Classifying Salt Concentrations from Acoustic Cavity Spectra via Group-Aware Supervised Learning and Interpretable Feature-Band Selection

We present and validate an audible-band, resonant-cavity acoustic system that operates both as a sensor and as an automatic classifier for the nondestructive analysis of aqueous solutions, enabling the discrimination of solute concentrations using small liquid volumes. Frequency spectra computed from impact recordings on a glass resonator were used to construct ten datasets spanning two salts (𝑀𝑔𝐶𝑙 2 and 𝐾𝐶𝑙), four concentrations (0.001, 0.01, 0.1, and 1 M), for a total of 1,918 normalized spectra represented by 511 features. We evaluate three supervised learning approaches coupled with feature selection to reduce dimensionality: Random Forest (RF), linear-kernel Support Vector Machine (SVM), and a hybrid Genetic Algorithm with Extreme Learning Machine (GA + ELM) as fitness function tailored to select frequency bands. Across all datasets, every method exceeds 99% accuracy on held-out validation sets; SVM attains the highest accuracy among the three strategies in the binary and 4-class settings, whereas GA + ELM yields the top scores in the 9-class setting, highlighting the value of band-oriented selection as spectral overlap increases. RF consistently ranks last both in accuracy and in the compactness of the selected frequency regions across the audible spectrum. For SVM and GA + ELM, the feature reduction surpasses 92%, evidencing a high signal-information ratio. Analysis of the selected bands reveals clear algorithmic profiles: RF tends to disperse selections; SVM concentrates on mid-to-high bands; and GA + ELM emphasizes a few contiguous intervals that are amenable to physical interpretation and to the design of bespoke sensing front-ends. These results indicate a practical route to rapid, low-cost, and interpretable acoustic characterization of aqueous solutions.