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In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
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Design & development of adulteration detection system by fumigation method & machine learning techniques.

Urvashi Agrawal1, Narendra Bawane2, Najah Alsubaie3

  • 1Department of Electronics and Telecommunication Engineering, Jhulelal Institute of Technology, Nagpur, India. urvashi.agrawal2000@gmail.com.

Scientific Reports
|October 25, 2024
PubMed
Summary

This study introduces novel methods using refractive index and electronic sensors to detect edible oil adulteration. Machine learning algorithms achieved 100% accuracy in identifying impurities, ensuring food quality.

Keywords:
CATBOOSTEdible Vegetable oilsOil AdulterationRandom ForestSensorsXGBOOST

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Edible oil adulteration poses a significant threat to public health and economic integrity.
  • Accurate and rapid detection methods are crucial for ensuring food safety and quality.
  • Current methods for detecting edible oil adulteration can be time-consuming and require specialized equipment.

Purpose of the Study:

  • To develop and validate novel methods for detecting adulteration in edible oils.
  • To investigate the efficacy of refractive index and electronic sensor-based techniques for impurity detection.
  • To compare the performance of various machine learning algorithms in identifying edible oil adulteration.

Main Methods:

  • Utilized spectral data from Advanced ATR-MIR Spectroscopy and refractive index measurements for dataset-based analysis.
  • Developed a fumigation technique integrating real-time hardware with MEMS and Multichannel Gas Sensors.
  • Employed machine learning algorithms including KNN, Random Forest, CATBOOST, and XGBOOST for classification.

Main Results:

  • Refractive index variations effectively indicated adulteration with vegetable oils (lower index) or animal fats (higher index).
  • The fumigation method successfully extracted volatile compounds, with sensor conductance changes correlating to contamination levels.
  • Random Forest and XGBOOST algorithms demonstrated superior performance, achieving 100% accuracy in adulteration detection.

Conclusions:

  • The proposed methods offer a reliable and accurate approach for identifying edible oil adulteration.
  • The integration of refractive index, electronic sensors, and machine learning provides a powerful tool for food quality control.
  • This research contributes to ensuring the availability of high-quality edible food products for consumers.