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Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

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Smartphone-Based Sensing System for Identifying Artificially Marbled Beef Using Texture and Color Analysis to Enhance

Hong-Dar Lin¹, Yi-Ting Hsieh¹, Chou-Hsien Lin²

¹Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.

Sensors (Basel, Switzerland)

|July 30, 2025

Summary

This summary is machine-generated.

A new smart sensing system uses mobile devices to detect artificially marbled beef, protecting consumers from mislabeled products and potential health risks. This technology ensures food safety and transparency in beef purchases.

Keywords:

artificially marbled beef detection color models food safety human-centered healthcare local binary patterns smart sensing technology support vector machine texture analysis

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Area of Science:

Food Science and Technology
Consumer Health
Artificial Intelligence in Food Safety

Background:

Beef fat injection technology creates artificial marbling, mimicking premium Wagyu beef.
Mislabeled products pose health risks due to undeclared additives and allergens.
High costs of Wagyu drive the market for cheaper, artificially enhanced alternatives.

Purpose of the Study:

To develop a consumer-accessible smart sensing system for real-time assessment of beef marbling.
To differentiate between genuine Wagyu, regular, and artificially marbled beef.
To enhance food safety and consumer trust through transparent product information.

Main Methods:

A mobile device-integrated smart sensing system captures beef images.
Image processing involves region of interest masking, grid partitioning, and feature extraction (Local Binary Pattern and RGB color).
A Support Vector Machine (SVM) model classifies beef types, with final classification via majority voting.

Main Results:

The system achieved a 95.00% recall rate for fat-injected beef.
A low misjudgment rate of 1.67% for non-fat-injected beef was recorded.
High overall performance metrics include a 93.89% correct classification rate and a 95.80% F1-score.

Conclusions:

The smart sensing system effectively identifies artificially marbled beef.
This technology serves as a valuable human-centered healthcare tool for ensuring food safety.
The system promotes informed consumer choices and transparency in the beef market.