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Visible and short-wave near infrared (Vis-SwNIR) spectroscopy was employed to determination the harvest time of laver. In order to eliminate useless variables and improve accuracy, uninformative variable elimination (UVE) was proposed to eliminate the uninformative information from full-range spectra (FRS). Then, the retained variables were processed by Principal Component Analysis (PCA), and finally...
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA)...
Visible and near infrared (NIR) spectroscopy was utilized to classify the verities of laver. As there are almost six hundreds of NMR variables which would cause poor classification and long calculation time, uninformative variables should be eliminated. Successive projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS). Finally 13 variables were selected,...
A novel method which is combination of uninformative variable elimination by partial least squares (UVE) and least-square support vector machine (LS-SVM) was proposed to discriminate soy milk powder. A total of 240 (60 for each variety) samples were characterized on the basis of visual and infrared spectroscopy (VIS-NIR), 160 (40 for each variety) samples were selected randomly for the calibration...
A novel method which was combination of uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) was proposed to extra relevant information among different varieties of panax. A total of 78 (26 for each variety) samples were characterized on the basis of visual and infrared spectroscopy (VIS-NIR), 63 (21 for each variety) samples were selected randomly for...
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