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Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.

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ColorNet: An AI-based framework for pork freshness detection using a colorimetric sensor array.

Guangzhi Wang¹, Yuchen Guo¹, Yang Yu²

¹School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Institute of advanced sensor technology, Northeast Electric Power University, Jilin 132012, China.

|January 10, 2025

Summary

This summary is machine-generated.

A new colorimetric sensor array (CSA) and ColorNet framework accurately detect pork freshness. This technology offers a cost-effective solution for real-time quality monitoring, ensuring food safety and consumer health.

Keywords:

Colorimetric sensor array Gradient activation mapping Neural network Pork freshness detection

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Area of Science:

Food Science
Analytical Chemistry
Sensor Technology

Background:

Pork freshness is vital for consumer health, flavor, and nutrition.
Existing colorimetric sensor array (CSA) systems for pork freshness detection suffer from high costs, low accuracy, and platform limitations.

Purpose of the Study:

To develop an efficient and accurate CSA and deep learning framework (ColorNet) for detecting pork freshness.
To create a cost-effective and reliable method for real-time pork quality assessment.

Main Methods:

A 53-point CSA was designed using pH and aldehyde/ketone indicators.
The Euclidean distance method optimized the sensor to 24 key array points.
A deep learning model, ColorNet, was employed to analyze color changes for freshness detection.

Main Results:

The ColorNet framework achieved 99.5% accuracy in real-time pork freshness detection.
A simplified 12-point CSA, optimized using gradient activation mapping, maintained 99% accuracy for practical applications.

Conclusions:

The developed CSA and ColorNet framework provide a highly accurate and efficient solution for monitoring pork freshness.
This technology offers significant potential for enhancing food safety, quality control, and consumer confidence in the pork industry.