Augmenting Spectroscopic Imaging for Analyses of Samples with Complex Surface Topographies

Abstract

Spectroscopic imaging has become a widely used tool for analyses of heterogeneous samples. Focal plane array detectors are incorporated into spectrometers that acquire a large number of spectra from different sample locations in parallel. This sensing technique facilitates analyses of spatial distributions of chemical information in an X-Y plane at high time resolution. In many cases, chemical reactions proceed in three spatial dimensions (X-Y-Z) and require the acquisition of spectroscopic information in an X-Y plane plus topographic (Z-dimension) information. However, capturing two-dimensional (2D, i.e., X-Y) images from three-dimensional (3D, i.e., X-Y-Z) samples inherently loses Z-dimension information. This technical note describes an augmented spectroscopic imager that gains both types of data, i.e., spatially resolved spectroscopic information and topography. For the latter purpose, a regular light pattern is generated and projected onto a sample. Due to its 3D topography, this light pattern is distorted. After extracting these distortions, the topography can be determined since the height structure is encoded in the light pattern. Because topographic probing must not affect infrared measurements, different wavelength ranges are used. Here spectroscopic information is acquired in the mid-IR while the light pattern probing the topography is generated in the visible. For relating distortions to physical height structures, the setup needs to be calibrated. For this purpose, calibration objects of known dimensions have been manufactured onto which the light pattern is projected. Determining distortions introduced by objects of known height derives a transform from distortions to topographies. Due to mechanical restrictions, the light pattern can only achieve a certain spatial resolution. In order to enhance the spatial resolution the topography is probed with, scanning the light pattern in X- and Y-direction is proposed.

Data Filtering in Instrumental Analyses with Applications to Optical Spectroscopy and Chemical Imaging

“…Applications of Fourier transforms (FTs) and Wavelet transforms (WTs) ,− in signal analyses have a common goal, namely, preserving the wanted signal frequencies and suppressing unwanted components. However, whereas a FT decomposes a signal h ( t ) into sines and cosines, a WT utilizes “wavelets” Ψ τ, s ( t ) instead.…”

“…Impact of threshold th , eq , and 2D-wavelet type on the 2D WT image filtering; the dark, grid-shaped light pattern was generated for an unrelated purpose but helps to assess filtering results; (top left) original, grainy image; (top right) Daub12 with th = 1% introduced considerable blurring possibly due to a too high threshold; (bottom left) Daub12 with th = 0.1% has removed much of the image’s graininess without introducing blurring; (bottom right) Daub2 (= Haar) with th = 1%; especially for such a large th , this wavelet type introduces a boxlike structure in the wavelet-filtered image similar to the results shown in Figure for 1D-signals (bottom, right) and thus should be avoided.…”

Process Analytical Chemistry

“…Because of mechanical restrictions, the light pattern can only achieve a certain spatial resolution. In order to enhance the spatial resolution the topography is probed with, the light pattern is scanned in the X-and Y-direction (73).…”

Accelerating kernel principal component analysis (KPCA) by utilizing two‐dimensional wavelet compression: applications to spectroscopic imaging

“…Combining spectroscopy with imaging techniques has greatly enhanced many studies of heterogeneous samples which require high spatial resolution [9][10][11][12]. In order to handle large amounts of data in a timely fashion, multi-dimensional wavelet transformations (WTs) [13][14][15][16][17][18][19][20][21] have been employed as an effective method of data compression prior to chemometric computations [22][23][24][25] (and references therein).…”