Authors: Huali Jin Linlin Li Junhu Cheng
Publish Date: 2015/03/17
Volume: 8, Issue: 10, Pages: 2524-2532
Abstract
Moisture content MC is a fundamental and very important quality indicator of peanut which has significant influence on the overall quality of peanut in the process of storage This study aimed to investigate the potential of hyperspectral imaging technique in the spectral range I 400–1000 nm and spectral range II 1000–2500 nm for predicting the MC of peanut kernels nondestructively Hyperspectral images were obtained and the corresponding spectral data was extracted The calibration models were built between the extracted spectral data and the measured MC using partial least squares regression PLSR analysis The established PLSR models using the full wavelengths showed good performance with determination coefficient R 2 p of 0908 and 0906 and root mean square errors by prediction RMSEP of 0063 and 0063 respectively Optimal wavelengths were then selected based on the regression coefficients of the established PLSR model The simplified PLSR models established only using identified optimal wavelengths also showed good performance with R 2 p of 0910 and 0900 and RMSEP of 0061 and 0060 respectively The best PLSR model established only using six optimal wavelengths 409 508 590 663 924 and 974 nm selected from the spectral range I was used to shift the spectrum of each pixel into its MC value for visualizing the distribution map of MC in peanut kernels The results demonstrated that hyperspectral imaging technique in tandem with chemometrics analysis has the potential for rapid and nondestructive prediction of MC in peanut kernels
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