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1. Editorial

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Chemical Abstracts
I am delighted to report that the INTERNET JOURNAL OF VIBRATIONAL SPECTROSCOPY is now indeed covered in CA. Coverage commenced with volume 4(1)2000. However, we picked up all volume 3 (1999) issues earlier this year. The most recent issue currently processed is volume 5(6)."

Citation Index
Chemical Abstracts cover our Journal. You authors who publish in the Journal need a decent Citation Index. THIS IS ENTIRELY UP TO YOU. CITE PAPERS IN IJVS and the rating will rise, improving the status of both the Journal and YOUR papers.

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Feature Article
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2. FT-NIR Spectroscopy
in the Ink and Paint Industry

Eszter Trenka and Joachim Oelichmann
Büchi Labortechnik AG
Meierseggstrass 40
CH-9230 Flawil

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Colour is one of the most demonstrative features of all kinds of goods. In most cases organic colored pigments are used to achieve the desired appearance. However pigments influence much more properties than just the colour, therefore the definition and maintenance of quality standards of the intermediates and raw materials used in pigment synthesis and paint production is crucial. The chemicals used range from organic fine chemicals for synthesis to products of inorganic basic chemistry and to special surface active agents, surfactants and resins.

These requirements lead to an increasing demand for quality control in the whole branch of industry. According to the extensive norms (ISO, TQM) the tests should include the various stages of production from raw material to end product. The necessity of many measurements cause increasing costs. Therefore a fast, precise, simple and cost saving analytical technique is needed.

FT-NIR spectroscopy is a universal and efficient analytical technique for substance identification and quality assurance. The main advantages are its speed, dependability and minimized sample preparation required, which make it the best qualified method.

Today quality control by NIR spectroscopy is an integral part of the process during the production of inks and paints. It is efficient and motivating for the employee in the production to control various chemical and physical parameters by himself and to introduce necessary corrections. The aim of the tests is to maintain the quality of the end products.

Not only the producer of inks and paints but also the producer of the raw materials need to perform quality tests, which are not only restricted to simple physical parameters. Legal requirements and specifications of his customers must be fulfilled. The last member of the simplified "quality chain" is the user. Every member contributes to the quality of the final end product.

Near-Infrared spectroscopy
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In the near-infrared range, in particular overtones and combinations of OH-, NH- and CH-vibrations can be observed. The most universal range is between 9`000 cm-1 and 4`000 cm-1, which delivers good spectra of most organic substances and is widely applied. The band intensities rarely vary very much between different compounds. In any case, they are such that even without sample preparation measurements can be performed either in transmission or in diffuse reflectance using an optical fiber probe. In concentrated systems and due to strong overlapping of the absorption bands, the Lambert-Beer law is normally not applicable. These circumstances make it necessary to use somewhat more comprehensive algorithms for the interpretation of the spectra. The most commonly used chemometric methods are principal components analysis, principal component regression and partial least squares regression.

Controlling of raw materials
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A lot of different raw materials are used during the production of inks and paints. The palette includes binders, solvents, pigments, fillers, additives and auxiliary materials. Normaly only simple physical tests are performed or to some extent wet chemical methods like acid-, saponification- or iodine-values are determined. Especially the latter procedures are time consuming and need chemicals, which need to be bought, stored and disposed of. Therefore these tests are expensive.

NIR spectroscopy is a very good tool for replacing such wet chemical tests. It is possible to assure positive identification of incoming raw materials within seconds. The risk of using incorrect materials is drastically reduced. Practical experience demonstrates, that the price of non-conformance (using wrong or out-of-specification materials) is in the range of some percent of the sales volume, which can easily be reduced.

Production and end product control
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During the last few years efforts have been undertaken in the industry to control process parameters that are critical for assuring the product quality. The conventional approach used to be to add a quality control stage after the process, which however had some drawbacks. If a production batch was rejected because of not-meeting the specifications, a series of costly corrective actions had to be taken. Today, instead, the process can be controlled so that all of the goods produced comply with the specifications.

Using NIR spectroscopy the shift worker can present a sample to the instrument and he can be told in a matter of seconds, what he has to do to ensure that everything will come out as it should be.

The goal of these measurements can be to identify raw materials or end products, to determine the concentrations or concentration ratios in blends or reaction feedstocks, or to establish the content and uniformity of content of formulations.

FT-NIR system
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All spectra of the examples shown had been measured with a NIRVis or NIRFlex N-400 FT-NIR spectrometer It is based on a polarization interferometer with two quartz wedges [ 1 ] and features high stability and robustness, which is essential especially for the use in harsh environments. Therefore it is possible to use the instrument not only in the laboratory but directly on the platform or in the production area as well.

For the calibrations the chemometric software package NIRCal (NIR Calibration) had been used. It supports material identification and qualitative or quantitative applications as well. NIRCal uses chemometric algorithms (PCR, PLS, MLR, Cluster Analysis) for efficient data compression and evaluation [ 2 ]. It is strongly dedicated to the needs of the operator and easy to use. During routine use the operator is forced to follow a defined procedure and all inputs can be done via a barcode reader and remote control.

For method development there are comprehensive and flexible tools to select the best model for each calibration. In order to optimize a method, a large number of different parameters are available to the user, which can be combined in any sequence and in any number. Until now comprehensive chemometric experience and and lot of time had been neccessary for the development of ideal calibrations.

With the NIRCal software the hurdles are greatly simplified. The implemented NIRCal Wizards guides the user step-by-step from the registration of the spectra to calibration and validation of the method.  The Calibration Wizard especially completely relieves the user of time and money consuming tasks. The user has simply to answer some questions regarding the used sampling option, the nature of the samples to be investigated, the desired calibration quality and type (qualitative or quantitative). The Wizard then uses its vast knowledge base, calculates the possible calibrations, and automatically documents all results obtained. The various calibrations can be investigated and judged in the NIR-Explorer using the Q-value – an objective measure of the quality of the calibrations. Main advantages offered by this approach are that the user`s chemometric knowledge will affect the results only slightly, and that the results therefore become reproducible. The new calibration tool offers a particularly large potential for reducing calibration effort and costs [ 3 ].

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In the following some applications are described. They do not represent the complete possibilities but only give some typical examples. More examples can be found under

Identity control of incoming raw materials

The identification and quality control of incoming materials is a straightforward application, which can be performed directly on the platform without any further time consuming tests.Organic solvents have different functional groups like alkyl-, hydroxyl-, aromatic CH-links or others. These show characteristic absorption bands, which support the automatic identification.

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Figure 1.  NIR spectra of different solvents used
frequently in the ink & paint industry.

Figure 1 shows the spectra of butyl acetate, acetone, cyclohexanone, methyl ethyl ketone, 1-butanol, 2-propanol, ethanol and ethyl acetate. All solvents are commonly used in the ink and paint industry. It can easily be seen that the spectra are sufficiently different for the establishment of an identification.

In this example a transmittance fiber optic probe had been used. With such an sampling option it is possible to do the measurements directly in a barrel or even in the tank lorry.

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Figure 2. Measuring liquids directly in the original
containers using a transmittance fibre optic probe.

It features a solid construction with integrated remote control for measurements even of highly corrosive liquids. With the remote control it is possible to start the measurement directly without the need of operating a PC. Additionally the result (OK or NOT OK) is indicated at the device.The pathlength (1.5 mm) of the probe is optimized for the investigation of various liquids.

Quality control of resins

Resins are complex polymers which often are dissolved in different solvents. Today water based systems are increasingly used. The quality control of such systems by classical chemical methods is time consuming and rather expensive. With NIR spectroscopy this can be done very easily and fast. The example shown below demonstrates how to check the quality of the phenolic resin. Bad qualities can be rejected at a very early stage of the production.

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Figure 3. Spectra of phenolic resins.

The measurements shown below had been performed in the transflectance mode. This can be done with a special attachement to the standard reflectance fiber optic probe, which is mounted directly on the reflectance probe head.

With this sampling option there is a small gap between the end of the fiber optic probe and a diffuse reflector. The pathlength can be optimized for the application. This gap is filled with the sample. The radiation passes the sample, is reflected and passes the sample again. In this way a transmission measurement is performed using the indirection of reflectance.

Quantitative anaylysis of resins

Additionally it is not only possible to differentiate between various qualities but also to quantify the components of resins. As an example the quantitative determination of formaldehyde, phenol and water in a phenolic resin is given. The measurements had been done with the transmission fiber optic probe described already above. Only a few scans had been accumulated, which means that the result is obtained within less than 30 seconds only. The standard error of prediction (SEP) is about 1.5. This shows that the amount of the components can be determined with high accuracy.

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Figure 4. Calibration and validation results for the
estimation of phenol (a) and water (b) content.

Determination of NCO content

As mentioned above classical analytical methods often involve wet chemical procedures. Titration is a common technique for the determination of NCO content. NIR spectroscopy is an indirect method, which means that a calibration is needed before it can be used for the analysis of unknown samples. The standards used must be well characterized by classical analytical techniques.The uncertainties of these techniques are inherently transfered to the chemometric methods used. However the following example (Du Pont, Wuppertal) demonstates that in fact it will deliver comparable results [ 4 ].

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Figure 5.
Comparison of NIR and titration results
for the estimation of NCO content.

Analysis of pigments

At the beginning it had been mentioned that pigments are very important for the quality of the paints. Therefore of course they are subject of an extensive quality control as well. The next example will adress this segment.

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Figure 6.
NIR spectra of copper phthalocyanine pigments.

Copper phthalocyanine pigments are often used as blue colour. They exist in two crystalline modifications, the so called alpha and beta forms. It is necessary to identify both forms, because of their different properties. In total 80 spectra of both forms had been measured in diffuse reflectance using the standard fiber optic probe decribed above. It is easily possible to set up a calibarion for the differentiation of both forms. The following cluster plot demonstrates the excellent seperation of both forms.

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Figure 7. Cluster plot for the differentiation of alpha and beta
forms of Cu-phthalocyanine pigments.

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FT-NIR spectroscopy allows both efficient identification and quantification for the quality assurance. It can be implemented at all stages of production and locations from the platform for incoming raw materials, the quality control laboratory to the production floor. The technique offers particular advantages and additionally offers fascinating prospects for online analysis.

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  1. J. Knecht, Design and Application of a Near-Infrared Polarization Interferometer for Industrial Use, Microchimica Acta (Suppl.) 14, 769-771 (1997).
  2. H. Martens, T. Naes, Multivariate Calibration, John Wiley & Sons, New York, 1989.
  3. J. Ebert, R. Bossart, J. Grabinski, H. Kellerhals, Wizard for the Calibration of Büchi NIR Spectrometers, best@buchi Industrial 07 2001.
  4. N. Basener, NIR zur Qualitätskontrolle in der Lackindustrie, XXIV. FATIPEC Kongress, 8-11.June 1998, Interlaken, Switzerland.

REF:  E. Trenka & J. Oelichmann, Int.J.Vibr.Spec., [] 6, 3, 2 (2002)

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Feature Article
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Infrared & Raman Spectra of solids
Why are they different from solutions?

Patrick Hendra

A few weeks ago, a colleague asked me to explain why two bands appeared in a spectrum of a solid, where he expected only one. I duly replied by FAX but I thought that perhaps my scribbled reply, duly edited and expanded, might be useful.

As we all know – molecules containing ‘n’ atoms do so in a set of well defined ways of numbers 3n – 6 where the molecule is non-linear and 3n – 5 if linear. We also know that these modes may give rise to infrared or Raman bands. If the molecule is centro-symmetric, no mode will occur in BOTH spectra but if not centro-symmetric, some modes will show dual activity. Further, a few modes will appear in neither spectrum.

To predict something of the characteristics of a mode and its activity (IR or Raman) one defines the symmetry properties of the molecule and hence it’s point group. Armed with a set of character tables the activity of each mode can be deduced. All of this is very true and it works well for molecules in the liquid or solution phases. For gases – no problem, but quantised rotation is allowed and this in turn leads to complications [I have discussed the infrared and Raman spectra of gases recently in IJVS [] 5, 4, 3 (2001)]

Vibrations in Solids
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Several important points need making before we start because the vast majority of spectra we record are on solids, most of which are crystalline.

  1. Spectra of crystals are almost always sharper than those of melts or solutions.
  2. Spectra of solids when compared to those of the solute in solution or their melts are invariably much more complex. To put it another way – comparing the spectrum of a liquid with its frozen solid, the latter have many more bands.
  3. The frequencies of the bands in the solid are shifted from those of itself in the liquid phase. Some bands can move considerably (30cm-1 is not unusual).

Let us now deal with the reasons in turn.

1. & 3. A molecule in solution or in the liquid phase is in a state of incredibly violent translation. Adjacent molecules continuously batter it, the violence increasing as the temperature rises. As a result, the intermolecular forces are random and very variable indeed. Intermolecular forces shift the frequencies of intra-molecular vibrations hence the randomness and violence of the collisions broaden the bands [the phenomenon is similar to pressure broadening in gases]. In crystals, each molecule lies in a precise site within an overall highly regular structure. It is surrounded in 3 dimensions by other molecules

As a result all the vibrators are similar in that they are exposed to the same intermolecular force field and hence they all vibrate similarly producing sharp spectra.

In a crystal, the number of vibrations equals 3n1- 6, where n1 is the number of atoms IN THE UNIT CELL. So, in a 10-atom molecule, one expects 24 modes in its melt, but if the unit cell contains 4 molecules, the number rises to 114. Further, the infrared and Raman activity is governed by the symmetry of the unit cell NOT that of the isolated molecule. Almost invariably, the symmetry of the unit cell is similar to or lower than that of the molecule.

It is perfectly possible to carry out a full and detailed analysis of the vibrational spectrum of a crystal (or at least a simple system such as CaCO3 or other simple ionics) but it is rarely done. In more complex molecular systems it is normal to follow changes in the spectra due to phase by adopting a rather crude but useful stepwise process.

Crystal or Davydov Splitting
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Let us consider a molecule, which is fairly highly symmetric and fortuitously crystallises with only 2 molecules per unit cell. Such an example is polyethylene. In polyethylene, the structure of the molecular is of class d2h, i.e. it is centro-symmetric with a good spread of planes of symmetry. Unusually, the crystal, which contains 2 molecules is also of symmetry class D2h [We are below using lower case for the molecule and upper for the crystal] i.e. the molecule and crystal are isomorphic. The structure looks like

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The planes through the zig-zag of carbon atoms is twisted by about 90º in adjacent molecules within the unit cell.

Let us consider just one vibration of the molecule – the symmetric C-H stretching mode

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In the molecule this will generate a Raman band but no infrared mode because the molecule is d2h and centro-symmetric. In the D2h crystal there are two possibilities
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the vibrations of each molecule can be in-phase or out-of-phase with respect to its neighbour in the unit cell. Obviously, the intermolecular effects in each mode are different and as a result, the frequencies will differ and hence we MIGHT see two bands rather than one. As it turns out, the effect of the phase splits the isolated molecule frequencies by 0 to 12cm-1.

In polyethylene, as I have emphasised, the unit cell and the molecule are isomorphous hence each mode of the isolated molecule splits into two modes of similar activity to its parent. So the Raman bands in polyethylene and also the infrared ones are seen as doublets e.g., the CH2 wag (isolated molecule ~725cm-1) shows as an infrared doublet at 719 and 730cm-1. The intensity of the bands is dissimilar but both crystal CH2 wags are IR active. Similarly, the Raman band at 1295 due to the twist of the CH2 shows up at low temperatures as a very close doublet.

If the molecule and the crystal are not isomorphous (and they rarely are) the Raman bands in the isolated molecule may (in fact usually do) have different activities in the crystal. Similarly the infrared active modes.

So we might have

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In this hypothetical case 4 modes are generated from each isolated molecular mode. The IR active one happens to be well split, the Raman one is not. The crystal modes are split 2 x IR and 2 x R but in the Raman mode in the isolated molecule no resolution occurs.

Lattice modes
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As we have seen, the number of modes in a crystal equal 3n1 – 6 where n1= the number of atoms in the unit cell. Not all of these will be intra-molecular modes, a few will result from the molecules moving as masses between themselves within the lattice. These will be, of course, low frequency (almost always below 400cm-1 and often below 100cm-1) and depend for their frequency on the mass of the molecule and the strength of the intermolecular force field. These modes are called ‘lattice modes’.

Infrared folk tend to ignore these because they occur below the lower limit of their routine scans (4000-400cm-1) but Raman enthusiasts see them all the time. They can be very confusing because nothing tells the poor old spectroscopist that they are lattice modes and he/she can well misidentify them as analytically useful features.

Experimental ruses
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Although it is rare to want to carry out a complete crystal analysis, some idea of the behaviour of the crystal as a vibrator is useful in some cases and can be crucial in the study of polymorphs.

Clearly, one can in principle record the spectrum of the melt or solution and compare it with the powder. Fine, but not always possible. Several snags may arise including

sample decomposes if melted and kept hot for too long.
sample doesn’t melt at all.
sample insoluble or only sparingly so.
sample is fluorescent hence Raman must be recorded on an F-T instrument. Very difficult to record spectra above 200ºC.

etc etc.

Clues as to behaviour can be deduced by using some experimental tricks.

Cooling the sample
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Cooling a sample tends to narrow the spectral line width. It also contracts the lattice increasing the intermolecular force field and hence increasing the splitting. Hence we might observe –
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The splitting increases and the line width falls as the temperature falls. Applying high pressure increases the splitting but doesn’t narrow the line-width.

Using polarised light
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The components of the crystal spectrum may well be more or less dichroic than each other each other hence, if the sample is oriented (e.g., a polymer film or a mineral wafer) the band intensities will change e.g.,

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The two components responded differently to the orientation.

Sometimes, one cannot see any change in the band shape or intensity (due to the lack of splitting and/or the bandwidth) but the band head WILL shift if only by a minute amount. Data subtraction will then yield the evidence.

The use of polarised light and an analyser in the Raman tends to be more useful than in the IR. I have discussed this matter before see IJVS [] 3, 1, 6 (1999) and also Neil Everall IJVS [] 3, 2, 2 (1999).

Indentifying lattice modes
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Lowering the temperature does not effect the average frequency of the bands in a crystal due to internal molecular vibrations i.e. vmax above doesn’t vary. Lowering the temperature usually (almost always) shrinks the lattice and so the lattice modes usually rise in frequency. So, if a low frequency band rises in frequency as the sample is cooled, you can bet good money on it being due to a lattice mode.


 P.J. Hendra, Int.J.Vibr.Spec., [] 6, 3, 3 (2002)

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Feature Article
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4. How FTIR works III

The Editor &
Fabrice Birembaut*

*Univeristy of Southampton
Highfield, Southampton
SO17 1BJ

In this piece we explore some of the problems you may encounter and how to deal with them. You, the readers – have prompted one or two areas, thanks for the ideas.

The finite slit width effect
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We have touched on this subject before but more detail is required. Back in the days of infrared spectrometers based on scanning mono-chromators, the effect of the slit-width on the spectrum was recognised. Let’s assume the spectral feature you want to examine is very, very sharp (say a w½ value of 0.1cm-1). The slit width is set at a wider width c x dispersion = 1cm-1 or if you prefer, the bandpass is 1cm-1. As the mono-chromator scans the image of the entrance slit across the open jaws of the exit slit we see

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and the magnitude of the signal at the detector is
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Thus, a spectral feature of width 0.1cm-1 appears as a band with a half width of 1cm-1. To correct this problem the slit width must be narrowed to less than 0.1cm-1. This may well be possible, but the quality of the optics and mechanical drive may not permit you to do so efficiently. However, narrow the entrance slit, there will be a minimum width of image. Optical imperfections could well be such that the lower limit is ½cm-1 so very narrow features (e.g., gas phase spectra) are simply not accessible without distortion. Now – what happens with an interferometer.

In a sense, the same thing – there is a limit to the quality of the optics and this defines the smallest value of the J stop you can usefully use. As the resolution is improved, you have to use a smaller J stop, so quite clearly for any given instrument its quality will define the limit of resolution available. In addition, as we have explained before, as you wish to improve the resolution, you must acquire more data points i.e. you must increase the length of the mirror scan. A path difference scan of 1cm will satisfy a need for 1cm-1 resolution but a huge 10cm movement is needed if you want 0.1cm-1 resolution – not easy to do without shake or wobble hence the cost of high resolution instruments.

All of this is academic if you habitually look at solids because the bands are broad – BUT IS IT? Assuming you are careful to keep the resolution better than the narrowest band in the spectrum, all seems well but not quite – there is the problem of the S:N ratio.

Returning to the old spectrometers. The amount of energy that passed through the mono-chromators was proportional to the (slit-width)2 so using very narrow slits could be a bit of a disaster. In interferometers, the energy that goes through the instrument is also proportional to the size of the J stop squared.

This might seem a little odd because only one J stop is fitted – the entrance one – the manufacturers leave out the exit aperture and hence the exit J stop is virtual to use optical parlance. Whether virtual or not, the exit J stop acts as if it were there because the detector has a limited size.

Be that as it may, higher resolution = noisier spectra requiring that you co-add more scans.

Again, as we have explained before, if the resolution is too low bands will appear broader than they should be but the area under the band is not effected by the resolution. Several of the points above are explained in Table 1.

Fixed J stop: 8.17mm
Resolution Peak Height Area Width Centre
1cm-1 278.77 1577.6 4.4643 988.52
2cm-1 260.18 1560.5 4.7772 989.23
4cm-1 218.96 1575.9 5.9274 989.23
8cm-1 85.178 1490.9 16.44 989.12
Fixed resolution @4cm-1
J stop Peak Height Area Width Centre
4.09mm 51.358 324.84 5.1546 989.03
5.78mm 124.69 821.61 5.3339 989.23
8.17mm 216.85 1555.2 5.9104 989.21
11.55mm 242.18 2009.5 6.9059 988.06
12.50mm 241.22 2041.2 6.9996 987.47

Table 1. Barium Sulphate results

In Raman spectroscopy, bands tend to be very narrow so we used an FTIR in its Raman mode to demonstrate. The sample was BaSO4 and the sharp clear band at 989cm-1 was our model. See Figure 1.

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Figure 1. Barium sulphate spectrum

In the first block of data the J stop was fixed at 8.17mm width. As the spectra were run at ever decreasing resolutions 1cm-1 arrow.gif (76 bytes) 16cm-1 the peak fell in height but not in area. The width of the band increased as expected. See Spectrum 2 The actual fundamental bandwidth of this feature in BaSO4 is about 4cm-1 , so the band has a limiting lower width or around 4cm-1 however fine the resolution.

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Figure 2. Barium sulphate spectrum using various resolutions:
blue 1 cm-1; red 2 cm-1; green 4 cm-1; pink 8 cm-1; black 16 cm-1.

In the lower block of data (see Figure 3) the resolution is fixed at the optimum value (4cm-1), but the J stop size is varied over a range of 3:1. Several interesting points arise. As you increase the J stop more energy passes through the instrument the amount varying as the J stop diameter increases but not linearly – it tails off at larger values.
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Figure 3. Barium sulphate spectrum using different J stop values:
blue 4.09 mm; red 5.78 mm; green 8.17 mm;
pink 11.55 mm; black 12.50 mm.

This is because the image of the imaginary exit J stop over fills the detector at large J stop values which has a delimited size (in this case ~1mm square).

To summarise: always try to use a resolution better than the sharpest line in the spectrum but only just better. For quantitative work only use the peak heights if you are certain that you have satisfied the first condition. If not always use the band areas.

A word of caution: Over the years the Southampton group have experimented widely with the use of band heights and areas in quantitative analyses (almost exclusively in Raman, but the principles apply equally to FTIR). We almost invariably found that peak heights are a more accurate measure of quantity than areas. Why? We think the reason is that the background is hard to precisely define. When measuring areas a small variation in the background height has an enormous effect on the integrated area for obvious reasons, but has little effect on the measured band height.

Note on Spectrographic Instruments
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All the above discussion refers to FT instruments. These days much of the Raman work done is carried out on spectrograph CCD detector microscope instruments. Some of the points still apply. In Table 2 we give some data recorded on a Renishaw Microscope Raman System.

Band Area W½ Peak Height
20 57 ~4 15
40 109 4 22
80 227 4 37
160 420 6 48
320µ 735 9cm-1 50K counts

Table 2.

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Figure 4. Barium sulphate spectra recorded using various slit width:
black 20 um; Red 40 um; Green 80 um; Pink 160 um; Blue 320 um.

The sample was again BaSO4 and the target band, the one at 988cm-1. Clearly, there is little point in this case in using a slit width less than 80 µ. Further note that the band area rises as the slit width NOT the slit width2. This is because the array type CCD detector ‘sees’ the image of the slit in its entirety and the peak height is the signal on the most brightly illuminated pixel. As a further consequence, the peak height rises at the power less than (slit width)1.

Way back in the past, when doing quantitative work, the use of peak heights was almost universal and it was accepted that the instrument response would be quirky and non-linear. As a result, calibration curves on a wide range of carefully prepared standards were always calculated and then used on unknowns. The quantities were read off the calibration curve. These days there is a tendency to check for linearity using a few samples and then let the computer calculate the quantity automatically. For good work a little humility and caution is indicated. Assume your methods are suspect, try various combinations of measurement method and calibration routine use the best and don’t overestimate the accuracy with which you claim your analyses – particularly at high and low concentrations.

Frequency accuracy
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As the venerable American economist Prof. John Kenneth Galbraith always insists, the study of economics is frequently dominated by myths so the same is true in our field – the myth of frequency accuracy.

Some manufacturers mutter that because the measurement of the optical delay is measured using a helium neon laser, the measurement is limited in accuracy only by our knowledge of the He/Ne wavelength and as an inevitable consequence, the position of the bands in an FTIR are precise. End of Story. Well its not the end.

The argument above would be valid if the measurement of the fringes as they fall on the fringe detector was perfectly precise, but of course it is not. This error although present is insignificant compared with the problem of uneven J stop illumination.

The path difference through the interferometer is defined as that along the optical axis but a cone of rays whose cone angle is defined by the size of the J stop in fact passes through the instrument. If the cone is symmetrical about the optic axis, the average optical delay will equal the measured one.

Let us assume that you are using an accessory (say a diamond ATR) or a small volume transmission cell or any microscope, the chances that the cone of rays passing through the interferometer will be precisely centred on the optic axis is zero. As a result, the measured delay is erroneous and the bands in the spectrum will be shifted.

Does the effect appear as a real problem?

In Figure 5, we show FT Raman spectra of potassium permanganate. [We use Raman again here because it is easy to misalign the experiment in the Raman. In using FTIR accessories it happens by inevitable accident and is impossible to control].

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Figure5. Raman spectra of a KMnO4 crystal.
Red is best position; green is right shifted and blue is left shifted.

The spectra demonstrate the point nicely. Using 4cm-1 resolution we looked at a crystal found the ‘best’ position and then misaligned so that the signal strength fell by 50% to one side and then the other. As you can see, the band shifts but not by the same amount each side. The shift is small but can in many cases exceed a wavenumber. Some physico chemical experiments rely on band head shifting (with temperature, pressure applied stress, concentration etc, etc.) and often the shift range is only a few cm-1. The myth that FTIR (and FT Raman) are inherently accurate leads people to assume that they can make measurement of significance to shift values down to ¼cm-1 or less – MAYBE.

The instrumental shift depends on misalignment so in making these measurements you must guarantee that the misalignment remains constant throughout the experiment e.g., using a small volume liquid cell take precautions that the cell is located in the sample area reproducibly as each spectrum is recorded and check using repeat runs that this is so. Similarly with ATR, but be very, very careful when using inhomogeneous samples – Many years ago I remember we ran the first Raman spectrum of polyacrylonitrile fibre using the then novel FT Raman machine at Southampton. Careful scrutiny showed that the dyed and undyed uCN bands were at different frequencies. We could see the interaction of the dye and the fibre. A note was hurriedly prepared – ‘t would be the note of the year! Fortunately, the next day Henry Mould visited us from Perkin Elmer, UK. He looked at the spectra, tugged his chin and suggested we bin the paper. He then explained the myth described above. The laser illuminating the bundle of fibres quite obviously generated an uneven patch of Raman scatter at the J stop and this would vary from sample to sample. Such was our wonderful earth shattering discovery.

Deconvolution process
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To finish this piece we decided to look at the band deconvolution process. The questions are: "how reliable are my results?", "which output should I chose?", "how many peaks should I use when deconvoluting my bands?" Once again we chose to work with Raman spectroscopy and the compound used was chloroform. At 368 cm-1, the spectrum exhibits the C – Cl umbrella vibration. With a 1 cm-1 resolution the band shows several peaks due to the different relative abundance of both chlorine isotopes in nature. Knowing that 35Cl has an abundance of 75.77 % and 37Cl one of 24.33 %, it is therefore possible to predict the overall probability of each vibration (see Table 3)

Isotope mass Required isotope Probability product Nº of permutation Overall probability
118 35 35 35 (0.7577)3 1 0.4350
120 35 35 37


3 0.4190
122 35 37 37 0.7577x(0.2433)2 3 0.1345
124 37 37 37 (0.2433)3 1 0.0144



Table 3.

The following figure shows the high resolution spectrum of CHCl3 in the 368 cm-1 region.

figure 6 (2).jpg (12004 bytes)
Figure 6. High resolution spectrum of Chloroform in the 368 cm-1 region.

The software used to do the deconvolution was GRAMS/32. The results are presented in the following figure and table.

figure 7 (2).jpg (32968 bytes)
Figure 7. Deconvolved spectrum of CHCl3








35 35 35





35 35 37





35 37 37





37 37 37




Table 4.

It is now possible to compare the deconvolution peaks intensity and the theory prediction (see Table 5)

Peak Predicted Intensity Deconvolution Intensity Deconvolution normlised Intensity
1 0.4350 16.485 0.46
2 0.4190 12.458 0.36
3 0.1345 5.2314 0.15
4 0.0144 0.96015 0.03

Table 5.

The first remark is that the intensities of the deconvolved peaks do not match the predicted intensities, especially for peak #2 and #3. One possibility is that the 2 middle modes are asymmetric. This must have an effect on the Raman scattering. However, it is very difficult to evaluate it.

The other remark that can easily been made is that the FWHM of the deconvolved peaks is not constant. We think, however, that in this case, they should be the same. This comes from a limitation of the software used. Hence, this introduces another source of error when calculating the the area of the peaks with the software data..

So, the message is clear - view the quantitative significance of deconcolved overlapping bands with caution.

Now, on the same spectrum, we tried to do a deconvolution using an enormous number of peaks. The result is shown in Figure 8.

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Figure 8. Deconvolution of CHCl3 using far too many bands

As can be seen in Figure 8, the fitting of the curve is PERFECT! However, the result of the deconvolution peaks does not mean anything either…… Put another way, you will be far better off using a restricted number of peaks. A good rule of thumb is: "you should only use the number of peaks that can actually spot in your high resolution spectrum". So, if you can see 3 peaks, use three peaks to deconvolve your spectrum. You will then limit the error of including peaks that simplt don't exist.

REF: F. Birembaut & P.J. Hendra, Int.J.Vibr.Spec., [] 6, 3, 4 (2002)

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