Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds

In vitro seed germination is a useful tool for developing a variety of biotechnologies, but cannabis has presented some challenges in uniformity and germination time, presumably due to the disinfection procedure. Disinfection and subsequent growth are influenced by many factors, such as media pH, te...

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Autores principales:	Marco Pepe, Mohsen Hesami, Andrew Maxwell Phineas Jones
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hydrogen peroxide sodium hypochlorite generalized regression neural network genetic algorithm scarification seed dormancy Botany QK1-989
Acceso en línea:	https://doaj.org/article/a0902fac3c0340ac99f103de96bb7cb2
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id	oai:doaj.org-article:a0902fac3c0340ac99f103de96bb7cb2
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spelling	oai:doaj.org-article:a0902fac3c0340ac99f103de96bb7cb22021-11-25T18:46:16ZMachine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds10.3390/plants101123972223-7747https://doaj.org/article/a0902fac3c0340ac99f103de96bb7cb22021-11-01T00:00:00Zhttps://www.mdpi.com/2223-7747/10/11/2397https://doaj.org/toc/2223-7747In vitro seed germination is a useful tool for developing a variety of biotechnologies, but cannabis has presented some challenges in uniformity and germination time, presumably due to the disinfection procedure. Disinfection and subsequent growth are influenced by many factors, such as media pH, temperature, as well as the types and levels of contaminants and disinfectants, which contribute independently and dynamically to system complexity and nonlinearity. Hence, artificial intelligence models are well suited to model and optimize this dynamic system. The current study was aimed to evaluate the effect of different types and concentrations of disinfectants (sodium hypochlorite, hydrogen peroxide) and immersion times on contamination frequency using the generalized regression neural network (GRNN), a powerful artificial neural network (ANN). The GRNN model had high prediction performance (R<sup>2</sup> > 0.91) in both training and testing. Moreover, a genetic algorithm (GA) was subjected to the GRNN to find the optimal type and level of disinfectants and immersion time to determine the best methods for contamination reduction. According to the optimization process, 4.6% sodium hypochlorite along with 0.008% hydrogen peroxide for 16.81 min would result in the best outcomes. The results of a validation experiment demonstrated that this protocol resulted in 0% contamination as predicted, but germination rates were low and sporadic. However, using this sterilization protocol in combination with the scarification of in vitro cannabis seed (seed tip removal) resulted in 0% contamination and 100% seed germination within one week.Marco PepeMohsen HesamiAndrew Maxwell Phineas JonesMDPI AGarticlehydrogen peroxidesodium hypochloritegeneralized regression neural networkgenetic algorithmscarificationseed dormancyBotanyQK1-989ENPlants, Vol 10, Iss 2397, p 2397 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hydrogen peroxide sodium hypochlorite generalized regression neural network genetic algorithm scarification seed dormancy Botany QK1-989
spellingShingle	hydrogen peroxide sodium hypochlorite generalized regression neural network genetic algorithm scarification seed dormancy Botany QK1-989 Marco Pepe Mohsen Hesami Andrew Maxwell Phineas Jones Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds
description	In vitro seed germination is a useful tool for developing a variety of biotechnologies, but cannabis has presented some challenges in uniformity and germination time, presumably due to the disinfection procedure. Disinfection and subsequent growth are influenced by many factors, such as media pH, temperature, as well as the types and levels of contaminants and disinfectants, which contribute independently and dynamically to system complexity and nonlinearity. Hence, artificial intelligence models are well suited to model and optimize this dynamic system. The current study was aimed to evaluate the effect of different types and concentrations of disinfectants (sodium hypochlorite, hydrogen peroxide) and immersion times on contamination frequency using the generalized regression neural network (GRNN), a powerful artificial neural network (ANN). The GRNN model had high prediction performance (R<sup>2</sup> > 0.91) in both training and testing. Moreover, a genetic algorithm (GA) was subjected to the GRNN to find the optimal type and level of disinfectants and immersion time to determine the best methods for contamination reduction. According to the optimization process, 4.6% sodium hypochlorite along with 0.008% hydrogen peroxide for 16.81 min would result in the best outcomes. The results of a validation experiment demonstrated that this protocol resulted in 0% contamination as predicted, but germination rates were low and sporadic. However, using this sterilization protocol in combination with the scarification of in vitro cannabis seed (seed tip removal) resulted in 0% contamination and 100% seed germination within one week.
format	article
author	Marco Pepe Mohsen Hesami Andrew Maxwell Phineas Jones
author_facet	Marco Pepe Mohsen Hesami Andrew Maxwell Phineas Jones
author_sort	Marco Pepe
title	Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds
title_short	Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds
title_full	Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds
title_fullStr	Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds
title_full_unstemmed	Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds
title_sort	machine learning-mediated development and optimization of disinfection protocol and scarification method for improved in vitro germination of cannabis seeds
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a0902fac3c0340ac99f103de96bb7cb2
work_keys_str_mv	AT marcopepe machinelearningmediateddevelopmentandoptimizationofdisinfectionprotocolandscarificationmethodforimprovedinvitrogerminationofcannabisseeds AT mohsenhesami machinelearningmediateddevelopmentandoptimizationofdisinfectionprotocolandscarificationmethodforimprovedinvitrogerminationofcannabisseeds AT andrewmaxwellphineasjones machinelearningmediateddevelopmentandoptimizationofdisinfectionprotocolandscarificationmethodforimprovedinvitrogerminationofcannabisseeds
_version_	1718410730801201152

Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds

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