Noninvasive deep Raman detection with 2D correlation analysis

Journal of Molecular Structure 1069 (2014) 223–228

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Journal of Molecular Structure journal homepage: www.elsevier.com/locate/molstruc

Noninvasive deep Raman detection with 2D correlation analysis Hyung Min Kim a,⇑, Hyo Sun Park b, Youngho Cho a, Seung Min Jin b, Kang Taek Lee c, Young Mee Jung d, Yung Doug Suh b,e,⇑ a

Department of Bio & Nano Chemistry, Kookmin University, Seoul 136-702, Republic of Korea Research Center for Convergence Nanotechnology, Korea Research Institute of Chemical Technology, Daejeon 305-600, Republic of Korea c Department of Chemistry, Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Republic of Korea d Department of Chemistry, Institute for Molecular Science and Fusion Technology, Kangwon National University, Chuncheon 200-701, Republic of Korea e School of Chemical Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea b

h i g h l i g h t s  Depth-dependent spatially offset Raman spectra are analyzed using generalized 2D correlation spectroscopy (2DCOS).  2DCOS analysis gives the resolved spectral components and their sequential order.  Linear shaped illumination is employed to reduce the potential photo-damage.

a r t i c l e

i n f o

Article history: Available online 23 January 2014 Keywords: Raman scattering Spatially offset Raman spectroscopy 2D correlation spectroscopy

a b s t r a c t The detection of poisonous chemicals enclosed in daily necessaries is prerequisite essential for homeland security with the increasing threat of terrorism. For the detection of toxic chemicals, we combined a sensitive deep Raman spectroscopic method with 2D correlation analysis. We obtained the Raman spectra from concealed chemicals employing spatially offset Raman spectroscopy in which incident line-shaped light experiences multiple scatterings before being delivered to inner component and yielding deep Raman signal. Furthermore, we restored the pure Raman spectrum of each component using 2D correlation spectroscopic analysis with chemical inspection. Using this method, we could elucidate subsurface component under thick powder and packed contents in a bottle. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction Raman spectroscopy has been a powerful analytical tool in various fields including pharmaceutical industry, environmental application, homeland security, forensic science, and archaeological analysis [1]. Rapid developments in laser technology and optical manufacturing industry have extended Raman applications to noninvasive detection of chemical or biological targets, such as pharmaceutical products [2], medical diagnostics [3], and in vivo imaging [4] in recent decades. However, the depth limit of noninvasive detection in turbid materials has been hardly overcome. Recent studies on deep Raman spectroscopy have improved the detection depth and provided chemical information of analytes ⇑ Corresponding authors at: Department of Bio & Nano Chemistry, Kookmin University, Seoul 136-702, Republic of Korea. Tel.: +82 29105770, fax: +82 29104415 (H.M. Kim), Research Center for Convergence Nanotechnology, Korea Research Institute of Chemical Technology, Daejeon 305-600, Republic of Korea. Tel.: +82 428607597; fax: +82 428607625 (Y.D. Suh). E-mail addresses: [email protected] (H.M. Kim), [email protected] (Y.D. Suh). http://dx.doi.org/10.1016/j.molstruc.2014.01.045 0022-2860/Ó 2014 Elsevier B.V. All rights reserved.

under turbid media [5,6]. Spatially or temporally resolved deep Raman methods including transmission Raman spectroscopy [7], spatially offset Raman spectroscopy (SORS) [8], and time-resolved Raman spectroscopy [9,10] have been developed and widely used in the area of deep Raman spectroscopy. Among them, SORS method has been considered as one of the most prominent method to investigate turbid media with increased deep Raman sensitivity [11,12]. The concept of SORS is that incident light can penetrate into the deeper region of the sample after it experiences multiple scattering accompanying spatial dislocations from irradiation position. This method yields deep Raman information from pharmaceutical tablets [13], hidden explosives [10], biological tissues [14,15], etc. Recently, surface enhanced spatially offset Raman spectroscopy (SESORS) was developed for in vivo detection of glucose [16]. In addition, SORS results include congested Raman spectra of multiple components, chemometric methods are necessary to disentangle the spectra. Principal component analysis (PCA) [17] and band-target entropy minimization (BTEM) [18] are typical methods that were introduced into SORS analysis [19].

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In this work, we introduce rapid and sensitive deep Raman method, called spatially offset Raman spectroscopy (SORS) coupled with two-dimensional correlation spectroscopy (2DCOS). Since illumination of circular, disk or linear geometry rather than point geometry has been reported in various SORS geometry with collection fiber optics [20–22], we employed linear near IR illumination combined with direct collection through the slit of polychromator to reduce the power density of light. Moreover, we could freely adjust scanning range of distance between illumination and detection depending on thickness and geometry of analytes, which provided sufficient information for 2DCOS analysis. We applied 2DCOS analysis to retrace each independent spectrum of layered analytes based on the acquired offset-dependent Raman spectra. 2DCOS is a well-established analytical technique to interpret spectral data sets obtained under some type of external perturbation. Historically, it has been developed to analyze complex spectra in the fields of nuclear magnetic resonance (NMR) [23] and other resonance spectroscopy. Afterwards Noda first proposed the applicability of generalized 2DCOS method to various fields including optical spectroscopy [24,25]. It turns out that this method is also advantageous for recovering the original spectrum from composite offset dependent Raman spectra for the first time. By means of experimental SORS method coupled alone or with 2DCOS analysis, we could disclose the identities of chemicals behind packing materials and potential hazard, pesticide, in daily necessity. We investigated the inner components in powder bilayer composed of polyethylene and trans-stilbene (PE-tSTB); glucose (Glu) in a HDPE bottle; and benomyl (BN) in a document envelope.

2. Experimental

spectrum acquisition were synchronously controlled using homemade LabVIEW software (National Instrument, LabVIEW 2010). 3. 2DCOS analysis 2DCOS analysis was performed on LabVIEW-based software, of which the algorithm is described by Noda and Ozaki [27,28]. Following equation expresses the key concept of 2DCOS algorithm at two vibration m1 and m2,

Xðm1 ; m2 Þ ¼< yðm1 ; tÞ  yðm2 ; tÞ >¼ Uðm1 ; m2 Þ þ iWðm1 ; m2 Þ

ð1Þ

X, y, U, and W are 2D correlation intensity between m1 and m2, spectral intensity variation as a function of external perturbation t, synchronous, and asynchronous correlation intensity, respectively. The simplified numerical calculation method to solve the above equation is described elsewhere and we followed the numerical algorithm applicable to evenly spaced t case [28]. 3.1. Chemicals Polyethylene (Alfa Aesar, 6400 lm), trans-stilbene (Sigma–Aldrich Co.), and a-D-glucose (Sigma–Aldrich Co.) powders were purchased and used without further purification. To precisely set the thickness of PE-tSTB sample, tSTB powder was put into an empty hole drilled in an anodized aluminum spacer, whose thickness was finely defined. And both sides of the spacer were blocked with thin cover glass of 0.13–0.17 mm thickness (Fisher Scientific) and the other spacer containing PE was laid on top of it. Pure benomyl was purchased from Sigma-Aldrich Co. and commercial form of pesticide, mixture of benomyl (50%) and surfactant (50%), was purchased from Dongbu Agrotech Co.

2.1. Instrumental

4. Results and discussion

SORS instrument mainly consisted of optical system, detection apparatus, and motorized stage. Laser-illumination and Ramancollection unit comprised the optical system and were translated separately. We used 785 nm diode laser system (PD-LD, Raman Boxx), and the output beam was transferred to the illumination unit through multi-mode fiber. The transferred beam was collimated with aspherical lens (Thorlabs) and linearly illuminated using cylindrical lens after passing through laser line filter (Semrock, MaxLine). The length of the focused beam at focal plane was controlled by selecting focal length of aspherical lens and cylindrical lens. In this work, typical width (0.7 mm) and length (4.3 mm) of the beam was measured using knife edge method and the power at the sample surface was maintained at 300 mW. Raman signal induced by multiply scattered light was collected by a 2 inch planoconvex lens (CVI, f = 75 mm) and finally focused by a planoconvex lens after passing through an edge filter (Semrock, RazorEdge). The image of line illumination was adjusted to overlap with entrance slit of polychromator (Princeton Instrument, SP2300i) and projected onto a thermoelectrically cooled CCD camera (Princeton Instrument, PIXIS 400BR). Slit width was fixed at 100 lm during the measurement and full range of CCD (1340  400 pixels) was used. The wavenumber scale of every spectrum was calibrated with respect to Raman spectrum of cyclohexane in accordance with ASTM standard [26]. The motorized actuator (Thorlabs, Z825B), which was assembled to the side of 3 dimensional translation stage (NAMIL Optical Components Co.), was manipulated by a computer regulated DC servo-motor controller (Thorlabs, TDC001). Wide range of distance (15 mm) was covered while we adjusted spatial offset between illumination and collection line. The stage movement and the

4.1. SORS spectra of bilayer system and depth profiling In principle, multiply scattered light penetrating into deeper region of analytes can lead to deep Raman signal as shown in Fig. 1(a), which describes the basic principle of SORS system. In our experiment, we focused light in line-shape to expand illumination area at the surface of analytes and incident laser penetrating into the sample collides elastically with inner particles multiple times and finally invoke Raman scattering. The induced Raman photons coming out of analytes were collected if propagating into the angular aperture of collection lens. Statistically considered, multiply scattered photons have higher probability to pierce into inner layer and induce Raman scattering at distant location. The longer the distance between irradiation and detection is, the more Raman scattering events from inner layer are observed. The frequencies of Raman scattering events in outer and inner layer are illustrated at the bottom of the figure. We employed the illumination linear shape and expand the illumination area longer than 4 mm in vertical direction. This large line-shaped light enabled us overall high excitation power accompanying low power density, which protects analytes from photodamage relative to point illumination with similar power. This effect is called as power distribution advantage [29]. This low powerdensity is especially important for study of biological or photo-labile sample. Unlike point illumination scheme, even at zero offset (d = 0 mm) the deep Raman signal can be observed because the scattered light can be observed at distant illumination and detection location in linear extension. And we collect deep Raman scattering directly through the entrance slit of polychromator in our optical configuration.

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Fig. 1. (a) Schematic diagram of linearly-illuminated spatially-offset Raman spectroscopy (SORS). Dark gray and light gray bar in the bottom graph represent the number of Raman scattering in the outer layer and inner layer, respectively. (b) Experimental setup of SORS: PL, planoconvex lens; EF, edge filter; CL, cylindrical lens; LF, laser line filter; AL, achromatic lens. The arrow in the inset photo illustrates the offset-direction of laser (gray line) from the detection (black line).

As a standard model system, we prepared PE-tSTB powder bilayer with precise thickness, as shown in inset of Fig. 1(b) and the thickness of PE and tSTB in this experiment were 3 mm and 2 mm, respectively. The power of 785 nm laser was 300 mW at sample surface and exposure time was 1 s for each spectra. We also observed SORS spectra of several different thicknesses, from 1 mm to 3 mm, while the results are not shown here. Fig. 2 shows Raman spectra of PE (bottom) and tSTB (top) alone, and SORS spectra of PE-tSTB bilayer as a function of spatial offsets between the illumination and the detection. The intensities are auto-scaled adjusted to show relative ratio between Raman peaks clearly, while the intensity variations of several peaks are shown in Fig. 3. From the figure, we can readily find that the longer the spatial offset becomes, the more the Raman signal from inner layer dominates. And the result shows that even at zero offset when the illumination and the detection line coincide, SORS spectrum includes the significant Raman characteristics of inner layer as well as of outer layer. This was proposed to be originated from the nature of the linear illumination-detection scheme as described in the above. To provide a clue for depth profiling of our model system, we tentatively select a few specific peaks of PE and tSTB; we recorded the offset-dependence change of signal intensity. Fig. 3 shows

intensity change of four characteristic peaks which represent PE (denoted as a, a0 in Fig. 2) and tSTB (denoted as b, b0 ) with respect to spatial offset. The baselines of the peak are corrected by subtracting the average of two end points and the signal intensities at each wavenumber within the peak width are summed. The result is plotted as shown in Fig. 3. As the collection efficiency decreases in accordance with spatial offset, the overall spectral intensity gradually decreases. While the Raman intensity of outer PE layer decreases earlier and more rapidly, that of inner tSTB layer decrease later and with increasing offset distance. This reflects the principle of SORS described in Fig. 1. At small offset limit, major portion of collected Raman scatterings are signals excited by almost non-scattered light, and at large offset, majority of detected scatterings are led by multiply scattered light. Subsequently, Raman signals from outer layer overwhelm those from inner layer when the offset is small, and vice-versa. 4.2. Detection of pesticide in an envelope As SORS method can lead to noninvasive detection to a depth, it can be applied in the field of homeland security, for example detection of chemical weapons delivered to anonymous people. We applied SORS technique to detect the toxic chemicals assuming the

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(a)

(b)

(c)

(d)

(e)

500

1000

1500

2000

Wavenumber (cm-1) Fig. 2. Raman spectra of pure PE (bottom) and tSTB (top) and auto-scaled SORS spectra of PE (3 mm)-tSTB (2 mm) bilayer according to increasing spatial offset from 0 to 7 mm (middle). Each spectrum was acquired for 1 s.

Fig. 4. (a) Raman spectrum of a vacant envelope. (b) Raman spectrum of pesticide BN in an envelope acquired with conventional Raman instrument. (c and d) SORS spectra of BN in an envelope with offset = 0 mm and 2 mm. (e) Raman spectrum of pure BN powder.

envelope using (b) commercial Raman microscope system, (c) homebuilt Raman system, and (d) SORS instrument. The spectra in (a)–(d) are baseline-corrected using asymmetric least square algorithm [30]. With conventional system, we observed (b) only the Raman spectrum of an envelope itself at any position of document without any feature of pesticide. And we performed Raman analysis using homebuilt typical Raman instrument equipped with 2 inch optical system and it shows the slight feature of Raman spectrum of pesticide in Fig. 4(c). Finally, we observed the Raman spectrum with SORS method of pesticide at d = 2 mm shown in Fig. 4(d), which clearly represents the Raman spectrum of commercial pesticide in envelope compared with Raman spectrum of pure BN. We note that Raman spectrum of commercial pesticide in an envelope is completely identical with that of pure BN except for arbitrary baseline. 5. 2DCOS analysis and spectral reconstruction Fig. 3. SORS intensities of peak a (filled circle) and a0 (hollow circle) of PE; peak b (filled square) and b0 (hollow square) of tSTB. X-axis is the spatial offset between illumination and detection.

case of intentionally delivered envelope or parcel. As a model system we prepared a toxic chemical in an envelope. We prepared a commercial pesticide purchasable from agriculture pesticide shop and put it into a document envelope before sealing. We selected benomyl (BN), a kind of pesticide, as a possible hazard. Pure BN was purchased from Sigma-Aldrich Co. and commercial pesticide composed of 50% BN and 50% surfactant for hydration was purchased from Dongbu Agrotech Co. The sample was probed using various instruments and conditions including conventional Raman system (Bruker, Senterra), homebuilt point Raman, and SORS instrument and the results are shown in Fig. 4. In the experiment, we observed Raman spectra of (a) a vacant envelope, (e) pure BN, and those of pesticide in an

The SORS spectra of bilayer system in Fig. 2 include the Raman spectra of both PE and tSTB with varying intensity ratio in accordance with spatial offsets. Thus it is critical to reconstruct and separate the independent spectra of inner and outer layer to reveal the identity of inner layer. To resolve the independent spectra, we employed 2DCOS as a mathematical tool which enables to analyze cross correlation among plural peaks in the SORS spectra. Spatial offsets are considered as external perturbation, t, in equation (1). Generally, 2DCOS analysis yields two distinct correlation intensity called synchronous correlation (U) and asynchronous correlation (W) in the presence of external perturbation. Synchronous and asynchronous 2D spectra map the synchronous and the asynchronous correlation intensities, respectively, onto colored individual points in two-dimension with respect to all the wavenumbers recorded. Synchronous 2D correlation spectrum illustrates the intensity correlation among entire peaks as a function of spatial offset; if one peak grows together with another peak, the correlation

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Fig. 5. (a) Asynchronous 2D correlation spectrum of SORS results of PE-tSTB bilayer. The red and blue lines represent positive and negative cross peaks, respectively. (b and e) Raman spectra of pure PE and tSTB powder. (c and d) Recovered spectra of PE and tSTB in bilayer obtained from the slice spectra at peak b (with inverted sign) and a. Each SORS spectra were acquired for 1 s. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

between them yields positive value and vice versa [28]. As shown in Fig. 4, spectral intensities of both inner and outer layer gradually decrease along positive axis and thus synchronous correlation shows all the positive cross peaks in synchronous 2D correlation spectrum. On the other hand, asynchronous 2D correlation spectrum represents the information on the phase of spectral intensity variation, in other words, it reflects sequential order of change correspondent to a given parameter [28]. If the signs of synchronous and asynchronous cross peaks are the same, the intensity change at m1 occurs before m2. If the signs of synchronousand asynchronous cross peaks are different, the intensity change at m1 occurs after m2. Fig. 5 shows the asynchronous 2D correlation spectrum of SORS results of PE-tSTB bilayer. In our experiment, the external perturbation in 2DCOS is spatial offset between illumination and detection position. We choose the SORS spectra between d = 2 mm and d = 7 mm at intervals of 0.5 mm to reflect the discrimination of intensity decrease clearly. The spectrum placed at the left and the bottom of asynchronous 2D correlation spectrum is the autopower spectrum, which is the diagonal slice spectrum of synchronous 2D correlation spectrum. Among various peaks in the SORS spectrum in Fig. 2, we tentatively choose peak a and peak b corresponding to Raman characteristics of outer layer (PE) and inner layer (tSTB), respectively. The slice spectrum at peak b along vertical axis represents the sequential order between vibrational peak b from inner layer and all the other vibrational peaks. (The negative sign of the slice spectrum at peak b is inverted in Fig. 5(c).) It includes mainly Raman peaks of outer layer in negative sign compared with Raman spectrum of pure PE powder shown in Fig. 5(b). In the view of 2DCOS analysis, all the Raman peaks from inner layer change almost simultaneously with peak b from the same layer, showing no correlation in asynchronous 2D correlation spectrum. However, the decrease of Raman intensity of vibrational peak b from inner layer occurs after that of Raman peaks from outer layer along the external perturbation in the region of interest (d = 2–7 mm), the correlation yields negative cross peak. In case of correlation at peak a, slice spectrum shown in Fig. 5(d) reconstructs pure Raman spectrum of inner layer in Fig. 5(e) for the same reason. The result verifies the advantages of 2DCOS in discovering the spectrum of unknown inner components based upon SORS results.

The combination of 2D correlation analysis and SORS data can effectively sort out the spectral data according to the sampling order. This method provides rich insight into the SORS spectra of complex systems. Such insight could not have been obtained through the application of other multivariate analysis techniques to the SORS data. 5.1. Deep Raman probing of powder in a bottle One of important applications of deep Raman technique is to predict unknown contents inside package wrapped up with diverse packing materials because of the noninvasive nature of Raman spectroscopy. To attest the utility in noninvasive probing of SORS, we performed SORS experiment on commercial packaged reagent, Glu in a large round bottle made from high density polyethylene (HDPE) and analyzed with 2DCOS method. According to the 2DCOS results, in which we selected offset range from d = 3 mm to d = 7 mm, synchronous correlation spectrum involves all the cross peaks among all compartment peaks because of their intensity declination at the same time, and the results are presented. Fig. 6(a) exhibits asynchronous 2D correlation spectrum of SORS spectra obtained for Glu powder in a HDPE bottle. Autopower spectrum obtained in synchronous 2D correlation spectrum is located at the bottom and the left of asynchronous spectrum to show the congested Raman spectrum of Glu and the HDPE bottle altogether. In the autopower spectrum, we picked two typical peaks representing the HDPE bottle and Glu denoted as c and d, respectively, and the slice spectra obtained at each peak are shown in Fig. 6 placed with Raman spectra of vacant bottle and pure Glu. The slice spectrum at peak d of Glu shown in Fig. 6(c) restores the dominant Raman spectrum of the vacant bottle with marginal positive values. (We inverted the sign of the spectrum in Fig. 6(c) for the convenience of understanding.) Negative peaks in the 2D spectrum at peak d originated from the fact that the spectral decrease of the peak d from Glu is slower than that from the HDPE bottle in SORS results in the 3–7 mm offset region. The slight positive peaks reflect imperfect synchronization of intensity variation with respect to multiple Glu peaks because of the experimental artifacts. On the other hand, the slice spectrum in Fig. 6(d), calculated at peak c of the HDPE bottle, corresponds to the typical Raman

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Fig. 6. (a) Asynchronous 2D correlation spectrum of SORS results of Glu in a HDPE bottle. The red and blue lines represent positive and negative cross peaks, respectively. (b and e) Raman spectra of the vacant bottle and Glu powder. (c and d) Recovered spectra of the HDPE bottle and Glu obtained from the slice spectra at peak d (with inverted sign) and c. Each SORS spectra were acquired for 2 s. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

spectrum of pure Glu inside the bottle in Fig. 6(e). It also includes insignificant peaks of the HDPE bottle in the opposite sign. The result means that the intensity decrease of peak c, attributed to molecular vibration of the bottle, alters faster than that of Glu according to offset distance. Consequently, Raman spectra from Glu can be reconstructed obviously using SORS method coupled with 2DCOS. This result emphasizes the virtue of noninvasive Raman probing which enables to identify the inner components without unpacking the analytes. Moreover, 2DCOS is found to be advantageous when we analyze SORS spectra with asynchronous interpretation corroborating chemical inspection. 6. Conclusion We have developed a deep Raman system, SORS, coupled with generalized 2D correlation analysis. Using modified SORS method, we could observe Raman spectra from inner components of PE-tSTB bilayer, Glu in a HDPE bottle, and pesticide in a sealed envelope. For reconstructing separate Raman spectrum from congested SORS spectra, we introduced 2DCOS analysis by simply picking several typical peaks of inner and outer component. Asynchronous correlation spectrum presents the information on individual Raman spectrum of each pure component depending on calculated positions. 2DCOS analysis expedites disentangling complex SORS spectra conveniently by graphical exploration. This analytical scheme of SORS analysis combined with 2DCOS method can be widely applicable to subsurface analysis. Acknowledgements This research was supported by the Public welfare & Safety research program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0020957) and H. M. K. acknowledges the financial support by the faculty research program 2012 of Kookmin University in Korea and by Basic Science Research Program through the National Research Foundation of Korea (NRF-2013R1A1A1008710). K. T. L. was supported by Basic Science Research Program (2013R1A1A1058451) of MSIP through NRF.

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