Munera, S.; Amigo, JM.; Blasco Ivars, J.; Cubero, S.; Talens Oliag, P.; Aleixos Borrás, MN. (2017). Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging. Journal of Food Engineering. 214:29-39. https://doi.org/10.1016/j.jfoodeng.2017.06.031
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/156115
Title:
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Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging
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Author:
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Munera, S.
Amigo, Jose Manuel
BLASCO IVARS, JOSE
Cubero, Sergio
Talens Oliag, Pau
Aleixos Borrás, María Nuria
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UPV Unit:
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Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica
Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments
Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària
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Issued date:
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Abstract:
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[EN] Visible near-infrared (450-1040 nm) hyperspectral reflectance imaging was studied in order to assess the internal physicochemical properties and sensory perception of 'Big Top' and 'Magique' nectarines (Prunus persica ...[+]
[EN] Visible near-infrared (450-1040 nm) hyperspectral reflectance imaging was studied in order to assess the internal physicochemical properties and sensory perception of 'Big Top' and 'Magique' nectarines (Prunus persica L Batsch var. nucipersica) (yellow and white-flesh cultivar, respectively) during ripening using the Ripening Index (RPI) and the Internal Quality Index (IQI). Hyperspectral images of the intact fruits were acquired during the ripeness under controlled conditions, and their physicochemical properties (flesh firmness, total soluble solids, titratable acidity and flesh colour) were analysed. IQI and RPI were used to relate the spectral information obtained from nectarines with the physicochemical properties and the sensory perception of their maturity using Partial Least Square (PLS) regression with proper variable selection. Optimal results were obtained with R-2 values higher than 0.87 for the two indices and the two cultivars. The ripeness of each fruit could be visualised by projecting the PLS models of the IQI on the pixels of the fruits in the images, showing great potential for further monitoring of the evolution of intact nectarine ripeness in industrial setups. (C) 2017 Elsevier Ltd. All rights reserved.
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Subjects:
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Stone fruit
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Internal quality
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Ripeness
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Monitoring
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Hyperspectral image
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Computer vision
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Copyrigths:
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Cerrado |
Source:
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Journal of Food Engineering. (issn:
0260-8774
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DOI:
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10.1016/j.jfoodeng.2017.06.031
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Publisher:
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Elsevier
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Publisher version:
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https://doi.org/10.1016/j.jfoodeng.2017.06.031
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Project ID:
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INIA/RTA2015-00078-00-00
INIA/43 (CPR2014-0082)
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Thanks:
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This work has been partially funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) through research project RTA2015-00078-00-00 with the support of European FEDER funds. ...[+]
This work has been partially funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) through research project RTA2015-00078-00-00 with the support of European FEDER funds. Sandra Munera thanks INIA for the grant FPI-INIA num. 43 (CPR2014-0082), partially supported by European Union FSE funds.
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Type:
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Artículo
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