Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

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• 作者：Di Wu^a ; ^b ; Hui Shi^a ; Songjing Wang^c ; Yong He^a ; ^{yhe@zju.edu.cn} ; Yidan Bao^a ; Kangsheng Liu^c
• 关键词：Hyperspectral imaging ; Prawn ; Successive projections algorithm ; Image processing ; Wavelength selection ; Imaging spectroscopy
• 刊名：Analytica Chimica Acta
• 出版年：2012
• 期刊代码：2_00032670
• 类别：ch
• 出版时间：13 May, 2012
• 卷：726
• 期：Complete
• 页码：57-66
• 文件大小：1021 K

摘要

Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380-1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on 鈥楳anual Prawn Mask鈥?and 鈥楢utomatic Prawn Mask鈥? respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. Successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428, 445, 544, 569, 629, 672, 697, 760, 827, 917, 958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round.