Abstract
Current study results on Support Vector Machines have been published. According to news reporting out of Nanning, People’s Republic of China, by NewsRx editors, research stated, “Oysters are extensively cultivated worldwide. However, significant variations in chemical composition, quality, and price exist between oysters from different geographical origins.” Funders for this research include Guangxi First-class Discipline Project for Pharmaceutical Sciences, Fangchenggang Science and Technology Program, Major Program. Our news journalists obtained a quote from the research from Guangxi Medical University, “This study employed portable near-infrared spectroscopy in conjunction with chemometric analysis to determine the geographical origin and glycogen content of oysters. Pretreatment methods (multiplicative scattering correction, first derivative, and second derivative) were used to preprocess the raw spectra. Partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and support vector machine (SVM) were then adopted to establish the qualitative models. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were compared for predicting the glycogen content. The results revealed that the PLS-DA, OPLS-DA, and SVM models classified the geographical origin of oysters with 100% accuracy. For quantitative analysis, the regression equations displayed high predictive ability. The SVMR model was superior to the PLSR model for glycogen content prediction, with a coefficient of determination of prediction (R2P) of 0.9253 and a residual prediction deviation (RPD) of 3.62.”
NETL
NSTL
Elsevier