School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu, China.
Department of Gastrointestinal Surgery, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
J Biophotonics. 2020 Apr;13(4):e201960176. doi: 10.1002/jbio.201960176. Epub 2020 Feb 9.
Surface-enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle-serum mixture formed coffee-ring-like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support-vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results.
表面增强拉曼光谱（SERS）在快速诊断病原体和异常生物状态（即癌症）方面引起了相当大的关注。在这项工作中，通过设计 SERS 采样和数据分析，开发了一种简单、快速和廉价的光学传感平台。光谱测量的预处理采用将胶体金纳米粒子与结直肠癌（CRC）患者的血清混合。粒子-血清混合物的液滴在边缘形成咖啡环状区域，提供了强而稳定的 SERS 谱。分别通过无监督主成分分析（PCA）和监督机器学习模型（如支持向量机（SVM））对来自癌症患者和健康志愿者的获得的光谱进行分析。结果表明，SVM 模型在 CRC 诊断分类中的性能优于 PCA。此外，将血液样本中的癌胚抗原值与相应的 SERS 光谱一起用于 SVM 计算，可提高预测结果。
