectra and images can be measured and collected simultaneously from the same point on a sample.

	Renishaw presents a compact benchtop Raman imaging system, designed for biological and clinical research. This instrument enables the rapid collection of detailed information from a range of biological samples, including tissues and biofluids.

	Rigaku combines a standard library of over 13,000 chemicals using a 1064 nm laser for identifying colored substances, or scanning through colored packaging.

	Spectra Solutions showed two Raman products. One is a portable battery-operated Raman spectrometer, and the other is a removable fiber optic Raman measurement probe.

	Tec5USA's Raman spectrometer, designed for industrial environments, includes multiple lasers as the sources for the Raman excitation.

	WITec's ParticleScout is a particle analysis tool for the alpha300 Raman microscope series. The device surveys, categorizes, analyzes, quantifies, and identifies particles, even over large sampling areas.

	Table IX presents the properties of the new Raman instruments.

, in the context of this review, consists of several different, albeit related, types of products. Actual computer programs, compilations of spectra, databases, and collections of chemometric calibration models are all included in this category.

	The ability to share information via vendor neutral archiving using the public domain data standard, Analytical Information Markup Language (AnIML), is a desirable goal. However, it remains to be seen if this will be widely implemented by the spectroscopy community. If so, this capability will enable detail reviewing, visualization, and reporting, even across different techniques and platforms.

	Domain-specific processing and data sharing capabilities are upcoming requirements for research and development (R&D) and QC departments within the spectroscopy user community. The AnIML industry data standard and related software implementations greatly help to enforce these requirements in a regulatory-compliant manner.

	Operators of laboratory and at-line instrumentation are becoming increasingly responsible for many other duties in production plants, reducing the time and resources available to spend on training and learning underlying technologies. Instrument networking allows production plants to train operators to analyze samples and perform basic diagnostic tests, while remote internal or supplier experts are able to configure, monitor, and troubleshoot instrumentation.

	Ametek now provides basic and Pro versions of TruVu software to integrate with the Spectro Scientific MiniLab on-site oil analysis system. The TruVu 360 Basic system for a single user can be installed on either a local computer or a company network server. It features multiple user licenses, which include site user, operator and reader privileges.

	Bio-Rad offers a new software package for identification of unknowns using Raman or IR spectra, combined with an appropriate corresponding spectral library.

	BSSN Software GmbH harmonizes data sets from biology and other measurement techniques, and brings raw data, results, and metadata to the user's fingertips.

	Eigenvector has upgraded both the PLS Toolbox and the Solo stand-alone software, to include new chemometric methods of analyzing the data.

	Tornado offers security, productivity, and industrial control modules, with each module integrating a single platform, and allowing fast and accurate results.

	Unity Scientific provides an advanced suite of digital tools designed for the secure configuration, monitoring, and data reporting of NIR analyzers over the internet.

	Wiley continues to expand its libraries of spectra collections.

Our annual review of products introduced at Pittcon or during the previous year

Review of New Spectroscopic Instrumentation 2019

) is assumed for all spectra used for the purposes of quantitative and qualitative work. Note that the assumption for regression modeling, as well as spectral comparison algorithms, is that the data channels, or 

-axis scales of the spectroscopic data, are precisely aligned, and that only amplitude in the 

-axis changes with respect to analyte concentration in a pre-defined mathematical relationship. This article provides a discussion and set of spectra to illustrate the use of reference materials to align the 

-axis for the major types of optical spectroscopy, to include the ultraviolet, visible, near-infrared, infrared, and Raman spectroscopic methods.

) for spectroscopic data is essential to the basic use of these spectroscopic techniques. The primary assumption of all quantitative regression and qualitative comparison methods is that the data channels, as wavelength or frequency, are precisely aligned for comparison. Without such a condition, no spectroscopic methods are valid. Thus, the alignment of the 

-axis requires a known standard material that may be measured to either align the axis, or to verify whether the axis is aligned. Multiple standards are available for each spectroscopic region. The typical standards used for spectrometer alignment or testing will be illustrated within this article (1–3).

Ultraviolet (UV) Wavelength Measurement Standards 

	There are several available standards used for initial alignment, or for verifying alignment, when using ultraviolet spectrophotometers. The ultraviolet spectral region is generally considered to include from 190 nm to 380 nm (non-vacuum UV region). Note that the standards shown are also useful and traceable for visible (vis) spectroscopic measurements. The certified peak positions are labeled within the figures and the data are given in accompanying tables.


	Figure 1: High resolution absorbance spectrum of holmium oxide liquid wavelength standard traceable to SRM 2034 (Data and spectrum provided with permission by Starna Ltd., Hainault, IG6 3UT, UK. www.starna.com).

Holmium Oxide Liquid Wavelength Standard Traceable to SRM 2034 

	Holmium oxide liquid wavelength standard is traceable to the National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) 2034, and consists of holmium oxide (4%) in perchloric acid (10%) sealed in a quartz cell. Specifically, the solution contains 4% (mass fraction) of holmium oxide (Ho

) in 10% (volume fraction) perchloric acid (HClO

). This solution is a stable material that has multiple peaks useful for aligning and maintaining a UV or vis spectrophotometer for the wavelength axis. The peaks are stable if the quartz cell remains clean and unopened (4).


	Figure 2: High resolution transmittance spectrum of holmium oxide liquid wavelength standard traceable to SRM 2034 (Data and spectrum provided with permission by Starna Ltd., Hainault, IG6 3UT, UK. www.starna.com).

	The spectra for this liquid standard material are illustrated in Figures 1 and 2, with the data table of certified wavelengths for the absorbance spectrum given in Table I.

Holmium Oxide Glass Wavelength Standard Traceable to SRM 2034 

	The holmium oxide glass wavelength standard is traceable to NIST SRM 2034, and has been available for many years. It consists of holmium oxide (Ho

) in a special glass. The glass was initially developed by Corning Glass (Corning, New York), and sold as glass No. 3130. The thickness of the glass selected is designed to be less than 3 mm, which provides an approximately 1% transmittance or greater at the specific absorption peaks used (5,6).


	Figure 3: Absorbance spectrum of holmium oxide glass wavelength standard traceable to SRM 2034 (Data and spectrum provided with permission by Starna Ltd., Hainault, IG6 3UT, UK. www.starna.com).

	NIST has made the material available as an SRM since 1961. NIST reports that, over 40 years of testing, there has been no recorded instance of a spectral shift of the certified bands for any of the [test] samples measured. The material has been characterized as being robust and insensitive to a standard range of humidity and temperature conditions inherent to laboratory use. Because of the demonstrated stability the standards are no longer recertified by NIST, as there is no need (7).

	The absorbance and transmittance spectra for the holmium oxide glass are shown in Figures 3 and 4, respectively with the table of certified wavelength values given for the observed peaks given in Table II.

Visible (Vis) Wavelength Measurement Standards

	Note that the visible spectral region is generally considered to be from 380 nm to 750 nm.

	NIST SRM 2036 glass is a mixture of the following mole fractions of materials: 3.00% holmium oxide (Ho

). This is mixed with a matrix material containing lanthanum oxide (La

). The SRM 2036 glass optical filter is 25 mm in diameter and 1.5 mm thick. The backing consists of sintered polytetrafluoroethylene (PTFE, FW99), which is 25 mm in diameter and approximately 6 mm thick (8).


	Figure 4: Transmittance spectrum of holmium oxide glass wavelength standard traceable to SRM 2034 (Data and spectrum provided with permission by Starna Ltd., Hainault, IG6 3UT, UK. www.starna.com).

	SRM 2036 is intended for the verification and calibration of the wavelength or wavenumber scale of near-infrared (NIR) spectrometers operating in diffuse reflectance or transflectance mode. It is also useful for the visible region and has 11 peak positions characterized within the visible spectral region (Figures 5 and 6, Table III). SRM 2036 is similar in composition to SRM 2035 Near-Infrared Transmission Wavelength–Wavenumber Standard and SRM 2065 Ultraviolet-Visible-Near-Infrared Transmission Wavelength–Wavenumber Standard. The difference is that 2036 contains a piece of sintered polytetrafluoroethylene (PTFE, FW99) in physical contact as a reflectance backing. The combination of the reference glass in contact with the diffuse reflection backing provides reflection–absorption bands ranging from 15% reflection ® to 40% R. Technical specifications state that SRM 2036 is certified for the 10% band fraction air wavelength centroid location, (10%)B, of seven bands spanning the spectral region from 975 nm to 1946 nm. It is also certified for the vacuum wavenumber (10%)B of the same seven bands in the spectral region from 10,300 cm

resolution. Informational reference values are provided for the locations of thirteen additional bands from 334 nm to 804 nm (9).


	Figure 5: NIST SRM 2036 reflectance wavelength standard reference material (SRM) measured using 8°/hemispherical spectral reflectance factor geometry and a calibrated double-monochromator instrument from 250 nm to 850 nm. Peak designations marked (Data and spectrum used with permission from Avian Technologies, New London, NH).

Near-infrared (NIR) Wavelength Measurement Standards

	Note that the near-infrared (NIR) spectral region is generally considered for commercial instruments to be from 780 nm to 2500 nm.


	Figure 6: NIST SRM 2036 reflectance wavelength Standard reference material (SRM) measured using 8°/hemispherical spectral reflectance factor geometry (reported as absorbance or log10(1/R)) and a calibrated double-monochromator instrument from 300 nm to 800 nm (Data and spectrum used with permission from Avian Technologies, New London, NH).

	An excellent summary paper is given related to reference standards in the NIR region (10). It includes details of the following standards: high-pressure mercury arc, water vapor, gas standards SRM 2517, SRM 2519. Also covered are solid materials SRM 1920, SRM 2035, SRM 2036; McCrone M-27 and M-42; and polystyrene. Liquids described include 1,2,4 trichlorobenzene and methylene chloride. For this column, we will describe polystyrene and NIST SRM 1920a rare earth oxide standards.

	Polystyrene in a highly crystalline structure is a useful wavelength or wavenumber standard for NIR spectrophotometers. It provides a number of useful bands to verify or align the wavelength axis for all types of NIR instrumentation. The specific wavelengths and wavenumbers used for verification and calibration of the spectrophotometer 

-axis measurements using polystyrene are illustrated in Figures 7 and 8, and described with resolution details in Tables IV–V (11).


	Figure 7: Polystyrene (crystalline form) as a reflectance spectrum, 1.0 mm thickness (Spectrum used with permission from Avian Technologies, New London, NH).

	For Figures 7–8 and Tables IV–V, 1.0-mm thick polystyrene samples of 28 mm diameter were measured using a PerkinElmer Lambda-9/19 UV-vis-NIR spectrometer with traceability of measurements to: ASTM Test Method E 1331-96, ASTM Test Method E903-96, NRC Certificate PO1948 (didymium oxide glass), NRC Certificate Cal PAR-2008-2614 (holmium oxide filter), NIST SRM 1920a, and NIST SRM 2036. Traceability to NIST is achieved through NORAMET, the North American Metrological Agreement (11).


	Figure 8: Polystyrene (crystalline form) as absorbance (from reflectance) measurement, 1.0 mm thickness (Spectrum used with permission from Avian Technologies, New London, NH).

	Measurement conditions are mean temperature of 21.9 °C with relative humidity at 45%. The instrumental parameters are: bandpass, n/a NIR sensitivity corresponding to a bandpass approximately 1.5 nm from 1000–2500 nm); reporting interval, 1 nm; recording interval, 1 nm; scan speed, 60 nm/min; integration time, 1 s; number of measurements averaged, 6; measurement range, 1000–2500 nm. The instrument was set up in 8° hemispherical geometry with the sample positioned normally to the beam. NIR sensitivity of 3 and scan speed of 1 nm/s were used with peak picking at a 0.1-nm interval over the wavelength range of 1000–2500 nm.


	Table IV: Polystyrene nominal wavelengths from NIST traceable instrument (11)

	SRM 1920a is a discontinued SRM that is still used for verification and calibration of the NIR wavelength scale from 740 to 2000 nm. It is applicable for reflectance spectrophotometers or reflectometers using hemispherical geometry. It consisted of a mixture of three rare earth oxides (REOs) as dysprosium oxide (Dy

) that is pressed into a cylindrical cavity with an NIR transmitting sapphire widow. The outer dimensions of the standard are 51 mm in diameter and 12 mm in height. The inner dimensions of the powder-holding cavity are 30 mm in diameter by 6 mm in depth. The SRM samples were calibrated using the NIST high precision spectrophotometer. The spectra of a replicate mixture are shown in Figures 9 and 10; with the verified wavelengths for various bandwidths given in Tables VI and VII (11,12).

Infrared (IR) Wavenumber Measurement Standards

	The infrared spectral region is generally considered to cover from 2500 to 25,000 nm (4000 cm


	Figure 9: Rare earth oxide similar to NIST SRM 1920a as absorbance (from reflectance) spectrum from a NIST calibrated double-monochromator instrument (Data used with permission from Avian Technologies, New London, NH).

NIST Wavenumber Standard Reference Material for Infrared

	The available NIST standard for infrared (IR) measurements is NIST SRM 1921b, IR Transmission Wavelength (Polystyrene Film), and comes as a card-mounted polymer film. This standard is useful for measurements from 3.2 to 18.5 µm, 3200 nm to 18,500 nm, or 3125.0 to 540.5 cm

 (wavenumbers). The physical dimensions of SRM 1921b consists of a matte finish polystyrene film approximately 38 µm thick with a 25 mm diameter exposed area, centered 38 mm from the bottom of a cardboard holder, which is 5 cm by 11 cm by 0.2 cm in size (14,16–18). The spectra of the polystyrene film are displayed in Figures 11–13, with the verified peak positions in wavenumbers given in Table VIII.


	Figure 10: Rare earth oxide similar to NIST SRM 1920a as a reflectance spectrum from a NIST calibrated double-monochromator instrument (Data used with permission from Avian Technologies, New London, NH).

	The Raman region overlaps the infrared region as 2500 to 25,000 nm (4000 cm

); with the Raman spectral region referred to as 

 being from 2857 to 200,000 nm or 3500 cm

Wavenumber Standard Reference Material for Raman

	Several standard materials are recommended for alignment of the Raman wavenumber axis. These include those listed in Table IX (20–24). A virtual spectrum of a silicon wafer is shown in Figure 14 with a sharp peak at 520.7 cm

. Table IX refers to multiple materials used to verify or calibrate a Raman spectrometer wavenumber scale. Figure 14 shows a virtual silicon wafer spectrum.

	A variety of materials exist for the important characteristic of aligning or testing the wavelength or wavenumber axis for spectrophotometers from the ultraviolet through infrared regions, and Raman. 

-axis alignment and maintenance is a critical step required for the reasonable application of quantitative and qualitative analysis using spectroscopic methods. The assumption for all analytical methods using spectrometers is that the 

-axis, representing unique data channels, be constant, with only the amplitude changing with respect to analyte concentration. All chemometrics relies on the assumption of a precisely aligned 

-axis for analytical techniques in order to compare data for the purposes of calibration and analysis.


	Figure 11: Polystyrene film measured in transmittance with film holder and 4 cm-

 resolution, 64 co-added scans per spectrum, Nicolet Model 510 FT-IR Spectrometer (from the authors).


	Figure 12: Close-up of extended fingerprint region from Figure 11: Polystyrene film measured in transmittance with film holder and 4 cm-


	Figure 13: Polystyrene film converted to absorbance from transmittance spectrum with film holder and 4 cm-


	Figure 14: Virtual silicon wafer spectrum with sharp peak at 520.7 cm-

 (wavenumbers). Other wavenumber standards include naphthalene, 1,4 bis (2-methylstyryl) benzene (BMB), sulfur, 50/50 (v/v) toluene/acetonitrile, 4-acetamidophenol, benzonitrile, and polystyrene (24); polystyrene is suggested as well. Note infrared spectra of polystyrene are given in Figures 11 through 13, with a table of wavenumber peaks (Table VIII).

	(1) Reference spectra and data provided with permission by Starna Ltd., Hainault, IG6 3UT, UK. 

The Concise Handbook of Analytical Spectroscopy: Physical Foundations, Techniques, Instrumentation and Data Analysis. In 5 Volumes, UV, Vis, NIR, IR, and Raman

 (World Scientific Publishing-Imperial College Press, New Jersey and Singapore, 2016), vol. 1, pp. 163–214; vol. 2, pp. 217–278; vol. 3, pp. 363–424; vol. 4, pp. 323–360; vol. 5, pp. 229–250. ISBN-13: 978-9814508056. (Permission from NIST for data and spectra related to SRM materials).

	(3) Each table refers to specific NIST SRM datasheets and published references and as referred in Reference (2).

	(4) Standard Reference Material (SRM)® 2034 Certificate, "Holmium Oxide Solution Wavelength Standard (240 nm to 650 nm)," National Institute of Standards and Technology, Gaithersburg, MD 20899, 7 pages, Certificate Issue Date: (29 August 2016).

	(5) H. J. Keegan, J.C. Schleter, V.R. Weidner, 

	(8) Standard Reference Material (SRM)® 2036 Certificate, "Near-Infrared Wavelength-Wavenumber Reflection Standard," National Institute of Standards and Technology, Gaithersburg, MD 20899, 7 pages (24 January 2013).

	(9) S.J. Choquette, D.L. Duewer, L.M. Hanssen, E.A. Early, 

	(11) Spectrum and data used with permission from Avian Technologies, New London, NH.

	(12) Standard Reference Material (SRM)® 1920a Certificate, "Near-infrared Reflectance Wavelength Standard from 740 nm to 2000 nm," National Institute of Standards and Technology, Gaithersburg, MD 20899, 6 pages (20 Feb. 2004).

	(13) V.R. Weidner, P.Y. Barnes, and K.L. Eckerle, 

	(15) T. Isaksson, H. Yang, G.J. Kemeny, R.S. Jackson, Q. Wang, M.K. Alam, P.R. Griffiths, 

	(16) D. Gupta, L. Wang, L. M. Hanssen, J.J. Hsia, and R.U. Datla, "Polysytrene Films for Calibrating the Wavelength Scale of Infrared Spectrophotometers SRM 1921," NIST Special Publication 260-122, National Institute of Standards and Technology, Gaithersburg, MD, 25 pages, Certificate Issue Date: (April 1995).

	(17) Standard Reference Material (SRM)® 1921 Certificate, "Infrared Transmission Wavelength Standard," National Institute of Standards and Technology, Gaithersburg, MD 20899, 6 pages, Certificate Issue Date: (7 April 1995).

	(18) Standard Reference Material (SRM)® 1921b Certificate, "Infrared Transmission Wavelength Standard," National Institute of Standards and Technology, Gaithersburg, MD 20899, 7 pages, Certificate Issue Date: (19 February 2014).

	(19) W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, 

Numerical Recipes, The Art of Scientific Computing

 (Cambridge University Press, Cambridge, UK, 3rd ed., 2007), pp. 120–124.

The use of reference materials to align or test the wavelength–wavenumber axis for optical spectroscopy is essential for quantitative and qualitative methods. This article provides details for using reference materials with ultraviolet, visible, near-infrared, infrared, and Raman spectroscopy methods.

Using Reference Materials, Part I: Standards for Aligning the X-Axis

This column is the continuation of our previous installments dealing with the question of outliers. Here we consider what to do about an outlier, once one is detected.

	You have developed a set of data and have a reading that is suspected to be an outlier. You have applied one or more of the tests described in our previous column (1) and confirmed that the reading is, indeed, an outlier. Now what do you do?

	There are three actions you might consider taking:

	These possible actions could be considered the 

 approaches to dealing with the outlier. Other approaches can also be considered, and, depending on the circumstances, might be preferred. For example, if the outlier arises when calibrating a spectrometer for quantitative analysis (using chemometrics, of course, which is the default activity for all our columns), then the origin of the outlier could be either in the reference laboratory values or among the instrumental values. In either case, an alternative (nonstatistical) approach would be to identify the source of the discordant value (instrument or laboratory), and then engage in investigation of the chemistry, physics, and background of the readings to find the fundamental cause. In a pharmaceutical context, for example, this is what would be called 

	A variant of this approach is to concentrate all of one's attention on just the outliers and ignore the rest of the data, with a view toward learning some new fundamental science, the rationale being that some new and unexpected effect has created the discordancy in the values. An outlier can be an indicator of a scientific accident, of the sort that has led to discoveries such as quinine, the smallpox vaccine, X-rays, insulin, penicillin, Teflon, and the cosmic microwave background (2-4) (Don't hold your breath for this, but it does happen!). After all, to a lesser degree, we saw previously (5) that the presence of a set of outliers that persisted through changes of data transformations and other manipulations of the data was indicative of a previously unsuspected systematic effect influencing the data. The discovery of this effect gave us much information about the meaning of calibration transferability, and how to achieve it. General scientific principles come into play here. Is the effect reproducible? Can it be created or avoided at will? Does it lead to predictions of new phenomena? Is there a causal link between the discordant data and fundamental physics, chemistry, biology, or math? Can you do a controlled experiment (or more generally, what is sometimes loosely called a 

)? Is there a theory (from another science, such as chemistry, physics, biology) that can explain the findings? Do other scientists get similar results, or at least have you had someone check your work? Here we will avoid departing from our mission of describing the chemometric and statistical effects on the data, and so we will not pursue those other topics. However, the reader should keep these alternate considerations in mind; after all, not everything has a statistical explanation! We will now consider the possibilities listed earlier.

	In the context of using chemometric calibration to perform chemical analysis, the most common action taken when an outlier is identified is to delete the discordant reading. As we saw in the discussion of the nature of outliers (6), however, it seems likely that samples summarily rejected this way are sometimes (perhaps even often) misidentified as outliers, when in actuality, they are simply the extreme samples of the distribution.

	Alternatively, as described above, the distribution could be other than expected. If an outlier is identified based on an expectation of a normal (or at least a symmetric) distribution but the actual distribution is exponential, or χ

What are the steps to take once an outlier is discovered? There are several options.

Author | Howard Mark

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Using Reference Materials, Part I: Standards for Aligning the X-Axis

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