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Articles

8 Miniaturized MIR and NIR Sensors for Medicinal Plant Quality ControlChristian W. Huck

This work shows that methods based on miniaturized near- and mid-infrared spectroscopy can be used effectively for the quality control of herbal medicines.

17 Tracking Microplastics in the Environment via FT-IR MicroscopyMichael Bradley, Suja Sukumaran, Steven Lowry, and Stephan Woods

Microplastics from clothing, abrasive action on plastics, or engineered microbeads as found in some exfoliating cosmetics are showing up in many environmental systems. FT-IR microscopy is a useful tool in the analysis of microplastics, providing visual information, particle counts, and particle identification.

24 Assessing False Noncompliance and False Compliance with Limits Stated for Physicochemical Parameters in Red Wines Using a MIR-PLS ModelM.C. Ortiz, L.A. Sarabia, M.E. Meléndez, and M.S. Sánchez

When analytical techniques that depend on multivariate calibration models are used to analyze commercial products, the risks of false compliance and noncompliance must be assessed. This study presents an example from the application of a mid-IR partial least squares model to the analysis of seven parameters in red wine.

33 Solving Polymer Problems Using IR SpectroscopyAn interview with Naoto Nagai

Naoto Nagai focuses on solving problems for industry. In this interview, he explains his research to determine the cause of resin cracks in polyoxymethylene mold plates using IR spectroscopy.

Cover image courtesy of zhekoss/shutterstock; Dima Sobko/shutterstock; on_france/shutterstock.

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Infrared (IR) is divided into near-infrared (NIR, 750–2500 nm, 4000–13,000 cm-1) (1), mid-infrared (MIR,

2500–25,000 nm, 400–4000 cm-1) (2), and far-infrared (25,000–106 nm, 400–10 cm-1) (3) subregions, from which mainly NIR and MIR are applied to medicinal plant analysis (4). NIR spectroscopy is concerned with the absorption, emission, reflection, and

diffuse ref lection of light; it is elec-tronic as well as vibrational spectros-copy dealing with overtones and com-bination modes following the model of the anharmonic oscillator. MIR is vibrational spectroscopy dealing with fundamental modes following the harmonic oscillator principle, and in comparison, NIR spectra are usually crowded containing manifold infor-

Miniaturized MIR and NIR Sensors for Medicinal Plant Quality Control

The trend in analytical method development follows two different strate-gies. One is the further improvement of spectrometers toward higher performance; the other is to develop small, portable, and relatively cheap instruments that can be easily used by anyone. Today, small near-infrared (NIR) sensors are either based on microelectromechanical systems (MEMS) or linear variable tunable filters (LVTF) for appropriate wavelength selection. For measurement in the mid-infrared (MIR) region, handheld attenuated total reflection (ATR) spectrometers give great promise based on the improved signal-to-noise ratio enabled by the evanescent field principle. Both instrumental technologies can be used to monitor quality and determine the ideal harvest time of medicinal plants, such as Verbena

officinalis and Rosmarini folium. For method development, quantum chem-istry for band assignment and two-dimensional correlation spectroscopy for a better understanding of the factors that determine the partial least squares (PLS) regression models can be conducted. From these studies it can be shown that the performance of NIR and MIR spectroscopy with miniaturized devices as a fast and noninvasive technique is able to replace time- and resource-consuming analytical tools, being of high interest for the continuous growing phytopharmaceutical industry and its quality control.

Christian W. Huck

August 2017 IR Spectroscopy for Today’s Spectroscopists 9

mation extracted from overlapping peaks with NIR intensities that are 10–1000 times lower (5). The further development of portable and handheld NIR spectrometers based on micro-electromechanical systems (MEMS) (6) and linear variable tunable filters (LVTF) (7) is making NIR spectros-copy very popular. Attenuated total reflection (ATR) is the most suitable form of applying MIR spectroscopy, benefiting from the evanescent field and penetrating at least a few microm-eters into the sample of interest pro-viding high signal-to-noise ratio (S/N) (8). Two-dimensional (2D) spectros-copy, first proposed by Noda in the 1980s (9), has been further developed (10) to be applied for quality control in food and agricultural products (11). Defined by two independent spectral axes, the 2D correlation spectrum is generated by applying correlation analysis to the dynamic fluctuations of spectral signals caused by external perturbations, for example, chemical, thermal, or mechanical stimulations. One notable feature of 2D correlation spectroscopy (2D-COS) is that the

cross-peaks can potentially be used to characterize intermolecular inter-actions. The advent of 2D-COS brings about a new avenue for the investiga-tion of intermolecular interactions.

In many applications it is impos-sible to correctly assign the corre-sponding vibration bands. Quantum chemical calculations are a great method for band assignment not only in the MIR, but also in the NIR region (12). Vibrational spectroscopy with multivariate statistical analysis (MVA) is a powerful, synergistic combination enabling the extraction of the required information from the spectrum in the first step (13,14). Clustering methods such as principal component analysis (PCA), hierarchi-cal cluster analysis (HCA), fuzzy-C-means clustering, and others enable the reduction of the number of vari-ables enabling qualitative analysis (15). For the establishment of quanti-tative regression models, partial least square regression (PLSR) (16) is the preferred method, correlating spec-tral data with those obtained from reference methods such as liquid

(a) (b)Hermetic/vacuum sealed cavity

Hermetic coatings

Feedthroughs

Active circuit

Exposed sensor

Encapsulated sensor

Polychromaticlight

Mirror

MirrorSpacer

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01100 1400

Wavelength (nm)1700

10

20

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40

50

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Inte

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Figure 1: Schematics showing (a) microelectromechanical system (MEMS) and

(b) linear variable tunable filter (LVTF) construction.


chromatography (LC) (17) and LC–mass spectrometry (MS) (18), solid-phase extraction (SPE) (19,20), gas chromatography (GC) (21), capillary electrophoresis (CE) (22), capillary electrochromatography (CEC) (23), and MS (24).

In this article, the potent combination of these methodologies as a novel strat-egy in medicinal plant analysis is intro-duced and summarized for the highly efficient quality control of Verbena of-ficinalis (25) and Rosmarini folium (26), two typical plants used in folk medicine.

MethodsNIR Spectroscopy The inexorable trend toward the min-iaturization of spectrometers has been continuing since the 1970s. The de-velopment of one of the smallest NIR spectrometers, with a current weight of only 60 g, was enabled by introducing MEMS in 2008 (Figure 1a) and LVTF in 2012 (Figure 1b) (27,28). A MEMS device is made up of components 1–100 μm in size, and it generally ranges in size from 20 μm to 1 mm (29). The active circuit contains upper and lower mirrors acting as wavelength-tunable parallel plates: The width of the air gap controls the resonant frequency of the cavity and allows certain wavelengths to pass. The upper mirror’s movement is controlled through an applied volt-age. Applying different voltages results in transmitting a spectrum of wave-lengths to be detected by the InGaAs PIN photodiode (30). This technology was added to smartphones in 2014; re-mote systems using wireless hyphen-ation to smart computers were created in 2016 (31).

LVTF are small-wedged Fabry-Perot etalons manufactured in a thin-film

optical coating process. The bottom layer is made of several Bragg reflectors forming a dielectric mirror. The next layer forms the cavity of the resonator. Atop this resonator, a second layer of Bragg reflectors is applied as the upper dielectric mirror (7,32,33).

2D-COS

This method offers a higher resolution where even strongly overlapping peaks can be distinguished and explicitly as-signed (9). Applying a second dimen-sion enables the interpretation of the sequential order of intensity change and offers the possibility of correlating different spectroscopic methods via hetero-spectroscopic correlation. For the generation of the needed dynamic data sets of spectra an external pertur-bation, such as temperature, time, or pressure, should be applied during the IR measurements. The following corre-lation analysis generates a synchronous and an asynchronous 2D spectrum (10). While the synchronous spectrum fea-tures so-called auto-peaks along the diagonal and the sign of its cross-peaks indicates a rise or fall in intensity, the asynchronous spectrum lacks in auto-peaks, but its cross-peaks provide the information of the sequential order of the intensity change.

Quantum Chemical Calculations

The generalized second-order vibra-tional perturbation (GVPT2) theory approach on a density functional the-ory (DFT) level for the calculation of the NIR spectrum was used, with the aim of obtaining a better explanation of the observed experimental patterns. For small molecules in a diluted phase this theoretical approach gives excellent results (37).


Results and DiscussionApplication of Benchtop and Porta-

ble NIR for Predicting the Optimum

Harvest Time of Verbena officinalis The applicability of NIR spectroscopy to determine the ideal harvest time of Verbena officinalis was demonstrated (25). As a reference method a novel high performance liquid chromatog-raphy–diode array detection–elec-trospray ionization-mass spectrom-etry (HPLC–DAD–ESI-MS) method

was developed and validated. HPLC analysis of the leaf drug showed that the content of the quality-determin-ing compound verbenalin fluctuated throughout the flowering period. Ver-bascoside as a further pharmacologi-cally important ingredient increased from the middle of flowering, but was also subjected to certain variations. The causes of these f luctuations can have various reasons, such as insola-tion, humidity, fertilization, and soil

Table I: PLS regression results for benchtop referred to the fresh and dried plant material*

Verbenalin

Dried Plant Material Fresh Plant Material

R2 LV SEPSEP–SEC

RPD Range R2 LV SEPSEP–SEC

RPD Range

CV 0.82 4 0.39 1.17 2.30 0.231–4.302

0.83 3 0.072 1.10 2.40 0.032–0.783

TSV 0.82 4 0.38 1.04 2.33 0.231–4.302

0.85 4 0.069 1.15 2.54 0.032–0.783

Verbascoside


R2 LV SEPSEP–SEC


RPD Range

CV 0.82 4 0.80 1.16 2.25 0.031–6.954

0.82 4 0.15 1.17 2.28 0.004–1.361

TSV 0.82 4 0.78 1.01 2.33 0.031–6.954

0.85 5 0.12 0.93 2.61 0.004–1.361

*Unit of SEP and range is %, R2 refers to validation, in TSV SEP = SECV.

Table II: PLS regression results for MEMS (miniaturization) referred to the fresh and dried plant material*

Verbenalin


R2 LV SEPSEP–SEC


RPD Range

CV 0.72 6 0.48 1.18 1.81 0.231–4.302

0.75 6 0.083 1.17 1.98 0.032–0.783

TSV 0.69 6 0.45 0.97 1.78 0.231–4.302

0.74 6 0.077 1.02 1.98 0.032–0.783

Verbascoside


R2 LV SEPSEP–SEC


RPD Range

CV 0.80 3 0.86 1.09 2.22 0.031–6.954

0.80 3 0.16 1.09 2.23 0.004–1.361

TSV 0.80 3 0.86 1.01 2.21 0.031–6.954

0.80 4 0.15 0.95 2.21 0.004–1.361

*Unit of SEP and range is %, R2 refers to validation.


Table III: Band assignments in NIR spectrum of rosmarinic acid, based on quantum chemical (DFT-B3LYP/N07D) calculation

Wavenumber

(cm-1) Major Contributions

Exp. Calc.

1 6854.9 6853 2νOH (ar)

2 6767.2 6741 2νOH (ar)

3 ~6680 6645 2νOH (carboxyl)

4 ~6044 6056 2νCH (ar, aliph, in-phase)

5 5986.5 6001 2νCH (ar, aliph, opp.-phase); 2νCH (ar)

6 5929.7 5930 2νCH (ar); 2νCH (ar)

7 5752.5 5780 [νasCH2, νCH (ar)] + [νas CH2, νCH (ar)]

8 5128.0 5126 [ν C=O, δipOH (carboxyl)] + [νOH (carboxyl)]

9 5075.8 5027 [δring, δipOH (ar)] + [νOH (ar, para-)]; [δring, δipOH (ar)] + [νOH

(ar, para-)]; [δring] + [νOH (ar, para-)]

10 4994.9 4980 [δring, δipOH (ar)] + [νOH (ar, meta-)]; [δring, δipOH (ar)] + [νOH (ar, meta-)]

11 4923.8 4906 [δring, δipOH (ar)] + [νOH (ar,para-)]; [δring, δipOH (ar)] + [νOH

(ar, para-)]

12 4860.0 4847 [δring, δipOH (ar)] + [νOH (ar, meta-)]; [δring, δipOH (ar)] + [νOH

(ar, meta-)]

13 4788.3 4798

[νCC] + [νOH (ar, para-)]; [νCC] + [νOH (ar, para-)]; [νCC] + [νOH (ar, meta-)]; [δCCH (carboxyl)] + [νOH (carboxyl)]; [δCH (ar), δipOH (ar)] + [νOH (ar, para-)];

[δCH (ar), δipOH (ar)] + [νOH (ar, para-)]

14 4701.0 4701 [δCH (aliph)] + [νOH (ar, meta-)];

[δCH (ar), δipOH (ar)] + [νOH (ar, meta-)]

15 4629.4 4632 [δCH (ar), δring, δipOH (ar)] + [νOH (ar, meta-)]; [δCH (ar), δring, δipOH (ar)] + [νOH (ar, para-)]; [δipOH (ar), δCH (ar), δring] + [νOH (ar, para-)]

16 4575.7 4757 [δCH (ar), δring, δipOH (ar)] + [νOH (ar, meta-)]; [δipOH (ar), δCH (ar), δring] + [νOH (ar, meta-)]

17 ~4508 4465

[δring, δipOH (ar)] + [νCH (ar)]; [δring] + [νCH (ar)];

[δring, δipOH (ar)] + [νCH (ar)]; [νC-O (carboxyl), δipOH

(carboxyl)] + [νOH (carboxyl)]; [δring] + [νCH (ar)];

[δring, δipOH (ar)] + [νCH (ar)]

18 4372.3 4360 [δring] + [νCH (ar)]; [δring] + [νCH (ar)]

19 4233.3 4237 [δscissCH2] + [νasCH2, νCH (ar)]; [δscissCH2] + [νasCH2, νCH (ar)]

20 4179.4 4194 [δCH (aliph)] + [νCH (ar, aliph, opp.-phase)]; [δsciss CH2] +

[νsCH2]; [δCH (aliph)] + [νCH (ar, aliph, in-phase)]

Abbreviations: aliph: moiety connected to aliphatic chain; ar: moiety connected to aromatic ring


condition. For these reasons, the de-velopment of a quick and noninvasive NIR method was essential for the de-termination of the optimum time to harvest Verbena officinalis.

PLS regression results obtained from the benchtop polarization in-terferometer–based FT-NIR spec-trometer demonstrated that NIR spectroscopy is suitable for the char-acterization of Verbena officinalis (see Table I). PLS results of cross and test-set validation models for verbenalin and verbascoside reached R2 values ranging from 0.82 to 0.85. A maxi-mum of five latent variables (LVs) was needed. The standard error of prediction–standard error of calibra-tion (SEP–SEC) quotient achieved values near to 1 (0.8–1.2) attesting to the robustness of the developed mod-els. The residual predictive deviation (RPD) was >2, confirming a satisfac-tory quality. A quick and simple de-termination of the ideal time to har-vest Verbena officinalis was possible.

To allow a laboratory-independent applicability of the NIR analysis, the portable MEMS was evaluated for its performance. Regression results for

verbascoside (see Table II) reached an R2 of 0.8. A maximum of four LVs was required. The SEP–SEC ratio ranged between 0.95–1.09 and the RPD values from 2.21–2.23, which indicate the ro-bustness and quality of the developed PLS models. The results of this study clearly revealed that the prediction of verbascoside applying the portable de-vice could be performed with nearly the same accuracy as with the bench-top spectrometer. PLS models for ver-benalin were less precise showing a value for R2 between 0.69 and 0.75 and RPD values < 2. Therefore, the quality of the calibration using the handheld device was not satisfactory. Repro-ducible quantifications of verbenalin applying NIR spectroscopy are quite feasible as the aforementioned study using the benchtop device has shown. The low resolution and the limited spectral range of the mobile NIR de-vice seem to be the responsible spec-tral features necessary for a successful quantification of verbenalin that were not sufficiently captured. Therefore, the application of a portable spectrom-eter with a different spectral range and

Table IV: Results of all PLS regression models

Spectrometer Benchtop MEMS LVTF

Samples 60 60 60

Outliers 6 8 4

Range (%) 1.138–2.425 1.138–2.425 1.138–2.425

Validation method CV TSV CV TSV CV TSV

R2 0.91 0.91 0.73 0.73 0.84 0.85

SECV (%)SEP (%)

0.072 0.069 0.12 0.11 0.091 0.11

SECV–SECSEP–SEC

1.46 1.43 1.28 1.24 1.55 2.09

Factors 8 8 5 5 11 12

RPD 3.27 3.41 1.88 2.06 2.46 2.14


higher resolution may lead to further improved calibration models.

Evaluation of Spectral Information

of Benchtop Versus Portable

Spectrometers for the Quality

Control of Rosmarini folium

The theoretical NIR spectrum of ros-marinic acid (RA) allowed a detailed band assignment for the molecule throughout the NIR region (8000–4000 cm-1), which is presented in Table III (26).

For the comparison of benchtop, MEMS, and LVTF devices, the mea-

(a) (b)

(c) (d)

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Figure 2: 2D spectra obtained using (a) a benchtop system (NIRFlex N-500), (b) a MEMS-

based system (microPHAZIR), (c) an LVTF system (MicroNIR 2200) with the pretreatment

of the moving average transform, (d) an LVTF system without the pretreatment of the

moving average transform, (e) hetero-correlation between the benchtop system (x-axis)

and the LVTF system (y-axis), and (f) heterocorrelation between the benchtop (x-axis) and

MEMS (y-axis) systems. The x- and y-axes are wavenumbers (cm-1).


sured spectra of Rosmarini folium were investigated by syn 2D-COS plot using MSC pretreated spectra. Figure 2a shows the syn spectrum of the entire range of the benchtop FT-NIR system (NIRFlex N-500, Buchi Corp.), which is compared to the spectrum of the MEMS sys-tem (microPHASIR, Thermo Fisher Scientific) in Figure 2b. The major d iscrepancies are t he pea ks at 4678 cm-1 (region of appearance of δCH+νOH bands of RA) and 4785 cm-1 (νCC+νOH[ar], δCCH+νOH[carboxyl], δCH[ar]+νOH[ar]) that appear in the 2D plot of the miniaturized device. Although these bands are unneces-sary for the benchtop-based analysis, it is a crucial region for the calcula-tion of an expedient PLS regression model for MEMS. Between 5600 and 6000 cm-1 (δCCH+νOH[carboxyl] and νasCH2+νasCH2), two peaks in the spectrum of the benchtop system are accompanied by one peak of the MEMS device. The conclusion of no further significant differences can be supported by the application of a hetero-2D-correlation between the spectra of the benchtop (x-axis) and the MEMS device (y-axis) (Fig-ure 2f). By comparing the benchtop device (Figure 2a) with the LVTF system (MicroNIR 2200, Viavi So-lutions) (Figure 2c), the significant differences are identif ied as the negative crosspeaks of the LVTF system in the range between 5400 to 7300 cm-1 (2νOH[ar and carboxyl], 2νCH, νasCH2+νasCH2) that appears positive in the 2D benchtop system spectrum. If this result is investi-gated with a hetero-2D-correlation, these opposite signs are shown in the resultant spectrum as well (Figure

2e). Hence, the negative crosspeak in the mentioned range of the LVTF system at the y-axis signals a con-trary behavior to the crosspeak of the benchtop device at the x-axis. In accordance with the homo-2D-cor-relation, the hetero-2D-correlation shows no other difference between the two devices.

In Figure 2d the 2D spectrum without moving average transform is illustrated, while Figure 2c includes the pretreatment. This shows that 2D-correlation provides an explanation about why moving average transform was a necessity for an improved PLS model calculation.

The results of the PLS regression models were ranked because of RPD and R2, respectively, evaluated by co-efficient of variation (CV) (see Table IV). The best NIR-PLS regression model was achieved by the benchtop NIR spectrometer (RPD = 3.27; R2 = 0.91), which covered the widest spec-tral range (10,000–4000 cm-1) with the highest resolution (Δν = 8 cm-1) compared to the other two NIR de-vices. The miniaturized LVTF NIR spectrometer was the second best (RPD = 2.46; R2 = 0.84), performing satisfactorily. The performance of the miniaturized MEMS NIR spectrom-eter (RPD = 1.88; R2 = 0.73) can be improved.

ConclusionsMiniaturized spectrometers show great promise for quality control in the phytopharmaceutical industry. The evaluation of the results leads to the conclusion that it is necessary to prove the applicability of NIR de-vices in dependence on recording wavenumber ranges and resolution.


References(1) W.F. McClure, J. Near Infrared

Spectrosc. 11, 487–518 (2004).

(2) J.M. Chalmers and P.R. Griffiths,

Handbook of Vibrational Spectroscopy

(J. Wiley, Hoboken, New Jersey, 2002).

(3) P.R. Griffiths and C.C. Homes in

Handbook of Vibrational Spectroscopy,

P.R. Griffiths, Ed. (John Wiley &

Sons, Ltd., Chichester, UK, 2006).

(4) C.W. Huck, Phytochem. Lett.

11, 384–393 (2015).

(5) O.M.D. Lutz, G.K. Bonn, B.M. Rode,

and C.W. Huck, Anal. Chim.

Acta 826, 61–68 (2014).

(6) L.P. Schuler et al., J. Phys. D. Appl.

Phys. 42, 133001 (2009).

(7) D.L. Wetzel, A.J. Eilert, and J.A. Sweat, in

Handbook of Vibrational Spectroscopy,

P.R. Griffiths, Ed. (John Wiley &

Sons, Ltd., Chichester, UK, 2006).

(8) S.A. Schönbichler, G.F. Falser, S.

Hussain, L. Bittner, G. Abel, M.

Popp, G.K. Bonn, and C. Huck, Anal.

Methods 6, 6343–6351 (2014).

(9) Y. Ozaki and I. Noda, J. Near Infrared

Spectrosc. 4, 85 (1996).

(10) X. Li, Q. Pan, J. Chen, S. Liu, A. He, C.

Liu, Y. Wei, K. Huang, L. Yang, J. Feng, Y.

Zhao, Y. Xu, Y. Ozaki, I. Noda, and J. Wu,

Appl. Spectrosc. 65, 901–917 (2011).

(11) A.L. Fudge, K.L. Wilkinson, R.

Ristic, and D. Cozzolino, Food

Chem. 139, 115–119 (2013).

(12) C.G. Kirchler, C.K. Pezzei, K.B. Bec, S.

Mayr, M. Ishigaki, Y. Ozaki, and C.W.

Huck, Analyst 142, 455–464 (2017).

(13) M. Blanco and I. Villarroya, TrAC Trends

Anal. Chem. 21, 240–250 (2002).

(14) W. Kessler and R.W. Kessler,

Anal. Bioanal. Chem. 384,

1087–1095 (2006).

(15) C.W. Huck, and V.A. Huck-Pezzei,

Multidimensional Approach of Infrared

Imaging Spectra and Morphology

of Oral Squamous Cell Carcinoma

(OSCC) (Nova Science Publishers,

Hauppauge, New York, 2014).

(16) C.H. Petter, N, Heigl, R. Bakry, G.K.

Bonn, A. Ritsch, and C.W. Huck

Curr. Med. Chem. 16, 8 (2009).

(17) C.W. Huck, C.G. Huber, I.M. Lagoja, K.-H.

Ongania, H. Scherz, G.K. Bonn, and M.

Popp, Planta Med. 65, 491 (1999).

(18) W. Stöggl, C. Huck, S. Wongyai,

H. Scherz, and G. Bonn, J. Sep.

Sci. 28, 1712–1718 (2005).

(19) C. Huck, Molecules 21, 633 (2016).

(20) C.W. Huck and G.K. Bonn, J.

Chromatogr. A 885, 51–72 (2000).

(21) S.M. Valtiner, G.K. Bonn, and

C.W. Huck, Phytochem. Anal.

19, 359–367 (2008).

(22) C.W. Huck, G. Stecher, W. Ahrer, W.M.

Stöggl, W. Buchberger, and G.K. Bonn,

J. Sep. Sci. 25, 903–908 (2002).

(23) W.M. Stöggl, C.W. Huck, G. Stecher,

and G.K. Bonn, Electrophoresis

27, 787–792 (2006).

(24) M. Najam-ul-Haq, M. Rainer, T.

Schwarzenauer, C.W. Huck, and

G.K. Bonn, Anal. Chim. Acta

561, 32–39 (2006).

(25) C.K. Pezzei, S.A. Schönbichler, C.G.

Kirchler, J. Schmelzer, S. Hussain, V.A.

Huck-Pezzei, M. Popp, J. Krolitzek, G.K.

Bonn, and C.W. Huck, Talanta 169,

70–76 (2017). Available from: http://

www.sciencedirect.com/science/

article/pii/S0039914017303636.

(26) C.G. Kirchler, C.K. Pezzei, K.B. Be, S.

Mayr, M. Ishigaki, Y. Ozaki, and C.W.

Huck, Analyst 8374, 837404 (2016).

(27) D. Sorak, L. Herberholz, S.

Iwascek, S. Altinpinar, F. Pfeifer,

and H.W. Siesler, Appl. Spectrosc.

Rev. 47, 83–115 (2012).

(28) C.W. Huck, GIT Lab J. 29, 2–5 (2016).

Continued on Page 32


Macroscopic plastics are show-ing up in many environ-mental systems, including

mid-Pacific zones (1) and tropical islands (2). Microplastics, 20–1000 (or 5000) μm fibers or granules of polymer, are more insidious because they can’t be picked up simply as trash. Microplastics originate both from engineered materials, such as the microbeads found in some ex-foliating creams, and from abrasion or wear of polymeric materials, even from laundering of synthetic fabrics

(3). Microplastics live a long time in the environment and unfortunately they are exactly the right size for fil-ter feeders to consume them. From there, they can move up the food chain and are now found in f ish, birds, and other wildlife.

The microplastic materials are small enough to be highly mobile in the environment, carried most easily by f lowing water. Adsorption of chemical or biological toxins on the microplastics can then enable transport of those toxins from one

Tracking Microplastics in the Environment via FT-IR MicroscopyMicroplastics are particulates, roughly 20–1000 μm in size, origi-nating from materials such as clothing, abrasive action on plastics, or engineered microbeads as found in some exfoliating cosmetics. The microplastics enter aquifers where the particles can be con-sumed by filter feeders. Microplastics are chemically stable, giving them a long lifetime in the environment and making excretion or digestion difficult. Analytically, the size and polymeric nature of microplastics makes Fourier transform infrared (FT-IR) microscopy an ideal tool for detection and identification. Standard analyses typically start with a filtration step, extracting the material from the matrix. The analysis can proceed directly on the dried filter without further sample preparation. This simplicity in both sam-pling and analysis enables the rapid assessment of microplastic encroachment and can assist in the development of remediation techniques. We show examples from both prepared and field samples using microattenuated total reflection (ATR) FT-IR.

Michael Bradley, Suja Sukumaran, Steven Lowry, and Stephan Woods


biome to another (mobilizing materi-als). More directly, the microplastics clog critical digestive pathways. The chemical environments in the diges-tive paths are insufficient to dissolve

these clogs, resulting in incapacita-tion or death of the organisms (3,4).

Remediation requires answering two critical questions: What are the particles and how many particles are

3000

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bso

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Acrylic fiber, Acrilan B57

15002500 2000

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1000

Figure 1: Fiber on filter from a river water sample. The top spectrum was collected (Ge-

ATR) directly using a Nicolet iN5 microscope; the lower spectrum is a library search result.

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Polyethylene

Polypropylene, isotactic

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Figure 2: Two particles from water samples, again using Ge-ATR, with search results.


present (number density)? Polyethyl-ene, polypropylene, polystyrene, and nylon are some of the more commonly found materials, from food packing, toys, and many other sources. The number density varies from negligible in isolated lakes and streams to severe in some lakes and estuaries. Changes in the microplastic population (type or number) indicate a new set of con-ditions, such as those generated by f looding which augments transport of these materials over a large area.

In the aquifer, simple filtration (sieves or simple filters) is typically sufficient to isolate representative populations of microplastics. Sam-pling within an organism requires separation of the microplastics from the organism, such as the digestive tract of fish (4). This generates a solu-tion that is then filtered. Observation

of these samples under a standard light microscope can lead to particle counts, but identification of the ma-terials using visible microscopy alone is problematic at best. Microplastics have been analyzed by gas chromatog-raphy–mass spectrometry (GC–MS), scanning electron microscopy with energy dispersive spectroscopy (SEM–EDS), and combustion analysis. Each of these techniques has strengths and weaknesses—sensitivity, specificity, time for analysis, and destruction of the sample being considerations. However, vibrational spectroscopy, both Fourier transform infrared (FT-IR) and Raman, can provide insights quickly and nondestructively with a high degree of confidence.

FT-IR microscopy is an excellent tool for the analysis of these materi-als. Filtration of a known volume of

3800

X = 3441.25 Y = 0.0954

3600 3400 3200 3000 2800 2600 2400 2200 2000 1800 1600 1200 1000 8001400

Wavenumber (cm-1)

Orange particle from auto fluid

Composite for match 1

Teflon

Pliolite 85 S-5

Silane A-1100

Figure 3: Particle filtered from a shock absorber. The material contains multiple

components; the search result from OMNIC Specta’s multicomponent search is shown

at the bottom. See text for details.


liquid (river water, for instance) fol-lowed by infrared identification and particle counting provides a thorough picture of the material present. Mod-ern software automates many of the steps, enabling an analyst to answer those critical questions quickly and efficiently. This article starts with the basics of sample collection and single-particle identification and then examines techniques for the analysis of larger regions. We present data from both model systems using manufactured microbeads and real world filtrates.

ExperimentalSamples prepared from both refer-ence materials and environmental sources were examined. The primary goal of the work was to demonstrate the utility of FT-IR for this analysis; no extrapolation in a particular envi-ronment was made. Recently, we ex-amined samples from the automotive, food, and cosmetics industries as well

as environmental samples; here only a brief overview is provided.

Single particles were targeted and analyzed using the Thermo Scien-tific Nicolet iN5 FT-IR microscope, a point-and-shoot, small frame, man-ually operated FT-IR microscope. Larger images and a particle analy-sis were carried out on the automated Thermo Scientific Nicolet iN10 FT-IR microscope with OMNIC Picta soft-ware. Both microscopes used a single element MCT-A liquid-nitrogen-cooled detector for speed, although a room temperature detector would be sufficient for the point and shoot studies. All data shown in this paper were collected using germanium-attenuated total reflection (Ge-ATR) modes, though we have successfully used both ref lection and transmis-sion in other studies. Identifications were made using commercial librar-ies and standard searching or the OMNIC Specta multicomponent search.

2 mm200 μm

Figure 4: Visual image from filter showing particles (polyethylene beads). The image is

a mosaic of over 200 individual images.


Results and DiscussionMost environmental studies of micro-plastics involve filtration of a liquid sample (typically but not always aque-ous), followed by drying. This results in a sample that can be placed directly into the microscope—there is no need to pick off pieces for analysis. The vis-ible capabilities of FT-IR microscopes enable location and selection of a par-ticle or a region for analysis.

Four point-and-shoot examples are shown in Figures 1–3. The fiber or particle was located visually and then centered in the aperture. The Ge-ATR accessory was then brought

into contact with the particle. With Ge-ATR, there is a fourfold increase in magnification (because of its high index of refraction). The FT-IR micro-scope has a 10× magnification; with the Ge-ATR accessory the magnifica-tion increases to 40×. The standard 1-mm circular pinhole thus results in an effective aperture at the sample of 25 μm. As most microplastics are this size or larger, this ensures good spectral purity.

The fiber in Figure 1 was filtered from a river sample. The spectrum matches with acrylic (the strong ni-trile peak at 2243 cm-1 because of

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Figure 5: An IR image constructed by profiling the IR data from the sample in Figure 4.

(a) Spectra of the beads and the filter paper. (b) Correlation map to the spectrum of

the beads.


–C≡N is strongly indicative of this), suggesting a fabric source as the likely origin. The two particles in Figure 2 were separated from another (differ-ent origin) water sample. Again, the ATR results are unambiguous, with the identification as polyethylene and polypropylene, respectively. These are common from many sources. Ex-cluding sample preparation time, the analysis of these three samples took less than 2 min each even with a user not trained in microscopy.

Microplastics circulate in the aquifer readily. This means particles from many origins can be present in a single location. Field sampling (such as sieving) results in an inho-mogeneous (in size and composition) filtrate, but each particle is typically one material. This was the case in the first three examples. In contrast, single microplastic particles deriving from complex origins like in moving automobile or food processing parts may consist of multiple components. Figure 3 shows a particle filtered from shock absorber fluid and the resulting FT-IR spectrum.

This sample shows two characteris-tics of many real world samples. First, it consists of several components—a mixture—meaning the software (or analyst) must combine multiple pieces of information to achieve a satisfactory result. Second, and more critically, the compounds present in the material have undergone changes because of physical, thermal, and chemical wear. These may change the molecular structure in subtle ways, causing peaks to shift and change shape. A fallacy of spectral search-ing is the expectation that all “good” results will give a high search metric,

say 90 or 90% or 0.9, depending on the definition of the metric. The induced changes seen in materials, especially those exposed to high temperatures and strong solvents, will lower the re-sulting metric. In such cases, search results become indicative rather than definitive.

Such is the case here. The use of standard (single component) search-ing provides some clues, but this is clearly a mixture. The multicom-ponent searching shown in Figure 3 identified three classes of material: PTFE (the core composition of the component) along with an acrylic coat-ing (maybe from paint) and a siloxane agent (a lubricant, most likely). The final composite search result is good but not perfect, assigning most of the peaks. Even with the discrepancies, the origin of the particle was easily tracked to a sliding joint in the device.

These first few examples entailed point and shoot analysis (find, tar-get, collect, analyze). Point and shoot can be done on simple, manual FT-IR microscopes. Automation enables the analyst to obtain a wider picture—a map or image—of a sample, such as the one shown in Figure 4. For this study, a liquid suspension of stan-dard polyethylene spheres was made and then filtered. The filter, after dry-ing, was placed on a piece of double stick tape (to flatten and hold the fil-ter), then inserted into an automated microscope with no further sample preparation. The visual image con-sists of more than 200 video captures combined into a mosaic covering ap-proximately 1 cm2. There were 17,500 spectra collected (50 μm steps, 0.1 s/spectrum resulting in ~30 min for collection). Example spectra from the


filter itself and the spheres are shown in the top of Figure 4.

The image in Figure 5 is a corre-lation map relating the polyethylene spectrum from a reference standard to each spectrum in the map. Cor-relation highlights the presence of a component, with a value of 1 being highly correlated (very similar to the reference) and 0 being uncorrelated (not similar to the reference). In this image, the blue field is uncorrelated to polyethylene while the red spots are strongly correlated. Extension of this to the general case would use multiple curve regression (MCR) to highlight and identify each particle in an inho-mogeneous population as well.

The FT-IR microscope’s software can complete the analysis automati-cally, using a particle wizard. The software allows the user to select a region from the video image. The software then identifies the target particles (which the user can filter using a size based sieve) and the anal-ysis proceeds to produce spectra for each particle. These are then searched and a report cataloging the number of particles in the inspection area with a specific identity is prepared. Those data can then be back extrapolated through the volume of liquid filtered to provide a semiquantitative measure of the particulate concentrations.

ConclusionMicroplastics are now deeply en-trenched in the food chain. Rapid, reliable measurements are needed to assist in the assessment of the prob-lem and the potential remediation. As evidence of the presence of micro-plastics even in our air is becoming available, this problem will grow in

importance. FT-IR, and specifically FT-IR microspectroscopy, is a useful tool in the analysis of microplastics, providing visual information, par-ticle counts, and particle identifica-tion. The prevalence of this problem is leading to further refinements in the techniques, which should improve the ability of FT-IR to address it.

References(1) “Great Pacific Garbage Patch: Pacific

Trash Vortex,” National Geographic

special report, https://www.

nationalgeographic.org/encyclopedia/

great-pacific-garbage-patch/

(2) N.P. Walsh, I. Formanek, J. Loo, and

M. Phillips, “Plastic Island: How Our

Throwaway Culture Is Turning Paradise

into a Graveyard,” CNN.com, http://

www.cnn.com/interactive/2016/12/

world/midway-plastic-island/.

(3) L. Messinger, “How Your Clothes Are

Poisoning Our Oceans and Food

Supply,” The Guardian Common

Ground report, https://www.

theguardian.com/environment/2016/

jun/20/microfibers-plastic-

pollution-oceans-patagonia-

synthetic-clothes-microbeads.

(4) J. Wagner, Z.-M. Wang, S. Ghosal, C.

Rochman, M. Gassel, and S. Wall, Anal.

Methods 9, 1479–1490 (2017).

Michael Bradley, Suja Sukumaran,

Steven Lowry, and Stephan

Woods are with Thermo Fisher

Scientific in Madison, Wisconsin.

Direct correspondence to:

[email protected] ◾

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


Assessing False Noncompliance and False Compliance with Limits Stated for Physicochemical Parameters in Red Wines Using a MIR-PLS Model

Several analytical procedures depend on multivariate calibration models, such as partial least squares (PLS) models fitted with near-infrared (NIR) and mid-infrared (MIR) spectroscopy signals. Most of these models are related to products that require the control of maximum (or minimum) legally defined limits so that assessing the risks of false noncompliance and false compliance when establishing the procedure is mandatory. This article shows an application in the field of oenology with red wines from the Spanish qualified denomination of origin (DOC) Rioja, with around 600 samples of wine available, enabling representative training-validation sets. Seven oenological parameters were studied, namely alcoholic degree, total acidity, pH, density, reducing sugar, volatile acidity, and malic acid.

M.C. Ortiz, L.A. Sarabia, M.E. Meléndez, and M.S. Sánchez

Mid-infrared spectroscopy (MIR) shows great potential for the noninvasive measure-

ment of quality parameters. Conse-quently, its use is spreading in the world of industry, covering many fields such as agriculture, biotechnology, food and beverages, forensic science, clinical chemistry, pharmaceuticals, and more. The wine sector clearly re-

quires this technology that offers rapid results and low cost.

Fourier transform infrared spectros-copy (FT-IR) is already widespread in wine laboratories and cellars. So much so that the International Organization of Vine and Wine (OIV) has published a guide (1) where the performance of FT-IR partial least squares (PLS) models are explained. This technique


can identify the spectral features that can provide information on the fac-tors that affect the quality of alcoholic beverages and, combined with multi-variate analysis, increases the chances to obtain quality parameters and their control (2,3). A search in Scopus (01/25/2017) with the keywords “wine” and “MIR” provided 87 publications since 2001, with 33 of them devoted to the determination of parameters in wine. However, no one evaluates the capability of PLS models for fulfilling the legal limits.

Regardless of whether it is a maxi-mum or a minimum limit, when a reg-ulated property is being measured, it is necessary to evaluate the probabilities of false noncompliance and false com-pliance. For univariate signals, the In-ternational Organization for Standard-

ization (ISO) and the International Union of Pure and Applied Chemistry (IUPAC) defined a procedure for de-termining the detection capability of an analytical method, evaluating the probabilities of false positive and false negative (when an analyte is forbid-den). The procedure was generalized for PLS in the literature (4,5) and also adapted to fulfill the official regulation for Zamorano cheese that demands only minimum permitted limits (6).

The MIR spectra combined with PLS regression could be used for the control of the maximum and mini-mum limits established for the certi-fication of the red wines from Rioja, Spain (7). For this purpose, the new figures of merit CDα and CDβ, anal-ogous to decision limit and detection capability, must be evaluated.

x0 = PL

yc

xn

CDβ CDα

α = pr {false noncompliance}

x0 = PL

β =

pr

{fals

e c

om

plian

ce}

(x1, β

1)

(x2, β

2)

(xn, β

n)

CDβ CDα

x2

x1

[c]

PLS

[c]

[c]

βn

β2

β1

Figure 1: Minimum permitted limit (PL). Probability of false compliance, β, as a function

of the concentration in an accuracy line (left top corner) and operating characteristic

curve (below to the right). The probability of false noncompliance, α, is fixed.


TheoryThe PLS regression method is based on latent variables computed as the linear combinations of the predictor variables that are highly correlated with the response variable and, at the same time, explain the variation in the predictor variables (8).

CDα and CDβTo formalize this section the notation of Commission Decision 2002/657/EC (9) for univariate signals will be used. In particular, permitted limit (PL) is the maximum residue limit, maximum level, or other maximum tolerance for substances established in any norma-tive. When PL = 0, the decision limit (CCα) is the concentration limit at and above which it can be concluded with an error probability of α that a sample is noncompliant when it is. In the case of forbidden substances, for which no per-mitted limit has been established, the

detection capability (CCβ) is the lowest concentration at which a method is able to detect truly contaminated samples with a statistical certainty of 1–β. In the case of substances with an established permitted limit, this means that the de-tection capability is the concentration at which the method is able to detect permitted limit concentrations with a statistical certainty of 1–β.

The capability of detection in the case of univariate calibration models is well established in the ISO standard 11843 (10) and IUPAC (11). Interest-ingly, it can be generalized to any type of calibration models (multivariate, multiway, nonlinear, neural-network based, and so on). This generaliza-tion is based on the mathematical proof that the capability of detection as is defined by ISO and IUPAC for univariate calibration (signal versus concentration) is invariant for linear transformations of the response (the

x0 = PL

yc

xn

CDβCDα

α = pr {false noncompliance}

x0 = PL

β =

pr

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co

mp

lia

nce

}

(x1, β

1)

(x2, β

2)

(xn, β

n)

CDβCDα

x2x

1[c

] P

LS

[c]

[c]

βnβ

2β1

Figure 2: Maximum permitted limit (PL). Probability of false compliance, β, as a function of

the concentration in an accuracy line (right top corner) and operating characteristic curve

(below to the left side). The probability of false noncompliance, α, is fixed.


signal). In particular, the capability of detection is determined by using the concentration predicted with PLS ver-sus true concentration, which is called the accuracy line (y = a + bx) (4,5).

CCα and CCβ, which are defined for PL = 0, are thus generalized for any other value of PL, and named in this paper as CDα and CDβ to distinguish them from the terms already estab-lished in other regulations (9–11). Pre-cisely, if it is about a minimum limit, PL = x0, stated for a given parameter, x, the following hypothesis test is posed:

H0: x = x0 [1]Ha: x < x0

In equation 1 the null hypothesis H0 is in fact stating that the parameter x is greater than or equal to x0 (that is, the sample is compliant), and thus

the alternative hypothesis Ha says that the sample is noncompliant be-cause the parameter x is less than the minimum limit admissible x0.

The decision limit (CDα) of the method is the value of the parameter, below which it can be decided with a probability α that x0 was not reached when it was truly exceeded. Thus, CDα is related to the probability of false noncompliance, α. That is, α is the probability of deciding that the tested sample is noncompliant when it was, the significance level in the hypothesis test of equation 1.

For a given α, the probability β of false compliant decision is the prob-ability of wrongly affirming that the tested sample has a value of the pa-rameter greater than or equal to x0, that is, to conclude that it is compli-ant, when it was not. The detection

(a) (b)

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(g/L

)

Figure 3: PLS predicted values versus true values, (a) volatile acidity model, (b) alcohol

degree model, (c) total acidity model, and (d) reducing sugar model.


capability (CDβ) is the value of the parameter related to this decision.

This is precisely computed as

CDβ = x0 -

Δ(α,β)wx0σ

b [2]

where Δ(α,β) is the value of noncen-trality parameter of a noncentral t-distribution related to the probabili-ties α and β, σ is the residual standard deviation of the accuracy line, and b its slope, and

wx0 = + +

1

K

1

I

(x0 - x–)2

(xi - x–)2

i=1

I

∑ [3]

depends on the position of the stan-dards in the accuracy line (xi), the number K of replicates and the number of standards I.

When the established limit, PL = x0 is a maximum limit, a similar hy-pothesis test is used but in this case the alternative hypothesis is Ha: x > x0

(the parameter is greater than x0, so the sample is noncompliant). Thus, the ca-pability of detection CDβ (analogous to CCβ when x0 = 0 in the Commis-sion Decision [9] and to xd in the ISO 11843-1 [10]) is defined by equation 4:

CDβ = x0 +

Δ(α,β)wx0σ

b [4]

In both cases (minimum or maximum permitted limit, x0), the limit of deci-

sion CDα (analogous when x0 = 0 to CCα in the Commission Decision and unnamed in the ISO 11843-2) is

CDα =y

c – a

b [5]

where a and b are the intercept and the slope of the accuracy line and yc is the value that satisfies either equation 6 for a minimum permitted limit

α = probabilityy < y

c

x = x0)( [6]

or equation 7 for a maximum permit-ted limit.

α = probabilityy > y

c

x = x0)( [7]

Figure 1 shows a schematic represen-tation for an oenological parameter with a minimum limit established, for example the total acidity in Span-ish qualified denomination of origin (DOC) Rioja. In this case, the test of equation 1 is applied. As it can be seen, setting the probability α is in practice setting a critical value, yc, (equation 6) that allows the computation of the probability of deciding that a sample does not reach the minimum value when it is false. This statement is re-lated to concentrations CDα (equa-tion 5) and CDβ (equation 2). The concentration CDβ, besides assess-

Table I: Oenological parameters, reference techniques, and equipment

Oenological Parameters Reference Technique Equipment

Alcoholic degree Near infrared (NIR)Technicon infraanalyzer 450, Bran Luebbe, S.L,

Total acidity and pH Automatic potentiometricAutomatic tritator H-PLUS,

Bran Luebbe, S.L.

Density Electronic densimetryElectronic densimeter Anton Paar, DMA 48

Reducing sugars, volatile acidity, and L-malic acid

Air-segmented continuous-fl ow analysis (SFA)

TRAACS 2000, Bran Luebbe, S.L.


ing the probability of false noncom-pliance (α), also assesses false compli-ance (β). Note that in the value CDα itself, the probability β1 to falsely assert that the wine exceeds the re-quired minimum values is 0.5, while in the concentration corresponding to CDβ this error is reduced to a much smaller amount βn.

Figure 2 shows the corresponding diagram for a maximum limit. Once the probability α is set, now the criti-cal value yc is determined by equa-tion 7 and CDβ is determined with equation 4. In CDα, the probability β1 of asserting that the wine does not exceed the maximum required limit when it is false is 0.5, whereas in CDβ this probability is largely reduced, βn.

In any case, the researcher chooses both risks. The operating curve of the test, the representation of β versus the concentration (for a fixed α) could be used to comparatively examine the performance in the detection capabil-ity of several procedures of analysis (or several analyzed parameters).

ExperimentalMore than 600 samples of red wine are available, all of them analyzed in the official laboratory of the Oenolog-ical Station of Haro (La Rioja, Spain) according to official controls with the accredited methods under the norm EN/ISO 17025, number 183/LE407 (Table I). The same samples were scanned, between 1500 and 1000 cm-1 each 5 cm-1 (101 predictor variables), with a Spectrum One FT-IR spec-trometer (PerkinElmer) to obtain the MIR spectra. PLS models were com-puted with PLS Toolbox 5.8.2 2. (11) over Matlab software. CDα and CDβ were estimated using an in-house written program.

Procedure to Build the PLS Models The procedure consists of the follow-ing steps:1. To build a PLS model for each pa-

rameter:• Preprocess the data matrix X by

standard normal variate (SNV).

Table II: PLS models and accuracy lines for the seven oenological parameters

Parameter

PLS ModelsAccuracy Lines

Predicted Versus True*

Latent

Variables

Explained

Variance (%) RMSECV RMSEP Slope Intercept R2 (%)

X Y

Alcoholic degree

7 91.6 92.6 0.1639 0.1585 0.97 0.27 95.4

Total acidity 10 95.7 93.5 0.1595 0.1503 0.88 0.68 93.6

pH 10 95.4 90.2 0.0364 0.0346 0.91 0.31 84.5

Density 10 95.9 90.6 0.0003 0.0002 0.85 0.15 91.4

Reducing sugar

10 95.5 60.8 0.2135 0.2164 0.63 0.70 56.6

Volatile acidity

15 98.8 87.1 0.0620 0.0553 0.81 0.11 86.5

L-Malic acid 12 97.6 94.4 0.1371 0.1734 0.92 0.05 96.1

*Computed with the values of the external data set


After that, both predictor and re-sponse variables are centered.

• Determine the number of latent variables by cross-validation (10 splits data) and compute the root mean squared error in cross-valida-tion (RMSECV).

• Remove those samples with stan-dardized residual (in absolute value) greater than 2.5 or with both Q and Hotelling T2 values larger than their corresponding threshold values at a 99% confidence level.

• Repeat the two previous steps until there are no outliers.

2. To evaluate the predictive capabil-ity of the PLS models by using an external set of data with 150 wines. The threshold values of Q and T2

obtained in the calibration step are used to detect outliers by assessing the similarity of the spectra of the evaluation samples with the calibra-tion ones. Moreover, the samples with spectrum similar to those of the training samples but with dif-ferent values of the calibrated prop-erty were also considered outliers. This last assessment is made with the least median of squares regres-sion (LMS) line computed between the PLS predicted values versus the true values of the corresponding pa-rameter. The outliers thus detected

were removed and a least squares (LS) regression line was finally fitted. This regression line is the accuracy line, whose characteristics are in the last three columns in Table II for the parameters analyzed.

3. By using the data from the accuracy line, to determine CDα and CDβ with probabilities of both false non-compliance and false compliance fixed at 0.05.

Results and DiscussionThe characteristics corresponding to the different PLS models are shown in Table II, in which the slope, intercept, and R2 values corresponding to the accuracy lines obtained with the pre-dicted and true values can be observed too. In all PLS models, the RMSECV values are similar to their correspond-ing root mean squared error of predic-tion (RMSEP) values, which indicates stability of the models built. Also, no-tice that the variance of the oenological parameters (Y) explained by the mod-els is high, ranging from 87.1% to 94.4%, except for the reducing sugar (60.8%).

On the other hand, the determina-tion coefficients R2 (explained Y vari-ance) of the accuracy lines with the wines in the external data set are greater than 84.5% (except for reducing sugar). Figure 3 depicts these accuracy lines,

Table III: Decision limit, CDα, and detection capability, CDβ, for stated permitted limits (α = β = 0.05)

Permitted

Limit

In Fitting In Prediction

# Wines CDα CDβ # Wines CDα CDβ

Alcoholic degree* 11.5% v/v 447 11.34 11.18 145 11.35 11.23

Total acidity* 3.5 g/L 426 3.34 3.17 142 3.32 3.13

Reducing sugar† 4 g/L 398 4.28 4.56 138 4.40 4.80

Volatile acidity† 0.8 g/L 450 0.87 0.93 152 0.87 0.93

*Minimum and †maximum limit required for the certifi cation of the red wines from DOC Rioja (7)


only for the models corresponding to the four parameters with compulsory limits. Table III shows the permitted limits and the values obtained for CDα and CDβ for both fitting (the accuracy line is made with the training set) and prediction (wines in the external data set). Also, the number of samples used to compute the values, after deletion of outliers, is shown in Table III.

In the case of alcoholic degree the minimum limit established is 11.50% v/v. According to the values in the first row of Table III, the PLS model for this parameter has a CDα in prediction equal to 11.35% v/v. This means that for values less than 11.35% v/v we say that the alcoholic degree of the wine is non-compliant with a 0.05 probability that this assertion is false. But in this case the probability β, to assert that a wine has an alcoholic grade greater than or equal to 11.50% v/v when it is false is 0.50. However, if the PLS model for a wine sample provides a value of 11.23% v/v (equal to CDβ) then β is reduced to 0.05 (see Figure 1).

For a parameter with a maximum limit established—for example, the volatile acidity (last row in Table III)—the values of CDα and CDβ in predic-tion are equal to 0.87 and 0.93 g/L, re-spectively, when the PL is 0.80 g/L. The interpretation is the same for the rest of the parameters with their correspond-ing values. CDα and CDβ in predic-tion and fitting are very similar, which means that the values are stable for the future routine use of the PLS models.

ConclusionsAll the MIR-PLS models built are highly predictive and stable, for the determination of alcoholic degree, total acidity, pH, density, reducing

sugar, volatile acidity, and malic acid in red wines.

This procedure is general, can be applied to products that require the control of maximum (or minimum) legally established limits, and should be taken into account when assessing the risks of false noncompliance and false compliance.

AcknowledgmentsThe authors thank the financial sup-port provided by the Ministerio de Economía y Competitividad project CTQ 2014-53157-R and FEDER funds.

References(1) International Organisation of Vine

and Wine, “Resolution OIV-OENO

390-2010,” http://www.oiv.int/en/

technical-standards-and-documents/

resolutions-oiv/oenology-resolutions

(Last access 01/24/2017).

(2) D.W. Lachenmeier, Food Chem.

101, 825–832 (2007).

(3) C.A. Teixeira dos Santos, R.N.M.J. Páscoa,

and J.A. Lopes, TrAC, Trends Anal. Chem.

doi:10.1016/j.trac.2016.12.012 (2017).

(4) M.C. Ortiz, L.A. Sarabia, A. Herrero, M.S.

Sánchez, M.B. Sanz, M.E. Rueda, D.

Giménez, and M.E. Meléndez, Chemom.

Intell. Lab. Syst. 69, 21–33 (2003).

(5) M.C. Ortiz, L.A. Sarabia, and M.S. Sánchez,

Anal. Chim. Acta 674, 123–142 (2010).

(6) M.L. Oca, M.C.Ortiz, L.A.Sarabia,

A.E.Gredilla, and D. Delgado,

Talanta 99, 558–565 (2012).

(7) BOE del 27 octubre de 2004, Ministerio

de Agricultura Pesca y Alimentación,

18384 Orden APA/3465/2004.

(8) H. Martens and T. Naes in Multivariate

Calibration (John Wiley and Sons

Ltd., Chichester, UK, 1989).

(9) Commission Decision of 17 August 2002

implementing Council Directive 96/23/


EC Concerning the “Performance of

Analytical Methods and the Interpretation

of the Results” (2002/657/CE),

(European Union, Brussels, 2002).

(10) International Standard ISO 11843-2,

“Capability of Detection. Methodology

in the Linear Calibration Case”

(International Organization for

Standardization, Geneva, 2000).

(11) J. Inczedy, T. Lengyel, A.M. Ure,

A. Gelencser, and A. Hulanicki,

Compendium of Analytical

Nomenclature (IUPAC, Blackwell,

Oxford, 1998), chap. 18, pp. 31–40.

(12) B.M. Wise, N.B. Gallagher, R. Bro, J.M.

Shaver, W. Windig, and R.S. Koch, PLS

Toolbox 5 (Eigenvector Research Inc.,

Wenatchee, Washington, 2008).

M.C. Ortiz is with the Faculty of

Sciences in the Department of Chemistry

at the University of Burgos in Burgos,

Spain. L.A. Sarabia and M.S. Sánchez

are with the Faculty of Sciences in

the Department of Mathematics and

Computation at the University of Burgos,

Spain. M.E. Meléndez is with the

Quality Control Department at the

Oenological Station of Haro in Haro,

Logroño, Spain. Direct correspondence to:



Continued from Page 16

(29) J. Antila, M. Tuohiniemi, A. Rissanen,

U. Kantojärvi, M. Lahti, K. Viherkanto,

M. Kaarre, J. Malinen, J. Antila, M.

Tuohiniemi, A. Rissanen, U. Kantojärvi,

M. Lahti, K. Viherkanto, M. Kaarre, and J.

Malinen, in Encyclopedia of Analytical

Chemistry (John Wiley & Sons, Ltd.,

Chichester, UK, 2000), pp. 1–36.

(30) T. Egloff, J. Knobbe, S. Sinzinger, and H.

Grüger, Appl. Opt. 48, 6583 (2009).

(31) Anonymous, “The Scientist

and the Smartphone” Nat.

Methods 7, 87 (2010).

(32) P.D. Atherton, N.K. Reay, J. Ring,

and T.R. Hicks, Opt. Eng. 20,

206805–206805 (1981).

(33) N.A. O’Brien, C.A. Hulse, D.M.

Friedrich, F.J. Van Milligen, M.K. von

Gunten, F. Pfeifer, and H.W. Siesler, in

Proceedings of SPIE, M.A. Druy and

R.A. Crocombe, Eds., 837404 (2012).

(34) D.P. Aykas et al., Anal. Methods

8, 731–741 (2016).

(35) G. Gudi, A. Krähmer, H. Krüger,

and H. Schulz, Julius-Kühn-

Archiv. 0, 143 (2016).

(36) A. Cooke, M.J. Cork, S. Victor, M.

Campbell, S. Danby, J. Chittock,

and T. Lavender, Acta Derm.

Venereol 96, 323–330 (2015).

(37) K.B. Be, J. Grabska, C.W. Huck, Y.

Ozaki, and J.P. Hawranek, J. Mol. Liq.

224, 1189–1198 (2016). Available

from: http://linkinghub.elsevier.com/

retrieve/pii/S016773221632387X.

Christian W. Huck is with the

Institute of Analytical Chemistry and

Radiochemistry, CCB – Center for

Chemistry and Biomedicine in Innsbruck,

Austria. Direct correspondence to:



TO

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ES


SPECTROSCOPY SPOTLIGHT

You have been investigating the IR

spectra of polyoxymethylene (POM)

mold plates to determine the submi-

crometer-scale morphology and mo-

lecular orientation (1). What factors

led you to perform this research?

We work in the business of informing companies, by using various analytical techniques, about the causes of prob-lems such as resin cracks. In this work, we referred to the many papers pub-lished so far on the topic. However, we were not able to understand the cause of POM resin cracks in some cases. The measurements of IR in the papers re-ferred to were usually conducted using KBr or Nujol methods, but curiously its spectral shapes more closely resemble the spectral shapes of specular reflec-tion than those of the attenuated total reflectance method. We think that this

phenomenon may be attributed to in-correct assignment due to optical effects. Since the problem in polymers seems to relate somewhat to their higher-order structure and orientation, we prepared a molding plate sample in which the flow of the melt can easily be imagined. We investigated how the spectral fea-tures changed from the gate position, in addition to observing the scanning electron microscopy (SEM) images. Furthermore, we applied the specular reflection method to avoid changes in the morphology resulting from pre-treatment before the IR measurement. When we used the SEM images, we found cellular structures on the surface. Thus, as a first step, we would like to research the relation between the cellu-lar structures and IR spectral shapes to consider the problems of POM.

Solving Polymer Problems Using IR Spectroscopy

Infrared (IR) spectroscopy is a versatile analytical technique that has found application in a great variety of industries. Naoto Nagai, of the Industrial Research Institute of Niigata Prefecture in Japan, has been studying the potential of IR spectroscopy for investigating higher-order structures of polymers. He and his colleagues recently looked at the IR spectra of polyoxymeth-ylene (POM) mold plates and the cause of occasional resin cracks. Nagai is the 2017 recipient of Sciex’s William F. Meggers Award in Applied Spectroscopy. Here, he describes his research in this area, the method used, and the challenges involved.



Can you please briefly describe the

method used in your investigation?

Considering the change in orientation by the specular reflection method, it is necessary to rotate the polarizer and perform a reflection measurement at each site. Analysis of the spectra ob-tained in this way revealed that there is a region where the real part of the dielectric function (RPDF) is greatly negative in the main chain direction of the molecule. We also found that the strange peaks found in earlier work in other laboratories coincided with the wavenumber range where the RPDF intersects with zero, and I thought that it was a polariton due to the delay effect of electromagnetic wave. To confirm this hypothesis, we assembled an ex-perimental setup to observe the Ber-reman effect; namely, we performed reflection measurements of a thin film on a metal plate with a large incident angle. The Berreman effect should be observed as a reflection response in p-polarization only at the position of the LO mode.

What conclusions did you draw from

the study and why are they significant?

So far, polaritons have been observed in inorganic ion crystals and polar semiconductors, and there have been no reports that surface phonon polari-tons have been observed in polymers. Although POM is a crystalline polymer, it seems surprising that polaritons ap-pear in weak ordered materials like polymers. Furthermore, we pointed out that we face the risk of misinter-preting the measured spectra obtained by the destructive techniques in the framework of the ordinary vibration analysis. Therefore, it is necessary to reconsider the interpretation of large

peaks in polymers in connection with higher-order structures. Thus, IR spec-troscopy has potential for use when in-vestigating the higher-order structures of polymers. In addition, we show that polar polymers with high f lexibility can also be candidates as materials for low-loss nanophotonic devices that require structures generating surface phonon polaritons.

What are the next steps in your

research?

We predict that a similar polariton mode will exist for polymers with a large dipole moment and relatively high-order structure. We would like to search for materials that behave similarly in polymers other than POM. Also, we will investigate the interme-diate state that changes from ordinary vibrations to polaritons. Polymers are considered to be the most suitable ma-terials to investigate the condition of changing from ordinary vibrations to polaritons in IR spectroscopy. More-over, since it is believed that some IR peaks reflect higher-order structures, we would like to analyze such struc-tures using finite-difference time-do-main (FDTD) methods.

Reference(1) N. Nagai, M. Okawara, and Y. Kijima,

Appl. Spectrosc. 70, 1278–1291 (2016).

This interview has been edited for length and clarity. To read the full in-terview please visit: www.spectroscopy-online.com/solving-polymer-problems-using-ir-spectroscopy ◾

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