Resonance features integration of multiple terahertz metamaterials sensors for qualification and quantification of trace fluoroquinolone antibiotics

The residual fluoroquinolone antibiotics (FQs) in the environment and food has raised public concerns over their potential impact on human health. Terahertz metamaterial sensors (TMSs) have garnered significant attention due to their capability to enhance the interaction between terahertz waves and antibiotic molecules, enabling the detection of trace antibiotics. However, conventional quantitative and qualitative methods based on TMSs suffer from low accuracy and cumbersome processes, respectively. Herein, this work proposed a novel approach that reconstructed optimal terahertz response features of different TMSs with machine learning algorithms, which allowed for analysis of three similar trace FQs with enhanced accuracy.

The prepared three patterned TMSs exhibited different resonance responses, which varied with changes in FQs types and concentrations. The resonance peak features of the three TMSs were fused to construct the resonance peak feature matrix (W₀) and combined with the K-Nearest Neighbor (KNN) algorithm to build the W₀-KNN classification model. The interval feature matrix was constructed by optimizing and expanding the resonance peak feature width. The optimal resonance peak interval feature matrix (W_t) was combined with Gaussian process regression (GPR) algorithms with different kernel functions to build the W_t-GPR quantitative model. The results showed that W₀-KNN achieved 100 % classification accuracy for the three FQs. W_t-GPR exhibited high quantitative accuracy for all three FQs with the determination coefficient (R²) of 0.94-0.98, and root mean square error (RMSE) of 6.4085-10.6540. The results of W_t-GPR with different kernel functions had small fluctuations, demonstrating high stability in predictive performance.

Reconstructing features from multi-TMSs in combination with machine learning algorithms enables rapid, precise, and reliable qualitative and quantitative analysis of trace FQs. Our research introduces innovative concepts and methodologies to detect trace FQs using TMS-based sensors, paving the way for future applications of TMS in the biomolecular sensing and detection.