## 1 Introduction  
As a typical optical detection technique, Raman spectroscopy has been widely applied across numerous fields, including bioanalysis, disease diagnosis, and molecular recognition [1, 2, 3, 4]. However, the acquisition of Raman spectral data is often subject to various typesof noise interference, such as CCD detector shot noise and dark current noise within the instrument, as well as the influence of the fluorescence background from the material. Especially under the stringent requirements for the on-site detection of special samples, such as a short exposure time and low power excitation lasers, the characteristic information carried in the Raman spectra can even be overwhelmed by the abundant noise. The weak Raman spectral signals usually become difficult to discern, thereby affecting the localization and identification of characteristic spectral peaks [5, 6]. Researchers have made abundant efforts and attempts in recent years to minimize the influence of noise, baseline drift, and other interfering factors on the analysis of Raman spectral data during subsequent data processing [7]. Existing Raman spectral denoising methods include fast Fourier transform, wavelet transform, and polynomial fitting, which can effectively eliminate noise in Raman spectra under specific conditions. However, there exist issues related to the empirical selection of filtering parameters, where different cut-off frequencies, filter window lengths, basis functions, thresholds, decomposition levels, and fitting orders can greatly influence the denoising effects [8, 9, 10]. [PERSON] et al. [11] employed the Savitzky-Golay convolutional smoothing (SG smoothing) method for further optimization processing of citrus leaf chlorophyll content Raman spectra after background interference subtraction, providing a simple analytical approach for quantitative analysis of citrus leaf chlorophyll content. However, it can cause issues of over smoothing the signal peaks. To address the current challenge of unsatisfactory denoising effects on confocal Raman microscopic images, [PERSON] et al. [12] proposed an omnidirectional denoising algorithm for Raman microscopic images based on wavelet analysis. It first performs wavelet transform on the Raman spectral images, followed by bilateral filtering of the Raman images, thereby suppressing noise interference in the Raman spectral microscopic images, and effectively improving the signal-to-noise ratio and accuracy of compositional analysis. However, when scanning new samples, suitable parameters need to be empirically selected based on human experience to achieve ideal outcomes, lacking flexibility and adaptivity. [PERSON] et al. [13] decomposed Raman spectra using empirical mode decomposition and realized adaptive denoising of Raman spectra through low-pass thresholding. However, for biological Raman spectra, the EMD method suffers from mode mixing issues, while low-pass thresholding fails to differentiate between high-amplitude noise and high-frequency Raman signals. [PERSON], [PERSON] et al. [14] proposed the variational mode decomposition (VMD) algorithm, which can adaptively decompose a signal into a finite number of band-limited intrinsic mode functions with their own center frequencies. Compared to other methods, the VMD algorithm has more accurate and stable feature extraction capabilities for the low-frequency band of signals. Since the primary features of most signals are contained in the low frequencies, this filtering approach can perform denoising of noisy signals more effectively. [PERSON] et al. [15] proposed a cherry quality inspection model based on VMD-SVD-MSR, which utilizes VMD to decompose near-infrared reflectance spectra into multiple IMF components and realizes multimodal layer decomposition. The correlation coefficient between each IMF component signal and SSC, as well as the moisture content is calculated separately, with larger correlation coefficients indicating that the corresponding IMF layers are more suitable as feature signals.