Image analysis-based quantification of the visual attributes of fish, with emphasis on color and visual texture


GÜMÜŞ B., GÜMÜŞ E., Odabasi-Kirli A., Balaban M. O.

INTERNATIONAL JOURNAL OF FOOD ENGINEERING, 2022 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1515/ijfe-2022-0014
  • Journal Name: INTERNATIONAL JOURNAL OF FOOD ENGINEERING
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, CAB Abstracts, Compendex, Food Science & Technology Abstracts, INSPEC, Veterinary Science Database
  • Keywords: color, goatfish, machine vision, Parupeneus forsskali, sensory analysis, visual texture

Abstract

Images of Red Sea goatfish Parupeneus forsskali were taken in a light box to perform color and visual texture analyses. The average L* and b* values did not change significantly during storage of 7 days, but the a* values decreased (P < 0.05). The change of visual texture parameters energy and entropy (calculated based on histograms, and based on co-occurrence matrices [COM]), box counting-based fractal results, and texture change index (TCI) values are presented. The appearance of fish became "smoother" over time. The entropy values calculated by histograms decreased with storage (P 0.05), and the maximum range was 0.395. That for COM-based entropies was 71.96. TCI also decreased with storage (P 0.05) with a maximum range of 10.67. However, energy values increased during storage. The maximum range of the energy values calculated by histograms over time for any color channel was 0.0036. That for COM-based energies was 5.7. There was no observable change in fractal dimension. These image analysis-based parameters were compared with sensory analysis. A trained sensory panel evaluated the visual texture of a sub-set of images. The R-2 values for equation fit between sensory score and texture features were, in increasing order: COM based energy (0.185), COM-based entropy (0.313), histogram-based energy (0.375), histogram-based entropy (0.386), TCI values (0.575). Since TCI correlated better with sensory values, it is recommended to be used in this type of visual texture evaluation.