Lectin-Modified Bacterial Cellulose Nanocrystals Decorated with Au Nanoparticles for Selective Detection of Bacteria Using Surface-Enhanced Raman Scattering Coupled with Machine Learning
Lectin-Modified Bacterial Cellulose Nanocrystals Decorated with Au Nanoparticles for Selective Detection of Bacteria Using Surface-Enhanced Raman Scattering Coupled with Machine Learning
复制标题
凝集素修饰的Au纳米粒子表面增强拉曼散射与机器学习相结合的细菌选择性检测
DOI：
10.1021/acsanm.1c02760
复制
发表时间：
2022-01-07
影响因子：
5.9
通讯作者：
Vikesland, Peter J.
中科院分区：
文献类型：
--
作者：
Rahman, Asifur;Kang, Seju;Vikesland, Peter J.
细菌纤维素纳米晶体（BCNCs）是一种可调的生物相容性纤维素纳米材料，可以很容易地进行生物缀合并用于生物传感应用。我们报道了伴刀豆球蛋白A（con A）凝集素修饰的BCNCs（con A + BCNCs）用于细菌分离和使用Au纳米颗粒（AuNPs）的无标记表面增强拉曼光谱（Sers）检测细菌物种的应用。聚集的AuNP +细菌+（con A + BCNC）缀合物产生Sers热点，其使得能够在10（3）CFU/mL水平下对菌株大肠杆菌8739进行Sers检测。利用优化后的检测方法对19株常见细菌进行了鉴别。使用支持向量机（SVM）分析了19种细菌菌株的大型Sers光谱数据集，SVM是一种基于优化的机器学习技术，用作二元分类器。SVM分类器在正确区分细菌菌株方面显示出87.7%的高总体准确率。这项研究说明了将低成本的基于纳米纤维素的Sers生物传感器与机器学习技术结合起来分析大型光谱数据集的潜力。
Bacterial cellulose nanocrystals (BCNCs) are tunable and biocompatible cellulose nanomaterials that can be easily bioconjugated and used for biosensing applications. We report the application of concanavalin A (con A) lectin-modified BCNCs (con A + BCNCs) for bacterial isolation and label-free surface-enhanced Raman spectroscopy (SERS) detection of bacterial species using Au nanoparticles (AuNPs). The aggregated AuNP + bacteria + (con A + BCNC) conjugates generated SERS hot spots that enabled the SERS detection of the strain Escherichia coli 8739 at the 10(3) CFU/mL level. The optimized detection assay was then used to differentiate 19 common bacterial strains. The large SERS spectral dataset for the 19 bacterial strains was analyzed using the support vector machine (SVM), an optimization-based machine-learning technique that worked as a binary classifier. The SVM classifier showed a high overall accuracy of 87.7% in correctly discriminating bacterial strains. This study illustrates the potential of combining low-cost nanocellulose-based SERS biosensors with machine-learning techniques for the analysis of large spectral datasets.