Nondestructive measurement of internal quality in pear using genetic algorithms and FT-NIR spectroscopy Original Research Article

An improved genetic algorithms (GAs) is used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression. The methods also allow the number of latent variables used in constructing the calibration models to be optimized along with the selection of the wavelengths. Studies are performed to characterize the signal and noise characteristics of the spectral data, and optimal configurations for the GAs are found for each data set through experimental design techniques. The experiments tested in this method were sugar content (SC), titratable acidity (TA) and valid acidity (pH).