Surface color distribution analysis by computer vision compared to sensory testing: Vacuum fried fruits as a case study

Fitriyono Ayustaningwarno, Vincenzo Fogliano, Ruud Verkerk, Matthijs Dekker*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)


Color is a main factor in the perception of food product quality. Food surfaces are often not homogenous at micro-, meso-, and macroscopic scales. This matrix can include a variety of colors that are subject to changes during food processing. These different colors can be analyzed to provides more information than the average color. The objective of this study was to compare color analysis techniques on their ability to differentiate samples, quantify heterogeneity, and flexibility. The included techniques are sensory testing, Hunterlab colorimeter, a commercial CVS (IRIS-Alphasoft), and the custom made CVS (Canon-CVS) in analyzing nine different vacuum fried fruits. Sensory testing was a straightforward method and able to describe color heterogeneity. However, the subjectivity of the panelist is a limitation. Hunterlab was easy and accurate to measure homogeneous samples with high differentiation, without the color distribution information. IRIS-Alphasoft was quick and easy for color distribution analysis, however the closed system is the limit. The Canon-CVS protocol was able to assess the color heterogeneity, able to discriminate samples and flexible. As a take home massage, objective color distribution analysis has a potential to unlock the limitation of traditional color analysis by providing more detailed color distribution information which is important with respect to overall product quality.

Original languageEnglish
Article number110230
JournalFood Research International
Early online date26 Feb 2021
Publication statusPublished - May 2021


  • Color distribution
  • Computer vision system
  • Fruit
  • Vacuum fried


Dive into the research topics of 'Surface color distribution analysis by computer vision compared to sensory testing: Vacuum fried fruits as a case study'. Together they form a unique fingerprint.

Cite this