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Colour and image texture analysis in classification of commercial potato chips

Mendoza, Fernando; Dejmek, Petr LU and Aguilera, Jose M. (2007) In Food Research International 40(9). p.1146-1154
Abstract
The images of commercial potato chips were evaluated for various colour and textural features to characterize and classify the appearance and to model the quality preferences of a group of consumers. Features derived from the image texture contained better information than colour features to discriminate both the quality categories of chips and consumers' preferences. Entropy of a* and V and energy of b* from imaees of the total chip surface, average and variance of H and correlation of V from the images of spots and/or defects (if they are present). and average of L* from clean images (chips free of spots and/or defects) showed the best correspondence with the four proposed appearance quality groups (A: 'pale chips', B: 'slightly dark... (More)
The images of commercial potato chips were evaluated for various colour and textural features to characterize and classify the appearance and to model the quality preferences of a group of consumers. Features derived from the image texture contained better information than colour features to discriminate both the quality categories of chips and consumers' preferences. Entropy of a* and V and energy of b* from imaees of the total chip surface, average and variance of H and correlation of V from the images of spots and/or defects (if they are present). and average of L* from clean images (chips free of spots and/or defects) showed the best correspondence with the four proposed appearance quality groups (A: 'pale chips', B: 'slightly dark chips, C: 'chips with brown spots', and D: 'chips with natural defects'), giving classification rates of 95.8% for training data and 90% for validation data when linear discriminant analysis (LDA) was used as a selection criterion. The inclusion of independent colour and textural features from images of brown spots and/or defects and their clean regions of chips improved the resolution of the classification model and in particular to predict 'chips with natural defects'. Consumers' preferences showed that in spite of the 'moderate' agreement among raters (Kappa-value = 0.51), textural features have potential to model consumer behaviour in the respect of visual preferences of potato chips. A stepwise logistic regression model was able to explain 86.2% of the preferences variability when classified into acceptable and non-acccptable chips. (c) 2007 Elsevier Ltd. All rights reserved. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
preference, classification, image texture, colour, potato chips, quality
in
Food Research International
volume
40
issue
9
pages
1146 - 1154
publisher
Elsevier
external identifiers
  • wos:000250195400005
  • scopus:34548601798
ISSN
0963-9969
DOI
10.1016/j.foodres.2007.06.014
language
English
LU publication?
yes
id
94d3c39d-f939-443e-b57f-1025ca8ca7d4 (old id 655291)
date added to LUP
2007-12-15 15:06:13
date last changed
2017-07-30 03:48:50
@article{94d3c39d-f939-443e-b57f-1025ca8ca7d4,
  abstract     = {The images of commercial potato chips were evaluated for various colour and textural features to characterize and classify the appearance and to model the quality preferences of a group of consumers. Features derived from the image texture contained better information than colour features to discriminate both the quality categories of chips and consumers' preferences. Entropy of a* and V and energy of b* from imaees of the total chip surface, average and variance of H and correlation of V from the images of spots and/or defects (if they are present). and average of L* from clean images (chips free of spots and/or defects) showed the best correspondence with the four proposed appearance quality groups (A: 'pale chips', B: 'slightly dark chips, C: 'chips with brown spots', and D: 'chips with natural defects'), giving classification rates of 95.8% for training data and 90% for validation data when linear discriminant analysis (LDA) was used as a selection criterion. The inclusion of independent colour and textural features from images of brown spots and/or defects and their clean regions of chips improved the resolution of the classification model and in particular to predict 'chips with natural defects'. Consumers' preferences showed that in spite of the 'moderate' agreement among raters (Kappa-value = 0.51), textural features have potential to model consumer behaviour in the respect of visual preferences of potato chips. A stepwise logistic regression model was able to explain 86.2% of the preferences variability when classified into acceptable and non-acccptable chips. (c) 2007 Elsevier Ltd. All rights reserved.},
  author       = {Mendoza, Fernando and Dejmek, Petr and Aguilera, Jose M.},
  issn         = {0963-9969},
  keyword      = {preference,classification,image texture,colour,potato chips,quality},
  language     = {eng},
  number       = {9},
  pages        = {1146--1154},
  publisher    = {Elsevier},
  series       = {Food Research International},
  title        = {Colour and image texture analysis in classification of commercial potato chips},
  url          = {http://dx.doi.org/10.1016/j.foodres.2007.06.014},
  volume       = {40},
  year         = {2007},
}