Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement

Area of Science:

• Food Science
• Colorimetry
• Digital Imaging

Background:

• Seafood flesh color is often non-homogenous, necessitating repeat measurements for accurate representation.
• Objective color measurement is crucial for quality assessment in seafood.

Purpose of the Study:

• To determine the optimal number of repeat color measurements for different devices.
• To compare machine vision, Nix Pro, and Minolta CR400 colorimeter performance across seafood species.

Main Methods:

• Evaluated color measurement accuracy using three devices: machine vision, Nix Pro, and Minolta CR400.
• Assessed raw and cooked samples of Atlantic salmon, rockling, and banana prawns.
• Employed two analytical methods to determine the required number of repeat measurements.

Main Results:

• Machine vision required an average of 4 (Method 1) or 1 (Method 2) repeat measurements.
• Nix Pro and Minolta colorimeters required an average of 12-13 (Method 1) or 9 (Method 2) repeat measurements.
• Machine vision demonstrated lower residual variance, indicating higher precision.

Conclusions:

• Machine vision offers a more efficient and precise method for measuring seafood color compared to traditional colorimeters.
• Fewer repeat measurements are needed with machine vision for accurate color assessment of salmon, prawns, and rockling.