ArticleFood science & nutrition2024
Automated detection of stale beef from electronic nose data.
Article in Food science & nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Utilization of nanosensors and nanomaterials to revolutionize the food industry: boosting product safety, quality, and sustainability.RSC advances · 2026Review
- AI-Driven Nondestructive Measurement Technologies for Meat Quality and Safety: A Review.Foods (Basel, Switzerland) · 2026Review
- Hybrid Sensor Array Electronic Nose for Pork Quality Monitoring.Foods (Basel, Switzerland) · 2026Article
- Research on an Intelligent Grading Method for Beef Freshness in Complex Backgrounds Based on the DEVA-ConvNeXt Model.Foods (Basel, Switzerland) · 2025Article
- Automated detection of stale beef from electronic nose data.Food science & nutrition · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K-nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10-fold cross-validation technique. All these methods reached an accuracy of 95% or above, with
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Registered trials
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