Non-destructive methods for mango ripening prediction: Visible and near[1]infrared spectroscopy (visNIRS) and laser Doppler vibrometry (LDV): Data
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O'Brien, Ciara
Falagan Sama, Natalia
Landahl, Sandra
Kourmpetli, Sofia
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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.
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Github
Keywords
Mangifera indica', 'Firmness', 'Food loss', 'chemometrics methods', 'resonant frequency', 'postharvest'
DOI
10.17862/cranfield.rd.25381456
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CC BY 4.0
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This work was funded by Orchard House Foods Ltd. and Cranfield University through the Cranfield Industrial Partnership PhD Scheme.