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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Honey Botanical Origin Authentication Using HS-SPME-GC-MS Volatile Profiling and Advanced Machine Learning Models

Amir Pourmoradian1, Mohsen Barzegar1, Ángel A Carbonell-Barrachina2

  • 1Department of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, Tehran P.O. Box 14115-336, Iran.

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Summary

This study uses Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) and machine learning to identify honey

Keywords:
GC—MSbotanical originchemometricschromatographyhoney authenticationneural networkvolatile compounds

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Area of Science:

  • Analytical Chemistry
  • Food Science
  • Cheminformatics

Background:

  • Honey botanical authentication is crucial for quality control and fraud detection.
  • Traditional methods often lack the sensitivity and specificity for complex mixtures.
  • Volatile organic compound (VOC) profiling offers a promising avenue for discrimination.

Purpose of the Study:

  • To develop and compare advanced machine learning models for multiclass honey botanical origin authentication.
  • To integrate Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) with supervised learning algorithms.
  • To identify key chemotaxonomic markers for differentiating honey floral sources.

Main Methods:

  • Analysis of 57 honey samples using HS-SPME-GC-MS to obtain VOC profiles.
  • Application of Principal Component Analysis (PCA) for data visualization and class separation.
  • Training and evaluation of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Neural Network (NN) models for classification.

Main Results:

  • Identification of specific VOC markers for coriander, orange blossom, astragalus, rosemary, and chehelgiah honeys.
  • PCA demonstrated clear separation of botanical classes.
  • The Neural Network model achieved the highest authentication accuracy (90.32%), outperforming RF and XGBoost.

Conclusions:

  • HS-SPME-GC-MS combined with deep learning models provides a rapid, sensitive, and reliable method for honey botanical authentication.
  • This approach offers significant potential for real-time quality control and combating honey adulteration.
  • The study highlights the efficacy of advanced machine learning in complex food analysis.