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6 FT-IR Technology for Today’s Spectroscopists August 2016
Articles 8 Vibrational Spectroscopic Discrimination of Herbal Medicines:
Polygala senega, Polygala tenuifolia, and Glinus oppositifoliusCornelia K. Pezzei, Oliver M.D. Lutz, Verena A. Huck-Pezzei, Sarah Kuderer, Brigitte Kopp, and Christian W. HuckA simple and rapid authentication method is presented for herbal medicine samples using commercially available mid-infrared and near-infrared benchtop spectrometers as well as using a handheld NIR device.
16 FT-IR Microscopic Analysis of Polymer Laminate Samples Including Transmission and ATR SpectroscopyRichard A. Larsen, Ken-ichi Akao, Jun Koshoubu, Kohei Tamura, and Hiroshi SugiyamaThe value of combining ATR and transmission spectra for the analysis of polymer laminates is illustrated here through the analysis of a multilayer polymer laminate from a food packaging sample.
26 Purity Analysis of Adulterated Essential Oils by FT-IR Spectroscopy and Partial-Least-Squares RegressionBrianda Elzey, Victoria Norman, Jamira Stephenson, David Pollard, and Sayo O. FakayodeThis study explores the use of FT-IR spectroscopy and PLS regression for the authentication of essential oils—wintergreen oil, tea tree oil, rosemary oil, and lemon eucalyptus oil—adulterated with either lemongrass oil or peppermint oil.
Cover images courtesy of Jasco, Inc.; TongRo Images/Getty Images; and Christopher Ames/Getty Images
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8 FT-IR Technology for Today’s Spectroscopists August 2016
A nalytical methods using infra-red (IR) spectroscopy have be-come indispensable tools in the
field of natural product analysis, rap-idly delivering results of high accuracy and robustness (1–6).
The Polygalaceae family is known to contain pharmaceutically important plants and the Polygala genus is espe-cially of medicinal importance because many Polygala species are used in folk medicine (7). Two important medicinal plants of the Polygala genus are Polygala tenuifolia and Polygala senega.
The root of Polygala tenuifolia is a well-known traditional Chinese medicine herb that is used as an ex-pectorant, a tranquilizer, and an anti-
psychotic agent (8), and for the treat-ment of neurological disorders such as dementia, amnesia, and neurasthenia (9). Polygala tenuifolia roots contain triterpenic saponins, and the dominat-ing compounds are tenuifolin and trit-erpene saponins with polygalacic acid as sapogenin. Furthermore, xanthone derivatives and oligosaccharides can be isolated (8).
Polygala senega roots show expec-torant and antitussive effects and are used to treat chronic bronchitis, cough, and tracheitis (10,11). Moreover an im-munopotentiation activity was proven (12). The root contains up to 12% tri-terpenic saponins with presenegin as a sapogenin. Further compounds are
Vibrational Spectroscopic Discrimination of Herbal Medicines: Polygala sen-ega, Polygala tenuifolia, and Glinus oppositifoliusThe noninvasive discrimination of powdered root material belong-ing to the Polygala genus and an adulterant is presented. The quality of the approach is assessed for attenuated total reflec-tance mid-infrared spectroscopy and diffuse reflectance near-infrared spectroscopy. Because of the pharmaceutical importance of Polygala-related plant material, conclusions are drawn toward a laboratory-independent discrimination of the samples.
Cornelia K. Pezzei, Oliver M.D. Lutz, Verena A. Huck-Pezzei, Sarah Kuderer, Brigitte Kopp, and Christian W. Huck
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10 FT-IR Technology for Today’s Spectroscopists August 2016
lipids, xanthone derivatives, and oligo-saccharides (11).
On the European market various Polygala adulterants are known. One of these adulterants is Glinus oppositi-folius (family: Molluginaceae), also known as Indian senega root (13), which is applied in Indian medicine for the treatment of skin diseases be-cause of its antiseptic and antiderma-titic properties (14).
The aim of this study is to establish a simple and rapid authentication method of Polygalae radix samples using com-mercially available mid-infrared (MIR) and near-infrared (NIR) benchtop spec-trometers alongside a topical laboratory independent handheld NIR device.
MethodsThe discrimination models were set up with 38 reference samples: 24 Polygala tenuifolia, 12 Polygala senega, and 2 Glinus oppositifolius root samples were subjected to MIR and NIR analysis. For the MIR measurements at a resolution of 4 cm-1 (1 cm-1 data point interval), a Spectrum 100 spectrometer (Perkin Elmer), equipped with an universal at-tenuated total reflectance (uATR) sam-pling accessory, was operated between 3000 and 650 cm-1, accumulating 16 scans for each spectrum. NIR analysis was performed with a NIRFlex N500 spectrometer (Büchi) operated at a resolution of 8 cm-1 (4 cm-1 data point interval), accumulating 64 scans for
PC-3 (5%)
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Figure 1: PCA score plot obtained with the benchtop NIR spectrometer. Green: Polygala tenuifolia, blue: Polygala senega, red: Glinus oppositifolius, pink: double-blind samples.
August 2016 FT-IR Technology for Today’s Spectroscopists 11
the spectral region between 10,000 and 4000 cm-1. The laboratory-independent measurements were obtained using a MicroPhazir GP 4.0 analyzer (Thermo Scientific) that was operated between 6267 and 4173 cm-1 at a resolution of 21 cm-1. For each spectrum, 10 scans were accumulated. The data pretreatment and the qualitative cluster analysis were performed with Unscrambler 10.3 soft-ware (Camo Software AS) using princi-pal component analysis (PCA) with the nonlinear iterative partial least squares algorithm. Every sample was measured in triplicate, and during the NIR mea-surements the sample compartment was rotated by 120° each time to average over the coarse grain of the ground samples.
Before the multivariate analysis, an aver-age spectrum was calculated for each set of replicates.
Besides full cross validation, the discrimination models were validated with two double-blind samples. After the spectroscopic analysis, the two double-blind samples were identified as Polygala senega root material via thin-layer chromatography (TLC) (15).
Results and DiscussionBenchtop NIR
For data pretreatment, full multiplica-tive scatter correction and a second de-rivative with nine smoothing points was used. The spectral region >6150 cm-1 was excluded from multivariate analy-
PC-3 (13%)
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Figure 2: PCA score plot obtained with the benchtop MIR spectrometer. Green: Polygala tenuifolia, blue: Polygala senega, red: Glinus oppositifolius, pink: double-blind samples.
12 FT-IR Technology for Today’s Spectroscopists August 2016
sis because of the absence of significant absorptions. The interspectral variance is optimally described with five latent variables of which the first, third, and fifth variables were found to correlate with the investigated problem. Figure 1 shows the three-dimensional (3D) score plot, indicating appropriate clustering of the samples. The two double-blind samples were correctly ascribed to the Polygala senega cluster. Apparently, the different grain size of the samples is suf-ficiently well accounted for by rotating the sample cuvette and multiplicative scatter correction.
Benchtop MIR
Since the samples were pressed firmly onto the ATR sampling accessory, the influence of the grain size can be con-
sidered to be of subordinate importance in this approach. Hence, only a second derivative with 21 smoothing points was used for the selected wavenumber regions 3000–2666 cm-1 and 1864–650 cm-1. The region between 2665 and 1865 cm-1 was excluded because of the absence of absorption bands and the fact that the spectrometer’s deuterated triglycinesulfate detector exhibited an impaired signal-to-noise ratio in this region. Figure 2 shows that the MIR-derived score plot has an inferior qual-ity, especially considering that one out-lier has been excluded from the sample group. Whereas the two double-blind samples were correctly assigned to the Polygala senega cluster, the Polyg-ala tenuifolia samples are distributed broadly over the score plot. Because the
PC-1 (76%)
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Figure 3: PCA score plot obtained for the mobile NIR spectrometer. Green: Polygala tenuifolia, blue: Polygala senega, red: Glinus oppositifolius, pink: double-blind samples.
August 2016 FT-IR Technology for Today’s Spectroscopists 13
NIR-derived score plot (Figure 1) also exhibited a wide distribution of the Po-lygala tenuifolia samples, the grain size may be ruled out from having a signifi-cant influence on the qualitative cluster analysis. Since the ATR technique in-herently defines the penetration depth of the incident light, an impaired per-formance of this MIR-based approach seems plausible, especially considering that NIR light per se allows for a larger sample thickness because of the higher energy.
Mobile NIR
As a consequence of the promising performance of the benchtop NIR spectrometer, a commercially avail-able mobile device was used to inves-tigate the in-field applicability of the approach. The principal component analysis in Figure 3 is based on spectra that have been subjected to multipli-cative scatter correction and a second derivative with five smoothing points. Considering the lower resolution and the limited spectral region, the most important spectral features necessary for a successful discrimination still seem to be captured with success. The absorption features responsible for the discrimination are summarized in Table I and have been identified from the loadings plots that were obtained during PCA.
Since the distribution of the Polyg-ala tenuifolia samples is much more evident as observed for the benchtop NIR score plot, more samples would be required until a definitive conclu-sion can be drawn toward applicability of the mobile appliance. As a tentative indicator of the discrimination’s plau-sibility, however, the two double-blind samples could still be ascribed to the appropriate group successfully.
Concluding RemarksFrom the data presented, it was con-cluded that a noninvasive discrimination of the two important Polygala species and an adulterant is plausible when using spectroscopic techniques. Even though the data obtained with the mobile NIR spectrometer are less precise, the recent development of microchip-based infra-red sensors (16) can be considered an important step toward reliable labora-tory independent identification of phar-maceutically relevant herbs. Moreover, such mobile devices are currently being applied in industrially relevant qualita-tive (2) and quantitative (1,17) contexts, emphasizing the demand for in-field spectroscopic techniques.
AcknowledgmentsSpecial thanks are given to Leopold-Franzens University Innsbruck, Aus-tria (Hypo Bank Forschungsförder-
Table I: Characteristic absorption features responsible for the successful discrimination of the samples
Band Center Assignment Functionality
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14 FT-IR Technology for Today’s Spectroscopists August 2016
ung), project “Quantum cascade lasers in mid infrared spectroscopy – a novel approach for infrared characterization of highly absorbing liquid and solids” (2012), the Ministry for Science and Research and the Ministry for Health, Family and Youth (Vienna, Austria) (Novel analytical tools for quality control of immunomodulatory, anti-inflammatory, and neuroprotective agents in traditional Chinese medicine).
References(1) M. Alcalà, M. Blanco, D. Moyano, N.
Broad, N.A. O’Brien, D. Friedrich, F.
Pfeifer, and H. Siesler, J. Near Infrar.
Spectrosc. 21, 445–457 (2013).
(2) N.A. O’Brien, C. Hulse, F. Pfeifer, and
H. Siesler, J. Near Infrar. Spectrosc. 21, 299–305 (2013).
(3) M. Popp, G. Bonn, C. Huck, and W.
Guggenbichler, Method for Classifying
Wine and Coffee, 2003. WO Patent App.
PCT/EP2002/004,988.
(4) M. Schmutzler, C.W. Huck, Vibr.
Spectrosc. 72, 97–104 (2014).
(5) L.M. Reid, C.P. O’Donnell, and G.
Downey, Trends Food Sci. & Techn. 17, 344–353 (2006).
(6) B.M. Nicola, K. Beullens, E. Bobelyn,
A. Peirs, W. Saeys, K.I. Theron, and J.
Lammertyn, Postharv. Biol. and Technol.
46, 99–118 (2007).
(7) L.C. Klein Júnior, S. Faloni de Andrade,
and V.C. Filho, Chem. and Biodiv. 9, 181–209 (2012).
(8) W. Tang and G. Eisenbrand, Pharmacol.,
Toxicol. 2, 919–922 (2011).
(9) H.-M. Chang, P.P. But, S.-C. Yao, L. Wang,
and S. Yeung, Pharmacology and
Applications of Chinese Materia Medica,
1, 1029–1032 (2000). (10) E. Steinegger, R. Hänsel, in
Pharmakognosie (Springer, 1999), pp.
212–213.
(11) M. Wichtl, J.A. Brinckmann, and M.P.
Lindenmaier, Schweizer Zeitschri. f.
Ganzheitsmed. 4, 464–466 (2002).
(12) A. Estrada, G.S. Katselis, B. Laarveld, and
B. Barl, Comp. Immunol., Microbiol.and
Infect. Dis. 23, 27–43 (2000).
(13) R. Hegnauer, inChemotaxonomie der
Panzen (Springer, 1964), pp. 30–35.
(14) B.N. Sastri, in The Wealth of India, Raw
Materials (Council of scientific and
industrial research, volume 6, New Delhi,
India,1962), pp. 396–397.
(15) S. Kuderer, “Wissenschaftliche
Erstellung der Monographie Polygalae
extractum uidum für das österreichische
Arzneibuch,” diploma thesis, University
of Vienna (2014).
(16) N.A. O’Brien, C.A. Hulse, D.M. Friedrich,
F.J.V. Milligen, M.K. von Gunten, F. Pfeifer,
and H.W. Siesler, “Miniature Near-
Infrared (NIR) Spectrometer Engine For
Handheld Applications” presented at
SPIE DSS, Baltimore, Maryland, 2012.
(17) O.M. Lutz, G.K. Bonn, B.M. Rode, and
C.W. Huck, Anal. Chim. Acta 826, 61–68 (2014).
Cornelia K. Pezzei, Oliver M.D. Lutz, Verena A. Huck-Pezzei, and Christian W. Huck are with Institute of Analytical Chemistry and Radiochemistry at the University of Innsbruck, in Innsbruck, Austria. Sarah Kuderer and Brigitte Kopp are with the Department of Pharmacognosy at the University of Vienna in Vienna, Austria. Direct correspondence to: [email protected] ◾
For more information on this topic, please visit our homepage at: www.spectroscopyonline.com
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Fourier transform infrared (FT-IR) microscope systems allow the analy-sis of extremely small samples (<250
μm) using IR spectroscopy. Mapping ex-periments made with a single-element de-tector can provide an IR image of a larger area of a sample with a defined spatial resolution. There are numerous applica-tions for FT-IR microscopes and imag-ing systems, including polymer analysis, pharmaceutical and materials analysis, forensic investigations, semiconductors, biochemistry, and chemical analysis, among others. For almost any sample that can be scanned with a traditional IR
method to obtain macro data from the sample, FT-IR microscopy can be used to collect the same spectra from a much smaller, defined area of the sample. FT-IR microscope systems can provide a simple spectrum of a very small contaminant in a larger matrix or detailed information about the distribution of the chemical constituents or other types of spatial in-formation—that is, the variation and dis-tribution of layers in a polymer laminate.
In the analysis of polymer laminates, information concerning the identity and spatial distribution of the various layers is of critical importance for verifying
FT-IR Microscopic Analysis of Polymer Laminate Samples Including Transmission and ATR SpectroscopyPolymer laminates typically make complex samples for infrared (IR) analysis, comprising multiple layers with defined thicknesses, in some cases <10 μm. When measuring extremely narrow laminate layers, the use of attenuated total reflectance (ATR) may provide improved spectra of the laminate cross-section, because ATR microscope objectives offer a greater spatial resolution than transmission because of additional magnification. This article details the preparation of polymer laminate sample cross-sections and the collection of transmission and ATR spectra of various layers. Further analysis of the laminate spectra is also explored using a multivariate curve resolution (MCR) algorithm. An example laminate sample is examined using all the tools available on a standard Fourier transform infrared (FT-IR) microscope.
Richard A. Larsen, Ken-ichi Akao, Jun Koshoubu, Kohei Tamura, and Hiroshi Sugiyama
August 2016 FT-IR Technology for Today’s Spectroscopists 17
the production quality of the laminate or inclusion of contaminants from the manufacturing process, or providing information about a competitive prod-uct. Collection of a lattice mapping file using a very small aperture can provide information about the laminate layers and their spatial arrangement. Polymer laminate samples include unique charac-teristics and are found in a wide array of applications, most often for food packag-ing. With the incorporation of a number of different polymer materials, laminates can include polymer layers favorable for printing, layers that provide oxygen or moisture barriers, as well as polymers that are ideal for heat sealing. Some lami-nates can be quite simple, consisting of two or three materials, while others can be a lot more complex, comprising multi-ple layers of various thicknesses. In some cases, the “tie layers” used to bond dis-similar polymer materials can be quite thin, at or below the spatial resolution capabilities of an IR transmission micro-scope (<10 μm). Thus, polymer laminates present a challenging analysis problem, often requiring advanced methods to properly measure and analyze the lami-nate components.
To provide visual images that are easily understood by nonusers, the IR spectral image data can be displayed as false color image maps based on peak in-tensity, calculations of peak height–ratio data, or peak area–ratio data. Additional calculations can also provide images based on peak shift or full-width at half maximum (FWHM) values. Image data from these various calculations can also be displayed as contour maps, or three-dimensional (3D) images to provide image contrast for information purposes. These various options are all available in the Jasco microscope data
processing software. This analysis soft-ware displays the automatically collected video images as well as individual spec-tra, image maps, and calculated data for easy interpretation of the data. If data are collected from multiple sample sites, the individual spectra can be selected with the corresponding video images for each sample area (1).
MCR AnalysisA sample with a multicomponent matrix can be difficult to analyze and provide a color image with spatial distinction be-tween components. There are numerous chemometrics processing algorithms that can be applied to data of this type, including partial least squares (PLS) regression or principal components analysis (PCA). A multivariate curve resolution (MCR) program can be used to process an array of spectral data and provide an analysis and spectral separa-tion without detailed knowledge of the components and their concentrations. Chemical components are separated by distinct spectral variations and a distri-bution map can be developed based on this component segregation. Standard chemometric methods that provide this capability are available, but they often re-quire a large amount of user interaction. The MCR method can analyze the array of spectral data unattended, providing the completed results for review and can, where necessary, be revised for the final selection of predicted components.
ExperimentalAn IRT-5200 FT-IR microscope system with an XYZ auto-stage, 16× transmis-sion cassegrain, a ClearView ZnS atten-uated total reflectance (ATR) objective and single-element mercury cadmium telluride (MCT) detector (all from Jasco)
18 FT-IR Technology for Today’s Spectroscopists August 2016
was used to collect transmission and ATR spectra from a multilayer polymer laminate sample (obtained from pack-aging material used for meat products). The microscope is coupled with an FT/IR-4600 FT-IR spectrometer (Jasco), an instrument that provides a high signal-to-noise ratio, spectral resolution to 0.7 cm-1 and a spectral range of 7800–350 cm-1.
To measure the transmission spec-tra of the multilayer laminate sample, a cross-section of the layered sample is required, with a sample thickness of ap-proximately 20–40 μm. Many different methods can be used to obtain a cross-section of a laminate—various devices have been used for the sample prepara-tion, including microtomes and other slicing tools, many of which require con-siderable time and patience to use. The laminate sample in this paper was cross-sectioned using a SliceMaster slicing tool (Jasco), a small cutting device that can be placed on the stage of a low power optical microscope so that the sample preparation can be observed during the cross-section cutting process. The sample is placed into a sample clamping device, with the laminate layers at 90° to the cutting device, a simple sharp blade. The sample movement under the cutting blade can be finely adjusted by the user to obtain the required thickness of the cross-section. Sample preparation can be completed within minutes and the slic-ing tool can be used to prepare samples of various thicknesses. Several cross-section samples were prepared using the slicing tool and samples with suitable thicknesses were selected and examined with the FT-IR microscope using both transmission and ATR measurement.
The laminate sample was placed onto a BaF2 window and a lattice map was specified across the breadth of the lami-
nate sample, ensuring collection of the transmission spectra of all the layers in the laminate cross-section. The trans-mission spectra of the laminate cross-section were measured using a 10 × 50 μm aperture, with 8 cm-1 resolution and accumulation of 64 background and sample scans for each spectrum using a mid-band MCT detector. Spectra of the laminate layers were also measured with the ClearView ZnS ATR objective using the IQ Mapping function of the IRT-5200 microscope.
For a standard ATR measurement, a sample is raised using the sample stage to make contact with the ATR objec-tive. A pressure stage on the microscope stage provides a monitor of the pressure applied to the sample against the ATR objective, ensuring that the ATR crys-tal in the objective is not damaged by excessive pressure. Conventional ATR objectives require that the center of the ATR crystal area be in contact with the sample position to be measured, and a microscope aperture is used to mask only the center portion of the total ATR contact area to select the measurement area of the sample. While this works well for many types of samples, in the case of a multicomponent sample, this data col-lection method can result in a spectrum that contains contributions from all of the components in the sample matrix. Alternatively, users can collect multiple ATR spectra, moving the sample to “cen-ter” the component of interest to the ATR objective element, but this approach can still result in spectra with contributions from the other components in the ma-trix.
The mapping feature used in the IRT-5200 microscope uses a movable mirror in the optical path of the microscope sys-tem to allow spectral measurement from
August 2016 FT-IR Technology for Today’s Spectroscopists 19
anywhere within the optical focal plane provided by the standard cassegrain ob-jective or, in the case of an ATR objec-tive, anywhere within the primary con-tact area of the ATR crystal. Using this dynamic measurement system, spectra can be obtained from either single or multiple points on the sample, or a line or grid map of the sample, all without moving the ATR objective or the sample stage. By optimizing the microscope ap-erture together with the mapping feature, the entire ATR objective–sample contact area can be used to collect individual spectra at multiple points on the sample without lowering, repositioning of the sample and then obtaining the ATR objective contact. The biggest advan-tage of the mapping feature is saving time, as well as not having to obtain multiple contacts with the sample area. This mapping system can provide data specific to the different components in the sample matrix in a single contact with the sample. The spectral matrix
obtained from this ATR mapping can be used to provide spectra of the in-dividual components without further data processing.
A suitable cross-section of the laminate was selected from the various samples prepared using the slicing tool and se-cured with tape onto a glass microscope
Ab
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Polyethylene vinylacetatecopolymer
Poly(ethylene-co-acrylic acid)
Ethylene vinyl acetate
Hydrogenatedpolyethylene (slip agent?)
Polyethylene
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Terephthalate
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Figure 2: Transmission spectra of polymer laminate layers.
(a)
(b)
Figure 1: Video micrographs of laminate sample collected (a) before the ATR contact and (b) after ATR contact, demonstrating the area of the sample compressed by the ATR element in the objective.
20 FT-IR Technology for Today’s Spectroscopists August 2016
slide, to prevent movement during mea-surement. A lattice map of the laminate sample cross-section was measured using a 5 × 5 μm aperture to obtain ATR spec-tra of the individual laminate layers in contact with the ATR objective. Figure 1 provides video photographs of the lami-nate sample before and after the ATR ob-jective contact with the sample.
Results and DiscussionReviewing the transmission data from the lattice mapping experiment, only five different polymer layers were identified within the laminate, based on functional group absorption peaks selected from the individual spectra, including the polyethylene, polyethylene terephthalate, ethylene-vinyl acetate, poly(ethylene-co-acrylic acid), and hydrolyzed polyethyl-ene layers (Figure 2). However, when the lattice transmission data were analyzed using the MCR software, six different polymer layers were identified, with dis-
tinct spectral differences among the spec-tra, adding the polyethylene vinylacetate copolymer layer just before the polyeth-ylene terephthalate (Figure 2). The iden-tification of the polymer compounds was verified by a spectral database search of polymer library spectra using the Sadtler Know-It-All search package (Bio-Rad).
The 3D spectra display outlined in Figure 3 provides a visual confirmation of the presence of the six different layers; the functional groups can be easily iden-tified in the 3D display. Using the graphic display in the analysis software package, the 3D model in Figure 3 can be rotated, expanded, and the axes modified to pro-vide a more extensive visual analysis of the data recorded by the microscope sys-tem, confirming the MCR results.
Based on this information, the absor-bance increase and decrease for the func-tional group absorptions can be exam-ined and overlaid to provide a trace of the laminate peaks identified in each layer, as
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Figure 3: 3D display of transmission spectra collected on the y-axis (breadth) of the polymer laminate cross-section, displaying spectra of the multilayer components in the laminate.
August 2016 FT-IR Technology for Today’s Spectroscopists 21
0.8
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OH Str.(PE-VA)
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Figure 4: Spatial distribution of laminate layers based on peaks from transmission spectra.
Hydrogenated
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Polyethylene
terephthalate
(PET)
Ethylene vinyl
acetate (EVA)
Polyethylene (PE)Poly (ethylene-
co-acrylic acid)
Polyethylene vinyl
acetate (PE-VA)
Figure 5: False-color maps of laminate transmission spectra, based on labeled peaks displayed for transmission spectra.
22 FT-IR Technology for Today’s Spectroscopists August 2016
displayed in Figure 4. This figure allows a calculation of the layer thickness based on the intensity changes recorded for the functional group vibrational modes associated with the laminate polymer peaks in the sample. Although the C-H bending mode at 1464 cm-1 is present in a majority of the polymer layers, there are other functional group peaks that can be identified, providing additional informa-tion for the layer thicknesses.
The color images for the various polymer layers, which were created using the MCR results and the specific absorption peaks found in the differ-ent polymers, are displayed in Figure 5. The false-color maps displayed in Figure 5 present a visual chemical and spatial distribution for rapidly visual-izing the features of interest within the images. These individual color maps can be overlaid to provide a compre-hensive, detailed image of the chemical and spatial distribution of the layers as presented in Figure 6, in which the indi-vidual color-coded layers are identified by the polymer compounds and their unique absorption peaks.
Figure 7 illustrates the ATR spectra selected from the lattice mapping of the
laminate. The same component layers identified in the transmission spectra are confirmed by library search of the ATR spectra. Individual false-color maps of the laminate components can be developed based on the ATR spec-tra, as well as the other types of visual displays, similar to the transmission data. A combined color overlay map of the chemical components, displayed in Figure 8, was developed from the peaks labeled in the ATR spectra (Figure 7). As can be observed in Figure 1, the ATR objective does compress the laminate polymer during contact, so layer thick-ness cannot be properly calculated from the spatial information provided by the lattice mapping file.
ConclusionsA multilayer polymer laminate from a food packaging sample was cross-sectioned using a slicing tool to obtain transmission and ATR spectra using an FT-IR microscope system. In addition, the mapping capability with the ATR objective provides a simpler method for analysis of multicomponent samples. Color image maps and overlays were derived from the transmission and ATR
Combined chemical image overlays
1681.5
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m)
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1740 cm–1
3310 cm–1
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3315 cm–1
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EVA
PE-OH
Polyethylene
PE-VA
PET
1547.5100 120130140150 0 0 0 0 0
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X (μm)
Figure 6: Chemical image overlay derived from individual color maps (Figure 5) of transmission spectra (Figure 2).
August 2016 FT-IR Technology for Today’s Spectroscopists 23
spectra collected from the laminate cross-section samples. Image overlays of the transmission and ATR data provide visual pictures that readily identified the individual components in the laminate. The transmission information can also be used to provide trace data that can be used to outline the thickness of the in-dividual layers, which provides another picture of the interaction of the various laminate layers. The ATR data can be used to provide similar views of the lami-nate layers, but no extra polymer layers were observed, even with the greater spa-tial resolution obtained when using the ATR objective.
Reference(1) R. Larsen, K. Akao, and J. Koshoubu, “An
Upgradeable FT-IR Accessory,” American
Laboratory 41, 14–17 (2009).
Richard A. Larsen retired from Jasco in July 2016. Ken-ichi Akao, Jun Koshoubu, Kohei Tamura, and Hiroshi Sugiyama are with Jasco Corporation in Tokyo, Japan. Direct correspondence to: [email protected]. ◾
2.3
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04001.57 3000 2000 1000
1710.55 cm–1
3316.96 cm–1
3314.58 cm–1
1737.55 cm–1
1465.63 cm–1
1560.13 cm–1
850Wavenumber (cm–1)
Ab
sorb
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ce
Figure 7: ATR spectra selected from the lattice map.
Combined chemical image overlays
0.2 0.01 0.03 0.03 0.04
0 0 0 0 0
170
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50
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μm
)
X (μm)
00 50 100 150170
1554 cm–1
1737 cm–1
3316 cm–1
1463 cm–1
3316 cm–1
1716 cm–1
PE co-acrylic
EVA
PE-OH
Polyethylene
PE-VA
PET
Figure 8: Chemical image overlay derived from the ATR spectra of the laminate polymers (Figure 7).
For more information on this topic, please visit our homepage at: www.spectroscopyonline.com
ADVERTISEMENT24 Molecular Spectroscopy
Combining a high precision FT-IR spectrometer with a long pathlength gas cell provides a powerful tool for analyz-
ing trace levels of contaminants in air and other gas mixtures (Figure 1). Two important applications of this are ensuring air quality and the purity of breathing oxygen and compressed air.
QA/QC and Air PurityInfrared gas analysis is critical in screening for trace con-tamination in aviator’s breathing oxygen (ABO). Both the U.S. Air Force and Navy have systems to verify that no dangerous contaminants are present.
Spectra were acquired in a 10-m gas cell from gas stan-dards used to calibrate the Air Force method. Spectra were acquired with a 1 min scan time at 1 cm-1 resolution. The Thermo Scientific™ TQ Analyst™ software method was calibrated with spectra obtained from gas standards pur-chased from Scott Specialties Gases® and tested on a spec-trum of the Navy test gas as shown in Figure 2.
These results (in ppm) show a standard error of less than 100 ppb for most of the 20 components. Similar methods are used in many industries to detect trace contamination and moisture.
Environmental and Air MonitoringVolatile organic species and other pollutants are regularly detected using IR spectroscopy. These hazardous com-pounds may originate from a manufacturing process, land fill off-gas, vehicle exhaust, or a chemical spill. A major
challenge in measuring pollutants in the air is the strong infrared absorbance for both water and CO2, which block many useful spectral regions. In many applications, con-tinuous extractive sampling, Summa canisters, or Tedlar®
Gas Analysis with the Nicolet iS50 FT-IR SpectrometerSteve Lowry, PhD, Senior Applications Scientist, Thermo Fisher Scientific
Figure 1: Rugged 10-m multi-pass white cell
mounted in the Thermo Scientific™ Nicolet™ iS™ 50
spectrometer provides sub ppm detection for many
pollutants.
Figure 2: Calculated concentrations from a sample of the Navy reference sample containing trace levels of
potential contaminants in aviator’s breathing oxygen.
4000 3500 3000 2500 2000 1500 1000 500
0.42
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Wavenumber (cm–1)
Navy Validation Standard: Nicolet iS50 Res=1760T 23C
ADVERTISEMENT Molecular Spectroscopy 25
bags are used to collect samples that can be pulled into the 10-m gas cell. In high humidity situations, the gas cell may be heated to ensure water does not condense.
A spectral resolution of 0.5 cm-1 may also be necessary to create analysis windows between the water and CO2 peaks, as shown in Figure 3.
ConclusionWe have presented an overview of gas analysis application areas where infrared spectroscopy has proven valuable. The Nicolet iS50 FT-IR spectrometer is designed with flexibility to analyze a variety of sample types with ease. The combina-tion of high sensitivity and a full suite of software features, specifically designed for gas analysis, creates a world- class solution to a broad range of applications.
105 Spectrum of water and CO2: 2M Heated Gas Cell 100C 650T
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Figure 3: Spectrum showing very strong features corresponding to water and CO2 that make air quality
and combustion monitoring a challenge.
Thermo Fisher Scientific
5225 Verona Road, Madison, WI 53711
tel. (800) 532-4752, (608) 276-6100; fax: (608)273-5046
Website: www.thermofisher.com/ftir
©2015 Thermo Fisher Scientific Inc. All rights reserved. Scott Gas is a part of Air Liquide America Specialty Gases LLC. Tedlar is a registered trademark of E. I. du Pont de Nemours and Company. All other trademarks are the property of Thermo Fisher Scientific Inc. and its subsidiaries. Specifications, terms and pricing are subject to change. Not all products are available in all countries.
26 FT-IR Technology for Today’s Spectroscopists August 2016
T he market for essential oils (EOs) was approximately $5.5 billion in 2014 and is projected to reach
$11.6 billion by the year 2022 (1). High therapeutic properties and market values of EOs (2–8) make them ideal candidates for potential counterfeiting or adulteration with low quality, cheap oils by unscrupulous traffickers, cartel members, or individuals for financial gain. Unsuspecting consumers often inadvertently purchase substandard, adulterated, or counterfeited consum-able goods with reduced therapeutic effects (2–8). The sales of substandard, adulterated, or counterfeited EOs also
have negative economic implications for the EO producers and marketers as well as potential health issues for unsuspecting customers. For instance, the use of adulterated EOs in cosmet-ics, therapeutics, or food may result in skin irritation and other related health issues (7). Frankly, the sale of sub-standard consumable goods and the associated negative health impact on consumers and the economic loss for the industry has become a global issue and a high-priority challenge in recent years (2–8).
Abating the sale of substandard, adulterated, and counterfeited con-
Purity Analysis of Adulterated Essential Oils by FT-IR Spectroscopy and Partial-Least-Squares RegressionThis study reports a combined use of ordinary Fourier transform infrared spectroscopy (FT-IR) in conjunction with partial-least-squares (PLS) multi-variate regression for accurate determination of the percent compositions of four essential oils (EOs) (wintergreen, tea tree, rosemary, and lemon eucalyptus oils) that were adulterated either with lemongrass essential oil or peppermint essential oil. The FT-IR spectra of the calibration sample sets of known compositions of adulterated EOs with lemongrass oil or pep-permint oil were measured and subjected to PLS multivariate regression analysis. The simplicity, low cost, and high accuracy of the protocol makes it appealing for routine industrial quality assurance of consumable goods.
Brianda Elzey, Victoria Norman, Jamira Stephenson, David Pollard, and Sayo O. Fakayode
August 2016 FT-IR Technology for Today’s Spectroscopists 27
sumer products can only be achieved through concerted efforts and strong collaborations between the major stake-holders, including the food industries, marketers, and government, regulatory, and enforcement agencies. The develop-ment of a low-cost analytical protocol that is capable of rapid, accurate analy-sis of the authenticity of adulterated and counterfeited consumable goods there-fore continues to be an active research area of a considerable interest to major shareholders both for economic reasons and to protect the safety and well-being of consumers. Toward this effort, regu-latory agencies such as the United States Food and Drug Administration (FDA), the United States Department of Agri-culture (USDA), and the World Health Organization (WHO) have been pro-active in the monitoring of the quality and purity of various consumer goods to ensure adequate quality control and quality assurance (2,4).
Analytical techniques including gas chromatography (GC) (9,10), high performance liquid chromatography (HPLC) (11–14), nuclear magnetic resonance (NMR) spectroscopy (15,16), and electroanalytical techniques (17,18) have been well developed for the analy-sis and evaluation of the authenticity of consumable products, with excel-lent accuracy and precision. However, some of these protocols have inherent drawbacks including long analysis times, lack of portability, and the high cost of instrumentation that limit their utility and wide applicability for rapid and routine food analysis. In addition, some of the available protocols may require specialized training and skills, further hindering their wide usage. To address these challenges, the utility of ordinary, inexpensive, and simple ana-
lytical spectroscopic techniques such as Raman (19–22), f luorescence (23–27), near infrared (28,29), and Fourier transform infrared (FT-IR) (30–35) in conjunction with multivariate regres-sion analysis has been explored for au-thentication and quality assurance of various food products. The combined use of analytical spectroscopy and multivariate regression analysis has become more appealing because it al-lows rapid analysis at reduced sample size with minimal to no sample prepa-ration. Most importantly, Raman and IR spectrometers are portable, easy to use, and relatively inexpensive, allowing affordable in situ field sample analysis.
A recent study in our laboratory (30) demonstrated the capability of the combined use of FT-IR spectroscopy and PLS multivariate regression for authentication and accurate determi-nation of purity and percent composi-tion of adulterated natural oils (neem and flaxseed oils) with edible vegetable and extravirgin olive oils, with excel-lent accuracy. The current study ex-plores the use of FT-IR spectroscopy and PLS regression for the authentica-tion of essential oils (EOs) (wintergreen oil [WO], tea tree oil [TTO], rosemary [RO], and lemon eucalyptus oil [LEO]) adulterated with either lemongrass oil or peppermint oil. Unlike the “natu-ral oils” in the previous study, that are primarily triacylglycerols, the investi-gated “essential oils” (WO, TTO, RO, and LEO) in the current study are ter-penoids and belong to different chemi-cal classes. Keeping in mind the high market values of WO, TTO, RO, and LEO, they are potential targets for adul-teration and counterfeiting, necessitat-ing the need for development of quality assurance and authentication protocols.
28 FT-IR Technology for Today’s Spectroscopists August 2016
For instance, the average market values of WO ($3.82 per ounce), TTO ($2.32 per ounce), RO ($3.54 per ounce), and LEO ($2.25 per ounce) are higher than the market values of lemongrass oil ($1.95 per ounce) and peppermint oil ($1.78 per ounce). As a result, a more expensive WO, TTO, RO, and LEO may potentially be adulterated with relatively cheaper lemongrass oil and peppermint oil. In addition to high market values, these essential oils were also selected for the study because of their various therapeutic, cosmetic, and agrochemical applications. For instance, wintergreen oil (Gaulthe-ria procumbens) is made up of 99% methyl salicylate. Wintergreen oil has been used to treat respiratory tract in-fections and musculoskeletal pain, to increase endurance and respiratory capacity, as a food flavoring, and as a common component in chewing gum and toothpaste because of its minty fla-vor (36). Tea tree oil, the essential oil of Melaleuca alternifolia, is made up of approximately 41% terpinen-4-ol. Tea tree oil has been extensively used in traditional medicine in Australia and more recently worldwide because of its antimicrobial and anti-inflammatory activity (37). Rosemary oil (Rosma-rinus officinalis L.) is composed of approximately 16% 1,8-cineole. Rose-mary plants are cultivated worldwide, and the essential oil has been used for its strong antioxidant and antimicro-bial activities (38–40). Rosemary plant species also have many other beneficial characteristics including antiviral, anti-inflammatory, and anticarcinogenic activity (38,41). Lemon eucalyptus oil (LEO) is a plant-based oil derived from the leaves of the Eucalyptus citrodora tree and is made up of nearly 76% cit-
ronellal. Lemon eucalyptus oil is widely used in perfumery, cleaning, cosmetics, pesticides, and room fresheners. The antibacterial, antifungal, and insecti-cidal activities of essential oil from Eu-calyptus citriodora have been previously reported (42).
ExperimentalMaterials and Methods Chemicals and SuppliesThe samples of primary essential oils—WO, TTO, RO, and LEO—and adulter-ant essential oils, lemongrass oil and peppermint oil, were purchased from New Directions Aromatics Inc.
Sample Preparation
FT-IR Measurement, and Chemometrics PLS Multivariate Regression AnalysisAll glassware used was thoroughly washed in acetone and rinsed with ul-trapure water (Thermo Scientific, Gen-Pure UV-TOC/UF). Between 40 and 50 samples of varying compositions of WO, TTO, RO, and LEO adulterated with either lemongrass oil or pepper-mint oil ranging from 1 to 90% (wt/wt) were prepared by carefully weighing the appropriate weights of the primary oil and the adulterant in sample vials. The samples were subsequently gently shaken and kept at room temperature for approximately 48 h to facilitate the proper mixing, equilibration, and homogenization of the samples.
The FT-IR spectra of the samples were recorded in reflection mode using an FT-IR spectrometer (IRAffinity-1, Shimadzu Corporation). The spec-trometer was equipped with a MiRacle ZnSe 3B crystal plate attenuated total reflectance (ATR) device mounted on a Shimadzu platform. The MiRacle ZnSe
August 2016 FT-IR Technology for Today’s Spectroscopists 29
3B crystal plate permits the rapid and ac-curate measurement of a small sample size with high sensitivity, accuracy, and reproducibility.
The FT-IR spectrum of each sample was scanned 100 times with a resolution of 4 cm-1 over a 600–4000 cm-1 wave-number range. The FT-IR spectrometer was routinely calibrated using a poly-styrene standard before each use to en-sure wavelength accuracy. Spectral data analysis and PLS multivariate regression analysis was performed using Unscram-bler software (CAMO Software, 9.8).
Results and DiscussionFT-IR Spectra of Adulterated WO,
TTO, RO, and LEO Samples with
Lemongrass and Peppermint Oil
Figure 1 shows the FT-IR spectra of the pure unadulterated WO, TTO, RO, LEO, lemongrass oil, and peppermint
oil, showing the expected characteristic C-H stretch (~2900 cm-1), C=O stretch (~1700 cm-1), broad O-H stretch (~3400 cm-1), and C-O stretch (~1100 cm-1) of terpenoid components in the essential oils. The compositions and constituents of essential oils may vary and highly depend on the geochemistry of the soil where it is cultivated. In general, essen-tial oils are made up of terpenes such as terpineol, cineole, citronellal, and oth-ers (43). The FT-IR spectra of samples containing multicomponent, aggregates, or composites of constituents are addi-tive. Thus, the observed FT-IR spectra in Figure 1 are the resultant FT-IR spectra of the components of essential oil.
Figures 2a–d (left) show the FT-IR spectra of the samples with varying percent compositions of WO, TTO, RO, and LEO adulterated with lemongrass oil used for PLS multivariate regression
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Figure 1: FT-IR spectra of pure samples of: (a) wintergreen oil (WO), (b) tea tree oil (TTO), (c) rosemary oil (RO), (d) lemon eucalyptus oil (LEO), (e) lemongrass oil (LO), and (f) peppermint oil (PO).
30 FT-IR Technology for Today’s Spectroscopists August 2016
calibration and model development. The FT-IR spectra of the samples were observed to change with percent compo-sitions of WO, TTO, RO, and LEO in the samples adulterated with lemongrass oil. A considerable overlapping of FT-IR spectra was obtained with changes in the percent compositions of WO, TTO, RO, and LEO in the lemongrass oil adul-terated samples. Figures 2a–d (right) are the cross sections of the FT-IR spectra in Figure 2a–d left. In the supplemen-tary section on-line, Figure 1 shows the
FT-IR spectra of the calibration samples of varying percent compositions of WO, TTO, RO, and LEO adulterated with peppermint oil. Once again, the FT-IR spectra of adulterated WO, TTO, RO, and LEO with peppermint oil samples vary with changes in the percent com-position of WO, TTO, RO, and LEO in the adulterated samples. Figures 1a-d (right) on-line show the cross sections of the FT-IR spectra in Figures 1a–d (left) on-line. However, the observed varia-tions and changes in FT-IR spectra of
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Figure 2: FT-IR spectra of calibration samples of: (a, left) WO, (b, left) TTO, (c, left) RO, and (d, left) LEO. Cross sections of FT-IR spectra of: (a, right) WO, (b, right) TTO, (c, right) RO, and (d, right) LEO adulterated with lemongrass oil. (Lemongrass oil adulteration ranged between 1% and 90% [wt/wt].)
August 2016 FT-IR Technology for Today’s Spectroscopists 31
adulterated oil of the calibration samples are highly dependent on the primary essential oil and the adulterant oil used for the oil adulteration. For instance, a more prominent variation in the FT-IR spectra in O-H region was observed for adulterated LEO with peppermint oil, suggesting differences in the interaction of the primary essential oil and adulter-ant oils (43).
Determination of Percent
Composition of Adulterated WO,
TTO, RO, and LEO with Lemongrass
Oil and Peppermint Oil Multivariate
PLS Regression Analysis
PLS regression analysis was used to cor-relate changes in FT-IR spectra data of WO, TTO, RO, and LEO adulterated with lemongrass oil (in Figure 2) or pep-permint oil (Figure 1 in the supplemen-tary section on-line) with wt/wt percent composition of WO, TTO, RO, and LEO of the adulterated samples. The advan-tages of multivariate regression over the use of univariate regression (analysis at one wavelength, typically at lambda max) for rapid and accurate determina-tion of analyte concentrations of agri-cultural, environmental, pharmaceuti-cal, and biomedical interest have been widely demonstrated (44–53). Com-pared to univariate regression calibra-tion, the combined utility of analytical spectroscopy and multivariate regres-sion analysis has become more appeal-ing for various reasons including rapid analysis at reduced sample size with minimal to no sample preparation. The use of multivariate regression analysis also allows accurate determination of analyte concentrations without prior analyte extraction (45,46,53). Most im-portantly, multivariate regression analy-sis of spectral data is the best choice of
sample calibration when there is spec-tral overlapping and spectral shifts or the presence of significant spectral interference that is often encountered in complex organic matrixes in agricul-tural, environmental, and biomedical samples (30,45,46,53). Several multi-variate regressions including multiple linear regression (MLR), PLS regression, unfolded PLS regression with residual bilinearization (U-PLS/RBL), principal component analysis (PCA), principal component regression (PCR), non-parametric linear-regression (NPLR), successive projections algorithm-linear discriminant analysis (SPA-LDA), and parallel factor analysis (PARAFAC) have been used for rapid and accurate sample calibration and analyses of molecules of agricultural, food, pharmaceutical, envi-ronmental, medical, and biomedical in-terest in recent years (53). However, the use of PLS regression for sample calibra-tion is particularly more attractive be-cause of its capability to simultaneously incorporate the dependent variable in the data compression and decomposi-tion operations. Consequently, both the x-variable (the FT-IR spectra data in this study) and the y-variable (the percent oil composition in this study) are actively involved in the construction of the new basis set made up of PLS components (47,48,51). In other words, the PLS re-gression primarily focuses on those as-pects of the data that are most important in predicting y.
The first step of a PLS multivari-ate regression is called the calibration phase, where a PLS regression model is built with a set of samples of known concentration. However, the model is carefully optimized to select the appro-priate wavelength region (x-variable) that correlates most with the y-variable.
32 FT-IR Technology for Today’s Spectroscopists August 2016
In addition, the appropriate number of PLS must be selected to eliminate over-fitting of the regression model. Theoreti-cally, n-1 number of PLS can be used to build the regression model, where n is the number of samples. The constructed model must then be validated and tested with an independently prepared test set in the validation phase to evaluate the predictive capability of the model devel-oped in the calibration phase. The pre-dictability of the regression models can be impacted by collinearity that is often present in the raw spectral data. Thus, the original raw spectral data must be presented in a new orthogonal variance-scaled eigenvector PLS component be-fore model development to eliminate the collinearity problem. Representa-tion of samples in this new orthogonal variance-scaled eigenvector or coordi-nate system, known as the score plot, is also advantageous because it can also be used as a data dimension reduction strategy. In general, only a few PLS com-ponents contain the most useful vari-ability in the data set and are the most critical to represent the data in the new
orthogonal variance-scaled eigenvector PLS component. Relatively higher PLS predominantly contain noise and can be disregarded. Most importantly, the score plots often reveal hidden and valu-able information for pattern recognition in the data set that may not be apparent from ordinary visual examination of the original raw data set.
Figure 3 is a representation of the score plot of the first PLS component against the second PLS component of the PLS re-gression analysis of FT-IR spectra of TTO adulterated with lemongrass oil (Figure 3) and WO samples adulterated with pep-permint oil (Figure 2 in the supplemen-tary section on-line). In Figure 3, the first two PLS components accounted for 88% of the variability in the FT-IR spectra (x-variable) and 93% of useful informa-tion in y-variable (percent compositions of TTO) in the TTO–lemongrass oil samples contributing to the PLS regres-sion model. Similarly, in Figure 2 in the supplementary section on-line, the first two PLS components accounted for 100% of the variability in the FT-IR spectra (x-variable) and 98% of the variability in y-
PLS-2
PLS-1
-100
-40
Samples with larger
% LO composition
Samples with larger
% TTO composition
-30
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Figure 3: Typical score plot of PLS-1 regression of TTO adulterated with lemongrass oil.
August 2016 FT-IR Technology for Today’s Spectroscopists 33
variable (percent compositions of WO) in the WO–peppermint oil samples. Care-ful examination of the score plots re-vealed an interesting pattern of the adul-terated samples. For instance, adulterated TTO samples with larger composition of lemongrass oil adulterant are grouped to-gether on the left hand side of the score plot. However, adulterated TTO samples containing larger percent compositions of TTO than the lemongrass oil adulter-ant are grouped together on the opposite, right-hand side of the score plot. The score plot of adulterated WO with pep-permint oil adulterant is also interesting and shows a similar pattern. Adulterated WO samples with larger percent compo-sition of peppermint oil adulterant are grouped together on the left-hand side of the score plot. In contrast, adulterated WO samples containing larger percent composition of WO than the peppermint oil adulterant are grouped together on the right-hand side of the score plot. An-other interesting pattern can be observed in this score plot, in which the pure WO sample with 99.9% WO is grouped alone in the lower right-hand side of the score plot. The obtained score plots of RO and LEO also show similar grouping of sam-ples containing larger percent composi-tion of RO and LEO in the adulterated samples. The results of the score plots are notable and can potentially be used for rapid screening and pattern recognition of the purity, authenticity, and possible adulteration of oil samples.
Table I is a summary of the figures of merit of the PLS regression analyses of the FT-IR spectral data, including the optimum wavenumber region, the slope, the square correlation coefficient, the offset, and the limit of detection (LOD) for WO, TTO, RO, and LEO adulter-ated with lemongrass oil. (Table I in the
supplementary section on-line shows the results with peppermint oil.) The de-veloped PLS models have good linearity (R2 > 0.999039). A perfect model would have a slope of 1, a correlation coefficient of 1, and an offset of 0.
Although the obtained figures of merit of the PLS models were excel-lent, the practical utility of any regres-sion model is the ability of the model to correctly determine the percent com-positions of independently prepared
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(~2900 cm-1)
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(~1740 cm-1)
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(~1740 cm-1)
Figure 4: FT-IR spectra of independent validation samples of (a) WO, (b) TTO, (c) RO, and (d) LEO adulterated with lemongrass oil.
34 FT-IR Technology for Today’s Spectroscopists August 2016
adulterated oil validation samples. Figure 4 shows the FT-IR spectra of independently prepared test validation samples of WO, TTO, RO, and LEO adulterated with lemongrass oil; Figure 3 in the supplementary section on-line shows the results with peppermint oil. It must be highlighted that the percent compositions of adulterated WO, TTO, RO, and LEO in the validation samples are totally different from the percent compositions of adulterated WO, TTO, RO, and LEO in the calibration samples used for the PLS regression model de-velopment.
The results of the validation study showing the plots of the determined and actual percent composition of WO, TTO, RO, and LEO in the validation samples adulterated with lemongrass oil are shown in Figure 5. Obviously, the de-termined percent compositions by PLS models favorably compared with the ac-tual percent compositions of adulterated essential oils in the validation samples. The capabilities of the PLS regression to accurately determine the percent com-position of WO, TTO, RO, and LEO in the adulterated validation samples were further evaluated using a root-mean-square-relative-percent error (RMS%RE) calculation shown in equation 1:
RMS%RE =(%RE
i)2
n
[1]
where %REi is the percent-relative-error calculated from the known and
predicted values for the ith validation sample, and n is the number of valida-tion samples in the set. The PLS regres-sion models correctly determined the percent compositions of independently adulterated WO, TTO, RO, and LEO with lemongrass oil with RMS%RE of determinations ranging between 0.81% and 2.42%, with an average of 1.57%. The summary of the corresponding validation study conducted for the de-termination of percent compositions of WO, TTO, RO, and LEO samples adul-terated with peppermint oil is shown in Figure 4 in the supplementary on-line section. The validation study for inde-pendently adulterated WO, TTO, RO, and LEO with peppermint oil resulted in RMS%RE of determinations ranging between 0.92% and 3.90%, with an aver-age of 2.36%.
ConclusionThe result of an analytical protocol that involved the combined used of FT-IR spectroscopy and multivariate PLS regression analysis for rapid and accurate determinations of percent composition of four essential oils (win-tergreen oil, tea tree oil, rosemary oil, and lemon eucalyptus oil) adulterated with lemongrass oil and peppermint oil is reported. PLS multivariate re-gression was used to correlate changes with percent compositions of essential oils in the adulterated samples and subsequently used to predict the per-
Table I: Figures of merit of PLS regression analysis of wintergreen oil (WO), tea tree oil (TTO), rosemary oil (RO), and lemon eucalyptus oil (LEO) adulterated with lemongrass oil
EOWavenumber
(cm-1)R2 Slope Offset
LOD
(w/w%)
WO 823–1011 0.999449 0.998899 0.070046 0.21
TTO 600–4000 0.999613 0.999226 0.037314 0.11
RO 600–4000 0.999039 0.998079 0.080606 0.24
LEO 600–4000 0.999275 0.998515 0.111855 0.34
August 2016 FT-IR Technology for Today’s Spectroscopists 35
cent compositions of adulterated WO, TTO, RO, and LEO of the validation samples. The simplicity, low cost, and high accuracy of the protocol are at-tractive, with potential real-world ap-plications for quality control and qual-ity assurance of consumable goods in food, cosmetics, pharmaceutical, and agrochemical industries.
AcknowledgmentsThe financial support for Brianda Elzey from Research Initiative for Scientific Enhancement (RISE) Program through a NIGMS/NIH R25GM0706162 grant is acknowledged.
Supplementary On-Line SectionThe peppermint oil results are presented in a supplementary section on-line. This on-line section includes Figures 1–4 and Table I. Please visit www.spectroscopyon-line.com/node/318164 to read the supple-mentary section.
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0
0
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% C
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po
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om
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Actual % WO composition Determined % WO compositionActual % TTO composition Determined % TTO composition
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36 FT-IR Technology for Today’s Spectroscopists August 2016
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For more information on this topic, please visit our homepage at: www.spectroscopyonline.com
ADVERTISEMENT38 Molecular Spectroscopy
A vibration-resistant FT-IR spectrometer is used
to monitor an industrially relevant fermentation
process. The production of two alcohols is moni-
tored in real time along with the consumption of
the sugar feedstock with concentrations ranging
from 0.1 to 25 g L-1, and is fi tted to HPLC data
using PLS methodology.
In situ chemical monitoring of chemical reactions is one of the most powerful tools in a modern spectroscopist’s
arsenal. In fact, some chemical processes can only be truly controlled if the reaction state is known in real time. However, standard FT-IR analytical instruments are highly sensitive to vibration, and often cumbersome, making it a challenge to use them in a production environment. Here we report the use of the vibration-resistant Keit FT-IR spectrometer for in-process monitoring of an industrial fermentation process. Th e spectrometer was used to monitor the production of the primary alcohol product, along with a secondary alcohol product and the consumption of the sugar feedstock.
Experimental ConditionsTh e Keit FT-IR spectrometer was incorporated into a lab-scale reactor assembly, with the reaction mixture fed through peristaltic pumps. Th e spectrometer was placed directly next to the pumps as their inherent vibration is not of concern for this instrument. Th e spectrometer was fi tted with a fl ow cell mounted onto a dip probe featuring an AMTIR ATR crystal. Spectra were recorded every 2 h over a period of 30 h, and an aliquot of the reaction mixture was removed for HPLC testing concurrently.
Partial least square (PLS) regression method was used for a calibration and prediction model for the produced alco-hols and consumed sugars. Th e spectral region from 900 to 1400 cm-1 was used for developing the model. Th e data was pre-processed with mean centering and three LVs were used to build the model.
Results and DiscussionBoth the HPLC and spectral data for the reaction run is shown in Figure 1. Th e results clearly show that the spec-troscopic data follows the HPLC data very closely, with minimal deviations between the two. Both alcohol species are observed to increase in concentration over the entire process, with a concentration range of 0.1 through to 9 g L-1. Moreover, the R2 values for correlations of predicted versus measured concentrations of alcohol 1, alcohol 2, and sugar feedstocks were 0.923, 0.912, and 0.983, respectively.
ConclusionsTh ese results show that the Keit FT-IR spectrometer can be eff ectively used to monitor an industrial process in operando, with the ability to track the concentration of at least three diff erent constituents simultaneously, and over a large range of sensitivity. Th e Keit FT-IR spectrometer helps improve quality, reduce waste, and improve facility utilization using a more robust and stable instrument.
Vibration-Resistant FT-IR for In-Process Monitoring of an Industrial Fermentation ProcessDr. Jonathon Speed, Keit Spectrometers
Keit Spectrometers
R71, Harwell Campus, Didcot, Oxfordshire, OX11 0QX, UK
tel. +44 (0) 1235 567176
Website: www.keit.co.uk
Figure 1: Reaction pathway for measured
(blue) and predicted (green) concentrations of
primary and secondary alcohol products, and
sugar feedstock.
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