Integration of fuzzy logic and computer vision in intelligent quality control of celiac-friendly products

Fatemeh Rezagholi*, Mohammad Ali Hesarinejad

*Corresponding author for this work

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

Automation in food industry demands intelligent and feasible techniques to replace the human brain with machine intelligence. Quality control examines product attributes which cannot be quantified exactly and thus the relationship amongst the attributes parameters is unclear. The visual properties of the product can be more accurately and quickly examined by machine. Hence, in the present paper, a sensory evaluation was carried out on one of the main quality attributes as taste and was combined with two others as appearance and texture acquired by computer vision to determine the acceptable level of ingredients of a gluten-free cake (GFC). Analysis of samples using the aforementioned method indicated that acceptable levels of 50% purslane flour (PF) and 1% quince seed gum (QSG). Sensory evaluation indicated that the quality attributes can be ranked in a descending order as texture, taste and color. Employment of fuzzy logic and image processing was promising to indicate the optimum formulation of compounds as the top rank was found to be the third sample. (c) 2018 The Authors. Published by Elsevier B.V.
Original languageEnglish
JournalProcedia Computer Science
Volume120
Pages (from-to)325-332
Number of pages8
ISSN1877-0509
DOIs
Publication statusPublished - 2017
Event9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception - Budapest, Hungary
Duration: 24 Aug 201725 Aug 2017

Conference

Conference9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception
CountryHungary
CityBudapest
Period24/08/201725/08/2017

Keywords

  • Image analysis
  • Sensory evaluation
  • Fuzzy logic
  • Gluten-free cake

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