期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：12
页码：49-58
DOI：10.14257/ijsip.2015.8.12.06
出版社：SERSC
摘要：Detection of starch content in potato is studied applying hyperspectral imaging technique in the paper. The original and preprocessing spectra were processed with partial least square(PLS) method to build prediction model of starch content. The original spectra between 400 and 1000nm was preprocessed with smoothing, second derivation, and multiplicative scatter correction (MSC). Prediction model was built with preprocessing spectra by applying principal component analysis (PCA). Known from the result, the model based on the preprocessing spectra preprocessed with smoothing and PCA is the best of all prediction models built in research. The determination coefficient (R 2 )of calibration set and prediction set was 0.8234 and 0.9031 respectively. The root mean square error of calibration set ( Ｒ MSEC) and root mean square error of validation set( Ｒ MSEV) was 0.5633 and 0.5025 ， respectively ． It indicated that this method could be applied in detection of starch content in potato. The study could offer theoretical and practical reference for further study in the future.
关键词：Hyperspectral imaging; Potato; Starch; Partial least square
