College of Food Science and Technology, Henan Agricultural University, Zhengzhou, 450002, People's Republic of China.
International Joint Laboratory of Meat Processing and Safety in Henan province, Henan Agricultural University, Zhengzhou, 450002, People's Republic of China.
Mikrochim Acta. 2023 Nov 21;190(12):472. doi: 10.1007/s00604-023-06031-3.
A new surface-enhanced Raman spectroscopy (SERS) biosensor of Graphene@Ag-MLF composite structure has been fabricated by loading AgNPs on graphene films. The response of the biosensor is based on plasmonic sensing. The results showed that the enhancement factor of three different spores reached 10 based on the Graphene@Ag-MLF substrate. In addition, the SERS performance was stable, with good reproducibility (RSD<3%). Multivariate statistical analysis and chemometrics were used to distinguish different spores. The accumulated variance contribution rate was up to 96.35% for the top three PCs, while HCA results revealed that the spectra were differentiated completely. Based on optimal principal components, chemometrics of KNN and LS-SVM were applied to construct a model for rapid qualitative identification of different spores, of which the prediction set and training set of LS-SVM achieved 100%. Finally, based on the Graphene@Ag-MLF substrate, the LOD of three different spores was lower than 10 CFU/mL. Hence, this novel Graphene@Ag-MLF SERS substrate sensor was rapid, sensitive, and stable in detecting spores, providing strong technical support for the application of SERS technology in food safety.
一种新型的基于石墨烯@Ag-MLF 复合结构的表面增强拉曼光谱（SERS）生物传感器已经被制备出来，该传感器通过在石墨烯薄膜上负载 AgNPs 实现。该生物传感器的响应基于等离子体传感。结果表明，基于石墨烯@Ag-MLF 基底，三种不同孢子的增强因子达到了 10。此外，SERS 性能稳定，重现性好（RSD<3%）。多元统计分析和化学计量学用于区分不同的孢子。前三个主成分的累积方差贡献率高达 96.35%，而 HCA 结果表明谱图完全可以区分。基于最优主成分，KNN 和 LS-SVM 的化学计量学被应用于构建用于快速定性识别不同孢子的模型，其中 LS-SVM 的预测集和训练集达到了 100%。最后，基于石墨烯@Ag-MLF 基底，三种不同孢子的 LOD 低于 10 CFU/mL。因此，这种新型的石墨烯@Ag-MLF SERS 基底传感器在检测孢子方面具有快速、灵敏和稳定的特点，为 SERS 技术在食品安全中的应用提供了有力的技术支持。
