Morphology Department, Federal University of Espirito Santo, Av Marechal Campos, 1468, Vitória 29040-090, ES, Brazil.
Federal Institute of Espírito Santo, Campus Serra, Serra 29173-087, ES, Brazil.
Biosensors (Basel). 2024 Oct 29;14(11):523. doi: 10.3390/bios14110523.
This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.
本工作报道了一种基于 SERS 结合机器学习工具检测血液样本中 SARS-CoV-2 抗体的有效方法。为此，直接将金纳米粒子与刺突蛋白偶联，用于鉴定人血样本中的抗 SARS-CoV-2 抗体。综合数据库利用了来自所有 594 个血清样本的拉曼光谱。使用 Scikit-Learn 库进行机器学习研究，并在 Python 中实现，使用一致流形逼近和投影（UMAP）技术提取 SARS-CoV-2 阳性和阴性样本的拉曼光谱特征。使用的机器学习模型包括 k-最近邻（kNN）、支持向量机（SVM）、决策树（DTs）、逻辑回归（LR）和轻梯度提升机（LightGBM）。kNN 模型的灵敏度为 0.943，特异性为 0.9275，准确性为 0.9377。本研究表明，结合拉曼光谱和机器算法可以成为一种有效的诊断方法。此外，我们强调了每种算法的优缺点，为未来的研究提供了有价值的信息。
