2025/09/19 by Xiaoyang Li, Hanjun Zhang, Zhong Wang +1 · 1 voice
Chemistry · Biochemistry, Genetics and Molecular Biology · Computer Science · #Spectroscopy and Chemometric Analyses #Spectroscopy Techniques in Biomedical and Chemical Research #Blind Source Separation Techniques
paper · doi:10.1177/00037028251384654
openalex publication_date 2025/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
As a preprocessing step of spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, electrophoresis, etc., the baseline correction is very important for improving the signal quality, thereby ensuring the reliability and accuracy of the data analysis. Methods such as polynomial fitting, wavelet transforms, and frequency-domain filtering are widely used for baseline correction, effectively reducing interference and enhancing the reliability of signal analysis. However, these methods have certain limitations: (i) Polynomial fitting faces challenges in determining the optimal order, which may affect the fitting quality, (ii) wavelet transforms are complex and require fine adjustments, and (iii) frequency-domain filtering may cause signal distortion. These shortcomings affect the implementation of the algorithm in spectral related industries. Therefore, finding an appropriate algorithm to optimize baseline removal is crucial for the development of automated spectral analysis equipment. Here, we propose a rolling ball baseline removal algorithm based on morphological operations. With its simple implementation and excellent baseline removal performance, this method effectively avoids the overfitting problems. It is suitable for baseline correction in not only Raman spectroscopy, but also various other types of spectral data. In all, this approach offers a convenient and efficient general solution for the processing of various spectral data.