Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments
Abstract Savory (Satureja rechingeri L.) is one of Iran’s most important medicinal plants, having low irrigation needs, and thus is considered one of the most valuable plants for cultivation in arid and semi-arid regions, especially under drought conditions. The current research was carried out to d...
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BMC
2025-01-01
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| Series: | BMC Plant Biology |
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| Online Access: | https://doi.org/10.1186/s12870-024-06044-x |
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| author | Amin Taheri-Garavand Mojgan Beiranvandi Abdolreza Ahmadi Nikolaos Nikoloudakis |
| author_facet | Amin Taheri-Garavand Mojgan Beiranvandi Abdolreza Ahmadi Nikolaos Nikoloudakis |
| author_sort | Amin Taheri-Garavand |
| collection | DOAJ |
| description | Abstract Savory (Satureja rechingeri L.) is one of Iran’s most important medicinal plants, having low irrigation needs, and thus is considered one of the most valuable plants for cultivation in arid and semi-arid regions, especially under drought conditions. The current research was carried out to develop a genetic algorithm-based artificial neural network (ΑΝΝ) model able of simulating the levels of antioxidants in savory when using soil amendments [biochar (BC) and superabsorbent (SA)] under drought. Data under different watering schemes and different levels of soil amendments showed that both BC and SA have mitigating effects over drought stress by optimizing enzymatic and non-enzymatic antioxidant traits (POD, CTA, and APX enzymes). Specifically, using biochar and superabsorbent led to improved homeostasis under water deficit as reflected by lower MDA levels. An ANN model with a 3-10-6 topology was found to be the best model to predict polyphenols (PHE), proline (PRO), peroxidase (POX), catalase (CAT), ascorbate peroxidase (APX) levels, and indicator of oxidative stress malondialdehyde (MDA). The model’s efficiency was established using the R-value as the statistical parameter, and simulated GA-ANN data were highly correlated with experimental findings. Across enzymatic antioxidants, APX had the best model fit, having an R-value of 0.9733. On the other hand, POX had a lower predictive correlation (R = 0.8737), indicating a lower capacity of the ANN system in forecasting this parameter. On the other hand, MDA (R = 0.9690) had an elevated assimilation performance over PHE (R = 0.9604) and PRO (R = 0.9245) levels. The current study shows the potential of the ANN model in predicting the content of enzymatic and non-enzymatic antioxidants in savory plants under drought stress as a non-invasive, low-cost experimental alternative. |
| format | Article |
| id | doaj-art-5bdb6c692d854210a33d71f1d8549cb0 |
| institution | Kabale University |
| issn | 1471-2229 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Plant Biology |
| spelling | doaj-art-5bdb6c692d854210a33d71f1d8549cb02025-01-12T12:14:10ZengBMCBMC Plant Biology1471-22292025-01-0125111410.1186/s12870-024-06044-xSmart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendmentsAmin Taheri-Garavand0Mojgan Beiranvandi1Abdolreza Ahmadi2Nikolaos Nikoloudakis3Mechanical Engineering of Biosystems Department, Lorestan UniversityDepartment of Agro-Ecology, Faculty of Agriculture, Lorestan UniversityDepartment of Plant Protection, Faculty of Agriculture, Lorestan UniversityDepartment of Agricultural Science, Biotechnology and Food Science, Cyprus University of TechnologyAbstract Savory (Satureja rechingeri L.) is one of Iran’s most important medicinal plants, having low irrigation needs, and thus is considered one of the most valuable plants for cultivation in arid and semi-arid regions, especially under drought conditions. The current research was carried out to develop a genetic algorithm-based artificial neural network (ΑΝΝ) model able of simulating the levels of antioxidants in savory when using soil amendments [biochar (BC) and superabsorbent (SA)] under drought. Data under different watering schemes and different levels of soil amendments showed that both BC and SA have mitigating effects over drought stress by optimizing enzymatic and non-enzymatic antioxidant traits (POD, CTA, and APX enzymes). Specifically, using biochar and superabsorbent led to improved homeostasis under water deficit as reflected by lower MDA levels. An ANN model with a 3-10-6 topology was found to be the best model to predict polyphenols (PHE), proline (PRO), peroxidase (POX), catalase (CAT), ascorbate peroxidase (APX) levels, and indicator of oxidative stress malondialdehyde (MDA). The model’s efficiency was established using the R-value as the statistical parameter, and simulated GA-ANN data were highly correlated with experimental findings. Across enzymatic antioxidants, APX had the best model fit, having an R-value of 0.9733. On the other hand, POX had a lower predictive correlation (R = 0.8737), indicating a lower capacity of the ANN system in forecasting this parameter. On the other hand, MDA (R = 0.9690) had an elevated assimilation performance over PHE (R = 0.9604) and PRO (R = 0.9245) levels. The current study shows the potential of the ANN model in predicting the content of enzymatic and non-enzymatic antioxidants in savory plants under drought stress as a non-invasive, low-cost experimental alternative.https://doi.org/10.1186/s12870-024-06044-xArtificial neural networksAntioxidantsBiocharSuperabsorbentWater stress |
| spellingShingle | Amin Taheri-Garavand Mojgan Beiranvandi Abdolreza Ahmadi Nikolaos Nikoloudakis Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments BMC Plant Biology Artificial neural networks Antioxidants Biochar Superabsorbent Water stress |
| title | Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments |
| title_full | Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments |
| title_fullStr | Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments |
| title_full_unstemmed | Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments |
| title_short | Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments |
| title_sort | smart estimation of protective antioxidant enzymes activity in savory satureja rechingeri l under drought stress and soil amendments |
| topic | Artificial neural networks Antioxidants Biochar Superabsorbent Water stress |
| url | https://doi.org/10.1186/s12870-024-06044-x |
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