Optimization of Informative Spectral Regions in FT-NIR Spectroscopy for Measuring the Soluble Solids Content of Apple

A novel potential method, linear combination weight PLS (LCW-PLS) model, was suggested for improving the performance of routine PLS model based on selected informative regions. Moving window partial least squares (MWPLS), genetic algorithms interval partial least squares (GAiPLS) and synergy interval partial least squares (SiPLS) were used to optimize informative spectral regions from FT-NIR spectra. A total of 660 apples harvested at 2006, 2007 and 2008, were divided into calibration and prediction sets by Kennard-Stone method. The best calibration model was obtained by LCW-PLS method based on informative spectral regions of 4328-4787, 5323-5512, 5982-7135 and 7159-7463cm(-1) selected by MWPLS procedure, and corresponding weights of 0.004, 0.070, 0.066 and 0.860, respectively. The LCW-MWPLS model was applied to predict samples, the prediction results were with R-P of 0.942, RMSEP of 0.649 %Brix and RPDP of 3.10. In addition, developed LCW-MWPLS model using random two years samples was used to predict one year samples excluded. The predictive results were with R-P of 0.921-0.927, RMSEP of 0.714-0.795 %Brix and RPDP of 2.44-2.88. The LCW-MWPLS model giving a prediction error equal to 4% of fresh weight was sufficiently accurate to determine the SSC of apple non-destructively.