As a novel and ultrasensitive detection technology that had advantages of fingerprint effect, high speed and
low cost, surface-enhanced Raman scattering (SERS) was used to develop the regression models for the fast quantitative
detection of thiram by support vector machine regression (SVR) in the paper. Meanwhile, three parameter optimization
methods, which were grid search (GS), genetic algorithm (GA) and particle swarm optimization (PSO), were employed
to optimize the internal parameters of SVR. Furthermore, the influence of the spectral number, spectral wavenumber
range and principal component analysis (PCA) on the quantitative detection was also discussed. Firstly, the experiments
demonstrate the proposed method can realize the fast and quantitative detection of thiram, and the best result is obtained
by GS-SVR with the spectra of the range of characteristic peak which are processed by PCA. And the effect of GS, GA,
PSO on the parameter optimization is similar, but the analysis time has a great difference in which GS is the fastest.
Considering the analysis accuracy and time simultaneously, the spectral number of samples over each concentration
should be set to 50. Then, developing the quantitative model with the spectra of range of characteristic peak can reduce
analysis time on the promise of ensuring the detection accuracy. Additionally, PCA can further reduce the detection error
through reserving the main information of the spectra data and eliminating the noise.