Herein we have stepped-up on a strategic spectroscopic modality by utilizing label free ultrasensitive surface enhanced Raman scattering (SERS) technique to generate a differential spectral fingerprint for the prediction of normal (NRML), high-grade intraepithelial lesion (HSIL) and cervical squamous cell carcinoma (CSCC) from exfoliated cell samples of cervix. Three different approaches i.e. single-cell, cell-pellet and extracted DNA from oncology clinic as confirmed by Pap test and HPV PCR were employed. Gold nanoparticles as the SERS substrate favored the increment of Raman intensity exhibited signature identity for Amide III/Nucleobases and carotenoid/glycogen respectively for establishing the empirical discrimination. Moreover, all the spectral invention was subjected to chemometrics including Support Vector Machine (SVM) which furnished an average diagnostic accuracy of 94%, 74% and 92% of the three grades. Combined SERS read-out and machine learning technique in field trial promises its potential to reduce the incidence in low resource countries.

在这里，我们已经通过利用无标记超灵敏表面增强拉曼散射（SERS）技术来生成战略性光谱模式，以生成用于预测正常（NRML），高级别上皮内病变（HSIL）和宫颈鳞状细胞的差异光谱指纹子宫颈脱落细胞样本中的细胞癌（CSCC）。通过Pap试验和HPV PCR证实，采用了三种不同的方法，即单细胞，细胞颗粒和从肿瘤学诊所提取DNA。金纳米粒子作为SERS底物，有利于拉曼强度的增加，分别对酰胺III /核糖核酸酶和类胡萝卜素/糖原表现出特征性，从而建立了经验辨别力。此外，所有光谱发明都经过了包括支持向量机（SVM）在内的化学计量学分析，该方法提供了三个等级的94％，74％和92％的平均诊断准确性。SERS读取和机器学习技术相结合的现场试验有望在低资源国家降低其发生率。