Temporal and spatial resolution in transmission Raman spectroscopy

Analytical Solution to the Depth-of-Origin Profile of Transmission Raman Spectroscopy in Turbid Media Based on the Kubelka-Munk Model

An analytical formula to the depth-of-origin profile of transmission Raman spectroscopy in turbid media was derived from the one-dimensional (1D) Kubelka-Munk model. The depth-of-origin profile of the transmitted Raman is proportional to the excitation intensity profile and the transmittance profile, which are two functions of similar forms. The effect of scattering, absorption, and signal-enhancing reflectors are incorporated into the formula. Depth-of-origin profile of a model sample was measured at better than 0.2 mm resolution and fits the formula reasonably well. Conical reflective cavities placed at the front and/or back of the sample enhanced the signal significantly; the relationship among the enhancement functions is verified by the formula. Optical parameters derived from the fitting are compared to theoretical value predicted by optical ray tracing and direct measurements; discrepancies are related to deficiency of the 1D Kubelka-Munk model.

Transmission Raman Measurements Using a Spatial Heterodyne Raman Spectrometer (SHRS)

A spatial heterodyne Raman spectrometer (SHRS) was used to measure transmission Raman spectra of highly scattering compounds. Transmission Raman spectral intensities of ibuprofen were only 2.4 times lower in intensity than backscatter Raman spectra. The throughput was about eight times higher than an f/1.8 dispersive spectrometer, and the width of the area viewed was found to be seven to nine times higher, using 50.8 mm and 250 mm focal length collection lenses. However, the signal-to-noise (S/N) ratio was two times lower for the SHRS than the f/1.8 dispersive spectrometer, apparently due to high levels of stray light.

Tissue phantoms to compare spatial and temporal offset modes of deep Raman spectroscopy

Tissue phantoms were created with embedded biomineral-simulating inclusions of varying size and depth, and formed of different mixtures of CaCO₃ and hydroxyapatite, for comparison of deep Raman spectroscopy techniques.

Studying the distribution of deep Raman spectroscopy signals using liquid tissue phantoms with varying optical properties

We studied experimentally the magnitude and origin of Raman signals in a transmission Raman geometry as a function of optical properties of the medium and the location of Raman scatterer within the phantom.

Raman imaging of pharmaceutical materials: refractive index effects on contrast at buried interfaces

Resolution targets composed of bilayer polydimethylsiloxane (PDMS) devices with buried polyethylene glycol (PEG) channels have been fabricated using traditional photolithographic and micromolding techniques to develop resolution targets that mimic pharmaceutical materials. Raman chemical images of the resulting PEG-in-PDMS devices composed of varying parallel line widths were investigated by imaging the PEG lines through a thin overlayer of PDMS. Additionally, a scattering agent, Al2O3, was introduced at varying concentrations to each layer of the device to explore the effects of scattering materials on Raman images. Features in the resulting chemical images of the PEG lines suggest that reflection at the PEG/PDMS interface contributes to the Raman signal. A model based on geometric optics was developed to simulate the observed image functions of the targets. The results emphasize the influence of refractive index discontinuities at the PEG/PDMS interface on the apparent size and shape of the PEG features. Such findings have an impact on interpretation of Raman images of nonabsorbing, opaque pharmaceutical samples.

Variation in the transmission near-infrared signal with depth in turbid media

Transmission near-infrared (NIR) measurements of a 1 mm thick aspirin disk were made at different positions as it was moved through a stack of eight 0.5 mm thick disks of microcrystalline cellulose (Avicel). The magnitude of the first derivative of absorbance for the aspirin interlayer at 8934 cm(-1) was lower when the disk was placed at the top or bottom of the stack of Avicel disks, with the largest signal observed when the aspirin was positioned at the central positions. The variation in signal with depth is consistent with that observed previously for transmission Raman spectrometry. In both cases, the trend observed can be attributed to lower photon density at the air-sample interface, relative to the center of the sample, owing to loss of photons to the air. This results in a reduction in the number of photons absorbed or Raman photons generated and subsequently detected when the interlayer occupies a near-surface position.

Amino acid quantification in bulk soybeans by transmission Raman spectroscopy

Soybeans are a commodity crop of significant economic and nutritional interest. As an important source of protein, buyers of soybeans are interested in not only the total protein content but also in the specific amino acids that comprise the total protein content. Raman spectroscopy has the chemical specificity to measure the twenty common amino acids as pure substances. An unsolved challenge, however, is to quantify varying levels of amino acids mixed together and bound in soybeans at relatively low concentrations. Here we report the use of transmission Raman spectroscopy as a secondary analytical approach to nondestructively measure specific amino acids in intact soybeans. With the employment of a transmission-based Raman instrument, built specifically for nondestructive measurements from bulk soybeans, spectra were collected from twenty-four samples to develop a calibration model using a partial least-squares approach with a random-subset cross validation. The calibration model was validated on an independent set of twenty-five samples for oil, protein, and amino acid predictions. After Raman measurements, the samples were reduced to a fine powder and conventional wet chemistry methods were used for quantifying reference values of protein, oil, and 18 amino acids. We found that the greater the concentrations (% by weight component of interest), the better the calibration model and prediction capabilities. Of the 18 amino acids analyzed, 13 had R(2) values greater than 0.75 with a standard error of prediction c.a. 3-4% by weight. Serine, histidine, cystine, tryptophan, and methionine showed poor predictions (R(2) < 0.75), which were likely a result of the small sampling range and the low concentration of these components. It is clear from the correlation plots and root-mean-square error of prediction that Raman spectroscopy has sufficient chemical contrast to nondestructively quantify protein, oil, and specific amino acids in intact soybeans.

Penetration of light into multiple scattering media: model calculations and reflectance experiments. Part II: The radial transfer

In continuation of our contribution to "The Axial Transfer" (Appl. Spectr. 2012. 66(8): 934-943), this paper describes the distribution of localized incident radiation in multiple scattering layers of arbitrary thickness and analyzes the lateral intensity profiles of radiation leaving the sample from its illuminated and non-illuminated surfaces. The theoretical profiles are calculated with different approximations of the equation of transfer. We derive for both non-absorbing and absorbing layers simple analytical expressions and verify their accuracy and range of applicability by comparison with Monte Carlo simulations. Particular emphasis is given to the analysis of the radial absorption, an under-theorized and under-investigated feature that can help to identify weak or hidden absorbers. In addition, we contribute to the description of how the radial reflectance is affected by anisotropy or by error sources like multiple surface reflection for samples in glass cells or deflectance (sideway loss) of radiation in small samples. Finally, the theoretical results are compared with experimental data of radial reflectance for quasi non-absorbing and absorbing powder layers.

Raman spectroscopy for the detection of AGEs/ALEs

Raman spectroscopy is a noninvasive, nondestructive tool for capturing multiplexed biochemical information across diverse molecular species including proteins, lipids, DNA, and mineralizations. Based on light scattering from molecules, cells, and tissues, it is possible to detect molecular fingerprints and discriminate between subtly different members of each biochemical class. Raman spectroscopy is ideal for detecting perturbations from the expected molecular structure such as those occurring during senescence and the modification of long-lived proteins by metabolic intermediates as we age. Here, we describe the sample preparation, data acquisition, signal processing, data analysis and interpretation involved in using Raman spectroscopy for detecting age-related protein modifications in complex biological tissues.

Transmission resonance Raman spectroscopy: experimental results versus theoretical model calculations

A laser spectroscopic technique is described that combines transmission and resonance-enhanced Raman inelastic scattering together with low laser power (< 30 mW) and good spatial resolution (< 200 μm) as operational features. The monitoring of the transmitted inelastic scattering provides an increased signal-to-noise ratio because the low fluorescence background and, on the other hand, the resonant character of the laser excitation, leads to enhanced analytical sensitivity. The spectroscopic technique was applied to investigate the carotenoid content (specifically the β-carotene concentration) of distinct samples that included fruits, reaching a detection limit of the order of hundreds of picograms in solid samples, which is below the level needed for typical food control analysis. Additional features of the present development are direct sampling, noninvasive character, and fast analysis that is not time consuming. From a theoretical point of view, a model for the Raman signal dependence on the sample thickness is also presented. Essentially, the model considers the sample to be homogeneous and describes the underlying physics using only three parameters: the Raman cross-section, the laser-radiation attenuation cross-section, and the Raman signal attenuation cross-section. The model was applied successfully to describe the sample-size dependence of the Raman signal in both β-carotene standards and carrot roots. The present technique could be useful for direct, fast, and nondestructive investigations in food quality control and analytical or physiological studies of animal and human tissues.

Protein and oil composition predictions of single soybeans by transmission Raman spectroscopy

The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructive quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may provide viable alternatives and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy provides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructive soybean measurements. We show that TRS uses the light scattering properties of soybeans to effectively homogenize the heterogeneous bulk of a soybean for representative sampling. Working with over 1000 individual intact soybean seeds, we developed a simple partial least-squares model for predicting oil and protein content nondestructively. We find TRS to have a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and validation sets, the predicative capabilities of the model were similar to the error in the reference methods.

Influence of tissue absorption and scattering on the depth dependent sensitivity of Raman fiber probes investigated by Monte Carlo simulations

We present a Monte Carlo model, which we use to calculate the depth dependent sensitivity or sampling volume of different single fiber and multi-fiber Raman probes. A two-layer skin model is employed to investigate the dependency of the sampling volume on the absorption and reduced scattering coefficients in the near infrared wavelength range (NIR). The shape of the sampling volume is mainly determined by the scattering coefficient and the wavelength dependency of absorption and scattering has only a small effect on the sampling volume of a typical fingerprint spectrum. An increase in the sampling depth in nonmelanoma skin cancer, compared to normal skin, is obtained.

Monte Carlo model of spatially offset Raman spectroscopy for breast tumor margin analysis

We have previously demonstrated the discrimination of two layers of soft tissue, specifically normal breast tissue overlying breast tumor, using spatially offset Raman spectroscopy (SORS). In this report, a Monte Carlo code for evaluating SORS in soft tissues has been developed and compared to experimental results. The model was employed to investigate the effects of tissue and probe geometry on SORS measurements and therefore to develop the design strategies of applying SORS for breast tumor surgical margin evaluation. The model was used to predict SORS signals for different tissue geometries difficult to precisely control experimentally, such as varying normal and tumor layer sizes and the addition of a third layer. The results from the model suggest that, using source-detector separations of up to 3.75 mm, SORS can detect sub-millimeter-thick tumors under a 1 mm normal layer, and tumors at least 1 mm thick can be detected under a 2 mm normal layer.

Measurement of spatial resolution and sensitivity in transmission and backscattering Raman spectroscopy of opaque samples: impact on pharmaceutical quality control and Raman tomography

A practical methodology is described that allows measurement of spatial resolution and sensitivity of Raman spectroscopy in backscatter and transmission modes under conditions where photon migration dominates, i.e., with turbid or opaque samples. For the first time under such conditions the width and intensity of the point spread function (PSF) has been accurately measured as a function of sample thickness and depth below the surface. In transmission mode, the lateral resolution for objects in the bulk degraded linearly with sample thickness, but the resolution was much better for objects near either surface, being determined by the diameter of the probe beam and collection aperture irrespective of sample thickness. In other words, buried objects appear to be larger than ones near either surface. The absolute transmitted signal decreased significantly with sample thickness, but objects in the bulk yielded higher signals than those at either surface. In transmission, materials are sampled preferentially in the bulk, which has ramifications for quantitative analysis. In backscattering mode, objects near the probed surface were detected much more effectively than in the bulk, and the resolution worsened linearly with depth below the surface. These results are highly relevant in circumstances in which one is trying to detect or image buried objects in opaque media, for example Raman tomography of biological tissues or compositional and structural analysis of pharmaceutical tablets. Finally, the observations were in good agreement with Monte Carlo simulations and, provided one is in the diffusion regime, were insensitive to the choice of transport length, which shows that a simple model can be used to predict instrument performance for a given excitation and collection geometry.