Most spectral data, such as those obtained via infrared, Raman, and mass spectroscopy, have baseline drifts due to fluorescence or other reasons, which have an adverse impact on subsequent analyses. Therefore, several researchers have proposed the use of various baseline-correction methods to address the aforementioned issue. However, most baseline-correction methods require manual adjustment of the parameters to achieve desirable performance. In this study, we propose a baseline-correction method based on a deep-learning model that combines ResNet and UNet. The method uses a deep-learning model trained with simulated spectral data to perform baseline corrections and eliminates the need for manual parameter adjustments. Based on the results of the qualitative and quantitative analyses of the simulated spectral data and actual Raman spectra, the proposed method is easier to apply and has better performance compared to the existing methods. As the proposed method can be applied to Raman spectra and other spectra, it is expected to be widely used.

Raman spectroscopy is widely used for its exceptional identification capabilities in various fields. Traditional methods for target identification using Raman spectroscopy rely on signal correlation with moving windows, requiring data preprocessing that can significantly impact identification performance. In recent years, deep-learning approaches have been proposed to leverage data augmentation techniques, such as baseline and additive noise addition, in order to overcome data scarcity. However, these deep-learning methods are limited to the spectra encountered during training and struggle to handle unseen spectra. To address these limitations, we propose a multi-input hybrid deep-learning model trained with simulated spectral data. By employing simulated spectra, our method tackles the challenges of data scarcity and the handling of unseen spectra encountered in traditional and deep-learning methods. Experimental results demonstrate that our proposed method achieves outstanding identification performance and effectively handles spectra obtained from different Raman spectroscopy systems.

We have proposed a novel deep learning architecture, the Triangular Deep Convolutional Network (TDCN), for Raman spectral baseline correction. TDCN enhances spectral pre-processing, thereby improving the reliability of downstream analyses.

Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substantially reduces computational demands compared to existing methods. The proposed method employs principal component transformation ($PCT$) as its foundational framework, similar to existing techniques. A running average filter is applied to reduce noise in the input data, which reduces the number of principal components ($PCs$) necessary to represent the data. Subsequently, a k-d tree is employed to identify a relatively similar spectrum, which efficiently constrains the search space. Additionally, fine search strategies leveraging precomputed distances enhance the existing pilot search method by dynamically updating candidate spectra, thereby improving search efficiency. Experimental results demonstrate that the proposed method achieves accuracy comparable to exhaustive search methods while significantly reducing computational complexity relative to existing approaches.

Efficient and accurate identification of spectra from large databases remains a critical challenge in spectroscopic analysis. Previous coarse-to-fine frameworks, typically combining Principal Component Analysis (PCA)-based preprocessing and k-d tree search, have shown that structured search can reduce computational cost without sacrificing accuracy. Building on this foundation, we propose an enhanced algorithm that integrates an improved preprocessing and a novel limited axis check (LAC) method. The preprocessing stage applies running average filtering, downsampling, and threshold-based noise-cutting, followed by PCA to construct a compact, noise-suppressed spectral representation. In the search stage, the proposed LAC algorithm replaces conventional tree-based structures by performing an axis-wise limited-range search and voting strategy to efficiently locate the candidate spectrum closest to the query within the reduced PCA domain. A subsequent refined search determines the closest spectrum by computing distances to the shortlisted candidates. Experimental results demonstrate that the proposed approach attains accuracy equivalent to that of the full search while markedly reducing computational complexity. These results confirm that the integration of enhanced preprocessing and LAC substantially accelerates the spectral search process.

DU-GridNet enables automated fluorescence baseline correction of Raman spectra through multi-resolution feature extraction, effectively removing background signals while preserving characteristic Raman peaks.