November 2019 Volume 34 Number 11 …files.alfresco.mjh.group/alfresco_images/pharma/2019/12/... ·...

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Protein Crystallization via QCL IR Microscopy

and 2D IR 2D-COS Raman to Analyze

Structural Changes in Polymers

Data Quality vs. Data Integrity

Book Review: Chemometrics in Spectroscopy

November 2019 Volume 34 Number 11 www.spectroscopyonline.com

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Data Quality vs� Data Integrity



Cover image courtesy of artegorov3@gmail/AdobeStock.

CONTENTS

Spectroscopy (ISSN 0887-6703 [print], ISSN 1939-1900 [digital]) is published monthly by MultiMedia Healthcare LLC 325 W 1st St STE 300 Duluth MN 55802. Spectroscopy is distributed free of charge to users and specifiers of spectroscopic equipment in the United States. Spectroscopy is available on a paid subscription basis to nonqualified readers at the rate of: U.S. and possessions: 1 year (12 issues), $74.95; 2 years (24 issues), $134.50. Canada/Mexico: 1 year, $95; 2 years, $150. International: 1 year (12 issues), $140; 2 years (24 issues), $250. Periodicals postage paid at Duluth, MN 55806 and at additional mailing of fices. POSTMASTER: Send address changes to Spec-troscopy, P.O. Box 6196, Duluth, MN 55806-6196. PUBLICATIONS MAIL AGREEMENT NO. 40612608, Return Undeliverable Canadian Addresses to: IMEX Global Solutions, P. O. Box 25542, London, ON N6C 6B2, CANADA. Canadian GST number: R-124213133RT001. Printed in the U.S.A .

COLUMNS

Molecular Spectroscopy Workbench � � � � � � � � � � � � � � 12Raman Analysis of Ethylene Vinyl Acetate Copolymers–Using 2D-COS for Identifying Structural ChangesFran AdarRaman 2D-COS spectral data provide information on conformational changes of polymers. Here, Raman spectra of ethylene vinyl acetate and vinyl acetate copolymer are measured and interpreted, enabling a description of morphological changes related to the vinyl acetate group.

Focus on Quality � � � � � � � � � � � � � � � � � � � � � � � � � 22Data Quality and Data Integrity Are the Same, Right? Wrong!R.D. McDowallMany analysts are confused about the terms data integrity and data quality. This article explores the differences between the terms, and explains how data integrity is an essential part of data quality.

IR Spectral Interpretation Workshop � � � � � � � � � � � � � � 30Organic Nitrogen Compounds VI: Introduction to AmidesBrian C. SmithAmides are an important functional group found extensively in polymers and proteins. There are three different families of amides. Here, is explained how to distinguish them using infrared spectroscopy.

PEER-REVIEWED ARTICLE

Quantum Cascade Laser Infrared Microscopy and 2D IR Correlation Spectroscopy Improves Crystallization Screening of a Protein Complex � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 34Sherly Nieves and Belinda PastranaWell-diffracting crystals are essential for X-ray diffraction of crystallized protein for structural determination. A quantum cascade laser (QCL) infrared microscope is used to determine protein aggregation, distinct from self-association, for the success of the crystallization effort.

BOOK REVIEWChemometrics in Spectroscopy, 2nd Edition, by Howard Mark and Jerry Workman, Jr� � � � � � � � � � � � � � � � � � � � � � � 40Husheng YangChemometrics in Spectroscopy is a collection of column articles that the authors have published over more than two decades. Collectively, the articles form a comprehensive reference for readers wanting to learn chemometrics, especially with its applications in spectroscopy.

Volume 34 Number 11 November 2019

November 2019 Volume 34 Number 11

ON THE WEBWEB SEMINARS

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DEPARTMENTSNews Spectrum . . . . . . . . . . . . . . . . . . . 10Products & Resources . . . . . . . . . . . . . . 43

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Editorial Advisory Board

Fran Adar Horiba Scientific

Russ Algar University of British Columbia

Matthew J. Baker University of Strathclyde

Ramon M. Barnes University of Massachusetts

Matthieu Baudelet University of Central Florida

Rohit Bhargava University of Illinois at Urbana-Champaign

Paul N. Bourassa Blue Moon Inc.

Michael S. Bradley Thermo Fisher Scientific

Deborah Bradshaw Consultant

Lora L. Brehm The Dow Chemical Company

George Chan Lawrence Berkeley National Laboratory

John Cottle University of California Santa Barbara

David Lankin University of Illinois at Chicago, College of Pharmacy

Barbara S. Larsen DuPont Central Research and Development

Bernhard Lendl Vienna University of Technology (TU Wien)

Ian R. Lewis Kaiser Optical Systems

Howard Mark Mark Electronics

R.D. McDowall McDowall Consulting

Gary McGeorge Bristol-Myers Squibb

Linda Baine McGown Rensselaer Polytechnic Institute

Francis M. Mirabella Jr. Mirabella Practical Consulting Solutions, Inc.

Ellen V. Miseo Illuminate

Michael L. Myrick University of South Carolina

John W. Olesik The Ohio State University

Steven Ray State University of New York at Buffalo

Jim Rydzak Specere Consulting

Jerome Workman Jr. Biotechnology Business Associates

Lu Yang National Research Council Canada

Spectroscopy ’s Editorial Advisory Board is a group of distinguished individuals assembled to help the publication fulfill its editorial mission to promote the effec-tive use of spectroscopic technology as a practical research and measurement tool. With recognized expertise in a wide range of technique and application areas, board members perform a range of functions, such as reviewing manuscripts, suggesting authors and topics for coverage, and providing the editor with general direction and feedback. We are indebted to these scientists for their contributions to the publica-tion and to the spectroscopy community as a whole.


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News Spectrum

SAS Atomic Technical Section Student Award Presented at SciX 2019

Four student researchers were presented with the Societ y for Applied Spec troscopy ’s (SAS) Atomic Technical Section Student Award at SciX 2019. Carlos Abad , a pos tdoc tora l researcher a t the Federal Institute for Materials Research and Testing (BAM) (Germany), Joseph E . Lesniewski , a PhD s tudent at Georgetown Universit y (Washington D.C .), Htoo Paing , a graduate s tudent at Clemson Universi t y (Clemson, South Carol ina), and Ingo St renge , a final-year PhD candidate at the University of Seigen (Germany), received their awards on Tuesday October 15, at SciX 2019 in Palm Springs, California.

A b ad e a r ne d h i s P h D i n t he sp r ing o f 2019 f rom the Humboldt-Universi tät zu Ber l in . He was a visiting scientist at the Le ibniz- Ins t i tu te for Anal y t ica l Science in Berlin, Germany from 2015–2018, and at the Lawrence Berkeley Nat ional Laborator y in 2018 . W h i l e w o r k i ng t o w a rd his PhD under the super v is ion

o f No rbe r t J akubow sk i and U l r i ch Panne , he developed a passion for spectrochemical analysis using optical and mass spectrometry. His research interests focus on the development and application o f op t i c a l spec t rome t r y o f t rans ien t d ia tomic molecules for trace analysis of non-metals and stable isotope analysis . He has been an ac t ive sc ience communicator and member of the SAS since 2017.

Lesniewski is working toward h i s PhD a t George town under the guidance of Kaveh Jorabchi . Prior to his work at Georgetown, he was twice awarded a Summer U n d e r g r a d u a t e R e s e a r c h Fellowship, which suppor ted his research at the National Institute of Technology (NIST) headquarters i n G a i t h e r s b u r g , M a r y l a n d .

A t George town , Lesn iewsk i ’s work focuses on addressing high-sensitivity elemental quantification of non-metals , such as chlor ine and f luor ine, for facile quantification of analytes without compound-spec i f ic s tandards . Lesniewsk i hopes to fur ther improve sensitivity of non-metal elemental analyses through exploring new ionization chemistries, and to expand the applications of elemental quantification in environmental and pharmaceutical investigations. Paing is working on developing the liquid sampling atmospheric pressure glow discharge plasma (LS–

APGD) as a miniature ionization and excitation source for various applications from nuclear security to pharmaceutical analysis under t h e m e n t o r s h i p o f K e n n e t h M a r c u s . W i t h h i s r e s e a r c h , Pa i n g h o p e s t o d e c o n v o l u t e some o f the mechan i sms and processes occurring in LS–APGD. Paing recently was awarded the

Innovations in Nuclear Technology R&D Award from the Department of Energy. He would like to pursue a career in academia.

Strenge’s research, under the direc t ion of Cars ten Engelhard, focuses on improv ing methods a n d i n s t r u m e n t a t i o n f o r t h e de tec t ion and charac ter i za t ion o f s ing le nanopar t i c l e s u s ing i n d u c t i v e l y - c o u p l e d p l a s m a –m a s s s p e c t r o m e t r y ( I C P -MS). He deve loped novel data a c q u i s i t i o n a n d p r o c e s s i n g

concepts to overcome current limitations of ICP-MS platforms such as insufficient time resolution, finite measurement duration, low-duty cycle, and other measurement artifacts known to occur in the realm of single-particle ICP-MS (spICP-MS). His current work is with NIST, where he continues his research and helps to establish, evaluate , and improve microsecond time-resolved spICP-MS within their laboratories. ◾

Our most recent Infrared Spectral Interpretation Challenge is seen in the figure below. Given its complexity, you only need to determine if there is an amine salt present. If so, justify your answer, and then determine the type of amine salt. Happy interpreteting!

To see the answer, please turn to page 33.

www.spectroscopyonline.com/ir-spectral-interpretation-workshop-quiz-19

Joseph Lesniewski

Htoo Paing

Ingo Strenge

The infrared absorbance spectrum of a solid.

Carlos Abad

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Fran Adar

Raman spectra of five reference samples of ethylene vinyl acetate (EVA) with vinyl ace-tate (VA) copolymer composition between 14 and 40% were measured. After normal-izing the spectra to the >CH2 deformation near 1450 cm-1, the spectra can be compared and band assignments made, enabling a description of morphological changes resulting from the introduction of the vinyl acetate group. The spectra were then analyzed by two-dimensional correlation spectroscopy (2D-COS). The 2D-COS enabled an easy way to use the spectral data to derive information on conformational changes that bypasses band-fitting, which can be non-unique.

Raman Analysis of Ethylene Vinyl Acetate Copolymers– Using 2D-COS for Identifying Structural Changes

Molecular Spectroscopy Workbench

Polymers are experiencing ever-expanding uses in many applications. The successful use of polymers depends on their chemical and physical proper-

ties which, in turn, depend on the molecular weight, chemical composition, and morphology. When a poly-mer is used to dissolve a small molecule (for instance, for drug delivery or for pigmentation), it needs to be noncrystalline. When it is used for a structural appli-cation, it needs to be tough, to be capable of resisting fracture when stressed. For those who are new to the characterization of polymers, it is useful to explain that morphology refers to the special arrangement of amor-phous versus crystalline composition, orientation of the molecular chains, and the state of amorphous chains in direct vicinity of the crystalline phase, all of which

affect the physical properties. In my journey into acquiring expertise in two-di-

mensional correlation spectroscopy (2D-COS), I have been looking for a system where I can connect what I know about the spectra with the changes that 2D-COS is showing. Some of the work that I have published in this column on the spectra of polyethylene terephthal-ate was designed with this in mind. However, this sys-tem is quite complicated, so I have been looking for a system where we can more easily use the combination of Raman spectroscopy and 2D-COS to reveal useful information about chemical or conformational changes. After I collected spectra of f ive reference samples of poly(ethylene vinyl acetate) (EVA), I dropped them into 2D-COS and asked my mentor, Isao Noda, if this

www.spec t roscopyonl ine .com November 2019 Spectroscopy 34(11) 13

Figure 1: Raman spectra of polyvinyl acetate (PVA, in red), polyethylene vinyl acetate (PEVA–40%, in black) and polyethylene (PE, in blue). (a) Full spectra, (b) Fingerprint part of spectra, and (c) CH part of spectra.

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←28

46

Table I. Method to assign the sequence of changes at each (ν1,ν2). Because the sequence of the input of the spectra into 2D-COS was from the minimal to the maxi-mal %VA, “preceding” means from lower to high VA.

Synchronous Spot Asynchronous Spot Comment

>0 >0 Change at ν1 precedes change at ν2

<0 >0 Change at ν2 precedes change at ν1

>0 <0 Change at ν2 precedes change at ν1

<0 <0 Change at ν1 precedes change at ν2

would be a convenient approach to try. His enthusiastic response was my go-ahead signal. For me, this is exciting, because I am able to rigor-ously follow the physical and chem-ical changes in a material. The focus is entirely different from my usual focus on microa na lysis . Ra ma n spectroscopy has not been used as much for chemica l characteriza-tion as infrared (IR) and nuclear magnetic resonance (NMR) spec-troscopy, for a variety of reasons. Its appl icat ion requires deta i led knowledge of spectral interpreta-tion, which is not being taught as it was 50 years ago. For me, this has provided an incentive to learn more about spectral interpretation, espe-cially of organic compounds.

BackgroundEthylene vinyl acetate is a copoly-mer of ethylene and vinyl acetate w it h t he lat ter usua l ly present at concentrat ions less than 50%. Whereas polyethylene (PE) can be quite well-ordered (chains parallel with long-range crystal line pack-ing order), the introduction of vinyl acetate interrupts that order. The spectrum of polyethylene has been extensively interpreted (1), so the changes introduced by the presence of vinyl acetate are amenable to in-terpretation. The first figure shows the Raman spectrum of poly(vinyl acetate) (PVA, top), EVA, 40% vinyl acetate (middle), and PE (bottom). Before examining the spectra, one might expect that the spectrum of EVA will be a superposition of PVA and polyethylene, but, in fact, that is an extreme oversimplif icat ion that becomes clearer by examining the spectra of solid and molten high density polyethylene (HDPE) in Fig-ure 2. The top of the f igure shows the spectrum of a pellet of polyeth-ylene (2) at room temperature where it is solid versus the bottom spec-trum of the same material, recorded at 200 ⁰C where it has melted. In the case of EVA, as the concentration of the VA is increased, the crystalline order of the polyethylene content is interrupted. Disorder in poly-


Figure 2: Raman spectra of (a) solid-crystalline (30 °C) versus (b) molten (200 °C) HDPE.

Molten HDPE 200˚C

←29

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(a)

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ethylene has well-known effects on the spectrum, which can be seen in Figure 2. In molten polyethyl-ene, there are three broad bands in the fingerprint region—the C-C single bond at about 1070 cm-1, the backbone twist near 1300 cm-1, and

the >CH2 deformations near 1440 cm-1. If polyethylene has a limited number of side branches, when so-lidified, the chains will stretch out with translational alignment; there are two methylene units in the re-peat seg ment of t he cha i n, a nd

two chains in the unit cel l . Using this in-formation, it becomes pos-sible to assign m o l e c u l a r motion to a l l t he pea k s in the spectrum of t he s o l id (1 , 3) . W i t h this informa-tion in mind, when we ex-a m i n e t h e spectra of the various com-p o s i t ion s of EVA, we note that bands of well-ordered, c r y s t a l l i n e polyet hylene a re decrea s-i n g a s t h e VA c o m p o -s i t ion i s i n-creasing, and

VA bands are increasing with VA concentrations. What the 2D-COS will tell us is what is happening si-multaneously (in the synchronous plots), and what is not happening simultaneously (in the asynchro-nous plots). W hat i s even more important is 2D-COS wil l tel l us the order in which the di f ferent bands are changing. Before think-ing this through thoroughly, it did not occur to me that the confor-mat iona l cha nges wou ld not be simultaneous. This has important implications for the study of tem-perature dependence and extrusion in polymers.

Band Assignments of PolyethyleneIn this sect ion, I enumerate the bands that I am using to fol low the inf luence of VA on the poly-ethylene order. Figure 2 shows the Raman spectrum of a pel let mea-sured just above room temperature (30 ⁰C), and then at 200 ⁰C, which is well above the range of melting temperatures (120–180 ⁰C). In the liquid phase, there are three broad and prominent bands in the finger-print region—the C-C stretch near 1070 cm-1, the backbone twist near 1300 cm-1, and the >CH2 deforma-tions near 1440 cm-1. Polyethylene is an unusual polymer in that it can exist in a highly crystal line state. In that form, the C-C stretch splits into two sharp bands at 1060 and 1125 cm-1, the backbone stretch is sharp and appears near 1295 cm-1, and the >CH2 deformation region ex hibits sharp bands near 1420, 1440, and 1460 cm-1. In t he CH stretching region, crystalline poly-ethylene is dominated by a strong sharp band near 2880 cm-1, with a second, somewhat broader band at about 2840 cm-1, with about 65% of the peak intensity of the first band. As disorder increases, the band at 2880 cm-1 loses intensity relative to the 2840 cm-1 band, and higher fre-quency shoulders grow in intensity and sharpen. These are the features that we will be following in the 2D-COS behavior.

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Figure 3a: Raman spectra of EVA (polyethylene vinyl acetate) samples at VA concentrations of 14, 18, 25, 33 and 40%, scaled to the >CH2 deformation at 1437 cm-1.

Inte

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0

1800 1600 1400 1200 1000 800 600

EVA 14% VA blEVA 18% VA blEVA 25% VA blEVA 33% VA blEVA 40% VA bl

Figure 3b: Raman spectra of the 5 EVA samples in the CH stretching region. The sharp peak near 3160 cm-1 is an artifact from room lights. These spectra have the same scaling as in Figure 3a.

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Table II. Sequence of direction of change in the atomic motion as a function of increasing VA content. The assignment of atomic motion for each band is indicated on the right column.

Peak Position (cm-1) Assignment Direction of Change

1346/1368 VA ↑

1305 a PE, backbone twist ↑

1440 VA – broadening of CH2 deformation ↑

1415 xtl PE CH2 deformation ↓

1425 VA – broadening of CH2 deformation ↑

2932 a PE, CH stretch ↑

2880 and 2840 xtl PE, CH stretch ↓

1080 a PE, C-C stretch ↑

1060 and 1125 xtl PE, C-C stretch ↓

1295 xtl PE, backbone twist ↓

1460 xtl PE CH2 deformation ↓

Results–Fingerprint RegionThe spectra of the EVA samples in the f ingerprint region are over-laid in Figure 3a, after scaling all the spectra to the intensity at the deformation near 1440 cm-1. Note that in this region there wi l l be contributions from the methylene groups in t he polyet hylene seg-ments, as well as the methyl group in vinyl acetate, and this may in-troduce some non-linearity to the spectra, but we wil l use the spec-tra scaled in this manner for this work , b e c au s e we a re lo ok i n g more for effects of loss of crystal-linity rather than small chemical changes. To make the examination of the spectra easier, I have color-coded the samples from violet to blue to green to red to brown as the concentration of VA changes f rom 14% to 18% to 25% to 33% and then 40%. The peak labels are brown (when the VA is at high con-centration) or violet (when the VA is at low concentration). Thus, the violet labeled peaks correspond to peaks from the crystalline polyeth-ylene units, and the brown labeled peaks correspond to peaks from the VA, and this can be confirmed by comparing these spectra from the various f igures to those in Figure 1. Figure 3b shows the same spec-tra but in the CH stretching region. (The sharp band near 3060 cm-1 is an artifact from the room lights and should be ignored.) The purple and brown color-coding is identical to that of Figure 2a, but the apparent peak at 2903 cm-1 is highest in the 30% sample, not 14 or 40%, so it is labeled in the color of that sample. What will become apparent is that this intensity at 2903 cm-1 is not due to a rea l peak, but the over-lap of bands in the two phases that happen to maximize in this sample.

2D-COS-Following Conformational and Morphological ChangesMy goal is to understand how the conformat ion of t he cr ysta l l ine phase of polyethylene is changing as the VA concentration increases, a nd , i n pa r t ic u la r, what i s t he


Figure 4: Fingerprint spectra between 950 and 1200 cm-1 superimposed on the 2D-COS results: synchronous spectra on top, asynchronous spectra on bottom.

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order of the changes. Before learn-ing about 2D-COS, my assump-tion was that as a polymer changed, a l l parts of the materia l changed together. If this were the case, the asynchronous plots would be blank. Now we are able to determine the order of changes in the polymer as ref lected by the order of changes in the spectral bands. In particular, we will follow bands of the crystalline versus amorphous forms of polyeth-ylene, keeping in mind that the in-tensity of the marker bands for the

crystalline phase go down, and the amorphous bands go up, as the VA is added. In addition, it is important to keep in mind that the intensity of the spots in the 2D-COS plots indi-cate the magnitude of the changes, not of the peaks themselves. In fact, in the diagonal points of the syn-chronous plots, all peaks have the same sign. That means that while the peaks indicating a disordered polyethylene lattice are increasing, those of the crystalline lattice are decreasing, but they are changing

together. It is only by examining the off-diagonal points that we can determine which peaks are increas-ing, and which are decreasing. But to determine the sequence of the changes, we have to examine the asynchronous plots.

If I plot the entire 2D-COS spec-tra, it will not be possible to see the details of interest. I will be plotting first blocks along the diagonal, then blocks off the diagonal, to examine the behavior of cross-peaks. For example, I know that the sharp CH peaks near 2840 and 2880 cm-1 are attributable to the crystalline phase. If I want to understand how the changes in the CH region line up with changes in the CH2 deforma-tion or the backbone stretch or the C-C stretches, then I will produce the 2D-COS plots with the CH re-gion on one axis and a f ingerprint region on the other axis.

Figures 4 t hrough 9 show t he 2D-COS results in the fol lowing regions:

Figure 4: 950−1200 cm-1

Figure 5: 1200−1500 cm-1

Figure 6: 2500−3100 cm-1

Figure 7: 2800−3000 cm-1 vs. 1200-1500 cm-1

Figure 8: 2775−3050 cm-1 vs. 950-1200 cm-1

Figure 9: 900−1200 cm-1 vs. 1200-1500 cm-1

T h e r u l e s f o r d e t e r m i n i n g whether the change at ν1 comes be-fore ν2, or visa versa (4) are shown in Table I. Table II shows the se-quence i n which t he ba nds a re changing that were derived from the 2D-COS plots. In order to pro-duce this table, one has to inspect each of the partia l 2D-COS plots and determine the sequence of the changes for the bands of interest in that plot. Then, when all the plots have been evaluated, it is possible to consolidate the results and produce the list shown in Table II. However, it is a bit tedious to get everything in a self-consistent order; it is more or less like solving a puzzle.

So what does this list mean? The


Figure 5: Fingerprint spectra between 1200 and 1500 cm-1 superimposed on the 2D-COS results: synchronous spectra on top, asynchronous spectra on bottom.

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Figure 6: CH region of spectra between 2500 and 3100 cm-1 superimposed on the 2D-COS results; synchronous spectra on left, asynchronous spectra on right.

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f irst bands listed are the two VA bands near 1350 and 1370 cm-1. Although polyethylene does have a band in this region (the so-called umbrel la mode of CH 3 g roups), most of the change here is from the VA. In fact, careful examination of the overlaid spectra in this region (Figure 3a) does indicate that at low VA concentration, the band at 1368 cm-1 is sharper, and more intense, than the band at 1346 cm-1, but at high VA concentration the two have similar widths and intensities exactly as in the VA and 44% EVA as shown in Figure 1. The next band to exhibit changes is the backbone twist in amorphous polyethylene, which, of course, grows in intensity with VA; note that this band is to-tally absent in PVA, so its presence indicates only the amorphous con-tent of polyethylene, and no contri-bution from VA.

Following this are changes around the >CH2 deformation that was used for normalization at 1437 cm-1. What is curious is that the two small spots that seem to indicate symmetric in-creases in intensity at 1427 and 1440 cm-1 in the synchronous plot do not behave in parallel. The 1440 cm-1 spot increases f irst, the 1415 cm-1 band of crystalline polyethylene de-creases next, and then the 1425 cm-1 spot increases. To convince myself that this conclusion really ref lects the data, I have plotted this region of the spectra in Figure 10. The fig-ure shows clearly the decrease in the crystal bands at 1415 and 1460 cm-1, but it also shows broadening around the centroid at 1437 cm-1. The spots appear in the 2D-COS plots because of the -CH3 group on the acetyl that replaces one of the protons on the chain. In fact, the normalization to the centroid of the >CH2 probably enhances the ability to see the in-crease in width. The spectral plot does not indicate directly the order of the changes, but at least it does support the conclusions from the 2D-COS.

Following these changes, there is the growth of the amorphous con-tributions in the CH region at 2932


Figure 7: 2D-COS Cross-peak correlation spectra between 2800 to 3000 cm-1 and 1200 to 1500 cm-1; synchronous spectra on left, asynchronous spectra on right.

1200

1300

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15002950 2900 2850 2950 2900 2850

Figure 8: 2D-COS Cross-peak correlation spectra between 2725 to 3025 cm-1 and 950 to 1200 cm-1; synchronous spectra on left, asynchronous spectra on right.

3000 2900 2800 3000 2900 2800

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cm-1. Then there is the loss in in-tensity of the crystalline CH bands near 2880 and 2840 cm-1. After that there is growth in the amorphous C-C stretch at 1080 cm-1, followed by decrease in the crystalline C-C stretches at 1060 and 1125 cm-1. Finally, there is loss in intensity of the backbone twist of the crystal-line polyethylene, and then the loss in intensity of the 1460 cm-1.

DiscussionWhen I examine these changes, the trend that I see is that f irst we ob-serve growth in the VA bands that have no overlap with polyethylene (1346 and 1368 cm-1), and then the changes seem to happen with the grow th of the amorphous phase before the loss of crystallinity. In most cases, changes of analogous bands (amorphous and crystalline) occur sequentially; see the bands in the CH region and then the C-C re-gion. The last bands to change are the backbone twist of the crystalline phase, and then the last component of the >CH2 at 1460 cm-1.

I found the results in the >CH2 deformation region puzzling. While both the 1415 and 1460 cm-1 are clearly associated with crystallin-ity, they did not behave the same. But the following will explain what is going on. In the amorphous phase

there is a single chain δ(>CH2) mode at 1440 cm-1 of A1g symmetry. In the crystal there are two chains per unit cell, and polarization studies have shown that this mode splits into bands at 1415 and 1440 cm-1. In order to explain the 1460 cm-1

band in the crystalline phase, the overtone of the IR active band is in-voked. The single chain >CH2 rock-ing IR band of B2u species appears at 720 cm-1 in the amorphous phase, but splits into a doublet at 720 and

730 cm-1 in the crystal line phase. Thus the overtone of the 720 cm-1 band will overlap with the 1440 cm-1 component of the split-off Raman band, and the overtone of the sec-ond at 730 cm-1 will be the Raman band observed at 1460 cm-1. These assignments have been summarized in (1).

It is tempting to try to rationalize the order in which the band changes occur. That the first change was the introduction of VA bands is easy to understand. But why would the appearance of the backbone twist of the amorphous phase appear so much earlier than the disappearance of the twist in the crystalline phase? Why does the disappearance of the 1415 cm-1 crystalline band occur be-tween the two spots that appear in the 2D-COS at 1440 and 1425 cm-1; perhaps because the magnitude of the changes on the two sides of the 1437 cm-1 band are not equal? One can argue that the changes in the CH stretches precede those of the CC stretches and crystal backbone because it is easier for the protons to adjust to changes than the CC units, which are constrained and coupled together. Likewise, it can be argued that it is easier for there to be adjustments in the single CC


bond stretches than the backbone t w is t , wh ich probably requ i res some minimum coherent sequence length along the chain.

After this work was completed experimental ly and I was prepar-ing to write, I picked up the text by Nod a a nd Oz a k i a nd i t fe l l open to pages in which ana lysis of this same chemical system was described (5)! The work cited in this section of the text goes back to 1999, in which Fourier transfer (FT) Raman spectra were analyzed only in the fingerprint region. The results were discussed in terms of the three-phase model (the ortho-rhombic crystal phase, a melt-like amorphous phase, and an isotropic disordered phase). Some of their conclusions are different than ours, but they did not describe how the spectra were normalized for com-parison. I believe that the analysis and conclusions can be dif ferent, because I have normalized spectra at 1437 cm-1.

SummaryOf what use is a l l this? The ques-t ion is important, because in de-r iv i ng Table I I , much t i me was involved to check and recheck the sequences and I have to ask my-self if it was worth doing. Because polymers are used for many appli-cations with quite differing physi-cal and chemical requirements, and the physica l and chemica l prop-er t ies depend not on ly on t heir chemical composition, but on their history, this type of analysis can provide information that is dif f i-cult to acquire by other means. In particular, polymers are often ex-truded from the melt and the speed with which they are extruded as well as the temperature and other factors wi l l determine the physi-cal properties, including (but not limited to) orientation, crystallin-ity, and ductility. Because the 2D-COS results provide information on the atomic/molecular scale, the polymer chemist can access this in-formation to engineer their materi-als. For instance, the ductility of a

polymer depends (inversely) on the crysta l l inity. If the EVA is being made for an adhesive application, it wil l need to be ducti le, but not so much that i t w i l l f low. U s i n g t h e i n for mat ion p r o v i d e d i n Ta b l e I I , a l o n g w i t h MVA (multi-variate ana l-y s i s pre d ic-t i o n s) , o n e s h o u l d b e a b l e t o d e -t e r m i ne t he mor pholog i-c a l c h a r a c -t e r i s t i c s e f-f e c t i n g t h e ductility, and t h e n a d j u s t the manufac-turing condi-t ions for op-t i m i z a t i o n . For example, t u n i n g t h e e x t r u s i o n temperature, o r a m o u n t

of shear, could potentially change the order in which these changes occur, which could affect the bulk physical properties.

Figure 9: 2D-COS Cross-peak correlation spectra between 900-1200 cm-1 and 1200 to 1500 cm-1; synchronous spectra on left, asynchronous spectra on right.

1250

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15001150 1100 1050 1000 950 1150 1100 1050 1000 950

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Figure 10: Raman spectra of the five samples of EVA in the region of the >CH2 deformations. All spectra were normalized to the peak intensity at 1437 cm-1 . It is clear that the “crystal” bands at 1415 and 1460 cm-1 are decreasing with VA concentration. It is also clear that the central band is broadening around the 1437 cm-1 centroid.

Inte

nsit

y (c

oun

ts)

Raman shift (cm-1)

6000

5000

4000

3000

2000

1000

0

1400 1420 1440 1460 1480

References(1) D. I . Bowers , and W.F. Maddams,

The Vibrat ional Spec troscopy of

Polymers (Cambridge Universi t y Press , Cambr idge , Uni ted K ing-dom, 1989).

(2) Scientific Polymer Products, Poly-e thy lene H igh Dens i t y Pe l le t s CAS#25213-0209 Cat#041, On-tario, NY.

(3) F. Ada r , Spec t ro scopy 24 (10) , 16–19 (2009).

(4) I . Noda and Y. Ozaki, Two-Dimen-sional Correlat ion Spec troscopy (John Wiley and Sons, Ltd. Chich-ester, United Kingdom, 2004).

(5) Section 10.4 Composit ion-based 2D Raman Study o f E VA Com-pounds in Reference 4.

For more information on this topic, please visit:

www.spectroscopyonline.com/adar

Fran Adar is the Principal Raman Applications Scientist for Horiba Scientific in Edison, New Jersey. Direct correspondence to: [email protected]

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Application Note

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New Approaches in Sample Preparation and Precise Multielement Analysis of Crude Oils and Refined Petroleum Products Using Single- Reaction-Chamber (SRC) Microwave Digestion and Triple-Quadrupole ICP-MSJohn F. Casey, Yongjun Gao, Weihang Yang, and Robert Thomas

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Focus on Quality

Do the terms data integrity and data quality mean the same? As “maybe” and “yes” are not the correct answers, we will explore the differences between the two terms, and how data integrity is a key part of data quality.

R.D. McDowall

Data Quality and Data Integrity Are the Same, Right? Wrong!

Data integrity has been the subject of many “Focus on Quality” columns over the past few years (1–5). However, we have never mentioned, let alone

discussed, data quality. In many people’s minds, data integrity and data quality are terms that have equiva-lent meanings, hence the first part of the title of this month’s column. However, in reading the MHRA (Med-icines and Healthcare Products Regulatory Agency, the UK regulator) GXP data integrity guidance, there is an indication from a regulator that the two terms are different (6). Newton and White have discussed the dif-ferences between data quality and data integrity in an ISPE blog in 2015 (7), and, in this column, we expand and explore the similarities and differences between the two terms.

MHRA GXP Data Integrity Guidance 2018The MHR A’s 2018 g u ida nce has in t he int roduc-t ion sect ion the fol lowing statement in clause 2 .7:

“This guidance primarily addresses data integrity and not data quality since the controls required for integrity do not necessari ly guarantee the quality of the data generated (6).”

Clearly, in the mind of the regulator, there is a differ-ence between data quality and data integrity. But what exactly is it?

ALCOA CriteriaOur data quality versus data integrity story starts to-ward the end of the last century with ALCOA—not the Aluminium Company of America, but an acronym standing for attributable, legible, contemporaneous, original , and accurate. These criteria have has been discussed in several data integrity guidances from the Food and Drug Administration (FDA), MHRA, the World Health Organization (WHO), and the Pharma-ceutical Inspection Convention and Pharmaceutical Inspection Co-operation Scheme (PIC/S) (6,8–10). The WHO guidance contains the most detailed discussion of ALCOA criteria in an appendix with a definition of each term, presentation of the expectations for paper and electronic records, plus special risk management factors (9).

A European Medicines Agency (EMA) publication on electronic source data extended ALCOA to include an additional four terms of complete, consistent, enduring, and available (11). The extension of these four addi-tional terms is known as ALCOA plus, or ALCOA+. A recent article in LCGC North America discusses all nine ALCOA+ criteria in more detail (12). Origin of ALCOAReading further through the MHRA guidance, there is clause 3.10, a part of which is presented below, discuss-ing ALCOA and ALCOA+ criteria:

November 2019 Spectroscopy 34(11) 23www.spec t roscopyonl ine .com

“….. ALCOA was historically regarded as defining the attributes of data quality that are suitable for regulatory purposes…. [6]”

This is interesting; ALCOA cri-ter ia were or ig ina l ly def ined as the attributes of data quality? How did this come about? The ALCOA acronym was invented by an FDA good laboratory practice (GLP) in-spector named Stan Wollen, who developed it as an aide memoire to remember the key requirements of data quality. This is outlined in a publ icat ion ent it led “Data Qua l-ity and the Origin of ALCOA” (13), available for download on the web. There is a lot of background in-formation together with Wollen’s inter pretat ion of ALCOA under the US GLP regulations 21 CFR 58 (14). For our discussion, ALCOA was originally associated with data quality, as noted by the MHRA in its GX P guidance document (6), long before it was stolen by vari-ous guidance documents for use as a cornerstone of data integrity.

Defining Data Quality and Data IntegrityBefore we go into more detail about the two terms, we need to def ine them. The definitions for both data integrity and data quality are taken from the 2018 MHR A GXP Guid-ance for Data Integrity (6), and are presented in Table I. We wil l con-sider and discuss the data integrity def init ion f i rst , and then spend

time looking at data quality. I am not going to discuss the last para-graph in the r ight-hand column of Table I about sound science and risk management, a lthough those are key inputs to data integrity, but not part of the data integrity and data quality debate in this column.

MHRA Data Integrity Definition ChangesWhat is interesting about the 2018 definition of data integrity is how far it has changed since the f irst M H R A g u id a nc e pu bl i s he d i n 2015. The earl ier def init ion was:

“The extent to which all data are complete, consistent and accurate throughout the data lifecycle (15).”

This def in it ion is a modi f ica-t ion of a 1995 FDA def init ion of data integrity (16). The 2015 def i-nition can be compared with that published in 2018, where the addi-tions are highlighted in bold text:

“Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable and that these characteristics of the data are maintained throughout the data life cycle (6).”

There is the addition of two cri-teria (trustworthy and reliable), as well as a requirement to maintain al l f ive data integrity characteris-tics throughout the data life cycle.

What is not stated in this defini-

tion, but in the explanation for raw data, is that dynamic data must be maintained in this format through-out the data life cycle. We shall re-turn to this regulatory expectation, and the total inability of industry and suppliers to meet it , in a fu-ture “Focus on Quality” column.

Additional Data Integrity Requirements?As discussed above and in Table I, we have addit ional criteria l isted i n t he M H R A def i n it ion : com-plete, consistent, trustworthy, and rel iable , in addit ion to t he f ive original ALCOA criteria (6). Two are ALCOA+ criteria that wil l be discussed below, plus two new re-quirements. ALCOA+ on steroids perhaps? Let us look at these four cr iter ia , which to ma ke mat ters worse are not def ined in the guid-ance document:

C omplete: T his i s one of t he ALCOA+ criter ia , and is a lso an FDA GMP regulatory requirement in 21 CFR 211.194(a) where complete data are mentioned (17). Put simply, every item of data and metadata col-lected during an analysis from sam-pling to generation of the reportable result is covered under “complete,” including (but not limited to) any instrument log entries, documen-tation of mistakes, investigations, dev iat ions , and inst rument and calibration failures. Of course, you won’t be a l lowing any laboratory user deletion privileges, will you?

C o n s i s t e n t : A n o t h e r o f t h e ALCOA+ criteria that requires that the data be consistent with the pro-cesses being followed, and that the t ime and date stamps ref lect the creation, modif ication, and audit trai l entries are as expected. The process (manual or automatic, hy-brid or electronic) should ensure that the acquisit ion, t ransforma-tion, or calculation of data is trans-parent and traceable.

Trustworthy: This is a new data integrity requirement that, together with “reliable,” harks back to the last century and the requirements outlined in 21 CFR 11 for electronic

Table I: MHRA Definitions of data quality and data integrity (6)

Data Quality Data Integrity

Glossary: The assurance that data produced is exactly what was intended to be produced and fit for its intended purpose.

This incorporates ALCOA.

Section 6.4: Data integrity is the degree to which data are complete, consistent, accurate, trustworthy, reliable and that these characteristics of the data are maintained throughout the data life cycle.

The data should be collected and maintained in a secure manner, so that they are attributable, legible, contemporaneously recorded, original (or a true copy) and accurate.

Assuring data integrity requires appropriate quality and risk management systems, including adherence to sound scientific principles and good documentation practices.

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• Sensitive hydride mode, provides simultaneous cold vapor and pneumatic nebulization with greater than 10-fold improvement in detection limits for the cold vapor elements without compromising performance of non-hydride forming elements

Element Pneumatic Nebulization Sensitive mode Simple mode

λ (ug/L) (ug/L) (ug/L)

As 188 3.7 0.2 0.5

Hg 194 1.2 0.07 0.1

Sb 206 3.6 0.2 0.4

Se 196 2.9 0.2 0.5

Cd 214 0.1 0.1 0.1

Co 238 0.4 0.5 0.7

Cr 267 0.5 0.3 0.3

Cu 327 0.5 0.6 0.6

Fe 238 0.4 0.3 0.4

Mn 257 0.05 0.04 0.03

Mo 202 0.5 0.7 0.7

Ni 231 0.7 1.0 1.0

Pb 220 1.9 2.3 2.6

Tl 190 2.1 0.2 0.2

Zn 213 0.2 0.2 0.2

Table 1. Measured 3σ detection limits (in ug/L) with an AgilentTechnologies 5100 SVDV in axial mode at 1.4 kW RF powerand 20 second integration time (SeaSpray DC nebulizer wasused, P/N A13-07-USS2)

Figure 1. Sensitive simultaneous cold vapor/pneumatic nebulization mode

Figure 2. Simple simultaneous cold vapor/pneumatic nebulization mode

ICP

ICP

REDUCTANT

REDUCTANT

SAMPLE

SAMPLE

WASTE

WASTE

SAMPLE & REDUCTANT INLET

REDUCTANT INLET



records and electronic signatures (18). In essence, can you trust the data or records? In some respects, it is the reverse of the ALCOA criteria. For example, if a record is attributed to an individual (ALCOA require-ment), did that person actually do the work? Was the person in the laboratory at the time the work was performed, or did somebody act on their behalf?

Reliable: Another new data in-teg r it y requirement t hat mea ns that all records and data are suitable and can be depended upon. Where a true copy has been made of an original record, is the verif ication process acceptable? The record set is not the nth iteration of testing to pass and that work has actually been performed as required under EU GMP Chapter 1.8 and 1.9 for in process and quality control testing, respectively (19)?

The addition of trustworthy and reliable to the ALCOA+ criteria are good. So, what does this mean for data integrity as a whole?

Data Integrity—Can I Trust the Data?Data integrity provides assurance that the analytical work in the lab-oratory from the sampling to the calculation of the reportable result has been carried out correct ly. In short, can you trust and rely on the analysis and the reportable results? If you cannot trust the analysis and the data, then you cannot make a quality decision based on poor, in-complete, or falsified data.

It is t he role of t he ana ly t ica l sc ient ist and t he second-person reviewer to ensure data integrity, but that a lso requires that the in-struments, computer systems, ana-lytical procedures, and laboratory environment are set up correct ly, and that the organization within which they work ensures that they are not pressured to cut corners or falsify data.

Therefore , ensur ing t he integ-rity of data is a major contributor to data qua l ity. But , in focusing on data integrity, we must not lose sight of data quality.

Understanding Data QualityYou can see from the definition of data quality in Table I that there are three main elements:• Data produced is exactly what was

intended to be produced • It is fit for its intended purpose• Data quality also includes ALCOA.

Hmmmm, what do these mean in practice? Let us look at item 1, that data produced is exactly what was intended to be produced. The problem with this is the wording of the def inition. Although a labora-tory produces mountains of data, we abstract and use the information within them to make decisions. For a detailed discussion of this process together with the controlled and un-controlled parameters, see the arti-cle by McDowall and Burgess (20). Therefore, the purpose of a labora-tory is to generate information from the analysis of samples in order to make decisions. These decisions could be to a id product develop-ment or release a batch of product.

To do this requires trained staff, qua l i f ied a na ly t ica l i nst r u men-tat ion, va l idated sof t wa re , a nd a na ly t ic a l pro c e du re s ver i f ie d or va l idated under actua l condi-tions of use, as required by 21 CFR 211.194(a)(2) (17). This is the same as the f irst three levels of the data integrity model discussed in earlier

articles (21–23). Where the second item comes in is that the output of the analytical procedure must be fit for use. As a simple example, there is no point calculating results of an analysis to three significant figures if all that was required was the pres-ence or absence of the analyte.

Data Quality—Can I Use the Data? Once you have ensured the integrity of data, we must consider data qual-ity. In part, this focuses on the labo-ratory’s ability to deliver the results to the sample submitter and meet their requirements for the analysis such as:• Reportable value format • Precision and accuracy or mea-

surement uncertainty of the result • Supporting information for the

analysis• Speed of ana lysis (for example,

for releasing a production batch or turnaround of analysis from a clinical trial)However, there is a compromise

with data quality that is best i l lus-trated by the data quality triangle.

Data Quality TriangleThere is a problem with the def i-nitions of data quality that is not me nt ione d by t he M H R A a nd t he A mer ic a n He a l t h I n for ma-t ion M a n a ge m e nt A s s o c i a t ion

Figure 1: The data quality triangle of quality, time and cost.

Quality

Time Cost

Data QualityTriangle


(AHIMA); t his i s t he data qua l-i t y t r i a n g l e , a s s h ow n i n F i g-ure 1. At each end of t he t r ia n-g le are t he fol low ing at t r ibutes:• Time: to perform the task

• Cost : cost in instrumenta l and human resources

• Quality: delivering data of accept-able quality to the end user of the information to make a decision.

The problem is that you can only select two of the three at any one t ime. For example, i f you want a f ixed t ime and high quality, how much a re you w i l l ing to pay or how much resource wil l you com-mit? Equa l ly, i f you want ana ly-sis performed quick ly and at low cost, quality suf fers. The quality triangle is applicable to any labo-rator y in any industr y, but in a regulated laboratory you still must ensure data integrity as well. The i n f luence of sen ior a nd labora-tory management on managing the three criteria of the data qua lity triangle wil l direct ly impact both data qua l it y a nd data integ r it y.

Integrity and Quality Use the Same Data SetIt is imperative to realize, as men-tioned earlier, that the data set for ensuring data quality and data in-tegrity is the same, and not two different ones. Furthermore, data integrity is not ensured f irst and followed by a second pass to ensure

Figure 2: Data quality and data integrity use the same records (modified from an original idea by Mark Newton).

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data quality. To ensure data quality and data integrity, both must occur in parallel, simultaneously and on the same dataset.

Because the two terms are differ-ent, as Newton and White (7) point out , you can have data integrity without data quality, and vice versa. A laboratory can have excellent data integrity, but if the analytical infor-mation to make a decision is deliv-ered late, or not at all, then the data quality is useless. Alternatively, if each laboratory is permitted to cre-ate its local practices for data man-agement, you can have integrit y without quality (or, at least, very low levels of quality). All the data are there, but consolidating them requires a standardization project: good integrity, lousy quality. That is the one place where quality and integrity do differ, and where the regulations are weak (Newton, per-sonal communication).

New ton and White (7) present a case where an organization out-sources analytical work to contract laboratories, but there is little over-sight of the work, resulting in data integrity lapses. When the results are received by the organizat ion, entry to the LIMS control led and delivery of the results to the deci-sion makers is rapid, and with good data quality.

The similarities and differences between data quality and data in-tegrity are shown in Figure 2. The

similarities are:• A common record set• A p p l i c a t i o n o f A L C OA a n d

ALCOA+ criteria to the record setThe differences between the two

terms are trusting the data versus making a decision using the results.

Does ALCOA Apply to Data Quality or Data Integrity? The problem a nd potent ia l con-f lict comes with the third element of the MHR A def init ion for data quality that states that data quality includes ALCOA. This is consistent with Wollen’s original view when devising the acronym ALCOA (13). However, when we see the MHRA definition of data integrity in Table I, each ALCOA criterion is spelled out in the definition. Puzzled?

Is ALCOA an integral part of data integrity, which in turn is a compo-nent of data quality, or is ALCOA applicable to both terms, as noted in the MHRA definition?

Look at it from the perspective of the data and records being gen-erated. Can you separate the data for data qua l ity f rom data integ-rity? The answer is no, because the record set for both data integrity and data quality wil l be the same. Therefore, ALCOA criteria apply equally to data quality and data in-tegrity. Think it through; otherwise you could have some data where all actions were attributable for data integrity, but not for data quality.

Figure 2 shows the relationship diagrammatically; in the middle is the ana lysis workf low from sam-pling to a decision based on the reportable result from the analysis. Throughout this process, data and records are generated that comprise the complete data from the analy-sis. This record set is impacted by the ALCOA criteria (as well as the four additional requirements) for data integr it y ment ioned in t he MHR A def init ion in Table I and the ALCOA criteria for data quality.

Data Quality AttributesApart from ALCOA, there are no criteria for data quality mentioned by MHRA (6), as shown in Table I. Looking outside of the pharmaceu-tical industry, Newton and White (7) quoted AHIMA, which has a def-inition of data quality as it ensures clear understanding of the mean-ing, context, and intent of the data. Additionally, there are 10 criteria or attributes for data quality that are summarized below. These defi-nit ions include attributes of data quality intended for a healthcare environment; where appropriate, I have edited the definitions, denoted by “…” to remove the references to healthcare data: • Accessibility: data items that are

easily obtainable and legal to ac-cess with strong protections and controls built into the process

• Accuracy: the extent to which the data are free of identifiable errors

• Consistency: the extent to which … data are reliable and the same across applications

• Comprehensiveness: all required data items are included and en-sures that the entire scope of the data is collected with intentional limitations documented.

• Currency: the extent to which data are up-to-date; a datum value is up-to-date if it is current for a spe-cific point in time. It is outdated if it was current at a preceding time yet incorrect at a later time.

• Definition: the specif ic meaning of a …. data element

• Granularity: the level of detail at

Figure 3: Interpretation of the MHRA definitions of data quality and data integrity (6).

Analysis Record and Data Set

AttributableLegibleContemporaneous

OriginalAccutate

AttributableLegibleContemporaneous

OriginalAccutate

CompleteConsistentTrustworthyReliable

Data Quality

ReportableResult

Comparisonwith

Speci�cationAnalysisSample

Data Integrity

Decision


which the attributes and values of … data are defined.

• Precision: data values should be strictly stated to support the pur-pose

• Relevancy: the extent to which… data are useful for the purposes for which they were collected.

• Timeliness: Concept of data qual-ity that involves whether the data is up-to-date and available within a useful time frame. Timeliness is determined by how the data are being used and their context.W hen look ing at t hese def in i-

t ions, there are some commonali-ties with ALCOA+ criteria; for ex-ample, comprehensiveness can be equated to complete.

Quality Does Not Own Quality Any MoreData quality is not owned by the quality assurance department, as qua l it y i s now ever ybody ’s job. Data quality starts in the labora-tory. Quality assurance staf f are not there to identify errors as that is a laboratory function of the two most important people in any analy-sis: the performer of a test, and the reviewer of the work undertaken. Data integrity and data quality are a laboratory responsibility. Quality assurance provides the advice, en-sures that work is compliant with regulations, and provides the qual-ity oversight via audits and data in-tegrity investigations (22,24).

SummaryIn this column, we have discussed the differences between data integ-rity and data quality for the same analytical data set. Although both terms use the ALCOA+ criteria, it is imperative that both data integ-rity and data quality are ensured so that the numbers can be trusted (data integrity) and decisions can be taken based on the results (data quality).

AcknowledgementsI would like to thank Chris Burgess and Mark Newton for helpful com-ments in preparation of this column.

References(1) R.D. McDowall, Spectroscopy 31(4),

15–25 (2016).(2) R . D . M c D o w a l l , Spe c t r o s c o py

32(12), 8–12 (2017).(3) R . D . M c D o w a l l , Spe c t r o s c o py

32(11), 24–27 (2017).(4) R . D . M c D o w a l l , Spe c t r o s c o py

31(11), 18–21 (2016).(5) R.D. McDowall, Spectroscopy 33(9),

18–22 (2018).(6) MHRA GXP Data Integrity Guidance

and Def ini t ions (Medic ines and Healthcare Produc ts Regulator y Agency, London, United Kingdom, 2018).

(7) M.E. Newton and C.H. White, Data Quality and Data Integrity: What is the Difference? (iSpeak Blog, June 15, 2015). https://ispe.org/pharma-ceutical-engineering/ispeak/data-qual i t y -and-data- integr i t y -what-difference.

(8) FDA Guidance for Industry Data In-tegrity and Compliance With Drug CGMP Ques t ions and Ans wer s (Food and Drug Administration, Sil-ver Spring, Maryland, 2018).

(9) WHO Technical Repor t Series No. 996 Annex 5 Guidance on Good Data and Records Management Practices (World Health Organiza-tion: Geneva, Switzerland, 2016).

(10) PIC/S PI-041-3 Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments (Draf t) (Pharmaceut ical Inspec-t ion Convention/Pharmaceutical Inspect ion Cooperat ion Scheme, Geneva, Switzerland, 2018).

(11) Reflection Paper on Expectations for Electronic Source Data and Data transcribed to electronic data Col-lection Tools in Clinical Trials (Eu-ropean Medicines Agency: London, United Kingdom, 2010).

(12) R.D. McDowall, LCGC N. Am. 38(9), 684-688 (2019).

(13) S.W. Wollen, Data Quality and the Or ig in of ALCOA (The Compass , Newsle t ter of the Southern Re -gional Chapter, Societ y of Qual-ity Assurance, 2010); http://www.southernsqa.org/newsletters/Sum-mer10.DataQuality.pdf

(14) 21 CFR 58 Good Laboratory Prac-t ice for Non-Clinical Laborator y

Studies (Food and Drug Adminis-tration, Washington, DC, 1978).

(15) MHRA GMP Data Integrit y Defini-t ions and Guidance for Industr y, 2nd Ed. (Medicines and Healthcare Products Regulatory Agency, Lon-don, United Kingdom, 2015).

(16) FDA Glossar y O f Computer i zed Sys tem And Sof t ware Deve lop -men t Te r m ino log y ( F ood and Drug Administration, Silver Spring, Maryland, 1995). ht tp://www.fda.gov/iceci/inspections/inspection-guides/ucm074875.htm#_top.

(17) 21 CFR 211 Current Good Manufac-turing Practice for Finished Pharma-ceutical Products (Food and Drug Administration, Silver Spring, Mary-land, 2008).

(18) 21 CFR 11 Electronic Records: Elec-tronic Signatures, Final Rule (Food and Drug Administration, Washing-ton, DC, 1997).

(19) EudraLex - Volume 4 Good Manu-facturing Practice (GMP) Guidelines (European Commission: Brussels, Belgium, 2013)

(20) R .D. McDowal l and C . Burgess , LC-GC North Am. 33(8), 554 - 557 (2015).

(21) R.D.McDowall, Validation of Chro-matography Data Systems: Ensur-ing Data Integr i t y, Meeting Busi-ness and Regulatory Requirements (Royal Society of Chemistry, Cam-bridge, United Kingdom, 2nd Ed., 2017).

(22) R.D.McDowall, Data Integrity and Data Governance: Practical Imple-mentation in Regulated Laborato-r ies (Royal Societ y of Chemistr y, Cambridge, United Kingdom, 2nd Ed., 2019).

(23) R.D.McDowall, LCGC N. Am. 37(1), 44–51 (2019).

(24) R.D.McDowall, LCGC N. Am. 37(6), 392–398 (2019).

R.D. McDowall isthe director of R.D. Mc-Dowall Limited and theeditor of the “Questionsof Quality” column forLCGC Europe, Spectros-copy’s sister magazine.Direct correspondence to:

[email protected]


IR Spectral Interpretation Workshop

Amides are an important functional group, because they are found extensively in polymers and proteins. They are unusual in that they contain nitrogen and a carbonyl group, giving a number of useful group wavenumbers. Also, hydrogen bonding affects their spectra.

Brian C. Smith

Organic Nitrogen Compounds VI: Introduction to Amides

T he amide functional group is important in biol-ogy and industry; every protein molecule on the planet contains amide linkages, and the back-

bones of the nylon family of polymers contain amide bonds. Amides are consistent with the theme of the last several columns in that they contain a nitrogen atom, but they also contain a carbonyl or C=O bond, which we have studied extensively in previous columns (1). The generic structural framework of amides is seen in Figure 1.

The f irst thing we have to decide is how to pro-nounce the word “amide.” Depending upon who I am talking to and where they are from, I have heard about nine different pronunciations for this word that, when spelled out phonetically, are: Ay-mids, Ay-mides, Ay-muds, Uh-mids, Uh-mides, Uh-muds, Am-mids, Am-mides, and Am-muds. My preferred pronunciation is “Ay-mides”, which I wil l use throughout the rest of this column.

The amide functional group consists of a central carbonyl group with a nitrogen atom single bonded to the carbonyl carbon. This nitrogen is called the “amide nitrogen”, and can have carbons or nitrogens attached to it. When we first started studying organic nitrogen compounds and were introduced to amines, we found that they came in three varieties, called primary, sec-ondary, and tertiary, depending upon the number of carbons attached to the nitrogen atom (2). Similarly

we speak of primary, secondary, and tertiary amides, and again the difference between them is the number of carbons attached to the nitrogen. The skeletal frame-works of primary, secondary, and tertiary amides are seen in Figure 2.

Note that, as in amines, we are counting the number of C-N bonds. Thus, a primary amide has one C-N bond and two N-H’s, a secondary amide has two C-N bonds and one N-H, and a tertiary amide has three C-N bonds and no N-H bonds. Given that this pri-mary/secondary/tertiary terminology applies to both amines and amides, we can speak more general ly of primary, secondary, and tertiary nitrogens. Therefore, when looking at the structures of amides, amines, and other nitrogen containing functional groups, a pri-mary nitrogen wi l l have one C-N and two N-Hs, a secondary nitrogen will have two C-N bonds and one N-H, and a tertiary nitrogen will have three C-Ns and no N-H bonds.

In prev ious columns when we studied carbonyl groups attached to benzene rings, we encountered the phenomenon of conjugation (3). Brief ly, the pi orbital on the carbonyl group overlaps sl ight ly with the pi electron cloud orbital on the aromatic ring, causing a small amount of electron density to be withdrawn from the carbonyl bond lowering its force constant. Spectroscopically, this is expressed as a ~30 cm-1 low-ering of the position of the C=O stretch compared to


a non-conjugated carbonyl (3), and we spoke of saturated and aromatic

versions of carbonyl conta ining functional groups (3).

Amides also engage in conjuga-tion as seen in Figure 3.

T he n it rogen atom i n a mides contains a p-orbital with a lone pair of electrons in it. This orbital hap-pens to point in space towards the pi-electron cloud of the carbonyl group. There is some orbital over-lap here, as illustrated in Figure 3, which leads to conjugat ion. Like w it h a romat ic ca rbonyl g roups , as a result of conjugation some of the electron density is withdrawn from the carbonyl bond, weakening it and lowering its force constant. This causes the amide C=O stretch to be on the low side compared to other carbonyl conta ining func-tional groups, typically from 1680-1630 cm-1. Note, however, that we do not speak of saturated or aro-matic amides as conjugation takes place in all amides so they all have the same carbonyl stretching peak range.

When we f irst started studying the infrared spectra of organic ni-trogen compounds, we saw how hy-

Figure 2: The skeletal frameworks of primary, secondary, and tertiary amides

C

Primary

Secondary

TertiaryC

O

H

H

N CC

O

HN

C

CC

O

N

C

C

Figure 1: The generic structural framework of the amide functional group.

O

CC N

CarbonylCarbon

AmideNitrogen

AlphaCarbon

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drogen bonded to each other (4). We also discussed at length how N-H stretching peaks appear in the same wavenumber region as O-H stretch-ing peaks, but that N-H stretching peaks are weaker and narrower than O-H stretching peaks, because the hydrogen bond strength for N-H is less than that for O-H (4). We then saw how amines hydrogen bond to each other, and what N-H stretching peaks look like (2,5). Like amines, amides a lso engage in hydrogen bonding, but it is structurally dif-ferent than in amines, as seen in Figure 4.

Because of the electronegat iv-ity difference between carbon and oxygen, the oxygen atom in a C=O bond contains a par t ia l negat ive charge, as denoted by the б- in Fig-ure 4. Because of the electronegativ-ity difference between nitrogen and hydrogen, the hydrogen in an N-H bond has a partial positive charge, as denoted by б+ in Figure 4. The result is that the carbonyl on one amide molecule coordinates with the N-H of a neighboring molecule, forming a hydrogen bond.

In the very f irst insta l lment of this column, we learned that infra-red peak widths are determined by the strength of the intermolecular interactions between neighboring molecules (6). Hydrogen bonds are an example of a strong intermo-lecular interaction. As a result, any functional group that engages in hydrogen bonding is going to have broadened peaks compared to non-hydrogen bonded functional groups. We saw this with amines (4), and it applies to amides as well.

Note that, to the lef t in Figure 5, the amine N-H stretching peaks come to a point, but are broadened at their base. Similarly, the amide N-H stretching peaks to the right in Figure 5 come to a sharp point, but are broadened at their base. Note that, for both these functional groups, the N-H stretching peaks fal l in about the same place, 3400 to 3200 cm-1. This means that the N-H stretching peaks by themselves cannot be used to distinguish am-

Figure 4: An illustration of how the C=O bond of one amide molecule hydrogen bonds to the N-H bond of a neighboring molecule.

HN

H δ+δ–O

RC Hydrogen

Bond

Figure 5: A comparison of the N-H stretching peaks of an amine (left) and an amide (right).

.6

.4

.2

0

.6

.8

1

.4

.2

0

Amine

Wavenumber (cm-1) Wavenumber (cm-1)

Amide

Ab

sorb

ance

Ab

sorb

ance33

6932

98

3366

3170

Figure 3: The orbital arrangement that leads to conjugation in amides.

C

O

NC


ides from amines. However, since amides contain a C=O bond and amines do not, these two functional groups are easy to distinguish. N-H stretches w it h t he presence of a C=O stretch indicate amides, N-H stretches without a C=O stretch in-dicate amines. As we will see in the next column, primary, secondary, and tertiary amides can be distin-guished from each other by infrared spectroscopy.

ConclusionsAmides are an important nitrogen containing functional group. Their s t ructure consists of a n it rogen atom attached to a carbonyl group. There are t hree t y pes of a mide, primary, secondary, and ter t iary depend i ng upon t he nu mber of C-N bonds present in the group. All amides are conjugated, result-ing in their a l l having a relatively low wavenumber C=O stretching peak. Amides a lso exhibit hydro-

gen bonding, resulting in somewhat broadened N-H stretching peaks. The three types of amides can be distinguished from each other by infrared spectroscopy as wi l l be seen in the next column.

References(1) B.C . Smith, Spectroscopy 33(11),

24−29 (2018).(2) B.C . Smith, Spec troscopy 34(3),

22−25 (2019).(3) B .C . Smith , Spec troscopy 32(9),





30−37 (2019).(8) ht tps://www.drugabuse.gov/pub-

licat ions/drugfac ts/mdma-ecsta-symolly

Given its complex ity, you only needed to determine if there is an amine salt present; if so, justify your answer; and then, determine the type of amine salt.

The broad feature that extends from 3000 to 2000 cm-1 by itself is a strong indication that this is the N-H+ stretching envelope of an amine salt. Note that the envelope tops out at about 2700 cm-1, there are C-H stretching shoulder peaks on its high wavenumber side, and overtone and combination bands on its low wavenumber side.

As we saw last t ime (7), both pr i ma r y a m i ne sa lt s a nd sec-ondar y amine sa lts have N-H+ stretching envelopes around 2700 cm-1. However, the position and number of N-H+ bending peaks can be used to distinguish these f rom each ot her. For pr i ma r y amine salts, there are two bend-ing peaks, which fa l l from 1625 to 1560 and 1550 to 1500 cm-1 re-spectively. whereas for secondary

Answers to the Previous Infrared Spectral Interpretation Challenge

Figure i: The infrared spectrum of a solid.

.8

.6

.4

.2

0

3500 3000 2500 2000 1500 1000

2945

2907 28

3027

6527

08

2617

2559

2510

2455

2418

2388

1590

1487

1439 12

42

1381

1029

9263

8635

7954

amine salts, there is only one N-H+ bending peak between 1620 and 1560 cm-1. Examination of Figure i shows that there are no peaks from 1550 to 1500, but there is a peak at 1590 cm-1. This means the molecule is a secondary amine salt, and that is the correct answer.

We discussed in the previous installment (7) how amine salts are im-portant in the legal and illegal pharmaceutical industries. The spectrum in Figure i is of Methylenedioxy-methamphetamine hydrochloride, or MDMA HCl, the amine salt form of the street drug Ecstasy (8).

Your Next Infrared Spectral Interpretation ChallengeThe Infrared Spectral Interpretation Challenge is on Christmas vaca-tion. It will return in the January 2020 edition of Spectroscopy.

Br ian C . Smi th , PhD, i s founder and CEO of Big Sur Scientific, a m a ke r o f p o r t a b l e mid - in f rared cannabis analyzers . He has over 30 years experience as an indus t r ia l in f ra red

spectroscopist, has published numerous peer reviewed papers, and has writ ten three books on spec t roscopy. A s a t rainer, he has helped thousands of people around the world improve their infrared analyses. In addition to writing for Spec troscopy, Dr. Smith wr i tes a regular column for its sister publication Cannabis Science and Technology and sits on its editorial board. He earned his PhD in phys ical chemis t r y f rom Dartmouth College. He can be reached at: [email protected]

For more information on this topic, please visit our homepage at: www.spectroscopyonline.com


P rotein cr ysta l l izat ion with X-ray di f f ract ion (XRD) is the preferred means of obtaining high-resolution structures of proteins and their com-

plexes. These structures are crucial for drug design, yet methodological barriers to their determination still exist (1−4). Efforts to make the structure determination process more efficient include data collection methods, such as the synchroton beam lines (5), detection via sensitive focal plane arrays, and improved cryogenic and mounting procedures for crystals. However, one key step in successful high-throughput X-ray crys-

tallography continues to be a bottleneck; namely, the screening and optimization of the conditions required to produce well-diffracting crystals. This process in-cludes the optimization of multiple variables, including, but not limited to, precipitant, pH, temperature, protein sequence, and concentration (4). New techniques are needed if we hope to increase the rate of success of this step, which is currently ~18%.

There are three stages in the crystallization process: nucleation, crystal growth (which is governed by diffu-sion), and cessation of crystal growth. Crystal growth

Sherly Nieves and Belinda Pastrana

X-ray diffraction of crystallized protein continues to be the preferred means of obtain-ing high-resolution structures of proteins and their complexes. These structures are cru-cial for drug design, but the screening and optimization of the conditions that produce well-diffracting crystals represent a bottleneck in the structure determination process. In this study, a quantum cascade laser (QCL) infrared microscope was used to deter-mine protein aggregation, distinct from self-association, which is crucial to the success of any crystallization effort. Hyperspectral images of an aliquot from a vapor diffusion hanging drop crystallization screen were acquired over a small temperature range (30–38 ºC), at intervals of 2 ºC. QCL infrared (IR) spectral data were subjected to 2D IR correlation analysis to describe Homo sapiens (Hs) centrin 2(E32A)-Sfi1p21 complex (1:1.5 molar ratio) and the selective aggregation of the target peptide. To our knowl-edge, this is the first time such a level of molecular understanding has been achieved.

Quantum Cascade Laser Infrared Microscopy and 2D IR Correlation Spectroscopy Improves Crystallization Screening of a Protein Complex


Table I. Summary of QCL IR spectral band assignments

Protein Wavenumber (cm-1) Band Assignment

Protein Backbone

Hs centrin 2(E32A) 1672.0 Apo-CaBS loop

Hs centrin 2(E32A) 1682.6 Holo-CaBS loop

Hs centrin 2(E32A) 1662.5 Hinge loop

Hs centrin 2(E32A) 1652.5 Helical

Hs centrin 2(E32A) 1632.0 β-Sheet

Hs Sfi1p21 1613.0 Aggregation

Side Chains

Hs centrin 2(E32A) 1717.5 Phe (overtones (p-aromatic))

Hs centrin 2(E32A) 1702.5 Phe (overtones (p-aromatic))

Hs centrin 2(E32A) 1577.0 Asp- (νas(COO-)

Hs centrin 2(E32A) and Hs Sfi1p21

1550.3 Glu- (νas(COO-)

Hs Sfi1 p21 1597.2 His (C=C)

Figure 1: Ribbon model representation for the yeast centrin homologue known as CDC31 CDC31-Sfi1p complex, based on high resolution X-ray structure determination PDB ID: 2DOQ (7). The centrin (light gray) wraps around the Sfi1p peptide (dark gray central helix), causing the peptide to adopt its helical conformation. Centrin, a calcium-binding protein belonging to the EF-hand superfamily, is also complexed with calcium, shown as orange spheres.

requires specif ic interactions be-tween individual protein molecules that lead to an organized crysta l lattice. If the crystallizability of the protein of interest is dependent on the type of protein aggregate gen-erated during the crysta l l izat ion process, then our proposed method should a l low for this assessment. Conceptually, the aggregate should be one of self-association, in which chemical associations driven by the weak interactions between proteins or the precipitating agent are criti-cal to maintaining the protein’s con-formational stability. Self-associa-tion in principle would account for the generation of a crystal lattice, while excluding solvent molecules from the immediate hydration shell of the protein and increasing the in-f luence of the surface charge of the protein to account for the crystal-lization event. The evaluation of the crystallization process of proteins and protein complexes using this direct and highly informative ap-proach would allow for further un-derstanding of the process of crys-tallization, and should be explored even further during crystallization optimization as part of the screen-ing process. It may also be possible to evaluate the precipitant’s role in changing the dynamics of the pro-

tein’s interaction with its aqueous environment, causing a synergistic effect that will lead to the successful crystallization of the protein or the protein complex.

Here, we have employed quantum cascade laser infrared microscopy (QCL IRM) for the determination of protein aggregation, distinct from self-association, under various crys-tallization conditions. The platform includes a QCL infrared (IR) micro-scope with enhanced signal-to-noise rat io, sampling accessories, cel ls under thermal control, and software for QCL IR spectra l analysis and

assessment of crystallizability. Hy-perspectral images (HSI) within the mid-IR spectral region of 1800–1000 cm-1 provide data distinguishing an aggregate from a crystal, as well as a rough determination of the crystal or aggregate size. The QCL IR spec-tral data are subjected to 2D IR cor-relation spectroscopy analysis (6,7) to determine the extent and molecu-lar mechanism of aggregation and crystallization under thermal stress.

A high-resolution structure for a yeast homologue of centrin known as CDC31 in the form of CDC31-Sf i1p complex ex ists as PDB ID: 2DOQ (Figure 1) (8), and our group has extensively studied Hscentrin isoforms using multiple biophysical techniques, including dif ferential scanning calorimetry and circular dichroism, Fourier transform infra-red (FT-IR), and two-dimensional (2D) IR correlation spectroscopies (9). The current study involved a complex between a centrin variant (Hscentrin 2 [E32A]) and its target peptide, Sfi1p21, which is presented as proof-of-concept. In this work, 2D IR correlation spectroscopy pro-vided molecular insight into which of the components was aggregated and how the initia l interaction of Sfi1p21 with the centrin variant led to the selective aggregation of the peptide. To our knowledge, this is the f irst t ime that such a level of molecular understanding has been


achieved for a crystal screen. The results obtained demonstrate the breakthrough capabi l it ies of the QCL IR microscope along with 2D IR correlation analysis for the eval-uation of the crystallizability of a protein or protein complex.

ExperimentalCrystal Screen SetupWe performed a vapor di f fusion hanging drop crysta l screen con-ta ining 2 µ L of t he Hscentr in 2 (E32A) variant and HsSfi1p21 syn-thetic peptide (1:1.5 molar ratio) for a 5 mM total protein concentration and 2 µL of the index screen from Hampton Research were placed in a vapor dif fusion setup tray from t he Easy Xta l Tool f rom Qiagen Sciences. The visual evaluation of the screen was done using a Nikon SMZU microscope for selection of potentially diffracting crystals. We identified one condition to contain both amorphous and microcrystals: condition #31 of the index screen. This crystal lization solution con-tained 0.1 M Tris at pH 8.5, 0.5% (w/v) polyethylene g lycol mono-methyl ether 5000, and 0.8 M potas-sium sodium tartrate tetrahydrate.

A 1 µL a l iquot from a selected hanging drop, identified to contain amorphous or microcrysta ls, was drawn from the hanging drop, placed in a predef ined wel l of a custom milled calcium f luoride slide, and covered with a fully polished crystal to make up the slide cell. The slide cell was then placed in a heated ac-cessory with accurate thermal con-trol, and HSIs were acquired within

the temperature range of 30–38 ºC with 2 ºC intervals and 4 min equil-ibration periods using the Protein-Mentor, a QCL IR microscope from Protein Dynamic Solutions, Inc.

Hyperspectral Image (HSI) AcquisitionThe QCL infrared microscope ac-quires the HSIs, allowing for a linear response microbolometer focal plane array (480 x 480 pixels) detector to be used. A low magnification objec-tive (4x), with a numerical aperture (NA) of 0.3 and a 2 x 2 mm2 f ield of view, provides a pixel size of 4.25 x 4.25 µm2. The HSIs are composed of 223,000 QCL IR spectra and were collected at 4 cm-1 resolution within the spectra l region of 1750−1500 cm-1. To prevent coherence effects due to QCL IR f luctuat ions, the

background was collected at each temperature once thermal equilib-rium (4 min) was achieved.

QCL IRM spectra l overlay and 2D IR correlation analysis for this sample was performed using the Ki-netics program of MATLAB, which was generously provided by Dr. Erik Goormaghtigh from the Free Univer-sity of Brussels, Belgium. However, we have developed fully automated Correlation Dynamics software to analyze an array of samples.

Results and DiscussionSize of the AggregateHSIs for the Hscentrin 2 (E32A)-Sfi1p21 sample at five different tem-peratures (30, 32, 34, 36, and 38 ºC) are shown in Figure 2. Initially, mi-crocrystals were observed in a cir-cular arrangement, due to the dis-pensing of the pipette, which allows for the amorphous or microcrystals to f low from the tip to the well dur-ing loading (Figure 2). A microm-eter sca le was used to determine the size of the aggregates observed in the HSI; the size of the aggregate was determined to be 30 µm x 40 µm at 30 ºC. Furthermore, as the temperature was increased, the ag-gregate continued to grow, reaching 300 µm x 400 µm in size at 38 ºC. The corresponding QCL IR spectra

Figure 2: HSIs of Hscentrin 2 (E32A)-Sfi1p crystallization screening sample. HSIs as a function of temperature (30–38 oC), where the amorphous aggregates were observed to grow. These hyperspectral images are generated from 223,000 QCL IRM transmission spectra.

T = 30˚C T = 32˚C T = 34˚C T = 36˚C T = 38˚C

Figure 3: QCL IRM spectra and 2D IR correlation spectra of Hscentrin 2 (E32A)-Sfi1p crystallization screening sample. (a) The corresponding QCL IRM spectral overlay with enhanced S/N ratio within the spectral region of 1750–1485 cm-1. Spectral features observed are commonly associated with protein aggregation. (b,c) 2D IR correlation plots using the QCL IRM spectral data. (b) The synchronous plot includes an auto peak due to protein aggregation as the main event during the thermal stress, which correlates with the HSIs shown in the top row. The color bar defines the synchronous intensity changes within the plot. (c) The asynchronous plot provides the enhanced resolution needed for the molecular understanding of the mechanism of aggregation for this crystallization condition. Also, shown is the color bar that defines the intensity changes within the asynchronous plot.

(a) (b) (c)QCL IRM Spectra Synchronous Asynchronous

Absorbance vs Wavenumber cm-1 Wavenumber cm-1, ν1 vs Wavenumber cm-1, ν2

1750 1700 1650 1600 1550 15000

1750 1700 1650 1600 1550 15001750

1700

1650

1600

1550

1500

1750

1700

1650

1600

1550

1500

1700 1600 1500

0.2

0.15

0.1

0.05

0

0.08

0.06

0.04

0.02

-0.02

-0.04

0

-0.06

-0.08

T = 30˚C

T = 32˚CT = 34˚C

T = 36˚C

T = 38˚C


(Figure 3a) were indicative of the presence of aggregate, thus confirm-ing the HSIs (Figure 2). Specifically, the full width at half height of the amide I band and the presence of a shoulder at 1613 cm-1 are consis-tent with the presence of aggregates. While 2D IR correlation analysis yielded the typical synchronous plot observed for a protein aggregation process during thermal perturba-tion (as indicated by the prominent auto peak 1613 cm-1 present in the synchronous plot), the asynchro-nous plot suggested an unfolding event during the aggregation pro-cess, as indicated by the cross peaks located within 1613 cm-1.

Band Assignments2D IR correlation spectroscopy was performed to improve our under-standing of the backbone dynamics and side chain interactions involved in the aggregation process within a crysta l l ization condit ion. Band assignments were made using the amino acid sequence of Hscentrin 2, obtained from the National Cen-ter for Biotechnology Information database (NCBI accession number: EAW72900). The band assignments are summarized in Table I. The vi-brat iona l modes associated with Hscentrin 2 (E32A) include the p-

aromatic overtones for phenylalanine residues (1702.0 and 1717.5 cm-1) and calcium binding site loops (1682.6 and 1672.0 cm-1). EF-hand proteins a lso have hinge loops, which we have assigned to 1662.5 cm-1. We also assigned the helical com-ponent (1652.5 cm-1), and the shor t β-sheet s e g m e n t s (1632.0 cm-1). Finally, the as-partate carbox-ylate st retch-ing side chain v i b r a t i o n a l mode ν (COO-), mainly located within the cal-cium binding s i t e s w i t h i n centrin, was as-signed to 1577.0 cm-1. Shared vi-brational modes were the gluta-mate carboxylate side chain mode ν(COO-) (1550.3 cm-1), found in b ot h c e nt r i n and the Sfi1p21

target peptide. Exclusive to Sfi1p21 were two vibrational modes due to aggregation (1613.0 cm-1) and the two sets of contiguous histidine res-idues with the stretching vibrational mode ν(C=C) (1597.2 cm-1). These band assignments are consistent with the secondary structure infor-mation available in the high-resolu-tion crystal structure of the complex and with previous work from our laboratory (8,9). Also, the side chain modes and their molar extinction coeff icients have been determined (10) and reviewed (11,12).

2D IR Correlation AnalysisThe protein sample is in an aque-ous environment and the molar ex-tinction coefficient of pure H2O is high at 55.5 M-1 cm-1, yet, like any protein-containing sample, it has effectively diluted the contribution of H2O in the overal l absorbance spectrum. Also important is the de-creased pathlength, allowing for the management of samples that exhibit

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Figure 4: Schematic representation of the sequential order of molecular events that led to the selective aggregation of the Sfi1p21 peptide. (a) Sequential order of molecular events. The 2D IR correlation analysis resulted in the elucidation of molecular events that were exclusive to either the centrin variant or the Sfi1p21 peptide, which provided direct evidence of the aggregation process. (b) Helical wheel representation of Sfi1p21 typically adopted when in complex with centrin (8). (c) Amino acid sequence of Sfi1p21 target peptide; the underlined residues are the hydrophobic triad (L19L23W26) required for binding.

(a)Sequential Order of Molecular Events

(c)

(b)

Tem

per

atur

e

Stab

ility

1 10 20 30Hs S�1p21 A A VQ Q Q Q Q QL L L L L L LA W TL GR R R R K HH H HE E H

1550.3

Glu-

ν(COO-)

ν(COO-) β-sheet

1577.0

Asp- 1632.0

\

\

\

\

\

\

\

1613.0

Aggregation 1597.2

His 1652.5

helical 1662.5

hinge loop 1672.0 1682.6 1702.0 1717.5

CaBSloop

CaBSloop

Pheovertones

Phe(p-aromatic)Hs cen2(E32A): Hs S�1p21 (1:1.5, mol ratio)

(T = 30 - 38˚C)

L

L

W


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high absorptivity. Finally, the QCL IRM transmission absorbance spectra have an enhanced signal-to-noise ratio allowing for the difference spectra approach to be used, thus overcoming the common deterrents of using IR spectroscopy for aqueous samples. The QCL IR spectrum for the region of interest at the initial tem-perature (30 ºC) was subtracted from all subsequent spectra, thereby generating the difference spectra. It is to this data set that the 2D IR correlation function is applied to generate the synchronous and asynchronous plots.

Difference spectra used in 2D IR cor-relation spectroscopy are defined as:

where Ā(υj) is the initial spectrum of the data set to generate the covariance spec-tra. Synchronous 2D correlation intensi-ties of the covariance spectral data are defined by:

where the resulting correlation intensity Φ(ν1,ν2) as a function of two independent wavenumber axes, ν1 and ν2, is the syn-chronous plot.

Asynchronous 2D correlation inten-sities of the covariance spectral data are defined by:

where the term Nij is the element of the so-called Hilbert-Noda transformation matrix, given by:

These 2D IR correlation plots provide enhanced resolution of the peak intensity changes and positions within the spec-tral region of interest that were due to the temperature increase. Specifically, the synchronous plot was used to establish relationships between peaks that change in the same phase with one another (Fig-ure 3b). This plot has peaks on the diago-nal known as auto peaks, which provide the main peak intensity changes. In addi-tion, peaks off the diagonal provide the relationship between the auto peaks and are known as cross peaks. In this case,

as shown in Figure 3b, the only intensity change was at 1613 cm-1, and was due to the aggregation process. The synchronous plot did not contain any negative peaks, thus simplifying the analysis even fur-ther. The asynchronous plot (Figure 3c) establishes the relationship between peaks that are changing out-of-phase from one another. In general, there are no peaks on the diagonal and only cross peaks are observed. This plot provides enhanced resolution, and is used to determine the sequential order of molecular events that describe the aggregation process during the temperature perturbation. The order of molecular events was determined by following Noda’s rules (6,7). Keeping in mind that asynchronous plots are sym-metrical in nature, and by convention we always refer to the top triangle for analysis, we apply the rules as follows: I. If asynchronous cross peak ν2 is posi-

tive, then ν2 is perturbed prior to ν1 (ν2 → ν1).

II. If asynchronous cross peak ν2 is nega-tive, then ν2 is perturbed after to ν1 (ν2 ← ν1).

III. If the corresponding synchronous cross peak is positive, then the order of the events is established using the asynchronous plot (rules I and II).

IV. However, if the corresponding syn-chronous cross peak is negative and the asynchronous cross peak is posi-tive, then the order is reversed.

The order of events can be established for each peak observed in the ν2 axis (Fig-ure 3c). In this specific case, the only two rules that apply are rules I and II. Our group has successfully applied 2D IR cor-relation spectroscopy to the study of nu-merous proteins and peptides, including a comparative analysis of related human centrins (9).

Description of the Molecular Aggregation ProcessFigure 4 gives a schematic representa-tion of the molecular events that lead to the initial interaction of Sfi1p21 with Hscentrin 2 (E32A) and the selective ag-gregation of the target peptide, based on the features of the asynchronous plot. Initially, the glutamate residues (1550.3 cm-1), which could be attributed to either

Hscentrin 2 (E32A) or the Sfi1p21 target peptide, were perturbed, followed by the aspartates (1577.0 cm- 1), which are ex-clusive to centrin and are located within the calcium binding sites. Next, the short β-sheets (1632.0 cm-1) that are located close to the calcium binding sites were perturbed, presumably due to the initial interaction with the target peptide. At this point, the optimum orientation was not achieved between the Hscentrin 2 (E32A) and Sfi1p21, leading to the selective aggre-gation (1613 cm-1) of the target peptide via its contiguous histidine residues (1597.2 cm-1), located both in the middle of the sequence (H13-H15, ε = 210 M-1 cm-1) and near the C-terminal end (H29-H30, ε = 140 M-1cm-1). These histidine vibrational modes are exclusive to Sfi1p21, and the histidine pairs are presumably involved in the aggregation process as two sepa-rate but simultaneous molecular events during the thermal stress. We theorize that the Hscentrin 2 (E32A) variant may predispose the initial interaction with Sfi1p21 to the EF-hand motif located in the N-terminal end instead of the EF-hand motif located within the C-terminal end, preventing the optimum orientation that would normally lead to Sfi1p21 adopting a helical conformation. The helical region of Hscentrin (E32A) (1652.5 cm-1) was then perturbed, followed by opening of the EF-hand motif, which perturbs the hinge loop (1662.5 cm-1) and associated calcium binding site loops (1672.0 and 1682.6 cm-1, which represent the apo- and holoforms of the calcium binding sites, re-spectively). The opening of the EF-hand exposes the phenylalanine residues (1715 and 1702 cm-1, p-aromatic overtones) to their aqueous environment, leading to an unstable centrin variant-target peptide in-teraction which resulted in the selective aggregation of Sfi1p21.

ConclusionThe QCL IRM platform, combined with 2D IR correlation spectroscopy, has proven useful for obtaining a molecular description of the aggregation distinct from the desired self-association of a protein-peptide complex during crystal screening. A successful crystal screen would include the self-association of pro-teins or the association observed in protein complexes, which would lead to nucleation and

[1]

[2]

[3]

[4]


crystal growth. 2D IR correlation spec-troscopy would be capable of distinguish-ing between the two types of processes, while providing an unprecedented level of molecular detail. This approach may provide valuable information leading to increased success rates for the crystalliza-tion of protein complexes by identifying the variables that lead to the optimum chemical and physical properties associ-ated with well-diffracting crystals.

Author ContributionsCrystallization screens were performed and HSIs acquired by Sherly Nieves. The 2D IR spectral data analysis was performed by Belinda Pastrana. Fig-ures were generated by Sherly Nieves.

AcknowledgmentsThe authors would like acknowledge Dr. Melissa Stauffer (Scientific Editing Solutions, Walworth, WI) for editing the manuscript.

FundingThe work presented herein was made possible by support from the National Science Foundation (SBIR PII Award 1632420 [BP]).

DisclosuresBel inda Pastrana is t he CEO of Protein Dynamic Solut ions, Inc. and a Professor in the Department of Chemistry at the University of Puerto Rico-Mayagüez, Mayagüez, PR. Sherly Nieves is a scientist at Protein Dynamic Solutions, Inc.

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(5) R.A. Judge, S. Takahashi, K.L. Longe-necker, E.H. Fry, C. Abad-Aapatero, and

M.L. Chiu, Cryst. Growth Des. 9, 3463–3469 (2009).

(6) I. Noda, Vibrat. Spectrosc. 36, 143–165 (2004).

(7) I. Noda, J. Mol. Struct. 1069, 23–49 (2014).(8) S. Li, A .M. Sandercock, P. Conduit,

C.V. Robinson, R.L. Williams, and J.V. Kilmartin, J. Cell. Biol. 173, 867–877 (2006).

(9) B. Pastrana-Rios, J. De Orbeta, V. Meza, M. Reyes, D. Narváez, A .M. Gómez, A. Rodríguez Nassif, R. Al-modovar, A . Díaz Casas, J. Robles, A.M. Ortíz, L. Irizarry, M. Campbell,

and M. Colón, Biochemistry 52, 1236–1248 (2013).

(10) S.Y. Venyaminov and N.N. Kalnin, Biopolymers 30, 1243–1257 (1990).

(11) J. Bandekar. Biochim Biophys Acta, 1120, 123–143 (1992).

(12) A . Bar th. Biochim. Biophys. Acta, 1767, 1073–1101 (2007).

Sherly Nieves and Belinda Pastrana are with Protein Dynamic Solutions, Inc., in Wakefield, Massachusetts. Direct correspondence to: [email protected] ◾


http://www.piketech.com




C hemometrics in Spectroscopy is a collection of column articles that the authors published in Spectroscopy over a period spanning more than

two decades. Each article is generally arranged as a chapter in the book, and chapters dealing with the same or similar topics are arranged closely as a section block rather than following the original sequence in the mag-azine. Although each article or series of articles only discusses one specif ic topic, collectively, the articles form a comprehensive reference that is a valuable source for readers wanting to learn chemometrics, especially with its applications in spectroscopy.

The book is div ided into 128 chapters, split over 24 sections, and a vast 1040 pages. Some might ask whether it is real ly necessary to wade through such a weighty tome to learn chemometrics. The answer could be “no,” with the argument that for the four multivariate methods delineated in the book, multiple linear regression (MLR), principal component regres-sion (PCR), partial least squares (PLS) regression, and classical least squares (CLS), each could be described in no more than perhaps two pages in the matr ix form. Furthermore, user-friendly software packages are readily available and generally easy to use, and in many cases it might only take a few mouse clicks to build a calibration model. At the end, if the predic-tions from the calibration model show marked differ-ences from the actual data, one can apply the famous “garbage in, garbage out” principle to declare the data garbage, which is not uncommon to see nowadays in real practice.

However, if one believes that good data should not be discarded as garbage without good reason, espe-cially when months, or even years, of effort may have been invested to collect them, it is then worthwhile to read this book, particularly the sections about CLS. Readers will f ind that even very experienced chemo-metricians could not develop the right model the first time, simply because the wrong unit of measure was

chosen for concentration. CLS is the simplest method among a l l the methods presented in the book, and could easily be described by two or three equations. However, the authors spend 14 chapters discussing the method and its applications in considerable length. The authors could have simply told the readers that volume percent or a comparable unit should be used as the concentration unit rather than weight percent. In-stead, the authors spend many pages describing their troubleshooting process along with the identif ication and verif ication of the root cause. Readers wil l per-haps benefit more by reading the lengthy description of the problem-solving process, rather than learning the simple reason in one sentence.

One cannot avoid mathematics when learning che-mometrics. Mathematics is needed to understand the equations in the multivariate methods and also many statistical methods for evaluating raw data and model predictions. A beginner does not need much mathemat-ics background to learn chemometrics using this book, however. Matrix a lgebra is introduced in Section 1, and then further expanded in Section 2, together with the description of MLR. Analytical geometry, which is needed for the understanding of regressions, is intro-duced in Section 4. The rather detailed description of the mathematical methods using simple language may seem somewhat tedious to an experienced reader, but is certainly beneficial to someone who is less comfortable with equations. PCR and PLS are introduced similarly by giving the detailed calculations using the simplest possible data set. The concept of principal components is perhaps the most difficult to understand for people who are new to multivariate analysis, or do not have a strong background in matrix algebra. To help readers understand principal components, the authors present an approach that uses only elementary algebra to derive the algorithm. This approach is long, complicated, and rather tedious, but could enable readers who are new to this topic to understand every single step intuitively.

Husheng Yang

Howard Mark and Jerry Workman, Jr.

Book Review: Chemometrics in Spectroscopy, 2nd Edition, by


Stat ist ics is a very big area for chemometrics, with many methods and books avai lable. The authors have very strong backgrounds in stat ist ics , and have cer ta inly in-c luded necessa r y a nd adequate statistical concepts or methods for chemometr icians . Design of ex-periments (DoE) or experimental design, including analysis of var iance (ANOVA) is intro-duced in Section 3 using easy-to-understand language. DoE is a ver y relevant topic , and the introduction is rather high level for such a significant area. Section 7, titled “Collaborative Laboratory Studies,” contains a detai led description of sta-tistical methods for comparing different analytical results and methods, although these meth-ods can also be found in regu-lar analytical chemistry books. Sect ion 10, “Goodness of Fit Statistics” describes statistical tools needed for analyzing lin-ear regression. Section 20, “Sta-tistics,” contains articles intro-ducing the three foundations of the subject, which are useful for people without much statistical background. Useful discussions of statistics can also be found in Sec-tion 12 (“Connecting Chemometrics to Statistics”) and Section 15 (“Clin-ical Data Reporting”).

Multivariate methods such as PLS can be, and have been, used on data from various scientif ic disciplines when the data can be arranged in formats that are suitable as inputs and outputs of the methods. The multivariate methods perform the same calculations regardless of the source of the data, whether the data a re f rom chemist r y or econom-ics. However, the methods for pre-processing the raw data to render them suitable for the multivariate methods to y ield the best predic-tion model can be very dif ferent, depending on the data source. The data a nd exa mples used in t h is book are mainly related to near-inf ra red (NIR) or mid-inf ra red (mid-IR) spectra. There are no dis-

cussions of infrared spectrometers or spectroscopic measurements in the book, perhaps because the au-thors assumed that the readers of Spectroscopy a lready had the nec-essar y k nowledge or experience. Readers who are unfamiliar with IR spectroscopy would need to ob-tain some basic knowledge through

other sources to get the most benefit from this book.

The mathemat ics descr ibed in this book are very basic, such as Section 1, “Elementary Matrix Al-gebra.” Conversely, the chemomet-rics-related discussions on spectro-scopic data are rather advanced, a ref lect ion of the k nowledge and experience that the authors possess through many years of practicing and writing in this field. The book contains deep discussions of deriva-tives, linearity, noise, and outliers, all of which are critical for building good chemometric models.

One very important area, perhaps as important as building the origi-nal calibration model, is calibration transfer. IR spectroscopic methods should be able to replace some tra-dit iona l wet chemistr y methods, such as chromatographic methods that consume solvents and generate significant volumes of waste, some-

thing that is tru ly needed in the green chemistry era. Many times it was the maintenance of a calibra-tion model, rather than the initial cost of building the model, that pre-vented the acceptance of the spec-troscopic methods within commer-cial manufacturing environments. Transferring calibration models is

not impossible from a techni-cal standpoint but the required knowledge of chemometrics is generally not sufficiently wide-spread. Calibration transfer is well covered in three sections or ten chapters in the book. All aspects of calibration transfer, such as reference standards, instrument performance, and modeling a lgorithms are de-scribed in the book. A review of published methods on cali-brat ion t ra nsfer i s a l so i n-cluded.

S i n c e t h e b o o k i s w r i t-ten in colu mn format f rom the authors’ own columns in Spectroscopy, it contains lots of authors’ comments. These com ment s were not a lways agreed upon by other experts in this field. As an example, the

initial article on linearity in Section 6 included discussions with several other experts, when a simple noise-free synthetic nonlinear set of spec-tra was used to compare MLR, PCR, and PLS. The discussions revea l that, even with such a very simple set of spectra of a single component, expert chemometricians could use different approaches and interpret the results dif ferently. Building a multivariate calibration model com-prises a fixed sequence of steps, but there are various approaches that can be taken at each step. The com-binations of these approaches across all steps can be numerous, resulting in many different models. Reading these discussions may inspire more efforts on building better calibra-tion models rather than concluding that the raw data are garbage!

Some improvements can perhaps stil l be made in future versions or in the book ’s companion website,


For more information on this topic, please visit our homepage at: www.spectroscopyonline.com

a publisher-hosted resource where the authors can add additional ma-terial in electronic format. Color f ig ures a re a l ready ava i lable on the companion website if they were printed as black-and-white in the hard copy. Since the book was not writ ten as a textbook by f irst in-tent, it does not provide example problems for the readers to practice with. The book does provide data or scripts that are in MATLAB or Mathcad format, however. Maybe in future edit ions or on the com-panion website, the authors can add Py thon or R versions of the data and scripts to benefit readers who do not have access to the named software packages.

For such a large book that en-compasses so many diverse topics, it would help readers navigate the information presented if the tit les of some chapters , a lt houg h not many, could be updated to more closely ref lect their contents and made available on the book ’s com-panion website. For example, the

book contains 15 chapters in Sec-tion 8, “Analysis of Noise.” Many types of noise and their effects on spectra are discussed, together with sophisticated equation derivations. However, the chapters themselves are simply named Part 1, Part 2 , and so on, up to Part 15. It would be very helpful to readers if the tit les indicated which type of noise is dis-cussed in each chapter.

Some minor topics or d iscus-sions are likely to be of particular interest to specific groups of read-ers, but, unfortunately, they cannot be found either from the table of contents or from the index. Aside from Section 15, “Clinical Data Re-porting,” there are three additional chapters that are particularly useful to users in the pharmaceutical in-dustry. However, readers would not know of their existence unless they happen upon them. Some readers might be comfortable to read the book sequentia l ly using the cur-rent arrangement of the chapters, but other readers will need to read

t he c hapter s in a dif ferent order, or wi l l on ly need to read some se-le c te d c h ap -ters. It might b e u s e f u l i f t h e a u t h o r s c a n p r o v i d e s o m e s t u d y g u i d e s , t a i -lored to d i f-ferent reader g r o u p s , a n d a dd t hem to t h e b o o k ’ s c o m p a n i o n website.

Neural net-work (NN) is one area that is mentioned, but not d i s -c u s s e d , i n the book. NN i s a u s e f u l method when dea l i ng w it h

nonlinear data. Simi larly, mult i-variate curve resolution (MCR) has found increased and successful use in recent years, but is not included in the book. MCR has the potential to remove the reliance on a primary analytical method for certain types of quantitative analysis. Both tech-niques are worth adding to future editions of the book. Classification of spectra may also need to be ex-panded in future editions.

T h is book cer t a i n ly cont a i ns suf f icient materia l for beginners to learn chemometrics and for ad-vanced users to strengthen their ski l ls for dealing with more chal-leng ing ca l ibrat ion problems or even to create innovative applica-tions in vibrational spectroscopy. Written original ly in journal col-umn format, the book contains lots of author’s comments, and very de-tailed explanations (in the author’s words, “to treat any topic at what-ever length is necessary”). Whether readers l ike the book or not may depend on whether the reader en-joys the writ ing style of the col-umn format, and if the reader can quick ly locate topics of interest . Readers might want to obtain some sample chapters, which should be possible through the publisher or past issues of Spectroscopy, to f ind out if they are comfortable with the writ ing style. An enhanced table of contents, or some type of study g uides , wou ld help readers f ind their desired contents faster.

Husheng Yang is a senior sci-enti f ic investigator and chemo-metrician at GlaxoSmithKline, in Collegeville, Pennsylvania. The review-er is not acting as a representative or agent of GSK. Direct correspon-dence to: [email protected] ◾

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UV-B and C LED wavelength accessoryDigital Light Lab’s PhotoDSC UB-B and C LED accessory is designed for the sample and reference cells of manufactur-ers’ differential scanning calo-rimeter, and has wavelength ranges from 265 to 340 nm. According to the company, the system offers continuous or pulse mode of operation, con-trol software, and built-in radiometer functionality, among other features. Digital Light Lab, Knoxville, TN. www.digitallightlab.com

Wire-grid polarizerMoxtek polarizers are designed for broadband UV-vis-IR wavelengths. According to the company, the polarizers have uniform performance over wide cone angles, are made from heat-tolerant inorganic materials, and can be exposed to temperatures up to 300 °C. Moxtek, Inc., Orem, UT.moxtek.com/optics-products

PolarizersREFLEX Analytical Corporation has a series of ultraviolet, visible, near infrared, mid-infrared, far infrared, and free-standing polarizers available. According to the company, the polarizers operate across a spectral range of 0.30–30 microns; materials include BaF2, CaF2, fused silica, Ge, glass, polyethylene, KRS-5, and ZnSe.REFLEX Analytical Corp., Ridgewood, NJ. www.reflexusa.com/polarizers.html

IR and Raman spectral ID softwareInfrared (IR) and Raman spectral identification packages are available from Bio-Rad Laboratories. According to the company, its KnowItAll Identification Pro and KnowItAll Raman Identification Pro software are combined with its KnowItAll spectral libraries, and includes tools for multi-technique complementary analysis and apps to identify, process, and manage IR and Raman spectra. Bio-Rad Laboratories, Hercules, CA. www.bio-rad.com

Raman microscopeThe RM5 Raman microscope from Edinburgh Instruments is designed for analytical and research purposes. According to the company, the truly confocal microscope has integrated narrowband Raman lasers, a five-position grating turret, integrated detectors, internal standards and auto-calibration, a four-position Raman filter turret, and more. Edinburgh Instruments, Livingston, UK. www.edinst.com

WDXRF spectrometerRigaku’s ZSX Primus 400 sequen-tial wavelength dispersive X-ray fluorescence (WDXRF) spec-trometer is designed to handle very large or heavy samples, and offers micro-mapping capabilities. According to the company, the spectrometer can adapt to varying specific sample types and analysis needs, accepts samples up to 400 mm diameter, 50 mm thick, and 30 kg mass, and is suited for analyzing sputtering targets, magnetic disks, or multilayer film metrology or elemental analysis of large samples. Rigaku Corporation, Austin, TX. www.rigaku.com

MALDI digital ion trap mass spectrometerShimadzu’s MALDImini-1 digital ion trap mass spectrometer is designed to fit in a space the size of a piece of paper, while allowing high-sensitivity measurements and detailed structural and quali-tative analyses over a mass range, even with sub-microliter sample volumes. According to the com-pany, the system’s digital ion trap uses rectangular wave RF to allow ion trapping up to 70,000 Da. Shimadzu Scientific Instruments, Columbia, MD. www.ssi.shimadzu.com

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Microwave digestionMilestone’s UltraWAVE microwave digestion system uses the company’s single reaction chamber technology for metals digestions. According to the company, the system uses a single pressurized vessel for all samples, allowing for simultaneous digestion of up to 22 samples. The system reportedly can accommodate a maximum temperature of 300 °C, and pressure of 199 bar. Milestone, Inc., Shelton, CT.www.milestonesci.com/ultrawave

ULF Raman filtersBragGrate Raman filters from OptiGrate are designed to enable access to Stokes and anti-Stokes Raman bands in the ultralow terahertz frequency range down to 5 cm-1. According to the company, laser line cleaning and light rejection notch filters are provided, and the filter production line is extended to cover many standard and custom laser wavelengths from 405 nm to 1550 nm. OptiGrate, Oviedo, FL.www.optigrate.com

Optics, prisms, and polarizersREFLEX Analytical’s selection of optical materials are designed for use in vacuum ultraviolet through far-infrared detection. According to the company, the materials are manufactured into transmission windows, lenses, viewports, beamsplit ters, attenuated total reflectance prisms, rods, hemispheres, and linear holographic infrared and free-standing wire grid polarizers. REFLEX Analytical Corporation, Ridgewood, NJ.reflexusa.com/noname.html

CRMs for spectrophotometer qualificationStarna’s range of certified reference materials (CRMs) for UV-visible spectrophotometer qualifications are designed to provide tailored solutions, advice, and support for a given situation. According to the company, the CRMs can help users of UV-vis spectropho-tometers comply with the new editions of U.S. Pharmacopeia Chapter <857> and European Pharmacopoeia Chapter 2.2.25. Starna Cells Inc., Atascadero, CA.www.starnacells.com

Cuvette holderThe Square One cuvette holder from Ocean Insight is designed for accurate, repeatable absorbance and fluorescence measurements. The cuvette holder reportedly has three collimating lenses with fiber optic connectors, and an integrated cover to reduce ambient light. It includes filter holders, a built-in mirror, cover, and three collimating lenses with fiber optic connectors.Ocean Insight, Largo, FL. https://oceanoptics.com/product/square-one-cuvette-holder/

All-purpose diamond ATR accessoryThe IRIS diamond attenuated total reflectance accessory from PIKE Technologies is designed for infrared sampling of powders, gels, liquids, solids, and more. According to the company, the accessory is suit-able for research, QA/QC, and sample identification.PIKE Technologies, Madison, WI.www.piketech.com

Raman analyzerThe Virsa Raman analyzer from Renishaw is designed for detailed remote analysis. According to the company, the analyzer is a fiber optic coupled spectroscopy system that includes a spectrometer with one or two internal lasers, with the dual excitation option enabling users to avoid fluorescence by switching between wavelengths at the touch of a button. Renishaw, West Dundee, IL.www.renishaw.com

Software for high-throughput experimentationThe Katalyst D2D software application from ACD/Labs is designed as a web-based digital environment that integrates informatics systems, laboratory hardware, and analytical instruments. According to the company, the chemically aware application eliminates manual data transcription between systems. ACD/Labs Toronto, Ontario, Canada.www.katalystd2d.com

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Sample introduction system for ICP–MSGlass Expansion’s single-cell sample introduction system for ICP–MS is designed to enhance the fundamental understanding in cellular biology, oncology, and drug discovery. According to the company, a high-efficiency, low uptake, concentric glass nebulizer introduces single-cell suspensions, without compromising cell integrity. Glass Expansion, Pocasset, MA. www.geicp.com

Microwave digestion systemThe CEM MARS 6 microwave digestion system is designed to digest challenging samples for trace metals analysis. According to the company, the system has hundreds of preprogrammed methods and vessel options for high throughput and difficult samples. CEM Corporation, Matthews, NC.www.cem.com/mars6

In-situ reaction monitoring analyzerABB’s MB-Rx in-situ reaction monitoring analyzer is designed to provide plug-and-play analyses for research laboratories and pilot plants. According to the company, the analyzer offers chemists direct access to real-time experiment data via an insertion probe and an intuitive software interface. ABB Measurements & Analytics, Quebec, Canada.www.abb.com/analytical

Diffraction databaseThe PDF-4+ 2019 database from ICDD is designed for phase iden-tification and quantitative analy-sis. According to the company, the database includes 412,083 entries with digital patterns for use in total pattern analysis; 312,395 entries with I/Ic values for quantitative analysis by refer-ence intensity ratio; and 311,225 entries with access to atomic coordinates sets for quantitative analysis by the Rietveld method. International Centre for Diffraction Data, Newtown Square, PA. www.icdd.com

TF fiber probe application noteAn application note from art photonics demonstrates an improved transflection (TF) fiber probe design, and shows the probe’s increased functionality in experimental and industrial applications. According to the company, possible applications of the probe range from biopharmaceutical analysis, real-time reaction monitoring, analytical characterization, and the production and development of biofuels. art photonics, Berlin, Germany.www.artphotonics.com

EBSD patterns simulations softwareOIM Matrix software from Ametek EDAX is designed to provide dynamic diffraction-based electron backscatter diffraction (EBSD) pattern simulations and dictionary indexing capabilities. According to the company, the software allows users to simulate EBSD patterns based on the physics of dynamical diffraction of electrons. EDAX, Inc., Mahwah, NJ.www.edax.com

X-ray system for XRFAmptek’s Mini-X2 miniature X-ray tube system is designed to simplify the X-ray fluorescence (XRF) process by providing a grounded anode, USB control of current and voltage, a collimator mount, and ease of operation. According to the company, the system is optimized for compact XRF applications. Amptek, Inc., Bedford, MA.www.Amptek.com/products/mini-x2-ray-tube

Raman spectrometerThe Mira P Raman spectrome-ter from Metrohm is designed for material varication in regulated industries. According to the company, the spec-trometer is barely larger than a smartphone, and provides results in seconds. Metrohm USA, Riverview, FL.www.metrohmusa.com

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Calendar of EventsJanuary 202012–18 2020 Winter Conference on Plasma Spectrochemistry Tuscon, AZ http://icpinformation.org/2020_Win-ter_Conference.html

February 20201–6 SPIE Photonics West San Francisco, CA https://spie.org/conferences-and-exhibi-tions/photonics-west/?SSO=1 15–20 SPIE Medical Imaging Houston, TX https://spie.org/MI/conferencedetails/medical-image-processing 23–26 IFPAC 2020 North Bethesda, MD http://www.ifpacglobal.org

March 20201–5 Pittcon Conference & Expo 2020 Chicago, IL https://pittcon.org 22–26 ACS Spring National Meeting & Exposition 2020 Philadelphia, PA https://www.acs.org/content/acs/en/meetings.html site 27–29 ACS Central Regional Meeting (CERM) 2020 Columbus, OH https://www.acs.org/content/acs/en/meetings/regional/central.html 29–April 2 53rd Annual International Meeting of the ESR Spectroscopy Group of the Royal Society of Chemistry Manchester, United Kingdom https://http://www.esr-group.org/conferences/2020-conference-manchester

29–April 2 MSACL 2020 Palm Springs, CA https://msacl.org/index.php?header=MSACL_2020_US 31–April 3 Analytica Munich, Germany https://www.analytica.de/index-2.html

April 20206-9 Spring SciX 2020 Liverpool, United Kingdom https://springscix.org 13-17 Materials Research Society Spring Meeting 2020 Phoenix, AZ https://www.mrs.org/spring2020 26–30 SPIE Defense + Commercial Sensing Expo 2020 Los Angeles, CA http://spie.org/x6776.xml 28–30 Interphex 2020 New York, NY https://www.interphex.com

May 202010–15 CLEO 2020 San Jose, CA https://www.cleoconference.org/home 31–June 4 ASMS 2020 Houston, TX https://www.asms.org/conferences/annual-conference/annual-conference-homepage

June 202012 ACS Middle Atlantic Regional Meeting (MARM) 2020 New York, NY https://www.acs.org/content/acs/en/meetings/regional/middle-atlantic.html

28-July 1 ACS Northwest Regional Meeting (NORM) 2020 Bellingham, WA https://www.acs.org/content/acs/en/meetings/regional/northwest.html

July 202026–29 North American Chemical Residue Workshop 2020 Fort Lauderdale, FL https://nacrw.org/site 26–30 AACC 2020 Annual Meeting & Clinical Lab Expo Chicago, IL https://www.aacc.org

August 20206–10 Denver X-ray Conference 2020 Rockville, MD http://www.dxcicdd.com 18–20 ACS Northeast Regional Meeting (NERM) 2020 Rochester, NY https://www.acs.org/content/acs/en/meetings/regional/northeast.html

October 202011–16 SciX 2020 Conference Reno-Sparks, NV https://www.https://facss.org 12–15 American Council of Independent Laboratories Long Beach, CA http://www.acil.org 13–14 Gulf Coast Conference Galveston, TX https://www.gulfcoastconference.com


Short CoursesFebruary 202024-28 Surface Analysis 2020 Tampa, FL www.surfaceanalysis.org/schedule.html

March 20201-5 Introduction to Gas Chromatography/Infrared Spectroscopy Mass Spectrometry Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Essentials of LC-MS Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Introduction to X-ray Photoelec-tron Spectroscopy (XPS) Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Elemental Analysis via Laser Induced Breakdown Spectroscopy and X-ray Fluorescence Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Introduction to Mass Spectrometry including Biomolecule Applications Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Primer on XRF Spectrometry: Instrumentation Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Introduction to ICP-MS Chicago, IL www.pittcon.org/short-courses/short-courses-preview

1-5 An Introduction to Scanning Electron Microscopy (SEM) and Associated Energy Dispersive X-ray Spectroscopy (EDS) Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Modern Portable Analytical Spectroscopy Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 How to Select an ICP-Mass Spectrometer: The Most Important Analytical Considerations Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 GC/MS Fundamentals for Operators Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Analytical Ionic Liquids in GC and Mass Spectrometry Chicago, IL www.pittcon.org/short-courses/short-courses-preview 1-5 Characterization of Biologics by Capillary Electrophoresis, Liquid Chro-matography, and Mass Spectrometry Chicago, IL www.pittcon.org/short-courses/short-courses-preview

May 202030-31 Protein Structural Analysis by Mass Spectrometry: Hydrogen Ex-change and Covalent Labeling Houston, TX www.asms.org/docs/default-source/conference-short-course-descriptions/11-protein-structural-analysis-by-msv2020.pdf

30-31 Clinical Diagnostics: Innovation, Validation, Implementation and Operation by Mass Spectrometry Houston, TX www.asms.org/docs/default-source/conference-short-course-descriptions/updated-v2_02-clinical-diagnostic-testing.pdf 30-31 Glycans and Glycoproteins in Mass Spectrometry Houston, TX www.asms.org/docs/default-source/con-ference-short-course-descriptions/04-glycans-and-glycoproteins-in-mass-spec-trometry.pdf 30-31 High Resolution Mass Spectrometry for Qualitative and Quantitative Analysis: An Introduction Houston, TX www.asms.org/docs/default-source/conference-short-course-descriptions/05-high-resolution-msv2020b.pdf 30-31 Peptides and Proteins in Mass Spectrometry Houston, TX www.https://www.asms.org/docs/default-source/conference-short-course-descrip-tions/updated_10-peptides-and-proteins-in-mass-spectrometry.pdf 30-31 Protein Therapeutics: Practi-cal Characterization and Quantita-tion by Mass Spectrometry Houston, TX www.asms.org/docs/default-source/confer-ence-short-course-descriptions/12-protein-therapeutics-course-descriptionv2020.pdf 30-31 Quantitative Mass Spectrometry Houston, TX www.asms.org/docs/default-source/conference-short-course-descriptions/13-quantitative-ms.pdf


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Special SupplementsA number of supplemental issues are planned for 2020, for which we also in-vite contributions. Manuscripts for the supplements on IR spectroscopy, ICP-OES & ICP-MS, and spectroscopy out-side the laboratory should be 2500–3000 words long, with up to six figures and tables combined, and should include an abstract of approximately 150–200 words. We are accepting submissions for the fol-lowing issues: IR Spectroscopy supplement, August 2020 Submission deadlines:Abstracts: March 19, 2020 Manuscripts: June 3, 2020 ICP–OES & ICP–MS supplement, September 2020 Submission deadlines:Abstracts: April 6, 2020 Manuscripts: June 15, 2020

Spectroscopy Outside the Laboratory supplement, November 2019 Submission deadlines:Abstracts: June 8, 2020Manuscripts: August 17, 2020

Current Trends in Mass Spectrometry supplement seriesOur supplement series on mass spec-trometry appears four times a year: in March, May, July, and October. Manuscripts for this series should be approximately 3500–4500 words long, including an abstract of ap-proximately 150–200 words, plus up to eight f igures and tables. We are still accepting submissions for the following issues: Submission Deadlines:

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