Wavelength-dependent label-free identification of isolated nontuberculous mycobacteria using surface-enhanced Raman spectroscopy

Early on-site detection of strawberry anthracnose using portable Raman spectroscopy

We developed a method for the early on-site detection of strawberry anthracnose using a portable Raman system with multivariate statistical analysis algorithms. By using molecular markers based on Raman spectra, the proposed method can detect anthracnose in strawberry stems 3 days after exposure to Colletotrichum gloeosporioides. A fiber-optic probe was applied for the portable Raman system, and the acquisition time was 10 s. We found that the molecular markers were closely related to the following subjects: i) an increase in amide III and fatty acids of C. gloeosporioides invading strawberry stems (Raman bands at 1180-1310 cm-1) and ii) a decrease in metabolites in strawberry plants, such as phenolic compounds and terpenoids (Raman bands at 760, 800, and 1523 cm-1). We also found that the increased fluorescence background caused by various chromophores within the invading C. gloeosporioides could serve as a marker. A two-dimensional cluster plot obtained by principal component analysis (PCA) showed that the three groups (control, fungal infection, and pathogen) were distinguishable. The linear discriminant analysis (LDA)-based prediction algorithm could identify C. gloeosporioides infection with a posterior probability of over 40%, even when no symptoms were visible on the inoculated strawberry plants.

Recent advances of Au@Ag core-shell SERS-based biosensors

The methodological advancements in surface-enhanced Raman scattering (SERS) technique with nanoscale materials based on noble metals, Au, Ag, and their bimetallic alloy Au-Ag, has enabled the highly efficient sensing of chemical and biological molecules at very low concentration values. By employing the innovative various type of Au, Ag nanoparticles and especially, high efficiency Au@Ag alloy nanomaterials as substrate in SERS based biosensors have revolutionized the detection of biological components including; proteins, antigens antibodies complex, circulating tumor cells, DNA, and RNA (miRNA), etc. This review is about SERS-based Au/Ag bimetallic biosensors and their Raman enhanced activity by focusing on different factors related to them. The emphasis of this research is to describe the recent developments in this field and conceptual advancements behind them. Furthermore, in this article we apex the understanding of impact by variation in basic features like effects of size, shape varying lengths, thickness of core-shell and their influence of large-scale magnitude and morphology. Moreover, the detailed information about recent biological applications based on these core-shell noble metals, importantly detection of receptor binding domain (RBD) protein of COVID-19 is provided.

Non-invasive discrimination of multiple myeloma using label-free serum surface-enhanced Raman scattering spectroscopy in combination with multivariate analysis

We report non-invasive discrimination of multiple myeloma (MM) using label-free serum surface-enhanced Raman scattering (SERS) spectroscopy in combination with multivariate analysis. Colloidal silver nano-particles (AgNPs) were used as the SERS substrate. High quality serum SERS spectra were obtained from 53 MM patients and 44 healthy controls (HCs). The SERS spectral differences demonstrated variation of relative concentrations of biomolecules in the serum of MM patients in comparison to HCs. Multivariate analysis methods, including principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM), were used to build discrimination models for MM. Leave-one-out cross-validation (LOOCV) was used to evaluate the performances of the models, in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUC). Using the SVM model, the accuracy for discrimination of MM was achieved as 78.4%, and the corresponding sensitivity, specificity, and AUC values were 0.830, 0.727, and 0.840, respectively. The results show that the serum SERS in combination with multivariate analysis could be a fast, non-invasive, and cost-effective technique for discrimination of MM.