SYNTHETIC RESEARCH REPORT 2017

Report on project achievement in the period September 1st 2017 – December 31st 2017

O1). Development of the experimental setup.
Designing the flow cell - After consulting the relevant bibliographic sources concerning portable, microfluidic, SERS-based detection systems, both partners met and discussed the technical details for designing the flow cell – technical parameters, dimensions, detection window type, etc.) and assembling the component parts of the portable device. A 2D/3D detailed technical scheme of the flow cell was designed by using a dedicated software and was provided in the extended scientific report. The documentation includes the decided design illustrated in several technical drawings, detailed cross-sections, 2D and 3D views of the final flow cell, etc.
Acquisition of the components and development of the flow system - Once the technical details for the experimental demonstrator were decided, each partner acquired the necessary components for developing the portable device: portable Raman spectrometer, optical microscope required for confocal Raman analysis of the sample, tubings, fittings and syringes that will be used in the assemblage step.
Outlook: The research teams will fabricate the flow cell and will assemble it to the handheld Raman spectrometer. The flow cell contains in its core the microchannel and will be made of an acrylic photopolymer, such as PDMS (one of the most actively developed polymers for microfluidics and can be autoclaved in order to reuse it and to avoid contamination of the detection system).

O2). Optimization and validation of the SERS detection methodology.
A) By using a novel label-free SERS detection optimized protocol with the conventional Raman system, the SERS spectral database was started to build up, containing the SERS fingerprints of 3 species of common fungal pathogens The relevant spectral domain from the whole SERS profile, which contains the main SERS marker bands, was determined in order to be analyzed by multivariate analysis (Principle Component Analysis – PCA; Linear Discriminant Analysis - LDA) for unbiased classification and discrimination of the investigated pathogens.
B) A PCA algorithm for accurate discrimination (>90 % specificity and sensitivity) between pathogens at strain level by using SERS single-organism fingerprints was developed and the PCA-LDA was successfully performed.

Project Manager, Dr. Nicoleta Elena DINA