Non-destructive methods for mango ripening prediction: Visible and near[1]infrared spectroscopy (visNIRS) and laser Doppler vibrometry (LDV): Data
Date
2024-03-12 09:10
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Journal Title
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Volume Title
Publisher
Cranfield University
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Dataset
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Format
Free to read from
Citation
Alamar Gavidia, Maria del carmen; O'Brien, Ciara; Falagan Sama, Natalia; Landahl, Sandra; Terry, Leon; Kourmpetli, Sofia (2024). Non-destructive methods for mango ripening prediction: Visible and near[1]infrared spectroscopy (visNIRS) and laser Doppler vibrometry (LDV): Data. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.25381456
Abstract
This data set includes reference measurements (firmness, colour [lightness, chroma and hue angle], total soluble solids [TSS], individual sugar concentrations [glucose, fructose, sucrose]), as well as visible and near-infrared spectroscopic (vis-NIRS) data (nm) and resonant frequency measured by laser Doppler vibroemetry (LDV) on 'Keitt' and 'Kent' mango fruit.
Description
Software Description
Software Language
Github
Keywords
Mangifera indica', 'Firmness', 'Food loss', 'chemometrics methods', 'resonant frequency', 'postharvest'
DOI
10.17862/cranfield.rd.25381456
Rights
CC BY 4.0
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Relationships
Supplements
Funder/s
This work was funded by Orchard House Foods Ltd. and Cranfield University through the Cranfield Industrial Partnership PhD Scheme.