Name
Title | ||
|---|---|---|
Fast discrimination of juicy peach varieties by Vis/NIR spectroscopy based on Bayesian-SDA and PCA |
Abstract | ||
|---|---|---|
Visible/Near-infrared reflectance spectroscopy (Vis/NIRS) was applied to variety discrimination of juicy peach. A total of 75 samples were investigated for Vis/NIRS using a field spectroradiometer. Chemometrics was used to build the relationship between the absorbance spectra and varieties. Principle component analysis (PCA) was executed to reduce numerous wavebands into 8 principle components (PCs) as variables of stepwise discrimination analysis (SDA). After execution of SDA through variables selection with 21 samples as validation set, the final results shown an excellent performance of 100% varieties discrimination which was better than the one only predicted by using partial least squares (PLS) model. The results showed the potential ability of Vis/NIRS coupled with SDA-PCA algorithm to discriminate the varieties of juicy peach. The analysis model was rapid, objective and accurate. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11816157_113 | ICIC (1) |
Keywords | Field | DocType |
near-infrared reflectance spectroscopy,variables selection,variety discrimination,fast discrimination,principle component,juicy peach,analysis model,juicy peach variety,principle component analysis,nir spectroscopy,sda-pca algorithm,stepwise discrimination analysis,varieties discrimination,variable selection | Pattern recognition,Absorbance,Computer science,Partial least squares regression,Near-infrared spectroscopy,Artificial intelligence,Reflectance spectroscopy,Spectroradiometer,Chemometrics,Principal component analysis,Bayesian probability | Conference |
Volume | ISSN | ISBN |
4113 | 0302-9743 | 3-540-37271-7 |
Citations | PageRank | References |
1 | 0.51 | 2 |
Authors | ||
3 | ||