Selection of the Optimal Wavebands for the Variety Discrimination of Chinese Cabbage Seed
This paper presents a method based on chemometrics analysis to select the optimal wavebands for variety discrimination of Chinese cabbage seed by using a Visible/Near-infrared spectroscopy (Vis/NIRS) system. A total of 120 seed samples were investigated using a field spectroradiometer. Chemometrics was used to build the relationship between the absorbance spectra and varieties. Principle component analysis (PCA) was not suitable for variety discrimination as the principle components (PCs) plot of three primary principle components could only intuitively distinguish the varieties well. Partial Least Squares Regression (PLS) was executed to select 6 optimal wavebands as 730nm, 420nm, 675nm, 620nm, 604nm and 609nm based on loading values. Two chemometrics, multiple linear regression (MLR) and stepwise discrimination analysis (SDA) were used to establish the recognition models. MLR model is not suitable in this study because of its unsatisfied predictive ability. The SDA model was proposed by the advantage of variable selection. The final results based on SDA model showed an excellent performance with high discrimination rate of 99.167%. It is also proved that optimal wavebands are suitable for variety discrimination.
KeywordsMultiple Linear Regression Partial Little Square Principle Component Analysis Chinese Cabbage Multiple Linear Regression Model
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