ArticleHeliyon2023
Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- The Antioxidant and Antibacterial Properties of Honeycomb-Structured Polylactic Acid/Astaxanthin@ZIF-8 Bio-Composite Film Applied in Beef Preservation.Biomolecules · 2026Article
- Vision-Based Quality Grading of Beef Steaks Using Marbling Distribution Analysis and Lean Meat Color Classification.Sensors (Basel, Switzerland) · 2026Article
- Bi-layer PVA/sodium alginate active films reinforced with clays and functionalized withFrontiers in bioengineering and biotechnology · 2026Article
- Effect of Linseed Feeding on Carcass and Meat Quality and Intramuscular Fatty Acid Profile of Simmental Bulls Slaughtered at Different Ages.Foods (Basel, Switzerland) · 2025Article
- Antioxidant and Antimicrobial Activity of Ferulic Acid Added to Dried Meat: Shelf-Life Evaluation.Foods (Basel, Switzerland) · 2025Article
- Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies.Foods (Basel, Switzerland) · 2024Review
- Images dataset of beef meat samples with different shelf life.Data in brief · 2023Article
- Molecular mechanisms underlying the impact of muscle fiber types on meat quality in livestock and poultry.Frontiers in veterinary science · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The quality of beef products relies on the presence of a cherry red color, as any deviation toward brownish tones indicates a loss in quality. Existing studies typically analyze individual color channels separately, establishing acceptable ranges. In contrast, our proposed approach involves conducting a multivariate analysis of beef color changes using white-box machine learning techniques. Our proposal encompasses three phases. (1) We employed a Computer Vision System (CVS) to capture the color of beef pieces, implementing a color correction pre-processing step within a specially designed cabin. (2) We examined the differences among three color spaces (RGB, HSV, and CIELab*) (3) We evaluated the performance of three white-box classifiers (decision tree, logistic regression, and multivariate normal distributions) for predicting color in both fresh and non-fresh beef. These models demonstrated high accuracy and enabled a comprehensive understanding of the prediction process. Our results affirm that conducting a multivariate analysis yields superior beef color prediction outcomes compared to the conventional practice of analyzing each channel independently.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.