{"title":"乙醇蜂胶提取物的人工神经网络建模","authors":"Sevgi Kolayli, Fatma Yaylaci Karahalil, Zeynep Berrin Celebi, Gizem Dilan Boztaş, Esra Capanoglu","doi":"10.1007/s11483-025-09995-2","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p><p>• Total phenolic content (TPC) indicates propolis extract quality.</p><p>• High TPC correlates with increased color intensity and dry matter (Brix)%.</p><p>• ANN modeling showed strong links between TPC, L* value, and Brix%.</p><p>• TPC can be predicted from color and Brix% via ANN models.</p></div>","PeriodicalId":564,"journal":{"name":"Food Biophysics","volume":"20 3","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Neural Networks (ANN) Modeling for Ethanolic Propolis Extracts\",\"authors\":\"Sevgi Kolayli, Fatma Yaylaci Karahalil, Zeynep Berrin Celebi, Gizem Dilan Boztaş, Esra Capanoglu\",\"doi\":\"10.1007/s11483-025-09995-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p><p>• Total phenolic content (TPC) indicates propolis extract quality.</p><p>• High TPC correlates with increased color intensity and dry matter (Brix)%.</p><p>• ANN modeling showed strong links between TPC, L* value, and Brix%.</p><p>• TPC can be predicted from color and Brix% via ANN models.</p></div>\",\"PeriodicalId\":564,\"journal\":{\"name\":\"Food Biophysics\",\"volume\":\"20 3\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Biophysics\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11483-025-09995-2\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Biophysics","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s11483-025-09995-2","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Artificial Neural Networks (ANN) Modeling for Ethanolic Propolis Extracts
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.
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.
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