Terahertz time-domain transmission spectroscopy of water and hydrogel thin films: Extraction of optical parameters and application to agarose gel characterization.

Terahertz time-domain spectroscopy (THz-TDS) is an emerging optical technique that has potential applications in the characterization of (bio)materials. However, the complicated extraction of optical parameters from multi-layered and optically thin samples is a barrier towards its acceptance by applied scientists. Therefore, the aim of this work is to provide a straightforward approach for the extraction of the THz absorption coefficient and index of refraction profiles of aqueous thin films in a window-sample-window configuration, which is ubiquitous in many laboratories (i.e., sample in a cuvette). A numerical approach-based methodology that accounts for multiple layers, Fabry-Pérot effect, and sample thickness is elaborated which involves an optical interference model based on a tri-layer structure and a simple thickness estimation technique. This method was validated on water samples where a good agreement was found with the THz optical parameters of water reported in the literature, while the use of a commercial software resulted in erroneous optical parameters estimates when used without due regard to its limitations. A case study was then performed to demonstrate the ability of the proposed method to characterize agarose hydrogels with varying degree of sulfation. It was demonstrated that THz-TDS can provide insight into the hydration state of the agarose hydrogels, including the relative number of the hydrogen bonds between the hydroxyl moieties of water and the polysaccharide network which is perturbed by the presence of sulfate. The trend in the index of refraction profiles suggested microstructural differences between the agarose hydrogels, which were confirmed by visualizing the agarose network morphology using cryo-SEM imaging.

Fabrication and characterization of porous tissue-mimicking optical phantoms as a tool for optical sensor validation.

This paper presents porous polydimethylsiloxane (PDMS) optical phantoms with tunable microstructural and optical properties to mimic porous biological tissues (e.g., fruit) during the design and optimization of novel optical setups. A well connected salt network formed using salt particles of various size distributions was used to obtain porous PDMS phantoms of different porous features including porosity, pore size distribution, pore number density and pore connectivity. These microstructural features are strongly related to the light scattering from the phantom where a higher reduced scattering coefficient ( µ s ' ) was observed from the porous PDMS phantom with a higher number of small pores compared to the optical phantom with a lower number of larger pores. The prepared phantoms were used to validate GASMAS (gas in scattering media absorption spectroscopy) H2 O and O2 sensors by quantifying the optical path length through the pores and the O2 concentration inside the pores.

Estimation of Particle Size Distribution from Bulk Scattering Spectra: Validation on Monomodal Suspensions.

A particle size distribution (PSD) estimation method based on light-scattering properties was validated on experimental visible/near-infrared scattering spectra of polystyrene suspensions, with a nominal particle size ranging from 0.1 to 12 µm in diameter. On the basis of µs and g spectra extracted from double integrating sphere measurements, good PSD estimates were obtained for particles ≥1 µm. The particle volume fraction estimates in the case of µs were close to the target concentrations, although influenced by small baseline fluctuations on the spectra. For submicrometer particles, on the other hand, the non-oscillating µs spectra lack discriminating power, resulting in erroneous PSD estimates. The reduced scattering coefficient spectra (µs') were found less useful for particle size estimation as they lack a characteristic shape, causing an over- or underestimation of the distribution width. In summary, the estimation routine proved to deliver PSD estimates in line with the reference measurements for micrometer-sized or larger particles based on their µs and g scattering spectra. Additional validation on more polydisperse samples forms the next step before going to bimodal PSD estimates.

Estimation of particle size distributions from bulk scattering spectra: sensitivity to distribution type and spectral noise.

A shape dependent method for particle size distribution (PSD) estimation based on bulk scattering properties was elaborated. This method estimates the parameters of a particle size distribution with predefined shape from the bulk scattering spectra. The estimation routine was validated on simulated data of polystyrene in water suspensions. To investigate the effect of measurement errors on PSD estimates, a sensitivity analysis was performed. The influence of spectral resolution and range was rather limited. Good PSD estimations were obtained on noise-free spectra, spectra with limited random noise and for estimations on µs or µs' in case of a multiplicative baseline. However, the PSD estimation deteriorated if an incorrect value for the refractive index of the particle relative to the medium was used as input parameter. Deviations caused by an incorrect distribution type were smaller for more narrow PSDs than for broader ones. Overall, this study showed the potential to estimate PSDs from bulk scattering spectra and indicated the factors affecting the accuracy.

Computational optimization of the configuration of a spatially resolved spectroscopy sensor for milk analysis.

A global optimizer has been developed, capable of computing the optimal configuration in a probe for spatially resolved reflectance spectroscopy (SRS). The main objective is to minimize the number of detection fibers, while maintaining an accurate estimation of both absorption and scattering profiles. Multiple fibers are necessary to robustify the estimation of optical properties against noise, which is typically present in the measured signals and influences the accuracy of the inverse estimation. The optimizer is based on a robust metamodel-based inverse estimation of the absorption coefficient and a reduced scattering coefficient from the acquired SRS signals. A genetic algorithm is used to evaluate the effect of the fiber placement on the performance of the inverse estimator to find the bulk optical properties of raw milk. The algorithm to find the optimal fiber placement was repeatedly executed for cases with a different number of detection fibers, ranging from 3 to 30. Afterwards, the optimal designs for each considered number of fibers were compared based on their performance in separating the absorption and scattering properties, and the significance of the differences was tested. A sensor configuration with 13 detection fibers was found to be the combination with the lowest number of fibers which provided an estimation performance which was not significantly worse than the one obtained with the best design (30 detection fibers). This design resulted in the root mean squared error of prediction (RMSEP) of 1.411 cm(-1) (R(2) = 0.965) for the estimation of the bulk absorption coefficient values, and 0.382 cm(-1) (R(2) = 0.996) for the reduced scattering coefficient values.