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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

296
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
296
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

307
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.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
307

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Related Experiment Video

Updated: Jun 5, 2025

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
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A rapid, non-destructive, and accurate method for identifying citrus granulation using Raman spectroscopy and machine

Rui Liu1, Yuanpeng Li2,3, Tinghui Li1,4

  • 1Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.

Journal of Food Science
|December 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a Raman spectroscopy and machine learning method to detect citrus granulation, a storage issue. The technique accurately identifies granulated citrus, reducing food waste and economic loss.

Keywords:
Raman spectroscopyagricultural product qualitycompetitive adaptive reweighted sampling algorithmnon‐destructive testing

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Citrus fruit quality is impacted by juice sac granulation during storage.
  • This condition presents a significant challenge to the citrus industry, affecting marketability and leading to economic losses.

Purpose of the Study:

  • To develop a rapid, non-destructive, and precise method for detecting citrus granulation.
  • To utilize Raman spectroscopy and machine learning for accurate granulation assessment.

Main Methods:

  • Analysis of 969 Raman spectral data points from granulated and non-granulated citrus samples.
  • Application of logistic regression, decision tree, and partial least squares discriminant analysis.
  • Refinement of models using principal component analysis, successive projection algorithm, and competitive adaptive reweighted sampling (CARS).

Main Results:

  • Identification of characteristic Raman peaks (1580 and 1661 cm⁻¹) indicative of granulation, linked to water, ferulic acid, and sugar content differences.
  • Partial least squares discriminant analysis achieved high accuracy (0.997), recall (0.994), and F-fraction (0.996).
  • A combined second derivative-CARS-partial least squares discriminant analysis model demonstrated 100% accuracy in test sets.

Conclusions:

  • The proposed Raman spectroscopy and machine learning approach offers a robust and reliable method for assessing citrus fruit quality and detecting granulation.
  • This technology can be applied for in-line screening of citrus during processing, minimizing waste and economic losses.
  • Provides technical support for classifying citrus crop quality, enhancing industry standards.