Fast and efficient deposition of broad range of analytes on substrates for surface enhanced Raman spectroscopy.

The majority of analytical chemistry methods requires presence of target molecules directly at a sensing surface. Diffusion of analyte from the bulk towards the sensing layer is random and might be extremely lengthy, especially in case of low concentration of molecules to be detected. Thus, even the most sensitive transducer and the most selective sensing layer are limited by the efficiency of deposition of molecules on sensing surfaces. However, rapid development of new sensing technologies is rarely accompanied by new protocols for analyte deposition. To bridge this gap, we propose a method for fast and efficient deposition of variety of molecules (e.g. proteins, dyes, drugs, biomarkers, amino acids) based on application of the alternating electric field. We show the dependence between frequency of the applied electric field, the intensity of the surface enhanced Raman spectroscopy (SERS) signal and the mobility of the studied analyte. Such correlation allows for a priori selection of parameters for any desired compound without additional optimization. Thanks to the application of the electric field, we improve SERS technique by decrease of time of deposition from 20 h to 5 min, and, at the same time, reduction of the required sample volume from 2 ml to 50 µl. Our method might be paired with number of analytical methods, as it allows for deposition of molecules on any conductive surface, or a conductive surface covered with dielectric layer.

Surface-enhanced Raman spectroscopy introduced into the International Standard Organization (ISO) regulations as an alternative method for detection and identification of pathogens in the food industry.

We show that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast, reliable, and easy method for detection and identification of food-borne bacteria, namely Salmonella spp., Listeria monocytogenes, and Cronobacter spp., in different types of food matrices (salmon, eggs, powdered infant formula milk, mixed herbs, respectively). The main aim of this work was to introduce the SERS technique into three ISO (6579:2002; 11290-1:1996/A1:2004; 22964:2006) standard procedures required for detection of these bacteria in food. Our study demonstrates that the SERS technique is effective in distinguishing very closely related bacteria within a genus grown on solid and liquid media. The advantages of the proposed ISO-SERS method for bacteria identification include simplicity and reduced time of analysis, from almost 144 h required by standard methods to 48 h for the SERS-based approach. Additionally, PCA allows one to perform statistical classification of studied bacteria and to identify the spectrum of an unknown sample. Calculated first and second principal components (PC-1, PC-2) account for 96, 98, and 90% of total variance in the spectra and enable one to identify the Salmonella spp., L. monocytogenes, and Cronobacter spp., respectively. Moreover, the presented study demonstrates the excellent possibility for simultaneous detection of analyzed food-borne bacteria in one sample test (98% of PC-1 and PC-2) with a goal of splitting the data set into three separated clusters corresponding to the three studied bacteria species. The studies described in this paper suggest that SERS represents an alternative to standard microorganism diagnostic procedures. Graphical Abstract New approach of the SERS strategy for detection and identification of food-borne bacteria, namely S. enterica, L. monocytogenes, and C. sakazakii in selected food matrices.