Artificial Neural Networks (ANN) Modeling for Ethanolic Propolis Extracts

IF 3.2 4区农林科学 Q2 FOOD SCIENCE & TECHNOLOGY

Food Biophysics Pub Date : 2025-08-04 DOI:10.1007/s11483-025-09995-2

Sevgi Kolayli, Fatma Yaylaci Karahalil, Zeynep Berrin Celebi, Gizem Dilan Boztaş, Esra Capanoglu

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引用次数: 0

Abstract

Total phenolic content (TPC) is a critical quality parameter evaluating the bioactive properties of ethanolic propolis extracts. This study aimed to investigate the relationship between color parameters (Hunter Lab), Brix% (dry matter), and total phenolic content, antioxidant capacity in the ethanolic propolis extracts. Four different percentages of ethanol (96%, 90%, 80% and 70%) and four different propolis concentrations (40%, 30%, 20% and 10%) were used in the study. Total phenolic substance amounts, and antioxidant values of the extracts were measured according to the ferric reducing power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity assays. Pearson correlation analysis and artificial neural network (ANN) modeling were utilized to examine these interactions. The study results showed that both ethanol percentage and propolis amount affected the amount of TPC in the extracts and accordingly the antioxidant capacity. A strong correlation between TPC and the Hunter L* color parameter, as well as Brix%, was identified through ANN modeling, yielding the predictive equation: TPC (mg GAE/mL)=[− 0.07×L + 0.87×Dry Matter − 0.0130]. The ANN-based model developed to predict total phenolic content (TPC) showed about 85% agreement with experimentally obtained values. However, it is predicted that the prediction accuracy of the model will improve with the addition of a larger and more diverse data set. In conclusion, ANN modeling offers a promising alternative for faster and economical evaluation of the quality of ethanolic propolis extracts.

• Total phenolic content (TPC) indicates propolis extract quality.

• High TPC correlates with increased color intensity and dry matter (Brix)%.

• ANN modeling showed strong links between TPC, L* value, and Brix%.

• TPC can be predicted from color and Brix% via ANN models.

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乙醇蜂胶提取物的人工神经网络建模

总酚含量（TPC）是评价乙醇蜂胶提取物生物活性的重要质量参数。本研究旨在探讨乙醇蜂胶提取物的颜色参数（Hunter Lab）、Brix%（干物质）与总酚含量、抗氧化能力的关系。实验采用四种不同的乙醇浓度（96%、90%、80%和70%）和四种不同的蜂胶浓度（40%、30%、20%和10%）。通过铁还原力（FRAP）和2,2-二苯基-1-苦味肼基（DPPH）自由基清除活性测定提取物的总酚类物质含量和抗氧化活性。使用Pearson相关分析和人工神经网络（ANN）建模来检验这些相互作用。研究结果表明，乙醇含量和蜂胶用量均影响提取物中TPC的含量，从而影响抗氧化能力。通过人工神经网络建模，发现TPC与Hunter L*颜色参数以及Brix%之间存在很强的相关性，得出预测方程：TPC (mg GAE/mL)=[−0.07×L + 0.87×Dry Matter−0.0130]。基于人工神经网络的预测总酚含量（TPC）的模型与实验结果的一致性约为85%。然而，预测模型的预测精度将随着数据集的增加而提高。总之，人工神经网络建模为更快、更经济地评价乙醇蜂胶提取物的质量提供了一种有前途的选择。•总酚含量（TPC）表示蜂胶提取物的质量。•高TPC与增加的颜色强度和干物质（白糖度）%相关。•人工神经网络模型显示TPC、L*值和Brix%之间有很强的联系。•TPC可以通过人工神经网络模型从颜色和Brix%预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Food Biophysics 工程技术-食品科技

CiteScore

5.80

自引率

3.30%

发文量

审稿时长

1 months

期刊介绍： Biophysical studies of foods and agricultural products involve research at the interface of chemistry, biology, and engineering, as well as the new interdisciplinary areas of materials science and nanotechnology. Such studies include but are certainly not limited to research in the following areas: the structure of food molecules, biopolymers, and biomaterials on the molecular, microscopic, and mesoscopic scales; the molecular basis of structure generation and maintenance in specific foods, feeds, food processing operations, and agricultural products; the mechanisms of microbial growth, death and antimicrobial action; structure/function relationships in food and agricultural biopolymers; novel biophysical techniques (spectroscopic, microscopic, thermal, rheological, etc.) for structural and dynamical characterization of food and agricultural materials and products; the properties of amorphous biomaterials and their influence on chemical reaction rate, microbial growth, or sensory properties; and molecular mechanisms of taste and smell. A hallmark of such research is a dependence on various methods of instrumental analysis that provide information on the molecular level, on various physical and chemical theories used to understand the interrelations among biological molecules, and an attempt to relate macroscopic chemical and physical properties and biological functions to the molecular structure and microscopic organization of the biological material.