Surface enhanced Raman spectroscopy (SERS) is a powerful molecular analytical tool that allows for highly sensitive chemical detection of low concentration analytes through the amplification of electromagnetic (EM) fields generated by the excitation of localized surface plasmons. SERS performance such as enhancement factor (EF), reproducibility and repeatability is highly related to distribution profile of Au nanoparticles on SERS substrates. The uniformity distribution of Au nanoparticles usually results in good SERS performance. We introduce a new SERS substrate that produces improved performance through surface modification of silicon wafers. For this purpose, hydrophilic silicon wafers are prepared and then their surfaces are coated with tannic acid (TA) by thermal treatment. TA is used as a surface modifier with low cost and high adhesion to synthesize uniform and dense Au nanoparticles. The direct synthesis of Au nanoparticles is carried out through the successive ionic layer absorption and reaction (SILAR) method. 2- naphthalenethiol (2-NAT) dye was utilized to confirm the SERS performance of the as-fabricated substrate. The SERS performance was optimized by controlling the thickness of the Au nanoparticle layer synthesized by repeating the SILAR cycle. We expect the proposed SERS substrate to exhibit good reproducibility and repeatability due to the high uniformity distribution of Au nanoparticles.
We have investigated a multiple label-free detection method based on Raman spectroscopy and multivariate curve resolution (MCR) analysis to classify breast cancer. Twenty breast tissues collected from five participants during breast surgery were used as biological samples. Ten samples were from malignant tumor mass (cancer core area) and the others were from the safety margin outside of the tumor mass (two sample groups). For each breast tissue sample, twenty Raman spectra were collected using a fiber-optics Raman system consisting of a fiber-optic Raman probe, a low dark current deep-depletion CCD connected to a Czerny-Turner spectrograph and 785-nm laser source. Using MCR analysis iteratively optimized by an alternative least squares (ALS) algorithm, biomarker-dominated spectral data can be obtained from the preprocessed Raman spectra. This allows a more accurate classification between the two sample groups (normal and cancer). We expect that the proposed method based on biomarker analysis using MCR-ALS will more accurately classify breast cancer.