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The Detection of Early-Maturing Pear’s Effective Acidity Based on Hyperspectral Imaging Technology

Abstract : The hyperspectral imaging technology is used to detect early-maturing pear’s effective acidity nondestructively, and effective prediction model is established. 145 pears’ hyperspectral images are obtained in the wavelength range of 400nm-1000nm. Total 145 pears are separated into the calibration set (77 samples) and prediction set (68 samples). Early-maturing pear’s effective acidity partial least squares (PLS) prediction model is built in different range of spectrum band. By comparison, the range 498 nm - 971 nm was selected in using partial least squares (PLS) to build early-maturing pear’s effective acidity prediction model. The experimental results show that, PLS prediction model of early-maturing pear’s effective acidity has the best effect in this range of wavelength. The correlation coefficient R between early-maturing pear’s actual effective acidity and predicted effective acidity is 0.9944 and 0.9233 for calibration set and prediction set respectively, the root mean squared error of prediction samples (RMSEP) is 0.022 and 0.072 for calibration set and prediction set respectively.
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Pengbo Miao, Long Xue, Muhua Liu, Jing Li, Xiao Wang, et al.. The Detection of Early-Maturing Pear’s Effective Acidity Based on Hyperspectral Imaging Technology. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.528-536, ⟨10.1007/978-3-642-18369-0_63⟩. ⟨hal-01564882⟩

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