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Machine learning links seed composition, glucosinolates and viability of oilseed rape after 31 years of long-term storage

Published online by Cambridge University Press:  12 July 2018

Manuela Nagel ,
Katharina Holstein ,
Evelin Willner  and
Andreas Börner
Manuela Nagel*
Affiliation:
Genebank Department, Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Seeland, Germany
Katharina Holstein
Affiliation:
Fraunhofer Institute for Factory Operation and Automation (IFF), Magdeburg, Germany
Evelin Willner
Affiliation:
Genebank Department, Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Seeland, Germany
Andreas Börner
Affiliation:
Genebank Department, Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Seeland, Germany
*
Author for correspondence: Manuela Nagel, Email: Nagel@ipk-gatersleben.de
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Abstract

Seed longevity is influenced by many factors, a widely discussed one of which is the seed lipid content and fatty acid composition. Here, linear and non-linear regressions based on machine learning were applied to analyse germinability and seed composition of a set of 42 oilseed rape (Brassica napus L.) accessions grown under the same single environment and at the same time following a period of up to 31 years storage at 7°C. Mean viability was halved after 27.0 years of storage, but this figure concealed a major influence of genotype. There was also wide variation with respect to fatty acid composition, particularly with respect to oleic, α-linolenic, eicosenoic and erucic acid. Linear regression (rL) revealed significant correlation coefficients between normal seedling appearance and the content of α-linolenic acid (+0.52) and total oil (+0.59). Multivariate regression using artificial neural networks including a radial basis function (RBF), a multilayer perceptron (MLP) and a partial least square (PLS) recognized underlying structures and revealed high significant correlation coefficients (rM) for oil content (+0.87), eicosenoic acid (+0.75), stearic acid (+0.73) and lignoceric acid (+0.97). Oil content or a combination of oleic, α-linolenic, arachidic, eicosenoic and eicosadienoic acids and glucosinolates resulted in highest model fitting parameters R2 of 0.90 and 0.88, respectively. In addition, the glucosinolate content, predominantly in the Brassicaceae family and ranging from 4.6 to 79.5 µM, was negatively correlated with viability (rL = ‒0.43). Summarizing, oil content, some fatty acids and glucosinolates contribute to variations in average half-life (15.2 to 50.7 years) of oilseed rape seeds. In contrast to linear regression, multivariate regression using artificial neural networks revealed high associations for combinations of parameters including underestimated minor fatty acids such as arachidic, stearic and eicosadienoic acids. This indicates that genetic and seed composition factors contribute to seed longevity. In addition, multivariate regressions might be a successful approach to predict seed viability based on fatty acids and seed oil content.

Keywords

artificial neural networksBrassica napusfatty acidsgene bankgerminationmultivariate regressionseed conservationseed longevityseed viability
Type
Research Paper
Information
Seed Science Research , Volume 28 , Issue 4 , December 2018 , pp. 340 - 348
Copyright
Copyright © Cambridge University Press 2018 

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