Nguyen Phuong H L, Hong Brandon, Rubin Shimon, Fainman Yeshaiahu
University of California San Diego, Electrical and Computer Engineering, La Jolla, CA 92161, USA.
Biomed Opt Express. 2020 Aug 18;11(9):5092-5121. doi: 10.1364/BOE.397616. eCollection 2020 Sep 1.
Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. In this manuscript we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train a linear regression as well as neural network models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for PCA (Principal Component Analysis) and NN (Neural Network) models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Furthermore, we show that NN model provides superior performance in the presence of complex noise and data dispersion factors that affect SERS signals collected from metal substrates fabricated on different days.
表面增强拉曼光谱（SERS）因其具有单分子灵敏度的潜力以及DNA组成分析的前景，是一种用于生化传感的有吸引力的方法。在本论文中，我们利用金属特异性化学增强效应来检测吸附在金或银纳米棒基底上的200个碱基长度的单链DNA（ssDNA）分子的SERS光谱差异，然后开发并训练线性回归以及神经网络模型来预测ssDNA的组成。我们的结果表明，使用承载给定吸附分子的不同金属基底会导致不同的SERS光谱，从而能够在不同的化学增强机制下探测金属 - 分子相互作用。利用这种差异并将来自不同金属的光谱组合作为主成分分析（PCA）和神经网络（NN）模型的输入，与手动特征选择分析以及使用单一金属数据的情况相比，能够显著降低检测误差。此外，我们表明，在存在影响从不同日期制备的金属基底收集的SERS信号的复杂噪声和数据分散因素的情况下，神经网络模型具有卓越的性能。
