Enzymatic Browning in Fresh-Cut Apple Slices Measured by Different Kinds of Image Algorithms

Lunadei, Loredana and Diezma Iglesias, Belen and Lleó García, Lourdes and Galleguillos, Pamela (2011). Enzymatic Browning in Fresh-Cut Apple Slices Measured by Different Kinds of Image Algorithms. In: "VI Congreso Ibérico de AgroIngeniería", 5-7 septiembre 2011, Évora. ISBN 978‐972‐778‐113‐3.

Description

Title: Enzymatic Browning in Fresh-Cut Apple Slices Measured by Different Kinds of Image Algorithms
Author/s:
  • Lunadei, Loredana
  • Diezma Iglesias, Belen
  • Lleó García, Lourdes
  • Galleguillos, Pamela
Item Type: Presentation at Congress or Conference (Poster)
Event Title: VI Congreso Ibérico de AgroIngeniería
Event Dates: 5-7 septiembre 2011
Event Location: Évora
Title of Book: VI Congreso Ibérico de AgroIngeniería - Comunicaciones finales
Date: 2011
ISBN: 978‐972‐778‐113‐3
Subjects:
Freetext Keywords: Fresh-cut apples; enzymatic browning; RGB image, IRRB image; multispectral images (Manzanas recién cortadas; pardeamiento enzimático; imagen RGB; imagen IRRB; imagen multiespectral).
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Rural [hasta 2014]
UPM's Research Group: LPF-TAGRALIA
Creative Commons Licenses: None

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Abstract

Resumen El objetivo final es el desarrollo de un sistema di visión multiespectral que permita asignar manzanas cortadas a clases de distinto nivel de pardeamiento. Se ha analizado un total de 240 imágenes IRRB y RGB, correspondientes a 240 gajos de manzanas de la variedad ‘Granny Smith’ (120 gajos = Set 1; 120 gajos = Set 2). Se analizaron 24 gajos por día: a tiempo cero y después de 1, 3, 7 y 9 días de almacenamiento a 7,5ºC. A las imágenes virtuales obtenidas como combinación del canal rojo y azul (B/R, R-B y (R-B)/(R+B)) se aplicó un procedimiento de clasificación no supervisada que, en todo los casos, generó tres clases de referencia. A la segunda serie de muestras (Set 2), sometidas los mismos tratamientos, se aplicó una validación externa, obteniendo un alto porcentaje de muestras correctamente clasificadas. La clasificación de las cámaras IRRB y RGB se evaluó de acuerdo a parámetros colorimétricos y sensoriales y las imágenes virtuales (R-B)/(R+B) y B/R mostraron la mejor sensibilidad para reflejar el cambio de color asociado con el pardeamiento. Abstract The main objective of this study was to develop a vision system able to classify fresh-cut apple slices according to the development of enzymatic browning. The experiment was carried out on ‘Granny Smith’ apple slices stored at 7.5°C (Set 1 = 120 n). Twenty-four samples were analyzed per day: at zero time and after storage for 1, 3, 7 and 9 days. Digital images were acquired by employing an IRRB camera and by employing a cheaper vision system, consisting in a RGB digital camera. A classification procedure was applied to the histograms of the following virtual images, acquired by the IRRB and by the RGB camera: (R-B)/(R+B), R–B and B/R. In all cases, a non-supervised classification procedure was able to generate three image-based browning reference classes. An internal and an external validation (Set 2 = 120 n) were carried out, with a high percentage of corrected classified samples. The camera classification was evaluated according to reference parameters (colorimetric and sensorial measurements) and the best results were obtained with the (R-B)/(R+B) and B/R virtual images.

More information

Item ID: 9591
DC Identifier: http://oa.upm.es/9591/
OAI Identifier: oai:oa.upm.es:9591
Deposited by: Investigador contratado Loredana Lunadei
Deposited on: 03 Nov 2011 14:45
Last Modified: 20 Apr 2016 17:57
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