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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: Mar 17, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
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Branch and bound algorithm for accurate estimation of analytical isotropic bidirectional reflectance distribution

Chanki Yu, Sang Wook Lee

    Applied Optics
    |July 14, 2016
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    Summary

    This study introduces a global optimization framework for estimating bidirectional reflectance distribution function (BRDF) parameters. Using L1-norm minimization, it offers a more accurate and reliable solution compared to traditional methods.

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    Area of Science:

    • Computer Graphics
    • Computer Vision
    • Materials Science

    Background:

    • Estimating parameters for analytical bidirectional reflectance distribution function (BRDF) models is crucial for realistic rendering.
    • Conventional local optimization methods often yield suboptimal results due to their sensitivity to initial parameter guesses and the inherent nonlinearity of BRDF models.

    Purpose of the Study:

    • To develop a reliable and accurate global optimization framework for estimating parameters of isotropic analytical BRDF models.
    • To address the limitations of local optimization in BRDF parameter estimation.

    Main Methods:

    • A branch and bound strategy combined with linear programming and interval analysis is employed.
    • The framework utilizes L1-norm error minimization for parameter estimation.
    • Interval arithmetic is used to define the feasibility problem and lower bounding function.

    Main Results:

    • The proposed method, applied to the Cook-Torrance model with various normal distribution functions (Beckmann, Berry, GGX), was validated using 100 isotropic materials from the MERL BRDF database.
    • Experimental results show that L1-norm minimization yields more accurate and reliable BRDF parameter estimates than L2-norm minimization.

    Conclusions:

    • The presented global optimization framework provides a robust and accurate approach for estimating BRDF parameters.
    • L1-norm minimization is demonstrated to be superior to L2-norm minimization for this specific task, enhancing the reliability of BRDF model fitting.