FSA-ILR: Explicit Relative-Scale Structure for Dynamic Odor Recognition

Identifying food ingredients from the dynamic responses of an electronic nose can provide odor-based evidence for ingredient identity verification. This task requires jointly using the magnitude of change, relative configurations across scales, and temporal dynamics in multichannel responses. Existing time-series methods typically encode these types of information jointly, leaving the contribution of cross-scale ratios to class discrimination largely to implicit learning. We propose the Finite-Structure Additive ILR Network (FSA-ILR), which uses an amplitude–composition reparameterization and an additive class-score readout to explicitly structure how different types of dynamic information contribute to classification. Amplitude anchors and full isometric log-ratio (ILR) coordinates jointly preserve the positive scale energies of active channels. Comparisons with learned references for each class–channel pair, constrained by an activity mask, produce composition-matching scores that are added classwise to temporal and activity/amplitude scores. On the 50-class food ingredient identification task in SmellNet-Base, FSA-ILR outperforms the comparison methods in Acc@1, Acc@5, and macro-F1 under all four sensor-only input settings, with an Acc@1 gain of up to 3.96 percentage points over the strongest comparator in each setting. Representation probes and independently retrained controls further support the joint roles of complete ratio information, scale identity, and structured class-score readouts.