Name
Title | ||
|---|---|---|
Application of Least Squares-Support Vector Machine for Measurement of Soluble Solids Content of Rice Vinegars Using Vis/NIR Spectroscopy |
Abstract | ||
|---|---|---|
Visible and near infrared (Vis/NIR) spectroscopy was investigated to predict soluble solids content (SSC) of rice vinegars based on least squares-support vector machine (LS-SVM). Five varieties of rice vinegars and 300 samples were prepared. After some preprocessing, PLS was implemented for calibration as well as the extraction of principal components (PCs). Wavelet transform (WT) was use to compress the variables. The selected PCs and compressed variables were applied as the inputs to develop PC-LS-SVM and WT-LS-SVM models. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.958, 1216, and -0.310 for PLS, 0.997, 0.357 and 0.121 for PC-LS-SVM, whereas 0.999, 0.199 and 0.030 for WT-LS-SVM, respectively. A high and excellent precision was achieved by LS-SVM models. The results indicated that Vis/NIR spectroscopy could be successfully applied as a fast and high precision method for the measurement of SSC of rice vinegars. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CIS.2007.104 | CIS |
Keywords | Field | DocType |
principal component,infrared spectra,spectroscopy,calibration,least squares support vector machine,solids,wavelet transforms,nir spectroscopy,near infrared,temperature,wavelet transform | Correlation coefficient,Analytical chemistry,Least squares support vector machine,Computer science,Infrared spectroscopy,Near-infrared spectroscopy,Mean squared error,Artificial intelligence,Spectroscopy,Calibration,Machine learning,Principal component analysis | Conference |
Volume | Issue | ISSN |
null | null | 2161-1890 |
ISBN | Citations | PageRank |
0-7695-3072-9 | 4 | 0.66 |
References | Authors | |
8 | 3 | |