Analysis of near infrared spectroscopy of beer alcohol content by correlation coefficients and genetic algorithms

In order to select wavelengths of near infrared spectroscopy in the prediction model of partial least squares regression of beer alochol content detection, correlation coefficients and genetic algorithms were used to select wavelength from near infrared spectroscopy in partial least squares regression model. The method was applied to the study of beer alcohol content with spectroscopy. The result shows that the numbers of wavelengths of original absorption spectra and first derivative spectra for developing models can be reduced by 83% and 82% by this method, while the root mean square error of prediction reduces by 0.42% and 0.64% respectively. This wavelengths selection method for PLS modeling not only simplifies and optimizes calibration model but also increases the prediction ability of calibration model. Therefore, correlation coefficients and genetic algorithms are effective and feasible methods applied in developing mutlivariate calibration model based on partial least squares regression.