David S. Paik

Publication Details

• Raman labeled nanoparticles: characterization of variability and improved method for unmixing

  Kode K, Shachaf C, Elchuri S, Nolan G, Paik DS. Journal of Raman Spectroscopy. 2012; 43 (7): 895-905

  Raman spectroscopy can differentiate the spectral fingerprints of many molecules, resulting in potentially high multiplexing capabilities of Raman-tagged nanoparticles. However, an accurate quantitative unmixing of Raman spectra is challenging because of potential overlaps between Raman peaks from each molecule, as well as slight variations in the location, height, and width of very narrow peaks. If not accounted for properly, even minor fluctuations in the spectra may produce significant error that will ultimately result in poor unmixing accuracy. The objective of our study was to develop and validate a mathematical model of the Raman spectra of nanoparticles to unmix the contributions from each nanoparticle allowing simultaneous quantitation of several nanoparticle concentrations during sample characterization. We developed and evaluated an algorithm for quantitative unmixing of the spectra called narrow peak spectral algorithm (NPSA). Using NPSA, we were able to successfully unmix Raman spectra of up to seven Raman nanoparticles after correcting for spectral variations of 30% intensity and shifts in peak locations of up to 10 cm−1, which is equivalent to 50% of the full width at half maximum (FWHM). We compared the performance of NPSA to the conventional least squares (LS) analysis. Error in the NPSA is approximately 50% lower than in the LS. The error in estimating the relative contributions of each nanoparticle with the use of the NPSA are in the range of 10–16% for equal ratios and 13–19% for unequal ratios for the unmixing of seven composite organic–inorganic nanoparticles (COINs); whereas, the errors from using the traditional LS approach were in the range of 25–38% for equal ratios and 45–68% for unequal ratios. Here, we report for the first time the quantitative unmixing of seven nanoparticles with a maximum root mean square of the percentage error (RMS%) error of less than 20%.