RamanNet Labeler: A PyQt5‐Based Raman Spectra Unmixing Tool via Physical Modeling for Generating Deep Learning Labels

ABSTRACT Raman spectroscopy is a powerful nondestructive analytical technique, yet its signals are frequently obscured by fluorescence background and noise, posing significant challenges for subsequent analysis—particularly in training deep learning models. The performance of deep learning models heavily relies on high‐quality training labels. However, conventional algorithm‐based label generation methods often fail to reflect the physical essence of Raman spectra, thereby limiting model performance. To address this limitation, we present RamanNet Labeler, a graphical user interface tool developed using PyQt5. The core innovation of this tool lies in its physics‐guided Voigt profile modeling for spectral unmixing, enabling the generation of high‐fidelity “ideal spectra” as training labels for deep learning. Integrated with adaptive preprocessing, interactive Voigt peak fitting, and a versatile synthetic data generator, RamanNet Labeler effectively extracts physically authentic components from complex raw spectra. Experimental results demonstrate its capability to batch‐generate standardized datasets for training denoising, peak extraction, and quantitative analysis models. This tool provides a critical solution for advancing reliable deep learning applications in Raman spectral analysis.