Bidirectional reflectance spectroscopy

An analytical model is developed for the opposition effects (heiligenshein) in the case of light scattering from a semi-infinite, particulate medium with particles that are large relative to the wavelength. The effect is common for natural materials, and comprises a bright surge in light diffusively reflected from a surface at near zero phase. A generalized expression is devised for the extinction coefficient of a particulate medium. Models are developed for step function and hyperbolic tangent distributions of light scattered from a stratified medium and exhibiting the opposition effect. A maximum brightness amplitude increase of 0.753 is projected for the effect. Greater values must have other causes. To illustrate the theory it is fitted to observations of the Moon, an asteroid, and a satellite of Uranus; Europa is also discussed

Reflectance spectroscopy in planetary science: Review and strategy for the future

Reflectance spectroscopy is a remote sensing technique used to study the surfaces and atmospheres of solar system bodies. It provides first-order information on the presence and amounts of certain ions, molecules, and minerals on a surface or in an atmosphere. Reflectance spectroscopy has become one of the most important investigations conducted on most current and planned NASA Solar System Exploration Program space missions. This book reviews the field of reflectance spectroscopy, including information on the scientific technique, contributions, present conditions, and future directions and needs

Detection of coherent magnons via ultrafast pump-probe reflectance spectroscopy in multiferroic Ba0.6Sr1.4Zn2Fe12O22

We report the detection of a magnetic resonance mode in multiferroic Ba0.6Sr1.4Zn2Fe12O22 using time domain pump-probe reflectance spectroscopy. Magnetic sublattice precession is coherently excited via picosecond thermal modification of the exchange energy. Importantly, this precession is recorded as a change in reflectance caused by the dynamic magnetoelectric effect. Thus, transient reflectance provides a sensitive probe of magnetization dynamics in materials with strong magnetoelectric coupling, such as multiferroics, revealing new possibilities for application in spintronics and ultrafast manipulation of magnetic moments.Comment: 4 figure

Visible and near infrared spectroscopy in soil science

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

A note on the comparison of three near infrared reflectance spectroscopy calibration strategies for assessing herbage quality of ryegrass

peer-reviewedPerennial ryegrass (n = 1,836), Italian ryegrass (n = 137) and hybrid ryegrass (n = 103) herbage was taken from harvested plots from the Irish national variety evaluation scheme and analysed for in vitro dry matter digestibility, water soluble carbohydrate concentration, crude protein concentration and buffering capacity. Spectral data were obtained using near infrared reflectance spectroscopy and three calibration strategies (global, species-specific or local) were utilised to relate the reference values to the spectral data. The local strategy generally provided the poorest estimation of herbage composition, with global and species-specific calibration strategies producing similarly accurate estimates of each quality trait. The higher accuracy and easier maintenance of the global strategy make it the recommended calibration method for analysing quality of ryegrass.Department of Agriculture, Food and the Marine Research Stimulus Fund (07 526

Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data

Objective: To assess if incorporation of DRS sensing into real-time robotic surgery systems has merit. DRS as a technology is relatively simple, cost-effective and provides a non-contact approach to tissue differentiation. Methods: Supervised machine learning analysis of diffuse reflectance spectra was performed to classify human joint tissue that was collected from surgical procedures. Results: We have used supervised machine learning in the classification of a DRS human joint tissue data set and achieved classification accuracy in excess of 99%. Sensitivity for the various classes were; cartilage 99.7%, subchondral 99.2%, meniscus 100% and cancellous 100%. Full wavelength range is required for maximum classification accuracy. The wavelength resolution must be larger than 8nm. A SNR better than 10:1 was required to achieve a classification accuracy greater than 50%. The 800-900nm wavelength range gave the greatest accuracy amongst those investigated. Conclusion: DRS is a viable method for differentiating human joint tissue and has the potential to be incorporated into robotic orthopaedic surgery

A holistic multimodal approach to the non-invasive analysis of watercolour paintings

A holistic approach using non-invasive multimodal imaging and spectroscopic techniques to study the materials (pigments, drawing materials and paper) and painting techniques of watercolour paintings is presented. The non-invasive imaging and spectroscopic techniques include VIS-NIR reflectance spectroscopy and multispectral imaging, micro-Raman spectroscopy, X-ray fluorescence spectroscopy (XRF) and optical coherence tomography (OCT). The three spectroscopic techniques complement each other in pigment identification. Multispectral imaging (near infrared bands), OCT and micro-Raman complement each other in the visualisation and identification of the drawing material. OCT probes the microstructure and light scattering properties of the substrate while XRF detects the elemental composition that indicates the sizing methods and the filler content . The multiple techniques were applied in a study of forty six 19th century Chinese export watercolours from the Victoria & Albert Museum (V&A) and the Royal Horticultural Society (RHS) to examine to what extent the non-invasive analysis techniques employed complement each other and how much useful information about the paintings can be extracted to address art conservation and history questions

Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)

This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra