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This research determines total phenolic content in green betel leaves using NIR spectroscopy and categorizes samples using SVM and LDA models.
One of the Indonesian plants that is used in traditional medicine is the green betel leaf. The main compound in green betel leaf is phenol. The aim of this study was to determine the classification model of green betel leaf powder to make it easier to determine the quality of green betel leaf based on the height of the planting site using NIR-chemometric spectroscopy and to determine the total phenolic content using UV-Vis spectrophotometry. The average yield of total phenol content in the lowlands, medium, and highlands was 12,349 mg GAE/g powder ± 0.866%; 22.042 mg GAE/g powder ± 0.924%; 28.827 mg GAE/g powder ± 0.801%. Statistical tests conducted with one-way ANOVA showed that there were significant differences in the average total phenolic content of green betel leaf powder at three altitudes (low, medium, and high). The results of determining the classification model used to categorize the 12 samples obtained 100% accuracy values on LDA, SVM, and SIMCA. The selected classification model was validated by internal validation and obtained an accuracy of 100% on the LDA and SIMCA models, while for the SVM model accuracy of 72.72% was obtained. On external validation, the accuracy is 100% on the LDA and SIMCA models. It can be concluded that the LDA and SIMCA models can classify all samples with the appropriate category.
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Nurani et al. (2024) studied this question.
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