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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
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IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
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Spectroscopy of Carboxylic Acid Derivatives01:26

Spectroscopy of Carboxylic Acid Derivatives

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Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and...
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¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Updated: Apr 10, 2026

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions
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Quantification of Soil Organic Carbon by Shifted-Excitation Raman Difference Spectroscopy with Machine Learning and

Ginger W Brown1, Natalia V Solomatova2, Naomi K Yamaoka2

  • 1Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1 Canada.

Analytical Chemistry
|April 9, 2026
PubMed
Summary

Shifted-Excitation Raman Difference Spectroscopy (SERDS) offers a rapid method for measuring soil organic carbon (SOC). Machine learning models, particularly nonlinear ones, improve accuracy by overcoming matrix effects in soil samples.

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Soil organic carbon (SOC) is crucial for soil health and productivity.
  • Conventional SOC measurement methods are time-consuming and resource-intensive.
  • Raman spectroscopy with machine learning presents a faster, field-deployable alternative.

Purpose of the Study:

  • To evaluate Shifted-Excitation Raman Difference Spectroscopy (SERDS) for SOC measurement in diverse soils.
  • To investigate the impact of matrix effects, like light absorption and fluorescence, on prediction accuracy.
  • To compare spectral preprocessing algorithms and machine learning models for optimal SOC quantification.

Main Methods:

  • Collected and analyzed 400 American farm soil samples using SERDS.
  • Applied machine learning to SERDS spectra, focusing on fluorescence-free measurements.
  • Utilized a synthetic dataset to isolate effects of light absorption and data skew.
  • Compared Asymmetric Least Squares and Common-Mode Rejection preprocessing algorithms.

Main Results:

  • Light absorption nonlinearly attenuates Raman signals at higher SOC concentrations.
  • Conventional machine learning models showed reduced accuracy for high SOC levels, especially with skewed calibration data.
  • Common-Mode Rejection preprocessing and nonlinear, tree-based models yielded the most accurate SOC predictions.
  • SERDS effectively minimized fluorescence interference inherent in soil samples.

Conclusions:

  • SERDS combined with appropriate machine learning and preprocessing can accurately quantify SOC despite matrix effects.
  • Nonlinear models and Common-Mode Rejection are essential for robust SOC prediction in complex soil matrices.
  • This approach offers a promising pathway towards rapid, field-deployable SOC assessment for improved soil management.