Leakage-Free Benchmarking of Electronic Noses for Beef Freshness: A Signal-Richness Criterion for Model Selection
Abstract
Low-cost metal-oxide-semiconductor (MOS) electronic noses promise rapid, non-destructive meat freshness screening, and published classifiers frequently approach perfect accuracy. Such figures are rarely tested against the two conditions that most inflate them: a target-derived label among the inputs, and random splitting of the correlated samples. Beef freshness is benchmarked here on a public 11-sensor, 12-cut MOS dataset using leakage-free leave-one-cut-out cross-validation in order to predict freshness class and total viable count (TVC) with paired significance tests. A gradient-boosted-tree pipeline is the strongest model (accuracy 0.81±0.10, macro-F1 0.68±0.15, TVC R2=0.77), significantly outperforming a multi-scale attention convolutional network (macro-F1 0.50±0.15; p
Fields of Study & Subjects
Advanced Chemical Sensor Technologies
Gas Sensing Nanomaterials and Sensors
Food Supply Chain Traceability
Publication details
- Journal / source
- Foods
- Publisher
- MDPI AG
- Volume
- 15
- Issue
- 16
- Pages
- 2798–2798
- Publication date
- 10 August 2026
- Language
- EN