MCR-ALS with sample insertion constraint to enhance the sensitivity of surface-enhanced Raman scattering detection

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Abstract
The multivariate curve resolution-alternative least square (MCR-ALS) algorithm was modified with sample insertion constraint to deconvolute the overlapping peaks in SERS spectra. The developed method was evaluated by the spectral data simulated using Gaussian distribution function to generate two independent peaks corresponding to a capping agent and an analyte, respectively. The spectra were generated with different overlapping levels and various intensity ratios of the analyte to the capping agent. By MCR-ALS with the sample insertion constraint, the peak from the capping agent was completely excluded to obtain the calibration model of the analyte with R2 > 0.95 in all conditions. Furthermore, our developed method was latterly performed on a real SERS measurement to quantify carbofuran (analyte) using azo-coupling reaction with p-ATP (capping agent) on the silver nanoparticles as a SERS substrate. The calibration model of derivative carbofuran phenol was generated with R2 =0.99 and LOD = 28.19 ppm. To access the performance of the calibration model, the model was used to estimate the concentrations of carbofuran in an external validation set. It was found that RMSE of prediction was only 2.109 with promising R2 = 0.97.
Funding Information
  • Center of Excellence on Petrochemical and Materials Technology
  • National Nanotechnology Center
  • National Science and Technology Development Agency

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