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New Developments and Applicationsof Handheld Raman, Mid-Infrared, andNear-Infrared SpectrometersDamir Sorak a , Lars Herberholz a , Sylvia Iwascek a , SedakatAltinpinar a , Frank Pfeifer a & Heinz W. Siesler aa Department of Physical Chemistry, University of Duisburg–Essen,Essen, GermanyAccepted author version posted online: 03 Nov 2011.Publishedonline: 02 Feb 2012.

To cite this article: Damir Sorak , Lars Herberholz , Sylvia Iwascek , Sedakat Altinpinar , Frank Pfeifer& Heinz W. Siesler (2012): New Developments and Applications of Handheld Raman, Mid-Infrared, andNear-Infrared Spectrometers, Applied Spectroscopy Reviews, 47:2, 83-115

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Applied Spectroscopy Reviews, 47:83–115, 2012Copyright © Taylor & Francis Group, LLCISSN: 0570-4928 print / 1520-569X onlineDOI: 10.1080/05704928.2011.625748

New Developments and Applicationsof Handheld Raman, Mid-Infrared,and Near-Infrared Spectrometers

DAMIR SORAK, LARS HERBERHOLZ, SYLVIA IWASCEK,SEDAKAT ALTINPINAR, FRANK PFEIFER,AND HEINZ W. SIESLER

Department of Physical Chemistry, University of Duisburg–Essen,Essen, Germany

Abstract: Recently, miniaturization of Raman, mid-infrared (IR), and near-infrared(NIR) spectrometers has made substantial progress. Though mid-infrared systems arebased exclusively on attenuated total reflection (ATR) measurements, near-infraredspectrometers operate in the diffuse reflection or transmission mode. The reduction insize, however, must not be accompanied by deterioration in measurement performance,and portable instrumentation will only have a real impact on quality and process controlif Raman, IR, and NIR spectra of comparable quality to laboratory spectrometers canbe obtained.

In the present communication, a short overview on the building principles ofnovel handheld systems will be provided and the results of qualitative and quantitativeanalyses of selected liquid and solid sample systems obtained with these Raman, Fouriertransform infrared (FTIR), and NIR spectrometers will be evaluated in terms of theircomparability with laboratory instruments and their suitability for on-site and fieldmeasurements.

Keywords: portable Raman, mid-infrared and near-infrared spectrometers, perfor-mance tests, qualitative and quantitative analyses, multicomponent analyses

Introduction

Miniaturization of vibrational spectrometers began approximately a decade ago, but onlywithin the last few years have real handheld Raman, infrared (IR), and near-infrared (NIR)scanning spectrometers become commercially available. These new developments werepartly driven by the potential and advantages of microelectromechanical systems (MEMS)production, and the near future will prove their impact in field and on-site analysis.

So far, primarily qualitative applications (1, 2) have been demonstrated in the field ofhomeland security, terror defense, and some forensic investigations, whereas only a fewquantitative studies have been reported (3–6). In the present article, four handheld systemswill be discussed with reference to performance tests in comparison to benchtop instrumentsand in a technique-selective comparison. The case studies discussed will demonstrate that

Address correspondence to Heinz W. Siesler, Department of Physical Chemistry, University ofDuisburg–Essen, Schuetzenbahn 70, D 45117 Essen, Germany. E-mail: [email protected]

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these instruments can be used for a broad range of on-site and field measurements beyondhomeland security and terror defense, thereby launching Raman, IR, and NIR spectrometersinto a new era of general-purpose analytical tools.

Instrumentation

Before discussing the analytical applications of the portable instruments, we will providean overview of the different spectrometers and some of their instrumental details. Thissection is intended to provide insight into the extent of miniaturization of the individualtypes of spectrometers and their most important instrumental and operational parameters.

Fourier Transform Infrared Spectrometer

The TruDefender Fourier transform (FT) spectrometer (Thermo Fisher Scientific Inc.,Wilmington, MA; formerly Ahura Scientific Inc.) operates on the basis of a Michelsoninterferometer applying the attenuated total reflection (ATR) technique for sample presen-tation. Figure 1 shows a schematic of the optical principle of the spectrometer. Spectraare recorded in the wavenumber range from 4000 to 650 cm−1 with a spectral resolutionof 4 cm−1 and the instrument can be applied in the temperature range between −25 and+45◦C. The small (19.8 cm × 11.2 cm × 5.3 cm), lightweight (1.3 kg), rugged handheldspectrometer, with more than 2 h of battery life and requiring little maintenance, is de-signed for rapid, field-based analysis of materials (7). The measurements are not limitedto liquids but can also be performed with powders or solids with smooth surfaces. Forthe investigation of powders, the so-called crusher accessory is recommended to press thesample against the diamond reflection element for an improved contact (Figure 2a). Viscoussamples can be conveniently measured in situ or after applying a layer to a metal or glasssurface (Figure 2b). The data can be exported as SPC files, text files, or as a JPEG report.The most relevant instrumental and operational parameters are summarized in Table 1.

Near-Infrared Spectrometer

Principally, NIR spectra are measured in transmission, transflection, and diffuse reflection(8, 9). The Phazir portable NIR spectrometer (Thermo Fisher Scientific Inc.; formerly

Figure 1. Optical scheme of the TruDefender FT spectrometer.

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New Developments and Applications of Spectrometers 85

Figure 2. (a) Measurement of powder samples with the crusher adapter for improved contact of thesample and the ATR reflection element and (b) investigation of a viscous bitumen layer on a glassslide.

Polychromix) used for the investigations discussed in this review can be operated in thetransmission and diffuse reflection modes. An optional adapter is available that can beattached magnetically to the front of the instrument to optimize sample presentation. Thesource and detector geometry of the instrument and a practical diffuse reflection measure-ment of a soil sample in a Petri dish are demonstrated in Figure 3. The optical beam pathin the spectrometer and the monochromator function of the MEMS chip are schematicallyoutlined in Figure 4. Thus, the chip operates like gold-coated piano keys that can be moveddown for a fraction of a micrometer by the application of an electrical voltage. If all ele-ments are in the up position, the surface reflects the light from each pixel; if an element isactivated it acts as a diffraction grating, directing the diffracted light out of the collectionorder. A tungsten halogen lamp serves as the source and the detector consists of InGaAsphotodiodes. The most important instrumental and operational parameters of this portableNIR spectrometer are summarized in Table 2.

Raman Spectrometer

FirstDefender TruScan Raman Spectrometer. The portable FirstDefender TruScan Ra-man spectrometer (Thermo Fisher Scientific Inc.; see Figure 5) is primarily used for the

Table 1Instrumental and operational parameters of the TruDefender FT portable

FTIR spectrometer

Specifications

Weight 1.3 kgDimensions 19.6 cm × 11.2 cm × 5.3 cmSpectral Range 4000–650 cm−1

Spectral Resolution 4 cm−1

Collection Optics ATR Diamond CrystalData Export Formats spc.txt.jpegOperating Temperature −25◦C −+ 45◦C

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Table 2Instrumental and operational parameters of the Phazir portable NIR spectrometer

Specifications


Spectral Resolution 19 cm−1

Method of Measurement Diffuse Reflection TransmissionData Export Formats CSVOperating Temperature + 5◦C −+ 40◦C

Figure 3. Source and detector geometry of the Phazir NIR spectrometer (right) and a practicalmeasurement of a soil sample in diffuse reflection (left).

Figure 4. Optical beam path in the Phazir NIR spectrometer (right) and principle function of theMEMS chip as a diffraction grating (left) (see Near-Infrared Spectrometer).

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Figure 5. Measurement of a plastic sheet sample with the FirstDefender TruScan Raman spectrom-eter.

qualitative in situ identification of unknown liquids and solids (10). Figure 6 shows theoptical configuration of the source, probe, and spectrometer modules of this Raman spec-trometer (Chris Brown, formerly Ahura Scientific, Wilmington, MA, personal communi-cation), and the most important instrumental specifications are summarized in Table 3.One of the advantages of the Raman technique compared to the infrared and near-infraredis that samples in plastic bags can be readily investigated without interference from thepackaging material because the laser focus can be positioned into the sample of interest. Todemonstrate the quality of the obtainable Raman spectra in terms of frequency positions

Figure 6. Optical configuration of the FirstDefender TruScan Raman spectrometer.

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Table 3Instrumental and operational parameters of the Raman FirstDefender TruScan

Specifications

Weight 0.8 kgDimensions 19.3 cm × l0.7 cm × 4.4 cmSpectral Range 2900–250 cm−1

Spectral Resolution 7 to 10.5 cm−1

Lightsource Laser (785 nm)Data Export Formats spc.txt.csvOperating Temperature −20◦C − + 40◦C

and relative intensity ratios, the spectra of acetaminophen measured with a laboratory in-strument (11) and with the portable FirstDefender TruScan Raman spectrometer (Figure 7)were compared. The 785-nm laser source can be operated with a maximum power of300 mW and a thermo-electrically cooled charge-coupled device (CCD) detector with2,048 pixels was used. At this point it should be mentioned, however, that for a largeproportion of samples, irradiation with 785-nm light causes fluorescence by additives orimpurities (or by the sample itself), which will superimpose and, in some cases, inundatethe Raman spectrum of the sample.

Xantus 1,064-nm Raman Analyzer. Xantus 1,064-nm Raman Analyzer with NIR laserexcitation has been recently launched by BaySpec Inc. (San Jose, CA). The use of NIRlaser excitation confers a number of advantages on a Raman system. Fluorescence is greatlyreduced in the Raman signal and, due to the lower energy of the excitation radiation, thermaldegradation is also less of a problem. However, these advantages are partly neutralizedby the disadvantages of using a low-frequency laser as source, because the NIR-Raman

Figure 7. Comparison of the Raman spectra of acetaminophen measured with a laboratory instru-ment (11) (with 2 cm−1 spectral resolution) and the FirstDefender TruScan Raman spectrometer(Chris Brown, formerly Ahura Scientific, Wilmington, MA, personal communication).

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Figure 8. Optical configuration of the Xantus 1,064-nm Raman Analyzer (with kind permission ofBaySpec Inc., San Jose, CA).

technique is less sensitive due to the ν4-dependence of the scattering efficiency (12). Themini-monochromator of this Raman spectrometer operates on the basis of a proprietarytransmission grating and uses a thermoelectrically cooled InGaAs detector (Figure 8). Themost important instrumental parameters of this handheld instrument are summarized inTable 4. To demonstrate the effect of fluorescence suppression, the Raman spectra of apharmaceutical gel formulation of acetaminophen, aspirin, and caffeine measured with theXantus 1,064-nm Raman Analyzer and a 785-nm system are compared in Figure 9.

Software

All of the instruments discussed above contain intuitive menu guides and graphical userinterfaces that allow simple and ready-to-use operation. For rapid qualitative materialidentification the operator can use the following:

1. Integrated libraries for specific substances (e.g., explosives, drugs of abuse, organicchemicals);

2. Imported libraries from third parties;3. A home-built compound library to meet specific identification needs.

Similarly, software packages for the quantititave determination of individual componentsare available or can be imported. Last, but not least, multiple data formats are available to

Table 4Instrumental and operational parameters of the Xantus 1,064-nm Raman Analyzer

Specifications


Spectral Resolution 10 to 12 cm−1

Detector TE cooled InGaAsData Export Formats spc.txt.csv

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Figure 9. Comparison of the Raman spectra of a pharmaceutical gel formulation (acetaminophen,aspirin, caffeine) measured with the Xantus Raman Analyzer with 785 and 1,064 nm excitation (withkind permission of BaySpec Inc., San Jose, CA).

allow users the maximum flexibility in data storage and retrieval as well as the export tothird-party software packages.

Application Examples

Quantitative Determination of a Pharmaceutical Drug Formulation

Comparison of the Handheld and a Benchtop NIR Spectrometer. NIR spectroscopy hasbeen increasingly developed as a routine tool for production and quality control in thepharmaceutical industry (13). In this section, a comparative performance test based onthe quantitative results obtained with the spectral data of a solid pharmaceutical drugformulation measured in diffuse reflection with a benchtop FT-NIR instrument and ahandheld NIR spectrometer will be reported (4). The portable instrument was the PhazirNIR spectrometer and the laboratory instrument was a Vector 22/N FT-NIR spectrometer(Bruker Optik GmbH, Ettlingen, Germany) equipped with a tungsten halogen light source, aquartz beam splitter, a thermoelectrically cooled InGaAs detector, and a light-fiber-coupleddiffuse-reflection probe.

The solid drug formulations investigated consisted of mixtures of the three crys-talline active ingredients acetylsalicylic acid (Sigma-Aldrich Chemie GmbH, Steinheim,Germany), ascorbic acid (Acros Organics, NJ), and caffeine (Sigma-Aldrich ChemieGmbH) with the two amorphous excipients cellulose (Fluka Chemie GmbH, Buchs,Switzerland) and starch (Carl Roth GmbH, Karlsruhe, Germany) (Figure 10). A set of48 samples was prepared by milling varying amounts of the three active ingredients in theconcentration range 13.77–26.43% (w/w) with equal amounts (40% (w/w)) of a 1:3 (w/w)mixture of cellulose and starch (Table 5) for 5 min in a Retsch mill (type BMO, RetschGmbH, Haan, Germany). The final particle size of the mixtures after the milling procedurevaried between 10 and 30 µm.

The measurements with the benchtop instrument were performed with a homemadesample cell consisting of a cylindrical brass cup with an internal diameter that fit the probehead (14). Each sample was measured in duplicate (repacks) with a spectral resolution of2 cm−1 and 256 scans were accumulated. The mean spectra of the replicate measurementsof 45 samples were used for development of the calibration models. Only the spectralranges 9050–7450, 7100–5570, and 5300–4000 cm−1 (to exclude possible absorption spikes

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Figure 10. Chemical formulas of the crystalline, active ingredients, and amorphous excipients ofthe investigated pharmaceutical formulations.

of water vapor at this high spectral resolution) were selected for quantitative evaluationand a multiplicative scatter correction (MSC) was applied. The data pretreatment and thedevelopment of the cross-validated partial least squares (PLS-1) calibration models for eachactive ingredient (based on 45 of the 48 samples) were performed using Unscrambler (v.9.6, CAMO Software AS, Oslo, Norway). The three samples not included in the calibrationset were selected on the basis of a representative variation of their composition and werefinally used as “unknown” test samples to predict the content of their active ingredients(see Table 7).

For measurements with the portable NIR spectrometer the samples were transferredinto a 6-mm-diameter glass vial that was then attached with the magnetic adapter in a smallhomemade brass sample holder to the front of the measurement nose of the spectrometer(Figure 11). Each sample was measured in triplicate (rotating the glass vial approximately120◦) by accumulating 64 scans, and the mean spectra of the replicate measurementswere used for development of the calibration models. The total available spectral regionbetween 6250 and 4166 cm−1 (1,600–2,400 nm) was used for calibration and an extendedmultiplicative scatter correction (EMSC) (15) was applied. The data pretreatment anddevelopment of the cross-validated PLS-1 calibration models for each active ingredient(based on 45 of the 48 samples) was also performed using Unscrambler.

Table 5Compositional ranges of the active ingredients in the calibration and test samples of the

pharmaceutical formulations

max. min. %(w/w)ASA 26.04 – 15.22ASC 26.19 – 14.77 � 60CF 26.43 – 13.77CE/ST (1 : 3) 40

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Figure 11. Sample presentation of the solid drug formulation for the Phazir NIR spectrometer (seeQuantitative Determination of a Pharmaceutical Drug Formulation).

The mean spectra of the three test samples measured with the laboratory and handheldspectrometers under the conditions described above are compared in Figure 12. Despite themuch higher spectral resolution of the laboratory FT-NIR spectrometer, the compositionaldifferences of the three test samples are hardly detectable in the spectra.

The calibration/cross-validation parameters and number of factors of the PLS-1 modelsfor acetylsalicylic acid (ASA), ascorbic acid (ASC), and caffeine (CF) developed with thespectral data measured using the laboratory and the handheld instrument are summarizedin Table 6. Based on these calibration models, the content of the active ingredients of thethree test samples was predicted and compared to the corresponding reference values inTable 7.

Table 6Selected calibration/cross-validation parameters of the PLS-1 models for ASA, ASC, and

CF based on the spectra of the two different spectrometers

RMSEC/RMSEP Corr. Coeff. Cal/Val Factors

Bruker VECTOR 22/NASA 0.375/0.487 0.9883/0.9802 6ASC 0.468/0.621 0.9830/0.9700 7CF 0.462/0.516 0.9911/0.9890 2

PhazirASA 0.572/0.645 0.9719/0.9641 3ASC 0.533/0.634 0.9789/0.9700 5CF 0.708/0.823 0.9784/0.9708 4

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Figure 12. NIR spectra of the test samples measured with the (a) portable Phazir NIR spectrometerand (b) benchtop Bruker IFS 28 FT-NIR spectrometer.

Inspection of Table 7 clearly reveals that accurate quantitative calibration models canbe developed on the Phazir and that the prediction performance of the portable instrumentis comparable to the benchtop FT-NIR spectrometer. Furthermore, the large differencein spectral resolution between the two spectrometers had no significant influence on theaccuracy of the quantitative determination. This observation, however, was not surprisingbecause it has been shown previously (16) that enhancement of the spectral resolution doesnot lead to calibration models with significantly higher accuracy.

Interdisciplinary Comparison of the Quantitative Determination of a Pharmaceutical DrugFormulation. The pharmaceutical sample set was also investigated with the portable Ramanand FTIR spectrometers and subsequently PLS-1 calibration models were developed for thethree active ingredients. The purpose of these investigations was to make a technique-relatedcomparison with reference to the measurement of pharmaceutical powder samples.

Details on the NIR spectra acquisition with the Phazir and calibration developmentwere described under Comparison of the Handheld and a Benchtop NIR Spectrometer.

Measurements with the handheld FTIR spectrometer in the ATR mode were performedwith the crusher accessory in order to optimize the contact between the samples andthe diamond reflection element (see Figure 2a). Sixty scans (spectral resolution 4 cm−1)were accumulated to improve the signal-to-noise ratio. Mean spectra of triplicate repackmeasurements were calculated for each sample. After baseline correction and selection ofrelevant wavenumber ranges (3700–2400 and 1850–720 cm−1), an EMSC was applied tothe 45 calibration spectra. For each active ingredient a PLS-1 model with cross-validationwas developed with these spectra using Unscrambler.

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Table 7Comparison of the prediction performance of the benchtop and portable NIR spectrometers

for the active ingredients ASA (a), ASC (b), and CF (c) in the three test samples

Reference Prediction | � | MeanSample (R) % (w/w) (P) % (w/w) % P-R | �|%

(a)Bruker Vector 22N

P22 20.01 20.12 0.11P24 16.52 16.65 0.13 0.28P38 23.39 22.78 0.61

PhazirP22 20.01 19.94 0.07P24 16.52 16.14 0.38 0.18P38 23.39 23.47 0.08

(b)Bruker Vector 22N

P22 25.26 25.51 0.25P24 20.87 19.93 0.94 0.62P38 16.27 16.94 0.67

PhazirP22 25.26 25.13 0.13P24 20.87 20.78 0.09 0.21P3S 16.27 15.87 0.40

(c)Bruker Vector 22N

P22 14.73 14.87 0.14P24 22.61 22.76 0.15 0.11P38 20.34 20.37 0.03

PhazirP22 14.73 14.81 0.08P24 22.61 22.69 0.08 0.16P38 20.34 20.67 0.33

For measurement of the pharmaceutical formulations with the portable Raman spec-trometer with 785-nm excitation, the samples were transferred into a small brass cylinderand the probe head of the spectrometer was set on top of this cylinder to focus the laserbeam into the sample. Sixty scans were co-added in the spectra acquisition process. Again,mean spectra of triplicate repack measurements were calculated for each sample. After aSavitsky-Golay smoothing and application of an EMSC procedure, the wavenumber range1800–400 cm−1 was selected for the development of PLS-1 calibration models for eachactive ingredient.

The calibration spectra of the pharmaceutical sample set measured by the three tech-niques with the handheld spectrometers are shown in Figure 13. The optimized PLS-1calibration models derived from these spectra and their parameters for the individual activeingredients are summarized in Figure 14. Based on these calibration models the test samples

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Figure 13. (a) NIR, (b) FTIR-ATR, and (c) Raman spectra of the pharmaceutical drug formulationcalibration samples measured with the portable instruments.

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Figure 14. Calibration and cross-validation parameters of the individual active ingredients based onthe (a) NIR, (b) FTIR-ATR, and (c) Raman data measured with the portable instruments.

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Table 8Technique-selective comparison of the prediction and reference values of the test samples

for the active ingredients ASA (a), ASC (b), and CF (c)

Portable spectrometer Test sample Predicted Reference

(a)TruDefender FT mid-infrared P22 19.29 20.01

P24 16.07 16.52P38 21.65 23.39

Phazir near-infrared P22 19.94 20.01P24 16.14 16.52P38 23.47 23.39

FirstDefender TruScan Raman P22 20.88 20.01P24 18.56 16.52P38 21.61 23.39

(b)TruDefender FT mid-infrared P22 24.36 25.26

P24 19.25 20.87P38 16.29 16.27


FirstDefender TrueScan Raman P22 26.54 25.26P24 21.80 20.87P38 18.01 16.27

(c)TruDefender FT mid-infrared P22 15.52 14.73

P24 22.77 22.61P38 21.13 20.34


FirstDefender TrueScan Raman P22 15.09 14.73P24 20.86 22.61P38 20.66 20.34

were predicted and, in order to provide a method-selective comparison of the predictionperformance, the prediction and reference values of the three spectroscopic techniques aresummarized separately for each active ingredient in Table 8. Upon a first rough inspection anacceptable agreement between the reference and prediction values of the three test sampleswas established for all three techniques; however, a closer look revealed that the NIR tech-nique yielded the best prediction data. The lowest prediction performance was obtainedwith the Raman spectrometer, whereas the FTIR-ATR system provided an intermediateprediction quality for the test samples. The slightly lower prediction performance of theRaman spectrometer can be explained in terms of interference from fluorescence effects

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and the rather small focus of the radiation on the sample powder. The negative influenceof fluorescence could certainly be suppressed by using the portable Raman system withNIR laser excitation instead (see Xantus 1,064-nm Raman Analyzer). In the case of theFTIR-ATR measurements, the imperfect contact between the diamond reflection elementand the powder sample was the reason for the lower prediction performance.

Quantitative Determination of a Technical Alcohol Blend

The determination of a single component in a multicomponent mixture is one of themost frequently encountered challenges in analytical chemistry. Before the developmentof powerful spectrometers and the availability of chemometric evaluation techniques, wetchemical analysis had to be applied for the quantitative determination of multicomponentsystems. Thus, the study on the titrimetric procedure-cascade published by Siggia et al.(17) for a mixture of primary, secondary, and tertiary amines, for example, conveys thepractical complexity and environmental contamination entailed by a wet chemical analysisof a multicomponent system. Taking into account that today such a problem for a liquidmulticomponent system can be readily solved by measuring the FTIR spectrum of a smalldroplet of an unknown mixture on the surface of an ATR reflection element and evaluatingthe spectrum based on a prior PLS calibration within a few minutes, the dramatic advance-ments in analytical chemistry during the last few decades can be assessed. This progresshas been further advanced by the local flexibility provided by portable instrumentation.

In the present communication, the quantitative determination of the individual compo-nents of a three-component mixture of different aliphatic alcohols using FTIR/ATR spec-troscopy will be discussed. Alcohols with high boiling points were chosen in order to avoiderrors due to evaporation during sample preparation and spectral recording (Figure 15).Generally, FTIR-ATR spectroscopy is the method of choice for liquids because, on theone hand, the sample preparation simply consists of positioning a drop of the liquid underinvestigation on the reflection element and, due to the small penetration depth of radiationinto the sample (18), excellent quality FTIR spectra are usually obtained. To prove theapplicability of the portable TruDefender FT spectrometer for such purposes the predictionperformance of unknown test samples was compared to the results obtained for the samemulticomponent system on a laboratory FTIR spectrometer with an ATR accessory.

Fifty-one calibration samples (50 mL each) with weight percentages varying between0 and 100% (w/w) for the individual components were prepared. A test set of four sampleswith a wide range of concentrations was also prepared.

All samples were measured in duplicate with the TruDefender FT (with the reflection el-ement pointing upwards) in the wavenumber range from 4000 to 600 cm−1 by accumulating60 scans with a spectral resolution of 4 cm−1. The same measurement procedure was

Figure 15. Individual components of the alcohol mixture.

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Figure 16. Baseline-corrected FTIR-ATR spectra of the calibration samples of the alcohol mixtures:(a) TruDefender FT and (b) Bruker IFS 28 FTIR-ATR spectrometer.

applied with the IFS 28 FTIR benchtop spectrometer (Bruker Optik GmbH) equippedwith a DuraScope diamond ATR accessory (Smith Detection, Danbury, CT) and an MCTdetector with 64 scans accumulated per spectrum. Thus, 102 and 8 spectra, respectively,were available for the calibration and the test set validation. The baseline-corrected spec-tra of the calibration samples measured on the portable and the laboratory instrumentare shown in Figures 16a and 16b. Only the wavenumber ranges 3700–2600 and 1600–800 cm−1 were selected for calibration. The spectra in Figure 16 clearly indicate that thesimilar structures of the alcohol components led to a strong overlap of their spectra; how-ever, the intensity changes and band shifts due to the variation in the hydrogen-bondingstructure as a function of concentration changes (19) complicated quantitative evaluation.Thus, univariate analysis of the data was not a feasible alternative and PLS-1 calibrationmodels were applied for quantitative determination of the individual components. Figures17a and 17b show the good calibration and validation parameters obtained with the datameasured on both instruments. The prediction values of the alcohol components of thetest samples were compared to the corresponding reference values for the portable andthe laboratory spectrometers as shown in Table 9. Taking into account the complex situ-ation regarding the changes in hydrogen-bonding structure as a function of variations in

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Figure 17. Calibration and validation results obtained with the FTIR-ATR spectral data of the(a) TruDefender FT and (b) Bruker IFS 28 spectrometers.

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Table 9Comparison of the prediction and reference values of the test samples for the individual

components of the alcohol mixture

a

TruDefender FT

Test sample Reference (R)% Prediction (P)% |�|%1-heptanol (w/w) (w/w) P − R

U1 1 10.15 11.54 1.39U1 2 10.15 9.97 0.18U2 1 20.24 20.16 0.08U2 2 20.24 21.34 1.10U3 1 50.38 51.71 1.33U3 2 50.38 55.74 5.36U4 1 60.32 61.30 0.98U4 2 60.32 60.87 0.55

b

TruDefender FT

Test sample Reference (R)% Prediction (P)% |�|%2-pentanol (w/w) (w/w) P − R

U1 1 20.04 22.24 1.74U1 2 20.04 20.50 0.46U2 1 39.98 39.64 0.34U2 2 39.98 40.25 0.27U3 1 19.91 18.72 1.19U3 2 19.91 18.10 1.81U4 1 29.79 28.47 1.32U4 2 29.79 28.78 1.01

c

TruDefender FT

Test sample Reference (R)% Prediction (P)% |�|%2-methyl-2-butanol (w/w) (w/w) P − R

U1 1 69.81 65.72 4.09U1 2 69.81 69.02 0.79U2 1 39.78 39.86 0.08U2 2 39.78 38.10 1.68U3 1 29.71 29.18 0.53U3 2 29.71 25.80 3.91U4 1 9.88 9.96 0.08U4 2 9.88 10.08 0.20

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Table 9Comparison of the prediction and reference values of the test samples for the individual

components of the alcohol mixture (Continued)

d

IFS 28 FTIR

Test sample Reference (R)% Prediction (P)% |�|%1-heptanol (w/w) (w/w) P − R

U1 1 10.15 10.42 0.27U1 2 10.15 10.98 0.83U2 1 20.24 21.08 0.84U2 2 20.24 20.44 0.20U3 1 50.38 48.03 2.35U3 2 50.38 50.93 0.55U4 1 60.32 59.84 0.48U4 2 60.32 59.32 1.00

e

IFS 28 FTIR

Test sample Reference (R)% Prediction (P)% |�|%2-pentanol (w/w) (w/w) P − R

U1 1 20.04 21.80 1.76U1 2 20.04 21.53 1.49U2 1 39.98 40.90 0.92U2 2 39.98 40.13 0.15U3 1 19.91 18.51 1.30U3 2 19.91 21.42 1.51U4 1 29.79 29.32 0.47U4 2 29.79 30.86 1.07

f

IFS 28 FTIR

Test sample Reference (R)% Prediction (P)% |�|%2-methyl-2-butanol (w/w) (w/w) P − R

U1 1 69.81 70.98 1.17U1 2 69.81 72.18 2.37U2 1 39.78 38.43 1.35U2 2 39.78 39.10 0.68U3 1 29.71 31.96 2.25U3 2 29.71 28.90 0.81U4 1 9.88 7.78 2.10U4 2 9.88 11.12 1.24

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concentration for both instruments, good and comparable predictions were obtained for theindividual components. Thus, the portable instrument can be used for quantitative qualitycontrol of liquid samples.

Quality Control in Road Construction Work: Quantitative Analysis of Additivesin Polymer-Modified Bitumen (PmB)

Bitumen is a complex mixture of high-molecular-weight hydrocarbons and it is oftenblended with different polymers to adjust the rheological and adhesional properties forapplication in road construction work (20–24). Bitumen is the main constituent of theasphalt binder layer between the coarse base layer and the fine-grained surface layer (25)and has to compensate for the unevenness of the base layer. Thus, the correct bitumencomposition is crucial for the long life cycle of the road surface. In this respect, in situcontrol before the application of the bitumen layer is an important step.

Due to the complex chemical matrix of bitumen, however, there are only a few ana-lytical methods available. Masson et al. developed a quantitative IR spectroscopic methodto determine the content of the styrene–butadiene copolymer additive in bitumen (26).For this purpose, however, the polymer had to be dissolved in CS2 and diluted in severalsteps. With the goal of an in situ measurement in mind, the quantitative FTIR-ATR spec-troscopic determination of polypropylene–maleic anhydride (PPMA) additive in bitumenwas tested using the handheld TruDefender FT portable FTIR spectrometer and comparedto the results obtained from the same samples using the Bruker IFS 28 laboratory FTIRinstrument.

The bitumen-additive formulations were prepared by admixture of polypropylene–maleic anhydride (Clariant GmbH, Gersthofen, Germany) to MBW 10-25 bitumen (Mit-teldeutsches Bitumenwerk, Hoehenmoelsen, Germany) in the practically applied concen-tration range 1–5% (w/w) by vigorous stirring at 165◦C for 1 h (22). A total of 42 calibrationsamples and 10 test samples with additive concentrations ranging from 0 to 5% (w/w) weremeasured with the two instruments. Two of the test samples with 3% (w/w) PPMA contentwere prepared at a later stage to assess possible influences from the sample preparationprocedure.

The data pretreatment of the FTIR-ATR spectra consisted of selecting the wavenum-ber regions 3250–2500, 1490–1130, and 930–680 cm−1 and a subsequent application ofa standard normal variate (SNV) transformation. Based on these pretreated calibrationspectra, PLS-1 calibration models with full cross-validation were developed for the spectrameasured on the portable and laboratory instruments. The calibration spectra measured onthe different spectrometers and the calibration/validation parameters of the correspondingPLS-1 models, respectively, are shown in Figures 18 and 19. Significantly better root meansquare error of calibration (RMSEC) and root mean square error of prediction (RMSEP)values were obtained (Figures 19a and 19b) with the laboratory spectrometer, indicat-ing better prediction accuracy for the test samples. The prediction results of the 10 testsamples summarized in Table 10 and Figure 20 corroborate this assumption and reflectsmaller deviations in test sample predictions using the benchtop instrument compared tothe portable instrument (see graphs below Table 10). It should be pointed out, however,that these unscrambler-specific deviations are not representative of the differences betweenthe reference and predicted values of the test samples but are calculated by correcting theRMSEC of the calibration model by the leverage of the investigated test sample (27). Thetwo grey-shaded test samples marked with italics (3.0% (w/w)) show that the time-shifted

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Figure 18. Calibration spectra of the polymer-modified (PPMA) bitumen samples measured on the(a) Bruker IFS 28 and (b) TruDefender FT spectrometers after wavenumber selection and SNVtransformation.

Figure 19. Calibration and validation parameters of the PLS-1 models developed with the (a) BrukerIFS 28 FTIR-ATR spectra and (b) TruDefender FT spectra for quantitative determination of the PPMAadditive in bitumen.

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Table 10Comparison of the prediction and reference values of the bitumen test samples with refer-ence to the PPMA additive for the TruDefender FT portable spectrometer and the Bruker

IFS 28 FTIR benchtop spectrometer

Sample Reference % (w/w) Prediction % (w/w)

TruDefender FTMBW 10-25 PPMA 1 1.85MBW 10-25 PPMA 2 2.54 2.30MBW 10-25 PPMA 3 2.42MBW 10-25 PPMA 4 2.46MBW 10-25 PPMA IT 3.02MBW 10-25 PPMA 2T 3.44 3.00MBW 10-25 PPMA 5 4.31MBW 10-25 PPMA 6 4.79 4.50MBW 10-25 PPMA 7 4.25MBW 10-25 PPMA 8 4.51

IFS 28 FT-IR/ATRMBW 10 25 PPMA 1 2.32MBW 10-25 PPMA 2 2.16 2.30MBW 10-25 PPMA 3 2.13MBW 10-25 PPMA 4 2.32MBW 10-25 PPMA IT 3.12MBW 10-25 PPMA 2T 3.04 3.00MBW 10-25 PPMA 5 4.86MBW 10-25 PPMA 6 4.92 4.50MBW 10-25 PPMA 7 4.19MBW 10-25 PPMA 8 4.20

sample preparation does not have a detectable effect on the prediction quality. In summary,it can be concluded that the disadvantage of slightly higher prediction errors obtainedwith the portable instrument is more than compensated for by the flexibility of its in situapplicability at road construction sites.

Figure 20. Unscrambler-specific “Predicted with Deviation” graph of the PPMA prediction andreference values of the bitumen test samples for the (a) TruDefender handheld spectrometer and the(b) Bruker IFS 28 FTIR benchtop spectrometer.

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NIR Spectroscopic Analysis of Hydrocarbon Contaminations in Soil

The contamination of soil with hydrocarbons (oil, gasoline, diesel) due to accidents orleakage is a widespread phenomenon with potential health risks. At present no techniquefor fast on-site identification and determination of the type and amount of contamination forthe assessment of decontamination measures for the area under consideration (excavation,biotechnical method) is available. All methods in current practice require isolation of thedecontamination with a subsequent quantitative spectroscopic or chromatographic deter-mination (28). Halogenated hydrocarbons were previously in use as extraction solvents, butdue to their ozone-depleting properties they have been replaced by halogen-free solvents.Microwave-assisted extraction has since been proposed as a time-saving alternative (29).In all cases, however, soil samples have to be retrieved and transported to the laboratory forqualitative and quantitative analysis (Figure 21), which is a time-consuming process. Ourapproach of diffuse reflection measurements with a portable NIR spectrometer completelyrejects the classical methods, with the future aim of a qualitative and quantitative in situanalysis. To reach this aim, the mobility of a handheld instrument is combined with on-siteevaluation of the spectroscopic data by chemometric calibration techniques. Thus, large-area investigations would allow a fast assessment of the measures to be taken in the caseof a hazardous incident. Furthermore, such on-site measurements would allow effectivemonitoring of the success of decontamination measurements. The method would not belimited to the characterization of areal pollution only but could also be easily extended todepth-profiling measurements.

In a first feasibility study, the same diesel-contaminated (0.23–4.85% (w/w)) soil sam-ples were investigated with a laboratory instrument (Bruker Vector 22/N) equipped with alight-fiber probe for diffuse reflection measurements and the handheld Phazir spectrometer.Figures 22 and 23 show the spectral evidence of the reproducibility of the effects of thediesel contamination by both instruments. In order to eliminate the influence of the variationin water content in the investigated soil the grey-shaded wavelength region of the watercombination band (ν(OH + δ(OH)) was generally not included in the calibration spectra.

Figure 21. Scheme of the conventional work schedule for the quantitative determination of hydro-carbon contaminations in soil.

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Figure 22. Measurement of diesel-contaminated soil samples with the Bruker Vector 22/N spec-trometer (the grey-shaded wavenumber range is characteristic of the water content and the arrowsmark the CH-characteristic combination and overtone absorption bands of diesel).

Based on these initial studies, PLS-1 calibration models for diesel and oil weredeveloped with spectra of contaminated soil samples measured using the Phazirportable NIR spectrometer only. Different standard soil types that were purchased fromLandwirtschaftliche Untersuchungs- und Forschungsanstalt (Speyer, Germany) were usedfor sample preparation. Here the results obtained with the standard soil type 2.1 will bediscussed in some detail. The most important property parameters of this soil type are

Figure 23. Measurement of diesel-contaminated soil samples with the Phazir portable spectrometer(the grey-shaded wavenumber range is characteristic of the water content and the arrows mark theCH-characteristic combination and overtone absorption bands of diesel).

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Table 11Selected property parameters of the standard soil type 2.1

Standard Soil Type 2.1

Sampling Date 22.03.2011Org. C in % 0.68 ± 0.15Nitrogen in % N 0.04 ± 0.01Particles <0.02 mm in % 7.7 ± 2.4pH-Valuc (.01 M CaCl2) 5.1 ± 0.4Cation Exchange Capacity (meq/100 g) 4.0 ± 1.0

summarized in Table 11. For the preparation of soil samples homogeneously contaminatedin the concentration range from 0 to 7% (w/w) the corresponding amounts of diesel oroil were dissolved in methylene chloride (CH2Cl2) and a certain amount of soil was satu-rated with this solution. Then, the methylene chloride was completely evaporated and thesoil sample was measured in a Petri dish at different positions (Figure 23). The replica-tion spectra were averaged and baseline corrected for development of calibration models.Additionally, the wavenumber region of the water combination band (5448–4686 cm−1)was eliminated before the PLS-1 calibration models were developed. The spectra of thecalibration sets for diesel and oil contamination are shown in Figures 24 and 25. Thecalibration/cross-validation parameters for the diesel and oil calibration models are sum-marized in Figures 26 and 27. The predictions of diesel- and oil-contaminated test sampleswith these calibrations are represented in Tables 12 and 13 and demonstrate excellentagreement with the reference values. Based on these promising results, further work willbe invested toward the transfer of this approach to real on-site areal and depth-profilingmeasurements for the evaluation of emergency cases.

Prior to the quantitative determination of hydrocarbon contamination, however, anidentification step is required. In this context, a principal component analysis (PCA) modelwas developed with the NIR spectra of differently contaminated soils measured using theportable Phazir in order to explore whether such a procedure is a feasible alternative for

Table 12Prediction of diesel-contaminated test samples (standard soil 2.1)

Reference % Prediction %Test sample (w/w) (w/w)

Test 1 0.74 0.63Test 2 0.91 0.82Test 3 1.43 1.20Test 4 2.18 1.89Test 5 3.12 2.97Test 6 3.57 3.38Test 7 3.79 3.58Test 8 4.54 4.60Test 9 5.38 5.37

Test 10 6.28 6.41

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Fig

ure

24.

NIR

calib

ratio

nsp

ectr

aof

soil

sam

ples

cont

amin

ated

with

dies

elin

the

conc

entr

atio

nra

nge

0–7%

(w/w

)(t

hegr

ey-s

hade

dw

aven

umbe

rra

nge

isch

arac

teri

stic

ofth

ew

ater

cont

ent)

.

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Fig

ure

25.

NIR

calib

ratio

nsp

ectr

aof

soil

sam

ples

cont

amin

ated

with

oili

nth

eco

ncen

trat

ion

rang

e0–

7%(w

/w)(

the

grey

-sha

ded

wav

enum

berr

ange

isch

arac

teri

stic

ofth

ew

ater

cont

ent)

.

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Table 13Prediction of oil-contaminated test samples (standard soil 2.1)

Test sample Reference % (w/w) Prediction % (w/w)

Test 1 0.40 0.65Test 2 1.18 1.26Test 3 1.76 1.74Test 4 2.34 2.21Test 5 3.02 3.35Test 6 3.40 3.22Test 7 3.93 3.62Test 8 4.48 4.36Test 9 5.33 5.51

Test 10 6.68 6.77

Figure 26. Calibration/cross-validation parameters of the PLS-1 models developed with the Phazirspectra for quantitative determination of diesel in standard soil type 2.1.

Figure 27. Calibration/cross-validation parameters of the PLS-1 models developed with the Phazirspectra for quantitative determination of oil in standard soil type 2.1.

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Fig

ure

28.

NIR

spec

tra

ofva

riou

sly

cont

amin

ated

and

unco

ntam

inat

edst

anda

rdso

ilty

pe2.

1.

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Figure 29. 3D score plot of the PCA analysis of NIR spectra of diesel-, oil-, and gasoline-contaminated soil type 2.1 and uncontaminated soil type 2.1.

discrimination of different hydrocarbons. Figure 28 shows representative NIR spectra ofvariously contaminated and uncontaminated standard soil 2.1. Visual inspection of thesespectra readily allows discrimination of the gasoline contamination, whereas oil and dieselcan hardly be differentiated. In the 3D score plot of a PCA of such spectra (Figure 29), thedifferent types of contamination can be separated and, based on this approach, an unknowncontamination can be rapidly predicted in an emergency case. Further improvement of theseparation of diesel and oil clusters can be achieved by the development of a PCA forthese two contaminants only. Thus, with the portable NIR spectrometer a complete chainof analytical procedures can be developed for qualitative and quantitative on-site analysisof hydrocarbon contamination.

Conclusions

A broad range of different applications was selected to demonstrate the performance ofhandheld Raman, FTIR, and NIR spectrometers for qualitative and quantitative chemicalquality control. The good identification and discrimination capability in combination withspectral libraries and PCA as well as the excellent quantitative determination of additives,active ingredients, and blend components in the liquid and solid state show that thesespectrometers can be used far beyond the originally planned scope of applications in thefield of homeland security and terror defense. The flexible use of these portable instrumentsfor on-site applications will open up a broad range of new applications in different industrialbranches and will revitalize the use of vibrational spectroscopy in quality and process controlin the near future.

Acknowledgments

The authors are grateful to Bundesanstalt fur Strassenwesen (Bergisch Gladbach, Germany)and Alfred Karcher Forderstiftung (Winnenden, Germany) for financial support of theseinvestigations. Furthermore, helpful discussions and the supply of useful data and spectrafrom Dietmar Keutel and Dieter Boehme (ServanTech GmbH, Rosbach von der Hohe,Germany), Chris Brown (formerly Ahura Scientific Inc., Wilmington, MA), and WilliamYang and Eric Bergles (BaySpec Inc., San Jose, CA) are gratefully acknowledged.

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