Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach

Hüseyin Şahin; Erol Eğrioğlu; Eren Baş

doi:10.35206/jan.1909322

TR EN

Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach

Öz

Pine honey is a high-value natural product distinguished by its unique phenolic composition and diverse bioactive properties. Among these compounds, protocatechuic acid has been identified as a major and characteristic phenolic marker in pine honey samples originating particularly from the Aegean region of Türkiye. Despite its importance, systematic optimization of extraction conditions for this compound from pine honey matrices remains limited. Therefore, the present study aimed to determine the optimal extraction conditions for protocatechuic acid using an artificial intelligence–based optimization framework integrating Pi-Sigma artificial neural networks (PS-ANN) with a genetic algorithm (GA). In this approach, critical extraction parameters, including solvent type, extraction temperature, extraction time, and medium acidity, were evaluated. Experimental data obtained at different parameter levels were used to construct a response surface through the PS-ANN model, enabling the modeling of nonlinear relationships between extraction variables and protocatechuic acid yield. The optimal extraction conditions were subsequently identified using the GA optimization strategy. This hybrid AI-based modeling and optimization approach provides a robust and efficient tool for identifying optimal extraction conditions in complex food matrices. The findings demonstrate that the integration of PS-ANN and GA not only improves prediction accuracy but also significantly enhances the efficiency of phenolic compound extraction processes. Overall, this study highlights the potential of AI-driven optimization strategies for advancing analytical methodologies and improving the recovery of bioactive compounds from natural products such as pine honey.

Anahtar Kelimeler

Genetic algorithm, Pine honey, Pi-sigma artificial neural network, Protocatechuic acid, Response surface methodology

Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach

Öz

Pine honey is a high-value natural product distinguished by its unique phenolic composition and diverse bioactive properties. Among these compounds, protocatechuic acid has been identified as a major and characteristic phenolic marker in pine honey samples originating particularly from the Aegean region of Türkiye. Despite its importance, systematic optimization of extraction conditions for this compound from pine honey matrices remains limited. Therefore, the present study aimed to determine the optimal extraction conditions for protocatechuic acid using an artificial intelligence–based optimization framework integrating Pi-Sigma artificial neural networks (PS-ANN) with a genetic algorithm (GA). In this approach, critical extraction parameters, including solvent type, extraction temperature, extraction time, and medium acidity, were evaluated. Experimental data obtained at different parameter levels were used to construct a response surface through the PS-ANN model, enabling the modeling of nonlinear relationships between extraction variables and protocatechuic acid yield. The optimal extraction conditions were subsequently identified using the GA optimization strategy. This hybrid AI-based modeling and optimization approach provides a robust and efficient tool for identifying optimal extraction conditions in complex food matrices. The findings demonstrate that the integration of PS-ANN and GA not only improves prediction accuracy but also significantly enhances the efficiency of phenolic compound extraction processes. Overall, this study highlights the potential of AI-driven optimization strategies for advancing analytical methodologies and improving the recovery of bioactive compounds from natural products such as pine honey.

Anahtar Kelimeler

Genetic Algorithm, Pine Honey, Pi-Sigma Artificial Neural Network, Protocatechuic Acid, Response Surface Methodology

Destekleyen Kurum

This work was supported and funded by Giresun University, Scientific Research Projects (BAP) with FEN-BAP-A-250221-34 project number

Proje Numarası

FEN-BAP-A-250221-34

Etik Beyan

The authors of this article declare that the materials and methods used in this study do not require ethical committee approval and/or legal-specific permission.

Teşekkür

The authors gratefully acknowledge the Turkish Central Beekeepers Association (TAB) and the Muğla Provincial Beekeepers Association (MAYBİR) for providing the pine honey samples used in this study.

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Gıda Mühendisliği

Bölüm

Araştırma Makalesi

Yazarlar

Hüseyin Şahin ^*
0000-0002-6018-1494
Türkiye

Erol Eğrioğlu
0000-0003-4301-4149
Türkiye

Eren Baş
0000-0002-0263-8804
Türkiye

Yayımlanma Tarihi

8 Mayıs 2026

Gönderilme Tarihi

13 Mart 2026

Kabul Tarihi

24 Nisan 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 9 Sayı: 1

DOI

https://doi.org/10.35206/jan.1909322

IZ

https://izlik.org/JA85TH26AK

APA

Şahin, H., Eğrioğlu, E., & Baş, E. (2026). Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach. Journal of Apitherapy and Nature, 9(1), 48-56. https://doi.org/10.35206/jan.1909322

AMA

1.Şahin H, Eğrioğlu E, Baş E. Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach. Journal of Apitherapy and Nature. 2026;9(1):48-56. doi:10.35206/jan.1909322

Chicago

Şahin, Hüseyin, Erol Eğrioğlu, ve Eren Baş. 2026. “Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach”. Journal of Apitherapy and Nature 9 (1): 48-56. https://doi.org/10.35206/jan.1909322.

EndNote

Şahin H, Eğrioğlu E, Baş E (01 Mayıs 2026) Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach. Journal of Apitherapy and Nature 9 1 48–56.

IEEE

[1]H. Şahin, E. Eğrioğlu, ve E. Baş, “Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach”, Journal of Apitherapy and Nature, c. 9, sy 1, ss. 48–56, May. 2026, doi: 10.35206/jan.1909322.

ISNAD

Şahin, Hüseyin - Eğrioğlu, Erol - Baş, Eren. “Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach”. Journal of Apitherapy and Nature 9/1 (01 Mayıs 2026): 48-56. https://doi.org/10.35206/jan.1909322.

JAMA

1.Şahin H, Eğrioğlu E, Baş E. Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach. Journal of Apitherapy and Nature. 2026;9:48–56.

MLA

Şahin, Hüseyin, vd. “Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach”. Journal of Apitherapy and Nature, c. 9, sy 1, Mayıs 2026, ss. 48-56, doi:10.35206/jan.1909322.

Vancouver

1.Hüseyin Şahin, Erol Eğrioğlu, Eren Baş. Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach. Journal of Apitherapy and Nature. 01 Mayıs 2026;9(1):48-56. doi:10.35206/jan.1909322

e-ISSN: 2667-4734 Yayın Modeli: Sürekli Yayın Atıf Dizinleri: TR Dizin

Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach

Öz

Anahtar Kelimeler

Determination of Optimal Extraction Conditions of Pine Honey Using a Pi-Sigma Artificial Neural Network–Genetic Algorithm Approach

Öz

Anahtar Kelimeler

Destekleyen Kurum

Proje Numarası

Etik Beyan

Teşekkür

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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