Quantifying nonhomogeneous colors in agricultural materials part I: method development

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.