Name
Title | ||
|---|---|---|
Detection of Protein Content of Oilseed Rape Leaves Using Visible/Near-Infrared Spectroscopy and Multivariate Calibrations |
Abstract | ||
|---|---|---|
Visible and near-infrared (Vis/NIR) spectroscopy was investigated for fast and non-destructive determination of protein content in rapeseed leaves treated with herbicide of Pyribambenz-propyl (PP). 64 samples were used in the calibration set, whereas 32 samples in the validation set. Partial least squares (PLS) analysis was the calibration method as well as extraction method of latent variables (LVs). Certain selected LVs were used as the inputs of back propagation neural networks (BPNN) and least squares-support vector machine (LS-SVM). The prediction results demonstrated that LS-SVM outperformed PLS and BPNN methods. The correlation coefficient, RMSEP and bias in validation set by LS-SVM were 0.999, 59.562 and 7.437 for protein content, respectively. The results indicated that Vis/NIR spectroscopy combined with LS-SVM could be successfully applied for the detection of protein content of rapeseed leaves. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.590 | ICNC |
Keywords | Field | DocType |
oil seed rape leaves,correlation coefficient,nir spectroscopy,extraction method,oilseed rape,protein content,bpnn method,proteins,near-infrared spectroscopy,partial least squares analysis,backpropagation,protein content detection,biology computing,least mean squares methods,visible/near-infrared spectroscopy,molecular biophysics,backpropagation neural networks,latent variable,validation set,calibration set,multivariate calibrations,calibration method,certain selected lvs,support vector machines,neural nets,squares-support vector machine,spectroscopy,kernel,predictive models,least squares support vector machine,calibration,near infrared spectroscopy,near infrared,correlation | Correlation coefficient,Rapeseed,Multivariate statistics,Computer science,Near-infrared spectroscopy,Partial least squares regression,Back propagation neural network,Artificial intelligence,Spectroscopy,Machine learning,Calibration | Conference |
Volume | ISBN | Citations |
3 | 978-0-7695-3304-9 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Liu | 1 | 20 | 6.06 |
| Hui Fang | 2 | 1 | 2.05 |
| Yong He | 3 | 487 | 65.25 |
| Fan Zhang | 4 | 0 | 0.34 |
| Zonglai Jin | 5 | 0 | 0.34 |
| Wei-Jun Zhou | 6 | 206 | 16.00 |