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Sensors 2015, 15(7), 16576-16588; doi:10.3390/s150716576

Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves

College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
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Authors to whom correspondence should be addressed.
Academic Editor: Mark A. Arnold
Received: 14 April 2015 / Revised: 27 June 2015 / Accepted: 3 July 2015 / Published: 9 July 2015
(This article belongs to the Special Issue Chemical Sensors based on In Situ Spectroscopy)
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Abstract

Visible and near-infrared hyperspectral imaging covering spectral range of 380–1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500–900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (rp) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with rp of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves. View Full-Text
Keywords: hyperspectral imaging; soluble protein content; weighted regression coefficient; successive projections algorithm; genetic algorithm-partial least squares hyperspectral imaging; soluble protein content; weighted regression coefficient; successive projections algorithm; genetic algorithm-partial least squares
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, C.; Liu, F.; Kong, W.; He, Y. Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves. Sensors 2015, 15, 16576-16588.

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