Selection of measurement variables for hyperspectra of total phenol content in grape seeds based on Monte Carlo frequency method

In order to solve the problems of too many variables and high model complexity, it is necessary to effectively reduce the dimension of the data according to the characteristics in establishing the prediction model of total phenol content in grape seeds by using hyperspectral data. In this paper, a Monte Carlo frequency (MCF) method was proposed to select the wavelength of hyperspectral data, and the support vector regression (SVR) prediction model of grape seed total phenols was established. The method uses Monte Carlo sampling to select wavelength subset, then establishes a large number of SVR sub-models, and selects sub-models with smaller root mean square error (RMSE) to count the frequency of each wavelength. Finally, the number of wavelengths is determined by exponential decline function, and the wavelength subset with the highest frequency is selected as the characteristic wavelength. The results show that the prediction performance of the model can be improved by using MCF method at the same time of dimensionality reduction. The number of wavelengths can be reduced from 196 to 9, the range of wavelengths is between 950 and 1400 nm, and the RMSE value can be reduced from 0.42 to 0.37. The prediction accuracy is better than other wavelength selection methods such as SPA. The results show that the proposed MCF method can effectively select characteristic wavelengths in hyperspectral data processing, which provides an effective method for the accurate establishment of prediction model.