Article pubs.acs.org/ac
Chemical Analysis of Molecular Species through Turbid Medium Rajan Arora,† Georgi I. Petrov,‡ Vladislav V. Yakovlev,*,‡,§ and Marlan O. Scully‡,§,⊥ †
Corning, Inc., Hickory, North Carolina 28602, United States Texas A&M University, College Station, Texas 77843, United States § Baylor University, Waco, Texas 76798, United States ⊥ Princeton University, Princeton, NewJersey 08540, United States ‡
ABSTRACT: Subsurface analysis of chemical species is imperative for biomedical diagnostics and imaging, homeland security, and pharmaceutical and other industries; however, the access to the object of interest is often obscured by an optically scattering medium which limits the ability to inspect the chemical composition of the sample. In this report, we employ coherent Raman microspectroscopy in a combination with a hierarchical cluster analysis to mitigate the effect of scattering and demonstrate the identification of multiple chemical species.
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laser beam is focused onto the area of high localization nonlinear Raman signal generated through the coherent antiStokes Raman scattering (CARS) process, it is known to be orders of magnitude stronger than the spontaneous Raman signal.16 In brief, CARS is a four-wave mixing, which in its simplest implementation scheme utilizes two frequencies, the pump, ω1, and the Stokes, ω2, which generate the signal at the frequency of 2ω1 − ω2. Any vibrational Raman resonance, which is characterized by Ω= ω1 − ω2, will be reflected in the CARS spectrum, which can be attained either by continuous scanning of the Stokes frequency, ω2, or by using a broadband pulse, as the Stokes pulse, to simultaneously excite different vibrational modes.17−21 A proper analysis of the spectrum, which takes into account a complex spectral shape of CARS resonances,22−24 allows analytical assessment of the interrogating volume.25 As with any other spectroscopic method based on a nonlinear optical interaction, the useful CARS signal originates predominantly from the focal region where the incident intensity of each incoming beam is reaching its maximum.25 CARS is a nonlinear optical spectroscopy method, and its signal is proportional to a higher order of the incident intensity implying that the maximum signal will be generated from a focal volume, where the intensity reaches its maximum.26 In scattering medium, beyond several mean free paths of a photon in such medium, light propagation becomes diffusive. In human skin and the near-IR illumination wavelength, this regime occurs at a depth of about 1 mm.27 While there are ongoing efforts on trying to overcome those limitations,28,29 in the present report, we limited ourselves to a depth of 1−2 mm. We estimated the reduced scattering coefficient, μ′s , of a sample to
n many practical applications, such as biomedical sensing and imaging, criminal investigations, homeland security, chemical processing, and quality control, there is a need to identify the chemical and structural composition without destroying the sample’s integrity.1−7 The challenge is not only to get chemical information but also to provide an assessment of the spatial homogeneity of the sample in terms of its chemical and structural composition. Vibrational spectroscopy is typically considered to be one of the better techniques suitable for in situ inspection, since the vibrational spectrum of a molecule is often treated as a fingerprint of a molecule.8,9 Most molecules have very strong absorption lines in the mid-infrared (2−20 μm) spectral region, and the mid-infrared spectroscopy is traditionally being considered for the assessment of a very thin surface layer of evaluating materials.10 Strong absorption in the mid-infrared part of the spectrum makes it difficult to attain a meaningful signal from the depth of a sample. On the other hand, Raman spectroscopy has been rather successfully used for chemical inspection of heterogeneous samples both on the surface and in the bulk.11,12 Some of the recent advances in Raman spectroscopy include surface-offset Raman spectroscopy13 and Raman spectroscopy based optical tomography,14 which have led to a dramatic advancement of chemical sensing through turbid medium. However, in many practical situations, the impurities, inclusions or, in general, points of interest are present in minute quantities which are often highly localized in space due to either agglomeration or natural arrangement. Clearly, by interrogating a much larger volume, spectral signatures of sought chemicals will be washed out, unless they are located in the spectral region where no other vibrational lines are present, making it hard or impossible to identify the presence of those potentially harmful compounds in a mixture. To deal with this problem, we propose the use of coherent Raman microspectroscopy,15 which serves several purposes. We assume that the chemicals are somewhat localized through either agglomeration or initial arrangement. In this case, if a © 2013 American Chemical Society
Received: July 29, 2013 Accepted: December 31, 2013 Published: December 31, 2013 1445
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Figure 1. (a) Schematic diagram of the sample’s assembly: Scotch tape was used to fix the position of the DPA powder, and calcium carbonate powder was placed on top of the structure to make a 1−2 mm thick layer of scattering material. The whole assembly is placed in between two pieces of paper. (b) Microscopic images of the sample’s architecture using a bright-field optical microscope. The top figure is a microscopic image of DPA powder deposited into a specific shape on Scotch tape. The middle figure is a microscopic image of the same structure with a layer of calcium carbonate powder on top of it. The bottom image was taken with a piece of paper on top of the above assembly. (c) Raman spectra of (a) paper, (b) dipicolinic acid (DPA) powder, (c) calcium carbonate powder, and (d) Scotch tape. All spectra were acquired using the excitation laser wavelength of 532 nm.
be 20 cm−1 at around 785 nm, which is similar to the scattering properties of a skin tissue at the same wavelength.30 One of the potential approaches to deal with the scattering is to increase the absorption of the medium, which makes light propagation in the medium more ballistic. In simple words, light photons, which experience stronger light scattering, travel
a larger distance and are more likely to be absorbed. This approach has been extensive for spontaneous Rama scattering imaging and sensing;31−33 however, this method results in a loss of a useful signal and requires a proper absorption to be present in the system. 1446
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copy using a home-built confocal Raman microscope employing 532 nm excitation wavelength and Horiba, Inc. 1/3 m spectrometer with the attached liquid nitrogen cooled CCD on the detection side. Those spectra are presented in Figure 2 and show some specific spectroscopic features, which could be used to potentially discriminate different chemicals.
The elastic light scattering is dramatically reduced for longer wavelengths,30 allowing for a deeper penetration.29 Since the CARS signal is blue-shifted with respect to the incident wavelengths, the detection can be arranged by employing a silicon based CCD detector for spectral measurements, if the pump wavelength is at the longer wavelength edge of the sensitivity of Si (1100 nm). A powerful combination of advantageous scattering properties and intrinsic sectioning capability of CARS microspectroscopy make the use of CARS microspectroscopy attractive for imaging through a highly scattering medium. The major challenge for the CARS measurements through the turbid medium is the vanishing intensity of the CARS signal, whose strength is substantially reduced because of the incident light propagation through scattering medium. As a result, the reduced signal-to-noise ratio (SNR) affects the ability of discriminating chemicals. While increasing the dwell time for each point on the sample improves the SNR, it also leads to enormously long acquisition times. To deal with this issue, we employed a hierarchical cluster analysis, which allows collection of hyperspectral data from each spatial location at the sample and clusters those spectra based on their similarity.34 This way, the SNR is tremendously improved by effective summation of multiple spectra within the same cluster, and the discrimination of different chemicals can be performed quantitatively based on a significance of spectral differences between different clusters. A graphical representation of such differences is typically achieved through a dendogram, which provides visual assessment and simple interpretation of the sample’s heterogeneity. We note that, while we have had a great success with the hierarchical cluster analysis, there are many other highly competitive approaches for spectral classifications, such as machine learning algorithms, like support vector machines,35,36 artificial neural networks, and many others. Without downplaying those powerful methods of spectral analysis, we note that the hierarchical cluster analysis was found to be appropriate for our present studies; however, no attempt was made in the course of this work to evaluate other approaches. Once the proper chemical species are identified, one can use a phase retrieval method described elsewhere37,38 to convert CARS spectra into more traditionally used Raman spectra, for which numerous databases exist for a large number of chemical species. We have found this retrieval algorithm, which is described in greater detail in ref 38, to be unaffected by the presence of strong scattering.
Figure 2. Schematic diagram of the experimental setup for broadband CARS microspectroscopy.
In the first set of measurements, we attempted to employ confocal Raman microscopy to image through the structure. Using a high numerical aperture objective lens (N. A. = 0.55; 50×; Mitutoyo, Inc.) to benefit the collection efficiency and spatial resolution for nonscattering samples, we utilized the above-mentioned setting to scan through the whole volume of a sample with an acquisition time of 10 s per data point; i.e., it took just over one day to acquire a full hyperspectral image of about 100 by 100 data points. We found that the recorded Raman spectra were not changing upon moving the focal spot of the incident laser beam in the axial direction demonstrating poor axial discrimination of the signal. We needed the assistance of CARS microspectroscopy to identify the focal plane corresponding to the plane where DPA was located; however, even with this support, no clear lines corresponding to the Raman spectrum of DPA were found in any of those spectra. Due to congested Raman spectra and poor axial spatial discrimination, it was not possible to obtain any clear image of the underlying DPA structure, as it is illustrated in several images shown in Figure 3. Hierarchical cluster analysis was unsuccessfully tried to recover the image information. That was expected, and it just provided an experimental confirmation of our prior arguments. However, the situation dramatically changed once we switched to the CARS microscopy system utilizing a fundamental radiation of Nd:YVO4 laser as a ω1 frequency (λ = 1064 nm) and a broadband continuum (from 1100 to 1500 nm) as a ω2 radiation. The technical side of the experimental setup is described in greater detail elsewhere.36,40,41 The incident radiation was focused onto the sample using an aspheric lens AR coated for the near-IR (f = 6.24 mm, NA = 0.4; ThorLabs, Inc.), and the CARS signal was collected using a high numerical aperture objective lens (f = 4.51 mm, NA = 0.54, Thorlabs, Inc.) from the back of the sample. A filter was used to block the residual radiation above 1 μm, and CARS spectra were collected using a 1/3 m imaging spectrometer (Shamrock, Andor, Inc.) with the attached TE-cooled CCD (iDus-401 BRDD; Andor, Inc.) optimized for the near-IR
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EXPERIMENTAL APPROACH To demonstrate the proof-of-principle and to illustrate the power of this approach, we prepared a sample, as it is shown in Figure 1a. We used Scotch tape to arrange a powder of dipicolinic acid (DPA) in a meaningful shape. DPA was chosen to imitate the presence of bacterial spores, which are known to have a high percentage volume of dipicolinic acid, which serves as a marker for detecting and imaging those spores.39 To achieve a desirable sample thickness and scattering properties, a fine powder of calcium carbonate (chalk) was added on top of the sample, and the whole assembly was placed in between two pieces of white printed paper (Office Depot, Inc.). As it is illustrated by microscopic images shown in Figure 1b, the resulted structure formed by DPA powder becomes totally invisible through several layers of scattering material (calcium carbonate and paper). Each of those substances used in our experiments was individually characterized by Raman spectros1447
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Figure 3. Confocal Raman images of the sample: (a) Raman signal is measured at around 1070 cm−1 corresponding to a Raman peak of calcium carbonate, (b) Raman signal is measured at around 1150 cm−1 corresponding to a Raman band associated with a paper, (c) nonspecific Raman image corresponding to the signal measured at Raman shift of 2000 cm−1, where no Raman signal is expected (signal is dominated by a residual fluorescence, which predominantly originated from a paper), and (d) combined (a) and (b) image, showing significant overlapping regions of localization of paper and calcium carbonate powder.
Figure 4. CARS images of the sample: (a) signal is measured at around 1000 cm−1 (the marker for DPA), (b) signal is measured at around 1070 cm−1 (the marker for calcium carbonate), (c) signal is measured at around 1450 cm−1 (the marker for Scotch tape), and (d) combined image, showing the substantial area of identified chemical substances. 1448
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spectral recording. CARS spectra were acquired with a preset acquisition time of 100 ms, and the step-size was allowed to be 10 μm to cover a larger area given a 128 × 128 size limitation for the number of data points in each direction of the scan imposed by a software package (CytoSpec, Inc.; version 1.4.02) for hyperspectral image analysis. We used D-values as a measure of similarity between the spectra and Ward’s algorithm to further combine the clusters after first iteration. This combination in spectroscopy is known to construct the most homogeneous groups.34 Each spectrum was recorded over the spectral region of about 1500 cm−1 and consisted of 1024 data points; the spectral resolution was determined by the spectrometer setting and was measured to be 6 cm−1, which was about the same as the spectral bandwidth of our pump pulse. Similar to our prior experiments,42 we had no troubles of localizing the plane where the tape was located by simply scanning the axial position through the sample and looking for characteristic spectral signatures of the Scotch tape. Once this position was found, a lateral scan was performed to image the underlying structure in this plane. The fastest way to analyze and display those images was by looking at the a priori known Raman and CARS spectra of substances and assigning a characteristic Raman band to each chemical as a “marker” for this chemical. This is how most of the signal-line CARS imaging systems perform,43 and by selecting those characteristic vibrations in the CARS spectra, those hyperspectral images can be decomposed into several images, which correspond to DPA, chalk, and tape images, as well as a nonspecific image, where we intentionally looked at the spectral region of around 2000 cm−1 where no vibrational signatures were expected for any of those chemicals. Clearly, as it is illustrated in Figure 4, such a selection allowed full retrieval of the underlying structure. However, one can notice that those images (see Figure 4d) have a significant portion of the imaged area, where no chemical is present. This was attributed to the fact that the level of signal at a particular Raman band was not strong enough, as compared to the regions, where Raman bands of DPA produced a very strong signal. The second shortcoming of those images, presented in Figure 4, is that they are all relying on the exact knowledge of the chemicals present in the imaged area. This information was successfully utilized to identify the most distinct vibrational lines for each of those chemicals. In the most general case, one is looking to find out what those chemicals are and what the distribution of those chemicals is in the whole volume of the sample. To demonstrate the proof of principle of such imaging, we used hyperspectral analysis software to process the spectral data collected from the imaging area. After recording all CARS spectra, the hyperspectral data was analyzed to create a dendogram, which is shown in Figure 5a. One can see that there is a clear difference between the first four chemicals, and the exact spectral analysis shows that those three chemicals are DPA, chalk, paper, and tape, as expected from the way the sample was prepared. The corresponding CARS spectra associated with those four clusters are shown in Figure 5b, and they represent a significant signal-to-noise improvement as compared to a single CARS spectrum taken from a single spatial location. The image displaying the overall chemical distribution is shown in Figure 5c, where every pixel of the image now shows a very specific chemical at this location. By introducing a larger number of clusters, we would essentially suggest that at a certain location we expect a mixture of
Figure 5. (a) The dendogram, showing hierarchical clusters for the hyperspectral image. Dashed line outlines the proposed four clusters used for further identification of chemical species. (b) CARS spectra in the spectral region from 850 to 1200 cm−1 averaged over all the spatial locations for each cluster identified using a dendogram shown in (a). All the chemicals were identified using their CARS spectra collected from 800 to 1800 cm−1. (c) Cluster distribution in the imaging plane of the sample, where different colors represent different clusters (red, DPA; blue, calcium carbonate; yellow, paper; and green, Scotch tape).
chemicals, and those additional clusters represent different possible mixtures. Now, having those clusters corresponding to different chemicals located, one can envision a possible identification of those chemicals using their corresponding CARS spectra (see Figure 6). To do so, we used a retrieval 1449
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somewhat limited to 128 × 128 pixels and 2048 data points per spectrum. It takes a single-CPU personal computer several hours to provide a cluster analysis of the image and retrieve Raman spectra for successful chemical identification. Since the software version currently available to us utilizes the MATLAB programming environment, it can be substantially accelerated by utilizing C++ programming language and multicore GPU platform, which gains its popularity for performing specific computational tasks. Data storage should be also optimized, since the raw hyperspectral data currently occupies over 200 MBt on a hard drive with limited capabilities of significantly extending the scanning area. The second type of challenge is related to the extended depth of imaging and ability to detect CARS signal in a more convenient epi-geometry. The latter problem can be easily addressed by rearranging the optics to maximize the collection efficiency of the backscattered CARS signal. Extending the depth of the CARS microspectroscopy might be more challenging, but recent advancements in twophoton microscopy utilizing even longer excitation wavelength29 and adaptive optical focusing44,45 hold a promise that those approaches can be directly applicable to CARS microspectroscopy as well.
Figure 6. (a) CARS spectra of all chemicals; (b) Raman spectra of all chemicals retrieved from CARS spectra using the phase retrieval algorithm described elsewhere.25,32 Chemical identification was performed by comparing those Raman spectra with experimentally measured Raman spectra presented in Figure 1c.
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algorithm25,38 developed earlier in our laboratory based on the previous work by Vartianinen.37 Those retrieved Raman spectra are shown in Figure 6b and are in a very good agreement with those recorded for pure chemicals using a significantly different excitation wavelength (see Figure 1c). However, some spectral shape differences are noticeable for DPA. The amended shape of the retrieved Raman spectrum of DPA is due to the fact that the nonresonant CARS background for this spectrum was quite small, and this tends to produce slighted dispersive lineshapes of retrieved Raman spectra, as it was discussed earlier in ref 38.
CONCLUSIONS In conclusion, we proposed and experimentally demonstrated a novel strategy of chemical identification of molecular species through an optically opaque medium. The proposed approach is based on advantages of CARS microspectroscopy, which allows utilization of the long incident wavelength to minimize the effect of scattering, superior spatial selectivity due to the nonlinear optical nature of CARS spectroscopy, and strong signals. The SNR can be further improved by utilizing hierarchical cluster imaging, which essentially, takes advantage of the averaging over a large number of similar spectra collected from different locations. The chemical identification is done using Raman spectra retrieved from the acquired CARS spectra.
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DISCUSSION CARS microspectroscopy is a powerful tool for imaging and identification of chemical species on a microscopic scale. While the vast majority of the work is focused on fast chemical analysis available by means of CARS spectroscopy in transparent media or in thin (100 μm or less) layers of scattering tissues, we have successfully demonstrated discrimination and identification of chemical species imaged through a thick (>1 mm) layer of scattering material. This became possible by utilizing a longer excitation wavelength, by using higher incident powers, and by utilizing a hierarchical cluster analysis for substantially improving the SNR of the acquired spectra. We have found CARS microspectroscopy to be substantially more useful in spatial discrimination of the generated signal than conventional Raman signal. Localized impurities create a high local concentration of chemical species benefiting the overall strength of the CARS signal. Due to a strong scattering, the generated CARS signal losses its coherence, while propagating to the detector, making it difficult to implement a number of methods of extracting Raman spectra from acquired CARS spectra;17,22−24 however, the phase retrieval based on the maximum entropy method25,37,38 has shown its robustness being insensitive to the scattering. Thus, the overall strategy of attaining chemical information about molecular species present in the bulk of the turbid media has proven to be successful. At present, there are several constraints, which currently limit the further extension of this approach to the three-dimensional case and larger interrogating volumes. First, it is a computational power. The largest area we were able to process is
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was financially supported by the NIH Grant R21EB011703 and the NSF Grants ECCS-1250360, DBI1250361, and CBET-1250363
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