Diffuse reflectance infrared fourier transform

Abstract: Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.

Abstract: Historically, our understanding of the soil and assessment of its quality and function has been gained through routine soil chemical and physical laboratory analysis. There is a global thrust towards the development of more time- and costefficient methodologies for soil analysis as there is a great demand for larger amounts of good quality, inexpensive soil data to be used in environmental monitoring, modelling and precision agriculture. Diffuse reflectance spectroscopy provides a good alternative that may be used to enhance or replace conventional methods of soil analysis, as it overcomes some of their limitations. Spectroscopy is rapid, timely, less expensive, non-destructive, straightforward and sometimes more accurate than conventional analysis. Furthermore, a single spectrum allows for simultaneous characterisation of various soil properties and the techniques are adaptable for don-the-goT field use. The aims of this paper are threefold: (i) determine the value of qualitative analysis in the visible (VIS) (400–700 nm), near infrared (NIR) (700–2500 nm) and mid infrared (MIR) (2500–25,000 nm); (ii) compare the simultaneous predictions of a number of different soil properties in each of these regions and the combined VIS–NIR–MIR to determine whether the combined information produces better predictions of soil properties than each of the individual regions; and (iii) deduce which of these regions may be best suited for simultaneous analysis of various soil properties. In this instance we implemented partial least-squares regression (PLSR) to construct calibration models, which were independently validated for the prediction of various soil properties from the soil spectra. The soil properties examined were soil pHCa ,p H w, lime requirement (LR), organic carbon (OC), clay, silt, sand, cation exchange capacity (CEC), exchangeable calcium (Ca), exchangeable aluminium (Al), nitrate–nitrogen (NO3–N), available phosphorus (PCol), exchangeable potassium (K) and electrical conductivity (EC). Our results demonstrated the value of qualitative soil interpretations using the loading weight vectors from the PLSR decomposition. The MIR was more suitable than the VIS or NIR for this type of analysis due to the higher incidence spectral bands in this region as well as the higher intensity and specificity of the signal. Quantitatively, the accuracy of PLSR predictions in

Abstract: This paper is concerned with the quantitative analysis of multicomponent mixtures by diffuse reflectance spectroscopy. Near-infrared reflectance (NIRR) measurements are related to chemical composition but in a nonlinear way, and light scatter distorts the data. Various response linearizations of reflectance (R) are compared (R with Saunderson correction for internal reflectance, log 1/R, and Kubelka-Munk transformations and its inverse). A multi-wavelength concept for optical correction (Multiplicative Scatter Correction, MSC) is proposed for separating the chemical light absorption from the physical light scatter. Partial Least Squares (PLS) regression is used as the multivariate linear calibration method for predicting fat in meat from linearized and scatter-corrected NIRR data over a broad concentration range. All the response linearization methods improved fat prediction when used with the MSC; corrected log 1/R and inverse Kubelka-Munk transformations yielded the best results. The MSC provided simpler calibration models with good correspondence to the expected physical model of meat. The scatter coefficients obtained from the MSC correlated with fat content, indicating that fat affects the NIRR of meat with an additive absorption component and a multiplicative scatter component.

Abstract: A series of titanium dioxide and graphene sheets (GSs) composites were synthesized with a sol–gel method using tetrabutyl titanate and graphite oxide (GO) as the starting materials. The obtained TiO2/GSs photocatalysts are characterized by X-ray diffraction, N2 adsorption analysis, Raman spectroscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and ultraviolet-visible (UV-vis) diffuse reflectance spectroscopy. The photocatalytic activity of the as-prepared samples was evaluated by hydrogen evolution from water photo-splitting under UV-vis illumination. The influence of GSs content and calcinations atmosphere on the photocatalytic activity was also investigated. The results show that both GSs content and the calcinations atmosphere can affect the photocatalytic activity of the obtained composites.

Abstract: A visible-light-active TiO2 photocatalyst was prepared through carbon doping by using glucose as carbon source. Different from the previous carbon-doped TiO2 prepared at high temperature, our preparation was performed by a hydrothermal method at temperature as low as 160 °C. The resulting photocatalyst was characterized by XRD, XPS, TEM, nitrogen adsorption, and UV–vis diffuse reflectance spectroscopy. The characterizations found that the photocatalyst possessed a homogeneous pore diameter about 8 nm and a high surface area of 126 m2/g. Comparing to undoped TiO2, the carbon-doped TiO2 showed obvious absorption in the 400–450 nm range with a red shift in the band gap transition. It was found that the resulting carbon-doped TiO2 exhibits significantly higher photocatalytic activity than the undoped counterpart and Degussa P25 on the degradation of rhodamine B (RhB) in water under visible light irradiation (λ > 420 nm). This method can be easily scaled up for industrial production of visible-light driven photocatalyst for pollutants removal because of its convenience and energy-saving.