期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：9
页码：35-44
出版社：SERSC
摘要：It usually contains a large amount of redundant information to use the hyperspectral information to create a model, which will increase the difficulty of the model analysis. Therefore, it’s so important to select the characteristic wavelength in an effective and quick way. This study is proposed by using the competitive adaptive reweighed sampling (CARS) to select the characteristic wavelength for detecting the reducing sugar content in the potatoes. In that experiment a total of 238 samples are prepared. Among them, 190 samples are selected as the calibration set and 48 samples as the validation set. The performance of CARS is compared with full spectrum and classical variable extraction methods such as Monte Carlo uninformative variable elimination (MC-UVE), genetic algorithm (GA) and moving window partial least squares (MWPLS). Experimental results show that the band screened by algorithm CARS has the best effect, compared to full spectrum modeling, the wavelength of the model reduces from 203 to 33, the model validation set coefficient R2 increases from 0.8464 to 0.8965, and the root mean square error prediction (RMSEP) decreases from 0.0758 to 0.0416. The results demonstrate that it is feasible to detect the reducing sugar content of potatoes by using CARS combined with hyperspectral imaging.
关键词：Hyperspectral; CARS; Potato; Partial least square
