Dynamic Discriminant Models for the Detection of Subtle Bruising in Huping Jujube Constructed Based on Their Visible/near-infrared Spectral Data
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
This study aimed to identify a rapid effective method to distinguish intact and subtly bruised Huping jujubes by the dynamic collection of their near infrared (NIR) spectral data. A combination of the Savitzky-Golay (S-G) and multiplicative scatter correction (MSC) methods were used for the preprocessing of spectral data. Full spectrum (FS) data obtained after preprocessing, major component data extracted by principal component analysis (PCA), and characteristic wavelength data extracted by successive projections algorithm (SPA) were used as input variables for the construction of models by partial least squares discriminant analysis (PLS-DA), or using the least squares-support vector machine (LS-SVM). The accuracy of these models in discriminating between the four types of intact and subtly bruised Huping jujubes was determined. The results of these analyses revealed the obvious advantages of PCA use for the extraction of the major components of Huping jujubes; in addition, this (PCA) data fulfilled all practical requirements for the accurate discrimination of all four types of subtly bruised samples. The PCA-LS-SVM model demonstrated optimal accuracy in the discrimination of four types of subtly bruised and intact Huping jujubes (100%, 86%, 100%, 100%, and 100%, respectively), , resulting in a total discrimination accuracy of 97.2%). In conclusion, this study provides a new theoretical basis for the dynamic discrimination of subtly bruised Huping Jujube.
