A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices

Lunadei, Loredana and Galleguillos, Pamela and Diezma Iglesias, Belen and Lleó García, Lourdes and Ruiz García, Luis (2011). A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices. "Postharvest Biology and Technology", v. 60 (n. 3); pp. 225-234. ISSN 0925-5214. https://doi.org/10.1016/j.postharvbio.2011.02.001.

Description

Title: A multispectral vision system to evaluate enzymatic browning in fresh-cut apple slices
Author/s:
  • Lunadei, Loredana
  • Galleguillos, Pamela
  • Diezma Iglesias, Belen
  • Lleó García, Lourdes
  • Ruiz García, Luis
Item Type: Article
Título de Revista/Publicación: Postharvest Biology and Technology
Date: June 2011
ISSN: 0925-5214
Volume: 60
Subjects:
Freetext Keywords: Fresh-cut apples; Enzymatic browning; CIE L*a*b* color space; Multispectral images; Image analysis.
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Rural [hasta 2014]
UPM's Research Group: LPF-TAGRALIA
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The main objective of this study was to develop a vision system that is 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 for 9 days (n = 120). Twenty-four samples were analyzed per day: at zero time and after storage for 1, 3, 7 and 9 days, which corresponds to treatments t0, t1, t3, t7 and t9 respectively. Multispectral images were acquired from the samples by employing a 3-CCD camera centered at the infrared (IR, 800 nm), red (R, 680 nm) and blue (B, 450 nm) wavelengths. Apple slices were evaluated visually according to a visual color scale of 1–5 (where 1 corresponds to fresh samples without any browning and 5 to samples with severe discoloration), to obtain a sensory evaluation index (ISE) for each sample. Finally, for each sample and for each treatment, visible (VIS) relative reflectance spectra (360–740 nm) were obtained. In order to identify the most related wavelengths to enzymatic browning evolution, unsupervised pattern recognition analysis of VIS reflectance spectra was performed by principal components analysis (PCA) on the autoscaled data. Maximum loading values corresponding to the B and R areas were observed. Therefore, a classification procedure was applied to the relative histograms of the following monochromatic images (virtual images), which were computed pixel by pixel: (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 (BRC): Cluster A (corresponding to the t0 samples), Cluster B (t1 and t3 samples) and Cluster C (t7 and t9 samples). An internal and an external validation (n = 120) were carried out, and the best classifications were obtained with the (R − B)/(R + B) and B/R image histograms (internal validation: 99.2% of samples correctly classified for both virtual images; external validation: 84% with (R − B)/(R + B) and 81% with B/R). The camera classification was evaluated according to the colorimetric measurements, which were usually utilized to evaluate enzymatic browning development (CIE L*a*b* color parameters and browning index, BI) and according to ISE. For both validation phases a*, b*, BI and ISE increased while L* values decreased with image-based class number, thereby reflecting their browning state.

More information

Item ID: 6423
DC Identifier: http://oa.upm.es/6423/
OAI Identifier: oai:oa.upm.es:6423
DOI: 10.1016/j.postharvbio.2011.02.001
Official URL: http://www.sciencedirect.com/science/journal/09255214
Deposited by: Memoria Investigacion
Deposited on: 18 Mar 2011 11:16
Last Modified: 20 Apr 2016 15:43
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