Classification of edible vegetable oils based on three-dimensional fluorescence spectroscopy and ISSA-SVM
[Objective]To improve the classification accuracy of edible vegetable oils,an identification model based on three-dimensional fluorescence spectroscopy and ISSA-SVM was established.[Methods]Combining the feature information of three-dimensional fluorescence spectroscopy,an improved sparrow search algorithm was used to optimize the parameters of the SVM model,constructing an edible vegetable oil identification method that integrates the characteristics of three-dimensional fluorescence spectroscopy information and the ISSA-SVM model.[Results]Compared with the SVM model,GA-SVM model,PSO-SVM model,and SSA-SVM model,the classification accuracy of the ISSA-SVM model for edible vegetable oils reached 100%.[Conclusion]The ISSA-SVM model has higher convergence efficiency,system stability,and the ability to avoid local optimal solutions,which can effectively cope with complex and variable sample classification tasks.
support vector machinesparrow search algorithmthree-dimensional fluorescence spectroscopyedible vegetable oils
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