Using genetic algorithm interval partial least squares selection of the optimal near infrared wavelength regions for determination of the soluble solids content of 'Fuji' apple

摘要

A near infrared (NIR) spectroscopy acquisition device was developed in this study using an apple as the test sample. With this device, the apple was rolled while collecting the NIR spectra. The feasibility of using efficient selection of wavelength regions in Fourier transform NIR for a rapid and conclusive determination of the inner qualities of fruit such as soluble solids content (SSC) of apples was investigated. Graphically-oriented local multivariate calibration modelling procedures called genetic algorithm interval partial least-squares (GA-iPLS) were applied to select efficient spectral regions that provide the lowest prediction error, in comparison to the full-spectrum model. The optimal SSC predictions were obtained from a seven-factor model using five intervals among 40 intervals selected by GA-iPLS. In the determination, a root mean square error of prediction of 0.42 ° Brix for SSC of apples was obtained. The result demonstrated that the new method is a very useful and effective method for developing high precision PLS models based on optimal wavelength regions.