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Related Concept Videos

Spectrophotometry: Introduction01:16

Spectrophotometry: Introduction

Spectrophotometry is the quantitative measurement of the absorption, reflection, diffraction, or transmission of electromagnetic radiation through a material as a function of the intensity and wavelength of the radiation. A spectrophotometer is a device used to measure the change in the radiation intensity caused by its interaction with the material.
The essential components of a spectrophotometer include a source of electromagnetic radiation, a slot for placing a material to be analyzed, and a...
UV–Vis Spectrometers01:14

UV–Vis Spectrometers

The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell. Samples for...
IR Spectrometers01:25

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

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Atomic Spectroscopy: Absorption, Emission, and Fluorescence01:23

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Atomic spectroscopy is a vital tool in elemental analysis, both qualitatively and quantitatively. It can be broadly divided into optical spectroscopy, mass spectroscopy, and X-ray spectroscopy methods. The optical spectroscopic methods are atomic absorption spectroscopy (AAS), atomic emission spectroscopy (AES), and atomic fluorescence spectroscopy (AFS). The first step in all three methods is atomization, where the solid, liquid, or solution-phase samples are converted into gas-phase atoms and...
IR Spectrum01:19

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Updated: Jun 22, 2026

Diffuse Reflectance Spectroscopy: Getting the Capillary Refill Test Under One's Thumb
06:50

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Published on: December 2, 2017

Does the spectral format matter in diffuse reflection spectroscopy?

James B Reeves1

  • 1EMBUL, Bldg 306, Rm 101, BARC East, Beltsville, Maryland 20705, USA. james.reeves@ars.usda.gov

Applied Spectroscopy
|June 18, 2009
PubMed
Summary
This summary is machine-generated.

Near-infrared and mid-infrared spectroscopy data formats do not significantly impact partial least squares regression calibration accuracy for forage analysis. Accurate calibrations for fiber, digestibility, and protein can be achieved using various spectral formats, including raw reflectance.

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Last Updated: Jun 22, 2026

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06:50

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Published on: December 2, 2017

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Diffuse reflection spectroscopy (DRS) in the near-infrared (NIR) and mid-infrared (MIR) regions is widely used for compositional analysis.
  • Spectra are typically converted to log (1/reflectance) for linearization, though some researchers use raw reflectance.
  • The necessity of this transformation for chemometric methods like partial least squares regression (PLSR) is debated.

Purpose of the Study:

  • To investigate the impact of different spectral data formats on calibration accuracy using PLSR.
  • To determine if data transformation is essential for successful NIR and MIR spectral analysis.

Main Methods:

  • Development of calibrations for forage analysis (fiber, digestibility, protein) using PLSR.
  • Utilized various spectral formats: reflectance, log (1/reflectance), single beam spectra, interferograms, and Kubelka-Munk transformed data.
  • Evaluated both pretreated and non-pretreated spectral data.

Main Results:

  • PLSR calibrations demonstrated that data format had minimal impact on accuracy.
  • Accurate calibrations were achieved across multiple spectral formats, including raw reflectance and single beam spectra.
  • Kubelka-Munk and interferogram formats showed slightly lower performance but remained effective.

Conclusions:

  • Accurate and equivalent calibrations for forage analysis can be developed using various diffuse reflectance spectral formats with PLSR.
  • Data transformation (e.g., log (1/reflectance)) is not strictly necessary for successful calibration development.
  • The choice of spectral format has limited influence on the performance of PLSR models in this context.