Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> Leaves
The study of the phenolic compounds present in olive leaves (<i>Olea europaea</i>) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to...
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/12/24/4483 |
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| author | Raquel Rodríguez-Fernández Ángela Fernández-Gómez Juan C. Mejuto Gonzalo Astray |
| author_facet | Raquel Rodríguez-Fernández Ángela Fernández-Gómez Juan C. Mejuto Gonzalo Astray |
| author_sort | Raquel Rodríguez-Fernández |
| collection | DOAJ |
| description | The study of the phenolic compounds present in olive leaves (<i>Olea europaea</i>) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANN<sub>Z-L</sub> model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANN<sub>R</sub>, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents. |
| first_indexed | 2024-03-08T20:46:53Z |
| format | Article |
| id | doaj.art-8083f08a38d047ecbec212c8f18beb1e |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-08T20:46:53Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-8083f08a38d047ecbec212c8f18beb1e2023-12-22T14:08:58ZengMDPI AGFoods2304-81582023-12-011224448310.3390/foods12244483Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> LeavesRaquel Rodríguez-Fernández0Ángela Fernández-Gómez1Juan C. Mejuto2Gonzalo Astray3Universidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, SpainUniversidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, SpainUniversidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, SpainUniversidade de Vigo, Departamento de Química Física, Facultade de Ciencias, 32004 Ourense, SpainThe study of the phenolic compounds present in olive leaves (<i>Olea europaea</i>) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANN<sub>Z-L</sub> model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANN<sub>R</sub>, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.https://www.mdpi.com/2304-8158/12/24/4483olive leavesultrasound-assisted extractionextract yieldTPCmachine learningrandom forest |
| spellingShingle | Raquel Rodríguez-Fernández Ángela Fernández-Gómez Juan C. Mejuto Gonzalo Astray Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> Leaves Foods olive leaves ultrasound-assisted extraction extract yield TPC machine learning random forest |
| title | Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> Leaves |
| title_full | Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> Leaves |
| title_fullStr | Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> Leaves |
| title_full_unstemmed | Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> Leaves |
| title_short | Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in <i>Olea europaea</i> Leaves |
| title_sort | modelling polyphenol extraction through ultrasound assisted extraction by machine learning in i olea europaea i leaves |
| topic | olive leaves ultrasound-assisted extraction extract yield TPC machine learning random forest |
| url | https://www.mdpi.com/2304-8158/12/24/4483 |
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