Abstract:Thickness is an important index for the grade of green soybean.In this manuscript,hyperspectral imaging technology was used to determine the thickness of two hundreds green soybean.The hyperspectral reflectance images of the samples were acquired using hyperspectral imaging system and the instrumental thickness values were measured by digital caliper.The range of 400 ～1000 nm spectral information was preprocessed using multiplicative scatter correction algorithm(MSC),standard normal variate algorithm(SNV) and derivation calculation algorithm,respectively.Then,partial least squares(PLS) and multiple linear regressions(MLR) were used to develop calibration and prediction model.Results show that optimal models are obtained by MSC coupled with PLS method,which the correlation coefficients of calibration set and prediction set were 0.956 and 0.933 and the root mean square error is 0.59 mm and 0.70 mm,respectively.The aboved results demonstrated that hyperspectral imaging technology is suitable for detection of green soybean thickness e.
