Sandra Munera
19 Papers
2 Citations
Sandra Munera is an academic researcher. The author has contributed to research in topics: Hyperspectral imaging & Ripeness. The author has an hindex of 8, co-authored 15 publications.
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Papers
Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging
TL;DR: In this article, the internal physicochemical properties and sensory perception of nectarine ripeness were assessed using visible-near-infrared (450-1040-nm) hyperspectral reflectance imaging.
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Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging
Sandra Munera,Cristina Besada,Nuria Aleixos,Pau Talens,Alejandra Salvador,Da-Wen Sun,Da-Wen Sun,Sergio Cubero,José Blasco +8 more
TL;DR: In this paper, the internal quality of intact persimmon cv. ‘Rojo Brillante’ was assessed trough visible and near infrared hyperspectral imaging, and the full spectrum was used to determine the internal properties and later principal component analysis (PCA) is used to select optimal wavelengths (580, 680 and 1050 nm).
Astringency assessment of persimmon by hyperspectral imaging
Sandra Munera,Cristina Besada,José Blasco,Sergio Cubero,Alejandra Salvador,Pau Talens,Nuria Aleixos +6 more
TL;DR: In this paper, partial least square (PLS) based methods were used to classify persimmon fruits by maturity and to predict flesh firmness from the average spectrum of each fruit, which showed a 97.9% rate of correct maturity classification and an R2P of 0.80 for firmness prediction with only five selected wavelengths.
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Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest
TL;DR: This chapter summarises the current state of the art in machine vision-based systems for inspecting fruit and vegetables, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.
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Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques
Sandra Munera,Juan Gómez-Sanchis,Nuria Aleixos,Joan Vila-Francés,Giancarlo Colelli,Sergio Cubero,Esteban Soler,José Blasco +7 more
TL;DR: In this article, two classifiers, random forest (RF) and extreme gradient boost (XGBoost), and different spectral pre-processing techniques were evaluated in terms of their capacity to distinguish between sound and defective features according to three approaches.
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