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A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics
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A double sliding-window method for baseline correction and noise estimation in Raman spectra of microplastics outperformed two commonly used methods for samples with low signal-to-noise ratios and elevated baselines, providing a more accurate automated preprocessing tool for environmental microplastic analysis.
When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.
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