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.

中文翻译：

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使用SERS中的化学增强功能对ssDNA进行成分分析的机器学习

表面增强拉曼光谱（SERS）由于具有单分子敏感性和DNA组成分析的潜力，因此是一种用于生物化学传感的有吸引力的方法。在此手稿中，我们利用金属特有的化学增强作用来检测吸附在金或银纳米棒基质上的200个碱基长的单链DNA（ssDNA）分子的SERS光谱差异，然后开发和训练线性回归以及神经网络预测ssDNA组成的模型。我们的结果表明，使用具有给定吸附分子的不同金属的底物会导致不同的SERS光谱，从而可以在不同的化学增强机制下探测金属与分子之间的相互作用。利用这种差异并将不同金属的光谱结合起来作为PCA（主成分分析）和NN（神经网络）模型的输入，与手动特征选择分析以及数据采集相比，可以显着降低检测误差。使用单一金属制成。此外，我们表明，在存在复杂噪声和数据色散因子的情况下，NN模型可提供出色的性能，这些噪声和数据色散因子会影响从不同日期制造的金属基板收集的SERS信号。