Leakage-Free Benchmarking of Electronic Noses for Beef Freshness: A Signal-Richness Criterion for Model Selection
FOODS, cilt.15, sa.16, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 15 Sayı: 16
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/foods15162798
- Dergi Adı: FOODS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Food Science & Technology Abstracts, Directory of Open Access Journals, Natural Science Collection (ProQuest)
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Recep Tayyip Erdoğan Üniversitesi Adresli: Evet
Özet
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 R-2=0.77 ), significantly outperforming a multi-scale attention convolutional network (macro-F1 0.50 +/- 0.15 ; p<0.001 ). The advantage of this study lies in the representation, not the model family: a network given the same window summaries reaches 0.64 +/- 0.17 , indistinguishable from the tree. Near-perfect accuracy returns only when TVC is supplied as a feature or samples are split at random (macro-F1 0.97). Under nested, per-fold selection, a five-sensor subset matches the full array. On a rich BME688 heater profile dataset, the network surpasses the tree, an advantage that vanishes as the profile shortens to one step. Evaluation and representation, not architecture, govern reported performance; a signal-richness criterion predicts when a deep temporal model is justified.