A terahertz metamaterial-based approach for detecting trace 1-naphthaleneacetic acid residues via AOO-optimized support vector regression

Abstract

Excessive residues of 1-naphthaleneacetic acid (NAA) pose a potential threat to people's health. This research aims to develop a highly sensitive method for detecting NAA residues. By integrating terahertz metamaterial sensors with intelligent optimization algorithms, it is intended to achieve rapid and accurate detection of trace amounts of NAA. A terahertz metamaterial sensor based on a composite dual-peak structure with L-shaped resonant elements is introduced. Spectral data of different concentrations of NAA solutions were collected using terahertz time-domain spectroscopy (THz-TDS), and Animated Oat Optimization (AOO) was used to optimize the hyperparameters of the support vector regression (SVR) model, thereby constructing an AOO-SVR quantitative prediction model. Furthermore, by optimizing the spectral data dimension through five feature extraction algorithms, the model performance was further enhanced. The results showed that the RFE-AOO-SVR model performed the best in terms of prediction accuracy, with a correlation coefficient (R) of 0.9715 and a limit of detection (LOD) of 0.036 µg mL⁻¹. This study verified the effectiveness of terahertz metamaterial sensors combined with intelligent optimization algorithms in the detection of NAA residues, providing a new method for the rapid quantitative analysis of pesticide residues.