[Study on application of Gaussian fitting algorithm to building model of spectral analysis]

In the present paper, Gaussian fitting algorithm is introduced. It is possible to separate some Gaussian peaks in one spectrum and get peak height value, peak position value and other parameters by this algorithm. These Gaussian parameters are used to describe original spectral information and extract spectral feature. The spectral model is optimized and explained by the combination of the Gaussian fitting algorithm and multivariate calibration methods. The relationship between the spectra and the chlorophyll of the corn's leaves is studied in this paper, every spectrum which contains 1551 absorbance data is fitted and separated by three Gaussian peaks, and then 1551 data are converted to 9 Gaussian parameters (approximately 0.58% of the whole original spectral data), and the chlorophyll content is estimated by the Gaussian parameters. This modeling method is fast and accurate for the estimation of a sample. The model of chlorophyll content with the Gaussian fitting algorithm and PLS is built in the range of 400-800 nm, and the experiment results show that the correlation coefficient between the estimated values and the real values is 0.960, and the relative standard deviation is 0.0485; The model of chlorophyll content with the Gaussian fitting algorithm and PCR is built in the same wavelength range, and the experiment results show that the correlation coefficient is 0.962, and the relative standard deviation is 0.048; while the correlation coefficient is 0.957 and the relative standard deviation is 0.051 for the model of PLS without Gaussian fitting algorithm; and the correlation coefficient is 0.919 and the relative standard deviation is 0.077 for the model of PCR without Gaussian fitting algorithm. The reliability of the prediction results shows that Gaussian fitting algorithm is satisfactory for building model of spectral analysis. Compared to the conventional methods, the method of Gaussian fitting algorithm can not only simplify the parameters of models, but also improve the explanation of analysis models. The result of the study shows that it is practical and feasible to apply the Gaussian fitting algorithm to quantitative analysis models.