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Related Concept Videos

Raman Spectroscopy: Overview01:20

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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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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...
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Related Experiment Video

Updated: Jan 10, 2026

Milk Collection in the Rat Using Capillary Tubes and Estimation of Milk Fat Content by Creamatocrit
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Milk powder adulteration identification and quantification based on shared encoder features using Raman spectroscopy.

Xiangchu Li1, Maoyuan Pang1, Yihua He1

  • 1Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Raman-based method combining data-driven soft independent modeling of class analogy (DD-SIMCA) and multilayer perceptron (MLP) for accurate milk powder adulteration detection, even at low concentrations and with multiple adulterants.

Keywords:
DD-SIMCAFood adulteration detectionMilk powderMulti-layer perceptronRaman spectroscopy

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Milk powder adulteration poses significant risks to public health and the food industry.
  • Current detection methods struggle with low concentrations and complex mixtures of adulterants.

Purpose of the Study:

  • To develop a robust and sensitive method for detecting and quantifying adulterants in milk powder.
  • To address the challenges of low-concentration detection and multi-adulterant mixtures.

Main Methods:

  • A two-stage approach utilizing Raman spectroscopy.
  • Nontargeted screening with data-driven soft independent modeling of class analogy (DD-SIMCA) for initial adulteration detection.
  • Multilayer perceptron (MLP) with a shared encoder for classification and quantification of adulterants in flagged samples.

Main Results:

  • DD-SIMCA achieved 100% accuracy in detecting adulteration, outperforming traditional methods.
  • MLP demonstrated 99% accuracy in classifying adulterant types.
  • MLP achieved excellent quantitative prediction (R²P = 0.99, RMSEP < 0.6), surpassing SVM, RF, and PLSR.

Conclusions:

  • The proposed DD-SIMCA and MLP combined approach effectively detects low concentrations and multi-adulterant mixtures in milk powder.
  • This method offers a practical solution for rapid, on-site screening, enhancing milk powder safety and quality control.