Optical-acoustic resonance fingerprinting of food-matrix perturbations in a phoxonic defect cavity.

This study presents a compact defect-mode phoxonic crystal framework for the dual optical-acoustic characterization of contamination-induced changes in food media. The proposed sensing platform consists of a food-sensitive defect cavity positioned between two Bragg mirrors, where perturbations in the effective optical and acoustic properties of the defect medium generate complementary resonance responses. The optical response was evaluated using the transfer matrix method, whereas the acoustic response was described through an effective defect-mode resonance model. Contamination was represented by a normalized perturbation parameter that modifies the refractive index, optical loss, density, and sound velocity of the investigated food media. Increasing contamination produced a systematic optical resonance redshift and acoustic resonance downshift, confirming that the two channels probe distinct but related physical properties. The optical sensitivity varied from 4.4025 to 7.6953 nm/fraction, while the acoustic sensitivity ranged from 77.901 to 105.314 kHz/fraction, with highly linear behavior throughout the studied range. Under the assumed readout noise floors, the estimated limits of detection were 0.00390-0.03716 fraction for the optical channel and 0.01558-0.01991 fraction for the acoustic channel. Optical-acoustic fingerprinting and a relative phoxonic contamination index were further introduced to compare the dual-channel responses of different food samples. The combined response increased the mean normalized endpoint separation to 0.761, representing a 46.7% improvement over the best single-channel result. Additional analyses of Q-factor degradation, representative field localization, thickness tolerance, surface roughness, phase response, and group delay were included to clarify the sensing mechanism and evaluate structural robustness. The results indicate that defect-mode phoxonic crystals can provide a useful numerical platform for contamination-sensitive food characterization. Nevertheless, the present work remains an effective numerical proof-of-concept, and experimental implementation, calibration, and validation using real contaminated food samples are required before direct biochemical or microbiological detection can be established.