Publication: Machine learning unmasked nutritional imbalances on the medicinal plant Bryophyllum sp. cultured in vitro
Authors
Lozano-Milo, Eva ; Landin, Mariana ; Gallego, Pedro Pablo ; García Pérez, Pascual
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Publisher
Frontiers Media
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DOI
https://doi.org/10.3389/fpls.2020.576177
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info:eu-repo/semantics/article
Description
© 2020 García-Pérez, Lozano-Milo, Landin and Gallego. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/. This document is the Published version of a Published Work that appeared in final form in Frontiers in Plant Science. To access the final edited and published work see https://doi.org/10.3389/fpls.2020.576177
Abstract
Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is
large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants
belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable “IF-THEN” rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro
culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative
per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.
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Citation
Front. Plant Sci. 11:576177
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Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by/4.0/