Data underpinning "Biochemical Profile of Heritage and Modern Apple Cultivars and Application of Machine Learning Methods to Predict Usage, Age, and Harvest Season"
Date
2017-06-05 09:32
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Supervisor/s
Journal Title
Journal ISSN
Volume Title
Publisher
Cranfield University
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Type
Dataset
ISSN
Format
Citation
Anastasiadi, Maria; Terry, Leon; Redfern, Sally; Mohareb, Fady; Berry, Mark (2017). Data underpinning "Biochemical Profile of Heritage and Modern Apple Cultivars and Application of Machine Learning Methods to Predict Usage, Age, and Harvest Season". Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.5002442
Abstract
This dataset contains the quantitative data used for statistical analysis and predictive modelling in the paper entitled "Biochemical Profile of Heritage and Modern Apple Cultivars and Application of Machine Learning Methods to Predict Usage, Age, and Harvest Season". Specifically it contains concentration of phenolic compounds per Fresh weight in the whole apples as well as sugars and organic acids. In addition the phenolic content of individual tissues (peel, flesh, seeds) is uploaded.
Description
Software Description
Software Language
Github
Keywords
Malus', 'phenolic compounds', 'sugars', 'organic acids', 'amygdalin', 'predictive modelling', 'machine learning', 'Bioinformatics', 'Biochemistry', 'Analytical Chemistry not elsewhere classified'
DOI
10.17862/cranfield.rd.5002442
Rights
CC BY 4.0
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Relationships
Supplements
Funder/s
TSB 101125 (Innovate UK), Unilever U.K. Central Resources Ltd, and the Biotechnology and Biological Sciences Research Council (BBSRC)