Abstract：
Surface-enhanced Raman spectroscopy (SERS) can quickly identify molecular fingerprints and has been widely used in the field of rapid detection. However, the non-uniformity inherent in SERS substrate signals, coupled with the finite nature of the detection object, significantly hampers the advancement of SERS. Nowadays, the existing mature immunochromatographic assay (ICA) method is usually combined with SERS technology to address the defects of SERS detection. Nevertheless, the porous structure of the strip will also affect the signal uniformity during detection. Obviously, a method using SERS-ICA is needed to effectively solve signal fluctuations, improve detection accuracy, and has certain versatility. This paper introduces an internal standard method combining deep learning to predict and process Raman data. Based on the signal fluctuation of single-antigen SERS-ICA test strip, the double-antigen SERS-ICA test strip was constructed. The full spectrum Raman data of double-antigen SERS-ICA test strip was normalized by the sum of two characteristic peaks of internal standard molecules, and then processed by deep learning algorithm. The Relative Standard Deviation (RSD) of Raman data of bisphenol A was compared before and after internal standard normalization of double-antigen SERS-ICA test strip. The RSD processed by this method was increased by 3.8 times. After normalization, the prediction accuracy of Root Mean Square Error (RMSE) is improved by 2.66 times, and the prediction accuracy of R-square (R2) is increased from 0.961 to 0.994. The results showed that RMSE and R2 were used to comprehensively predict the collected data of double-antigen SERS-ICA test strip, which could effectively improve the prediction accuracy. The internal standard algorithm can effectively solve the challenges of uneven hot spots and poor signal reproducibility on the test strip to a certain extent, so as to improve the semi-quantitative accuracy.
摘要：
表面增强拉曼光谱(SERS)能够快速识别分子指纹图谱,在快速检测领域得到了广泛的应用。然而,SERS衬底信号固有的非均匀性，再加上检测对象的有限性质，大大阻碍了SERS的发展。如今,现有成熟的免疫层析(ICA)方法通常与SERS技术相结合来解决SERS检测的缺陷。然而，条的多孔结构也会影响检测过程中的信号均匀性。显然,需要一种使用SERS-ICA的方法来有效解决信号波动，提高检测精度，并具有一定的通用性。本文介绍了一种结合深度学习的内标方法来预测和处理拉曼数据。基于单抗原SERS-ICA试纸的信号波动,构建了双抗原SERS-ICA试纸。双抗原SERS-ICA测试条的全光谱拉曼数据通过内标分子的两个特征峰之和进行归一化,然后通过深度学习算法进行处理。比较双抗原SERS-ICA测试条内标归一化前后双酚A拉曼数据的相对标准偏差(RSD)。通过该方法处理的RSD增加了3.8倍。归一化后,均方根误差(RMSE)的预测精度提高了2.66倍,R平方(R2)的预测精度从0.961提高到0.994。结果表明,用RMSE和R2对收集的双抗原SERS-ICA试纸数据进行综合预测,能有效提高预测精度。内标算法可以在一定程度上有效解决试纸上热点不均匀、信号再现性差的挑战,从而提高半定量精度。
