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

Zhang C, Liu F, Kong W, He Y - Sensors (Basel) (2015)

Bottom Line: SPA-PLS model obtained the best performance with r(p) 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 Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China. chuzh@zju.edu.cn.

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 (r(p)) 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 r(p) 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.

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Prediction results of the prediction set (a) and the calibration set (b) of PLS model using full spectra. (The unit of RMSECV, RMSEC and RMSEP was mg/g in the entire paper).
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sensors-15-16576-f004: Prediction results of the prediction set (a) and the calibration set (b) of PLS model using full spectra. (The unit of RMSECV, RMSEC and RMSEP was mg/g in the entire paper).

Mentions: The prediction of soluble protein content of rape leaves was performed by using PLS with full cross validation. The optimum number of latent variables for the PLS model was 13. The rcv and RMSECV of the calibration set were 0.9337 and 0.1776 mg/g, respectively, and the cross validation result indicated that hyperspectral imaging could be efficiently used to estimate the soluble protein content of rape leaves. The PLS prediction result was presented in the scatter plot (Figure 4). Moreover, rc and rp were both higher than 0.9, and the RPD (residual prediction deviation) value was 2.98 indicated that PLS model was robust in predicting soluble protein content of rape leaves.


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

Zhang C, Liu F, Kong W, He Y - Sensors (Basel) (2015)

Prediction results of the prediction set (a) and the calibration set (b) of PLS model using full spectra. (The unit of RMSECV, RMSEC and RMSEP was mg/g in the entire paper).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541894&req=5

sensors-15-16576-f004: Prediction results of the prediction set (a) and the calibration set (b) of PLS model using full spectra. (The unit of RMSECV, RMSEC and RMSEP was mg/g in the entire paper).
Mentions: The prediction of soluble protein content of rape leaves was performed by using PLS with full cross validation. The optimum number of latent variables for the PLS model was 13. The rcv and RMSECV of the calibration set were 0.9337 and 0.1776 mg/g, respectively, and the cross validation result indicated that hyperspectral imaging could be efficiently used to estimate the soluble protein content of rape leaves. The PLS prediction result was presented in the scatter plot (Figure 4). Moreover, rc and rp were both higher than 0.9, and the RPD (residual prediction deviation) value was 2.98 indicated that PLS model was robust in predicting soluble protein content of rape leaves.

Bottom Line: SPA-PLS model obtained the best performance with r(p) 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 Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China. chuzh@zju.edu.cn.

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 (r(p)) 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 r(p) 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.

Show MeSH
Related in: MedlinePlus