Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times
Bay laurel leaves, also known as bay leaves, are an important herb in many cuisines around the world. In addition to their use in cooking, bay leaves have also been used for their medicinal properties and are thought to have anti-inflammatory and antimicrobial effects. Gas chromatography/mass spectr...
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Wiley
2023-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2023/3942303 |
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| author | Emel Karaca Öner Meryem Yeşil Mehmet Serhat Odabas |
| author_facet | Emel Karaca Öner Meryem Yeşil Mehmet Serhat Odabas |
| author_sort | Emel Karaca Öner |
| collection | DOAJ |
| description | Bay laurel leaves, also known as bay leaves, are an important herb in many cuisines around the world. In addition to their use in cooking, bay leaves have also been used for their medicinal properties and are thought to have anti-inflammatory and antimicrobial effects. Gas chromatography/mass spectrometry (GC-MS) device was used to determine the secondary metabolites in the essential oil of bay laurel leaves samples kept at different temperatures (−22, −20, −18, 2, 4, 6, and 22°C) and storage times (1, 2, and 3 months). In this research, temperature (°C) and storage time (month) were used as input parameters in the neural network. On the other hand, alpha-pinene, beta-pinene, sabinene, 1.8-cineole, gamma-terpinene, cymenol, linalool, borneol, 4-terpineol, caryophyllene, sabinene, alpha-terpineol, germacrene-D, alpha-selinene, methyl eugenol, caryophyllene oxide, spathulenol, eugenol, and beta-selinenol were used as an output parameter. Considering the R2 values obtained from the artificial neural network analysis, R2 values of 0.97156 for the test, 0.98978 for the training, 0.98998 for the validation value, and 0.98831 for all values were obtained. |
| format | Article |
| id | doaj-art-d1cbb0c698d247b1856070716a5af8fa |
| institution | DOAJ |
| issn | 2090-9071 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Chemistry |
| spelling | doaj-art-d1cbb0c698d247b1856070716a5af8fa2025-08-20T03:23:59ZengWileyJournal of Chemistry2090-90712023-01-01202310.1155/2023/3942303Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage timesEmel Karaca Öner0Meryem Yeşil1Mehmet Serhat Odabas2Ordu UniversityOrdu UniversityOndokuz Mayis UniversityBay laurel leaves, also known as bay leaves, are an important herb in many cuisines around the world. In addition to their use in cooking, bay leaves have also been used for their medicinal properties and are thought to have anti-inflammatory and antimicrobial effects. Gas chromatography/mass spectrometry (GC-MS) device was used to determine the secondary metabolites in the essential oil of bay laurel leaves samples kept at different temperatures (−22, −20, −18, 2, 4, 6, and 22°C) and storage times (1, 2, and 3 months). In this research, temperature (°C) and storage time (month) were used as input parameters in the neural network. On the other hand, alpha-pinene, beta-pinene, sabinene, 1.8-cineole, gamma-terpinene, cymenol, linalool, borneol, 4-terpineol, caryophyllene, sabinene, alpha-terpineol, germacrene-D, alpha-selinene, methyl eugenol, caryophyllene oxide, spathulenol, eugenol, and beta-selinenol were used as an output parameter. Considering the R2 values obtained from the artificial neural network analysis, R2 values of 0.97156 for the test, 0.98978 for the training, 0.98998 for the validation value, and 0.98831 for all values were obtained.http://dx.doi.org/10.1155/2023/3942303 |
| spellingShingle | Emel Karaca Öner Meryem Yeşil Mehmet Serhat Odabas Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times Journal of Chemistry |
| title | Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times |
| title_full | Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times |
| title_fullStr | Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times |
| title_full_unstemmed | Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times |
| title_short | Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times |
| title_sort | prediction of secondary metabolites content of laurel laurus nobilis l with artificial neural networks based on different temperatures and storage times |
| url | http://dx.doi.org/10.1155/2023/3942303 |
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