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Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm

BACKGROUND: Contamination−free culture is a prerequisite for the success of in vitro − based plant biotechnology. Aseptic initiation is an extremely strenuous stride, particularly in woody species. Meanwhile, over−sterilization is potentially detrimental to plant tissue. The recent rise of machine l...

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Autores principales: Gammoudi, Najet, Nagaz, Kamel, Ferchichi, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9254583/
https://www.ncbi.nlm.nih.gov/pubmed/35790933
http://dx.doi.org/10.1186/s12870-022-03674-x
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author Gammoudi, Najet
Nagaz, Kamel
Ferchichi, Ali
author_facet Gammoudi, Najet
Nagaz, Kamel
Ferchichi, Ali
author_sort Gammoudi, Najet
collection PubMed
description BACKGROUND: Contamination−free culture is a prerequisite for the success of in vitro − based plant biotechnology. Aseptic initiation is an extremely strenuous stride, particularly in woody species. Meanwhile, over−sterilization is potentially detrimental to plant tissue. The recent rise of machine learning algorithms in plant tissue culture proposes an advanced interpretive tool for the combinational effect of influential factors for such in vitro − based steps. RESULTS: A multilayer perceptron (MLP) model of artificial neural network (ANN) was implemented with four inputs, three sterilizing chemicals at various concentrations and the immersion time, and two outputs, disinfection efficiency (DE) and negative disinfection effect (NDE), intending to assess twenty−seven disinfection procedures of Pistacia vera L. seeds. Mercury chloride (HgCl(2); 0.05–0.2%; 5–15 min) appears the most effective with 100% DE, then hydrogen peroxide (H(2)O(2); 5.25–12.25%; 10–30 min) with 66–100% DE, followed by 27–77% DE for sodium hypochlorite (NaOCl; 0.54–1.26% w/v; 10–30 min). Concurrently, NDE was detected, including chlorosis, hard embryo germination, embryo deformation, and browning tissue, namely, a low repercussion with NaOCl (0–14%), a moderate impact with H(2)O(2) (6–46%), and pronounced damage with HgCl(2) (22–100%). Developed ANN showed R values of 0.9658, 0.9653, 0.8937, and 0.9454 for training, validation, testing, and all sets, respectively, which revealed the uprightness of the model. Subsequently, the model was linked to multi−objective genetic algorithm (MOGA) which proposed an optimized combination of 0.56% NaOCl, 12.23% H(2)O(2), and 0.068% HgCl(2) for 5.022 min. The validation assay reflects the high utility and accuracy of the model with maximum DE (100%) and lower phytotoxicity (7.1%). CONCLUSION: In one more case, machine learning algorithms emphasized their ability to resolve commonly encountered problems. The current successful implementation of MLP–MOGA inspires its application for more complicated plant tissue culture processes.
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spelling pubmed-92545832022-07-06 Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm Gammoudi, Najet Nagaz, Kamel Ferchichi, Ali BMC Plant Biol Research BACKGROUND: Contamination−free culture is a prerequisite for the success of in vitro − based plant biotechnology. Aseptic initiation is an extremely strenuous stride, particularly in woody species. Meanwhile, over−sterilization is potentially detrimental to plant tissue. The recent rise of machine learning algorithms in plant tissue culture proposes an advanced interpretive tool for the combinational effect of influential factors for such in vitro − based steps. RESULTS: A multilayer perceptron (MLP) model of artificial neural network (ANN) was implemented with four inputs, three sterilizing chemicals at various concentrations and the immersion time, and two outputs, disinfection efficiency (DE) and negative disinfection effect (NDE), intending to assess twenty−seven disinfection procedures of Pistacia vera L. seeds. Mercury chloride (HgCl(2); 0.05–0.2%; 5–15 min) appears the most effective with 100% DE, then hydrogen peroxide (H(2)O(2); 5.25–12.25%; 10–30 min) with 66–100% DE, followed by 27–77% DE for sodium hypochlorite (NaOCl; 0.54–1.26% w/v; 10–30 min). Concurrently, NDE was detected, including chlorosis, hard embryo germination, embryo deformation, and browning tissue, namely, a low repercussion with NaOCl (0–14%), a moderate impact with H(2)O(2) (6–46%), and pronounced damage with HgCl(2) (22–100%). Developed ANN showed R values of 0.9658, 0.9653, 0.8937, and 0.9454 for training, validation, testing, and all sets, respectively, which revealed the uprightness of the model. Subsequently, the model was linked to multi−objective genetic algorithm (MOGA) which proposed an optimized combination of 0.56% NaOCl, 12.23% H(2)O(2), and 0.068% HgCl(2) for 5.022 min. The validation assay reflects the high utility and accuracy of the model with maximum DE (100%) and lower phytotoxicity (7.1%). CONCLUSION: In one more case, machine learning algorithms emphasized their ability to resolve commonly encountered problems. The current successful implementation of MLP–MOGA inspires its application for more complicated plant tissue culture processes. BioMed Central 2022-07-05 /pmc/articles/PMC9254583/ /pubmed/35790933 http://dx.doi.org/10.1186/s12870-022-03674-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Gammoudi, Najet
Nagaz, Kamel
Ferchichi, Ali
Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm
title Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm
title_full Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm
title_fullStr Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm
title_full_unstemmed Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm
title_short Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm
title_sort establishment of optimized in vitro disinfection protocol of pistacia vera l. explants mediated a computational approach: multilayer perceptron–multi−objective genetic algorithm
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9254583/
https://www.ncbi.nlm.nih.gov/pubmed/35790933
http://dx.doi.org/10.1186/s12870-022-03674-x
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