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May 15, 2026AgriEngineeringOpen Access

Geographical Origin Discrimination of Aniseed (Pimpinella anisum) Based on Machine Learning Classification of Agricultural and GC-MS Parameters

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Authors

MAMilica AćimovićBLBiljana LončarOŠOlja Šovljanski

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Overview

Randomized trial evaluates geographical origin classification of aniseed, suggesting machine learning enhances traceability.

Key Points

  • The research aims to classify the geographical origin of aniseed based on agronomic, productivity, and chemical data.
  • Analyzed 144 samples from three locations with varied fertilizer treatments over two years.
  • Employed a factorial design with four replications for data robustness.
  • Used multiple machine learning models, including artificial neural networks and support vector machines, for classification.
  • Trans-anethole was identified as the dominant compound in all samples, ranging from 89.508% to 101.441%.
  • Machine learning models effectively captured complex non-linear relationships, improving geographical classification accuracy.
  • Demonstrated potential for enhanced agro-food authentication and quality control through machine learning.

Cite This Study

Aćimović et al. (2026) studied this question.

synapsesocial.com/papers/6a06b983e7dec685947ac28bhttps://doi.org/10.3390/agriengineering8050194
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Characterization of Pimpinella anisum Germplasm: Diversity Available for Agronomic Performance and Essential Oil Content and Composition2026
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  3. 3Efficacy of Pimpinella anisum L. in Menopausal Women with Psychological Symptoms: A Randomized Controlled Study Integrated with Machine Learning Analysis2026
  4. 4Machine Learning–Discriminant Analysis of Rice Origin Traceability2026
  5. 5Identification of Spices and their Adulterants by Integrating Machine Learning and Analytical Techniques: A Representative Study2024 · 6 citations