The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Tsachi Livneh - One of the best experts on this subject based on the ideXlab platform.
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monitoring the in situ oxide growth on uranium by ultraviolet visible Reflectance Spectroscopy
Journal of Applied Physics, 2012Co-Authors: Danielle Schweke, Chen Maimon, Zelig Chernia, Tsachi LivnehAbstract:We demonstrate the in-situ monitoring of oxide growth on U-0.1 wt. % Cr by means of UV-visible Reflectance Spectroscopy in the thickness range of ∼20-150 nm. Two different approaches are presented: In the “modeling approach,” we employ a model for a metallic substrate covered by a dielectric layer, while taking into account the buildup of oxygen gradient and surface roughness. Then, we fit the simulated spectra to the experimental one. In the “extrema analysis,” we derive an approximated analytical expression, which relates the oxide thickness to the position of the extrema in the Reflectance spectra based on the condition for optical interference of the reflected light. Good agreement is found between the values extracted by the two procedures. Activation energy of ∼21 kcal/mole was obtained by monitoring the oxide growth in the temperature range of 22-90 °C. The upper bound for the thickness determination is argued to be mostly dictated by cracking and detachment processes in the formed oxide.
Bas Van Wesemael - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
PLoS ONE, 2013Co-Authors: Antoine Stevens, Marco Nocita, Gergely Tóth, Luca Montanarella, Bas Van WesemaelAbstract:Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse Reflectance Spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of Reflectance Spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ?5 times larger than the reproducibility error of the laboratory method, Reflectance Spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.
Daniel Cozzolino - One of the best experts on this subject based on the ideXlab platform.
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potential of near infrared Reflectance Spectroscopy and chemometrics to predict soil organic carbon fractions
Soil & Tillage Research, 2006Co-Authors: Daniel Cozzolino, A MoronAbstract:The potential of near-infrared Reflectance Spectroscopy (NIRS) to predict soil organic C in different particle-size fractions was evaluated. Soil samples (n = 180) from various crop rotations in Uruguay were analysed by standard chemical and NIRS methods. Partial least squares (PLS) regression with cross validation was used to develop calibrations between reference data and NIRS spectra (n = 87) and validated using an independent set of samples (n = 87). Coefficients of determination in calibration (RCAL2) and standard errors in cross validation (SECV) were 0.90 and 0.6 for coarse-sand C, 0.92 and 0.4 for fine-sand C, and 0.96 and 2.1 for clay + silt C, respectively. Calibrations were poor for C/N ratio (RCAL2 < 0.65). Although NIRS demonstrated great potential to predict soil organic C in different particle-size fractions, the nature of sampling and number of samples analysed should be considered in future developments.
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identification of animal meat muscles by visible and near infrared Reflectance Spectroscopy
Lwt - Food Science and Technology, 2004Co-Authors: Daniel Cozzolino, Ian MurrayAbstract:Visible (VIS) and near infrared Reflectance Spectroscopy (NIRS) was used to identify and authenticate different meat muscle species. Samples from beef (n: 100), lamb (n: 140), pork (n: 44) and chicken (n: 48) muscles were homogenised and scanned in the visible (VIS) and near infrared (NIR) region (400-2500 nm) in a monochromator instrument in Reflectance. Both Principal Component Analysis (PCA) and dummy partial least-squares regression (PLS) models were developed to identify different meat species. The models correctly classified more than 80% of the meat sample muscles according with the muscle specie. The results showed the potential of VIS and NIR spectra as an objective and rapid method for authentication and identification of meat muscle species.
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visible and near infrared Reflectance Spectroscopy for the determination of moisture fat and protein in chicken breast and thigh muscle
Journal of Near Infrared Spectroscopy, 1996Co-Authors: Daniel Cozzolino, Ian Murray, R M Paterson, Jeremy R ScaifeAbstract:Near infrared (NIR) Reflectance Spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned ...
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visible and near infrared Reflectance Spectroscopy for the determination of moisture fat and protein in chicken breast and thigh muscle
Journal of Near Infrared Spectroscopy, 1996Co-Authors: Daniel Cozzolino, Ian Murray, R M Paterson, Jeremy R ScaifeAbstract:Near infrared (NIR) Reflectance Spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned from 400 to 2500 nm, both as intact muscle and as comminuted (minced) tissue. Modified partial least squares (MPLS) regression on scatter corrected spectra (standard normal variates and Detrend) gave calibration models for chemical variables from NIR measurements on the defrosted minced breast samples having multivariate correlation coefficients and standard errors of calibration of 0.995 (2.4), 0.974 (2.11) and 0.946 (4.55) for moisture, crude protein and fat in g kg −1, respectively.
Kezhao Liu - One of the best experts on this subject based on the ideXlab platform.
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oxidation kinetics of nitrided uranium determined by ultraviolet visible Reflectance Spectroscopy
Journal of Alloys and Compounds, 2018Co-Authors: Huoping Zhong, Guangfeng Zhang, Yongbin Zhang, Ping Zhou, Kezhao LiuAbstract:Abstract The oxidation kinetics of a uranium surface treated by pulsed laser nitriding was studied using Reflectance Spectroscopy. The growth of the oxide thickness generally followed a parabolic growth model for the nitrided uranium with pure O2, which agreed with the diffusion barrier model. The activation energy was 98 kJ/mol and was obtained by monitoring the oxide growth in the temperature range 400–460 K. As a comparison, the growth of the oxide thickness of untreated uranium was also monitored, which indicated that the nitriding treatment improved the corrosion resistance of uranium. This may be due to the formation of UNO compounds on the surface of nitrided uranium after oxidation, which can resist the diffusion of oxygen ions into the inner layer.
Ian Murray - One of the best experts on this subject based on the ideXlab platform.
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the use of visible and near infrared Reflectance Spectroscopy to predict beef m longissimus thoracis et lumborum quality attributes
Meat Science, 2008Co-Authors: Sonia Andres, A Silva, A L Soarespereira, C Martins, A M Brunosoares, Ian MurrayAbstract:Abstract Visible and near infrared Reflectance Spectroscopy was used to predict pH at 24 h (pH24) post-mortem , sarcomere length (SL), cooking loss (CL), Warner–Bratzler Shear Force (WBSF) and colour parameters ( L ∗ , a ∗ , b ∗ ) in beef cattle samples. Samples from M. longissimus thoracis et lumborum from 30 bulls were aged at 4 °C for 1, 3, 7 and 14 days and analysed for pH, SL, CL, WBSF and colour. NIRS calibrations for pH24, luminosity at 0 ( L ∗ t 0) and 60 min ( L ∗ t 60) showed good predictability ( R 2 = 0.97, 0.85 and 0.82; SECV = 0.10, 1.16, 1.36, respectively), whereas those related to the rest of the parameters were poorer.
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identification of animal meat muscles by visible and near infrared Reflectance Spectroscopy
Lwt - Food Science and Technology, 2004Co-Authors: Daniel Cozzolino, Ian MurrayAbstract:Visible (VIS) and near infrared Reflectance Spectroscopy (NIRS) was used to identify and authenticate different meat muscle species. Samples from beef (n: 100), lamb (n: 140), pork (n: 44) and chicken (n: 48) muscles were homogenised and scanned in the visible (VIS) and near infrared (NIR) region (400-2500 nm) in a monochromator instrument in Reflectance. Both Principal Component Analysis (PCA) and dummy partial least-squares regression (PLS) models were developed to identify different meat species. The models correctly classified more than 80% of the meat sample muscles according with the muscle specie. The results showed the potential of VIS and NIR spectra as an objective and rapid method for authentication and identification of meat muscle species.
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visible and near infrared Reflectance Spectroscopy for the determination of moisture fat and protein in chicken breast and thigh muscle
Journal of Near Infrared Spectroscopy, 1996Co-Authors: Daniel Cozzolino, Ian Murray, R M Paterson, Jeremy R ScaifeAbstract:Near infrared (NIR) Reflectance Spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned ...
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visible and near infrared Reflectance Spectroscopy for the determination of moisture fat and protein in chicken breast and thigh muscle
Journal of Near Infrared Spectroscopy, 1996Co-Authors: Daniel Cozzolino, Ian Murray, R M Paterson, Jeremy R ScaifeAbstract:Near infrared (NIR) Reflectance Spectroscopy was used to determine the chemical composition of chicken breast and thigh muscles. Samples from twenty-four males and twenty-four females were scanned from 400 to 2500 nm, both as intact muscle and as comminuted (minced) tissue. Modified partial least squares (MPLS) regression on scatter corrected spectra (standard normal variates and Detrend) gave calibration models for chemical variables from NIR measurements on the defrosted minced breast samples having multivariate correlation coefficients and standard errors of calibration of 0.995 (2.4), 0.974 (2.11) and 0.946 (4.55) for moisture, crude protein and fat in g kg −1, respectively.