[Near infrared determination of sugar content in apples based on GA-iPLS]

To improve and simplify the prediction model of sugar content, genetic algorithm interval partial least square (GA-iPLS) methods, the evolution of iPLS described by Lars Nørgaard, were proposed and used to establish the calibration models of sugar content against apple spectra. The apple spectra data were divided into 40 intervals, among which 5 subsets, i. e. No. 4, 6, 8, 11 and 18, containing 362 data points were selected by GA-iPLS. The optimum GA-iPLS calibration model was obtained with the correlation coefficient (rc) of 0.962, the root mean square error of cross-validation (RMSECV) of 0.3346 and the root mean square error of prediction (RMSEP) of 0.3846. Compared with the whole spectra data model, the data points and the factors in the GA-iPLS were decreased significantly. Consequently, the running time of the PLS model build by GA-iPLS was shorter than that of the whole spectra data model. Furthermore, the GA-iPLS model could not only improve precision, but also simplify the model.