Monitoring of fresh-cut spinach leaves through multispectral vision and sensory evaluation

Lunadei, Loredana and Diezma Iglesias, Belen and Lleó García, Lourdes and Ruiz García, Luis and Cantalapiedra, Susana and Ruiz-Altisent, Margarita (2012). Monitoring of fresh-cut spinach leaves through multispectral vision and sensory evaluation. "Postharvest Biology and Technology", v. 63 (n. 1); pp. 74-84. ISSN 0925-5214. https://doi.org/10.1016/j.postharvbio.2011.08.004.

Description

Title: Monitoring of fresh-cut spinach leaves through multispectral vision and sensory evaluation
Author/s:
  • Lunadei, Loredana
  • Diezma Iglesias, Belen
  • Lleó García, Lourdes
  • Ruiz García, Luis
  • Cantalapiedra, Susana
  • Ruiz-Altisent, Margarita
Item Type: Article
Título de Revista/Publicación: Postharvest Biology and Technology
Date: January 2012
ISSN: 0925-5214
Volume: 63
Subjects:
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

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

Abstract

This paper reports the development of image processing methods for the detection of superficial changes related to quality deterioration in ready-to-use (RTU) leafy spinach during storage. The experiment was performed on spinach leaves stored at 4.5 °C for 21 days (Set 1) and at 10 °C for 9 days (Set 2). Regarding Set 1, 75 units were evaluated beginning at time zero and after 7, 14, and 21 days of storage (treatments t1.0, t1.1, t1.2, and t1.3, respectively). In the case of Set 2, 24 samples were measured at time zero and after 3, 6, and 9 days (treatments t2.0, t2.1, t2.2, and t2.3, respectively). Multispectral images were acquired using a 3-CCD camera centered at the infrared (IR), red (R), and blue (B) wavelengths. Opportune combinations of these bands were calculated using virtual images, and a non-supervised classification was performed. A large number of spinach leaves belonging to Set 2 showed injuries due to the effects of in-pack condensation; thus, an image algorithm was developed to separate these defective leaves before applying the classification. For Set 1, Set 2 and all the calculated virtual images, the classification procedure yielded two image-based deterioration reference classes (DRCs): Class A, including the majority of the samples belonging to t1.0 and t1.1 (Set 1) and to t2.0 and t2.1 (Set 2) treatments and Class B, which comprised mainly the samples belonging to t1.2 and t1.3 (Set 1) and to t2.2 and t2.3 (Set 2) treatments. An internal validation was performed, and the best classification was obtained with the virtual images based on R and B bands. For each sample, camera classification was evaluated according to reference measurements (visible (VIS) reflectance spectra and CIE L*a*b* coordinates); in all cases, VIS reflectance values corresponded well with storage days, and Classes A and B could be considered homogenous with regards to L* and a* values. Taken together, these results confirmed that a vision system based on R and B spectral ranges could constitute an easy and fast method to detect deteriorating RTU packed spinach leaves under different refrigeration conditions.

More information

Item ID: 10121
DC Identifier: http://oa.upm.es/10121/
OAI Identifier: oai:oa.upm.es:10121
DOI: 10.1016/j.postharvbio.2011.08.004
Official URL: http://www.sciencedirect.com/science/article/pii/S0925521411001967
Deposited by: Memoria Investigacion
Deposited on: 23 Jan 2012 12:31
Last Modified: 03 Nov 2016 17:20
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM