Raman signal optimization based on residual network adaptive focusing

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2024-04, Vol.310, p.123949, Article 123949
• Main Authors: Chen, Haozhao, Yang, Liwei, Zhu, Weile, Tang, Ping, Xing, Xinyue, Zhang, Weina, Zhong, Liyun
• Format: Article
• Language: English
• Subjects: Autofocus based on residual network; Cell; Discrete cosine transform; Gradient; Raman spectrum; Signal-to-noise ratio
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2024.123949

Due to its high sensitivity and specificity, Micro-Raman spectroscopy has emerged as a vital technique for molecular recognition and identification. As a weakly scattered signal, ensuring the accurate focus of the sample is essential for acquiring high quality Raman spectral signal and its analysis, especially in some complex microenvironments such as intracellular settings. Traditional autofocus methods are often time consuming or necessitate additional hardware, limiting real-time sample observation and device compatibility. Here, we propose an adaptive focusing method based on residual network to realize rapid and accurate focusing on Micro-Raman measurements. Using only a bright field image of the sample acquired on any image plane, we can predict the defocus distance with a residual network trained by Resnet50, in which the focus position is determined by combining the gradient and discrete cosine transform. Further, detailed regional division of the bright field map used for characterizing the height variation of actual sample surface is performed. As a result, a focus prediction map with 1μm accuracy is obtained from a bright field image in 120 ms. Based on this method, we successfully realize Raman signal optimization and the necessary correction of spectral information. This adaptive focusing method based on residual network is beneficial to further enhance the sensitivity and accuracy of Micro-Raman spectroscopy technology, which is of great significance in promoting the wide application of Raman spectroscopy. [Display omitted] •Adaptive focusing based on residual net-work isproposed to optimize Raman spectrum.•Only a bright field image is required to achieve the adaptive focusing.•Gradient and Dis-crete cosine Transform are combined to determine the accurate focus.•SNR and peak-to-peak ratio accuracy of cellular Raman spectra are optimized.