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Machine vision (MV) offers superior food color measurement compared to subjective tests or color meters. The developed "color change index" (CCI) effectively quantifies color non-uniformity in agricultural materials.

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Area of Science:

  • Agricultural Science
  • Food Science
  • Computer Vision
  • Colorimetry

Background:

  • Consumer perception of food quality is significantly influenced by color.
  • Traditional methods like subjective testing and color meters have limitations in assessing color variations.
  • Machine vision (MV) presents an objective and detailed approach to food color analysis.

Purpose of the Study:

  • To introduce and develop the "color change index" (CCI) for quantifying color non-uniformity in food and agricultural materials.
  • To compare the effectiveness of MV-based methods, including CCI, with traditional color measurement techniques.
  • To provide a quantitative measure for assessing color variations that impact consumer perception.

Main Methods:

  • Utilizing machine vision (MV) to capture images of food and agricultural materials.
  • Applying image analysis techniques such as color blocks, contours, and the newly developed "color change index" (CCI).
  • Quantifying color non-uniformity based on the desired level of detail.

Main Results:

  • MV can accurately measure average color values for uniform materials, comparable to color meters.
  • The color blocks method effectively quantifies non-homogeneity for images with a wide hue range.
  • The "color change index" (CCI) proves to be a superior indicator of color non-homogeneity for images with a narrow hue range.

Conclusions:

  • Machine vision offers advanced capabilities for detailed food and agricultural material color analysis.
  • The "color change index" (CCI) provides a valuable tool for quantifying color variations, especially in materials with narrow hue ranges.
  • Objective color measurement using MV and indices like CCI can better align with consumer perception of food quality.