Infrared spectroscopic imaging: the next generation

Abstract

Infrared (IR) spectroscopic imaging seemingly matured as a technology in the mid-2000s, with commercially successful instrumentation and reports in numerous applications. Recent developments, however, have transformed our understanding of the recorded data, provided capability for new instrumentation, and greatly enhanced the ability to extract more useful information in less time. These developments are summarized here in three broad areas--data recording, interpretation of recorded data, and information extraction--and their critical review is employed to project emerging trends. Overall, the convergence of selected components from hardware, theory, algorithms, and applications is one trend. Instead of similar, general-purpose instrumentation, another trend is likely to be diverse and application-targeted designs of instrumentation driven by emerging component technologies. The recent renaissance in both fundamental science and instrumentation will likely spur investigations at the confluence of conventional spectroscopic analyses and optical physics for improved data interpretation. While chemometrics has dominated data processing, a trend will likely lie in the development of signal processing algorithms to optimally extract spectral and spatial information prior to conventional chemometric analyses. Finally, the sum of these recent advances is likely to provide unprecedented capability in measurement and scientific insight, which will present new opportunities for the applied spectroscopist.

Fig. 1

(A) Typical layout of a Fourier transform infrared (FT-IR) spectrometer. Instrumentation is usually general purpose, with transmission and reflection sampling configurations in the same system (only the transmission path is shown here), dual detectors enabling both point mapping and imaging technology, as well as external interferometer and computer to acquire and process data [figure by Kevin Yeh]. (B) Conceptually, the optical path proceeds from a source to a spectrometer, microscope, sample, and to the detector. The computer serves to operate the spectrometer and process signals from the detector into information. (C) The ongoing evolution of technology will make computing and control central to imaging. The source/spectrometer combination will essentially function as a known and controlled light source. Information about the sample and microscope configuration will be fed to the computer and used to process signals from the detector via new theory and algorithms as described in this article. The quality of information extracted, as a consequence, will be considerably improved.

Fig. 2

Hybrid spatial–spectral data recording is possible using a grating or a prism-based spectrograph coupled to a microscope. (Left) Experimental arrangement of components in the PA-IR spectrograph. (Middle) Spectra acquired (across a row) using a prism-based spectrograph. The detector image of Parylene can be used to extract spectra (right) that change with the optical parameters of the system. Spectra recorded at slit width settings of (a) 46, (b) 64, (c) 108, and (d) 153 lm with the sodium chloride single-pass prism spectrograph. (e) The CH stretching and fingerprint regions of the FT-IR spectrum were acquired at 22 and 14 cm⁻¹ resolution, respectively. [Figure at left reproduced with permission from Ref. 67, copyright Society for Applied Spectroscopy. Figures in the center and to the right reproduced with permission from Ref. 75, copyright Society for Applied Spectroscopy.]

Fig. 3

(A) Essentials of the FT-IR and (B) DF-IR Setup. A broadband source (C) is encoded and decoded by FT-acquisition using (D) interferograms. (E) A small number of filters are used in DF mode to measure the informative spectral sections.

Fig. 4

Components and results from DF-IR imaging. (A) Schematic of a designed filter. (B) Characterization of the designed filter by microscopy and atomic force microscopy shows the ability to fabricate structures as per design and their uniformity. (C) The imperfect prediction and fabrication capabilities at this early stage, however, do present challenges in translating the narrowband predicted performance (for example, on the left) to actual filter performance (right). (D) Tens of filters can be placed on a single substrate and fabricated together on the same optical path using the concept of a filter wheel. (E) Schematic of setup for characterization of the filter, spectroscopy or for comparison with FT-IR imaging. (F) An absorbance image from an unoptimized system showing the antisymmetric CH stretching band absorbance of an SU-8 sample. The image size is approximately 500 μm × 500 μm. White levels indicate a higher beam attenuation and gray/black levels indicate little to no attenuation.

Fig. 5

USAF 1951 optical resolution target absorption images (cycle 3, elements 5 and 6) as acquired by three different instruments. As opposed to chrome-on-glass targets, these samples were designed to be a cross-linked photolithographic polymer (SU-8) on polished barium fluoride substrates. Top row: QCL + bolometer system without diffuser plate. Middle row: QCL + bolometer system with rotating diffuser plate. Bottom row: commercial FT-IR instrument. [Reproduced with permission from Ref. 126, copyright American Chemical Society.]

Fig. 6

Reconstruction of data from a small number of spectral measurements. (Top) The basis of the approach relies on reconstructing spectra from a representation of the data as a combination of sparsely measured data, parameters from a dictionary of known samples, and correlation coefficients. (Middle, left) Different parameters of the process result in reconstructed spectra (dashed lines) that may be lower resolution or closer to the recorded data (solid line). (Bottom, left) Gray colors indicate different cell types deduced from IR data measured at full resolution while the same information is sought from a reconstructed data set (bottom, right).

Fig. 7

(A) The original object consists of a set of bars of sizes with transmittance indicated to the left. Absorbance images from simulations (at 3950 cm⁻¹) are shown for the focusing Schwarzschild with a (B) NA = 0.65, (F) NA = 0.50, and (G) NA = 0.50. The effective sample pixel size at the detector in (B) and (F) is 1.1 μm, whereas the pixel size in (G) is 5.5 μm. Measured absorbance images (also at 3950 cm⁻¹) are shown with configurations (C) NA = 0.65, pixel size = 1.1 μm and (H) NA = 0.50, pixel size = 5.5 μm. (D), (E), (I), and (J) show magnified regions from corresponding images. [Reproduced with permission from Ref. 144, copyright Society for Applied Spectroscopy.]

Fig. 8

Comparison of the rescaled absorbance spectra from the same toluene film acquired in two different configurations, in transmission mode on a BaF₂ substrate and in transmission-reflection (transflection) mode on a gold substrate. Simply changing the thickness of the film changes the recorded data. [Reproduced with permission from Ref. 151, copyright American Chemical Society.]

Fig. 9

(A) Pixels in the vicinity of an idealized edge are examined for responses from a single material. (B) Spectra (colors correspond to the arrows indicating positions in (A)) demonstrate baseline offsets, relative changes in band intensities, and shifts in peak positions. (C) Detailed examination in the fingerprint region shows the effect of scattering on the apparent position and relative ratio of peak absorbance. [Reproduced with permission from Ref. 157, copyright American Chemical Society.]

Fig. 10

Refractive index mismatches cause baseline offsets and apparent absorption in a polymer dispersed liquid crystal. Chemically specific absorbance (top, left) and apparent absorbance (baseline offset) arising from optical mismatch of refractive indices can be observed in recorded data. The application of a voltage along the light propagation direction aligns liquid crystals in the microdomains, thereby changing the refractive index and matching the real part of the index closely to the surrounding matrix. Matching the refractive indices eliminates the baseline offset in spectra and the apparent absorbance at the interface that arises from scattering. [Reproduced from Ref. 158, copyright American Chemical Society.]

Fig. 11

(A) RGB image of a fingerprint obtained using oil, protein, and particulate matter-specific absorbance modes, respectively. (B) Expanded view of the box in A. (C) Spectra of the components showing the bands indicative of the specific components. (D) A single fiber can be seen in the print with a particle next to it. The trace evidence can also be idealized to be cylindrical or spherical in shape. Modeling predicts the shape dependence of spectra, dramatically altering the recorded data for the same material. [Reproduced with permission from Refs. 163 (A–D, copyright Springer), (cylinder spectra, copyright American Chemical Society), and (sphere spectra, copyright Society for Applied Spectroscopy).]

Fig. 12

The state of the art in available algorithms can successfully estimate the correct absorbance and refractive index from samples of known shapes. Reconstructions and true values of the complex refractive index are shown for fibers of radius 5 μm (A–C) and 10 μm (D–F). [Reproduced with permission from Ref. 167, copyright American Chemical Society.]

Fig. 13

(A) Acquired high SNR data and simulated noisy spectra (peak-to-peak noise = 0.001, 0.01, 0.1, and 0.4 a.u.), showing the degradation in data quality. Spectra are offset for clarity. (B) Spectra after noise reduction demonstrate the dramatic gains possible by chemometric methods. (C) Noise reduction was implemented to classify breast tissue and application of noise rejection allowed the same quality of classification (accuracy) to be recovered at higher noise levels. (D) In another example, image fidelity (here the nitrile stretching vibrational mode at 2227 cm⁻¹) is much enhanced as a result of spectral noise rejection. [Figures A and C reproduced with permission from Ref. 188, copyright Royal Society of Chemistry. Figure D reproduced with permission from Ref. 184, copyright Society for Applied Spectroscopy.]

Fig 14

Overview of IR imaging and developing areas of activity.