Classification of edible vegetable oils based on three-dimensional fluorescence spectroscopy and ISSA-SVM
Year: 2024
Type: article
Abstract: ObjectiveTo improve the classification accuracy of edible vegetable oils, an identification model based on three-dimensional fluorescence spectroscopy and ISSA-SVM was established.MethodsCombining 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.ResultsCompared 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%.ConclusionThe 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.
Authors: ZHANG Jing, QI Guohong, CHEN Jingzhao, CAO Xiaoli, LI Lili
Institutions: Sias University, Henan Agricultural University
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6941459528
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