Precise Temperature Prediction for Small-Sample Fiber Optic Spectra Based on Deep Learning

Abstract

This study addresses the issues of strong data dependency and limited prediction accuracy in temperature prediction using optical fiber spectroscopy technology by proposing a small-sample optical fiber spectroscopy-based temperature prediction method leveraging deep learning. This method aims to achieve high-precision temperature prediction with limited data samples through the powerful feature extraction and generalization capabilities of deep learning models. To achieve this goal, we first designed a precise experimental protocol to collect optical fiber spectroscopy data covering a temperature range from 30°C to 130°C, resulting in 84 high-quality data samples under controlled temperature variations. In the data processing stage, advanced signal processing techniques were employed to remove noise and outliers, and the data were normalized to ensure reliability and consistency. Subsequently, various models from the deep learning domain were utilized to train and learn from the processed spectroscopy data. By optimizing the model structures and parameters, we successfully established a nonlinear mapping relationship between spectroscopy and temperature. Experimental results demonstrate that compared to traditional methods, the deep learning model exhibits higher prediction accuracy and stronger robustness in spectroscopic temperature prediction, particularly under small-sample conditions. This study not only provides a novel and effective approach for spectroscopic temperature prediction with limited samples but also expands the application scope of deep learning in spectral analysis. Furthermore, this method holds broad application prospects in various fields such as industrial production, environmental monitoring, and biomedicine, promising to offer more precise and efficient solutions for temperature monitoring and control in related areas.