Abstract：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 NΦrgaard, 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 0.334 6 and the root mean square error of prediction (RMSEP) of 0.384 6. 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.
Key words：NIR spectra;Genetic algorithm;Partial least square;Sugar content;Apple
李艳肖,邹小波*,董英. 用遗传区间偏最小二乘法建立苹果糖度近红外光谱模型[J]. 光谱学与光谱分析, 2007, 27(10): 2001-2004.
LI Yan-xiao,ZOU Xiao-bo*,DONG Ying. Near Infrared Determination of Sugar Content in Apples Based on GA-iPLS. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(10): 2001-2004.
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