Abstract
Artemisia argyi has been widely used as an herbal medicine and food in South Korea. However, the harvesting time of A. argyi is still not known. In addition, quality-based classification of A. argyi used for consumption is necessary because of its pharmacological properties. To this end, the A. argyi harvesting time was determined, and quality grading of A. argyi was performed based on the following factors: the contents of eupatilin, jaceosidin, essential oils and the leaf weight ratio. It is appropriate to harvest A. argyi at the end of June in 2019 and the plants harvested in early July exhibit the highest quality, considering weather condition and statistical analyses. To grade the quality of A. argyi, machine learning algorithms were applied based on the aforementioned four components. Among the various models, the logistic regression model showed the best performance, with an area under the receiver operating characteristic curve of 0.906. In this study, we developed methods for determining the harvest time and classifying its quality so that it can be applied to different weather conditions every year with precision.
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Data are archived in S. Chung’s lab and available upon request.
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This work was supported by a research grant (RS-2023-00259086) from RDA, Korea.
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Supplementary file 1: Figure S1.
Weather conditions at the study site in 2019. Figure S2. Pairwise scatterplot matrices, density plots, and Pearson correlation coefficients of all variables. Figure S3. Diagnostic plots to evaluate several assumptions of regression model. Figure S4. Relative importance of predictor variables in the predictive linear model. Figure S5. Simulation-based histograms for estimating the coefficients of linear regression equation.
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Heo, S., Chung, S. Development of phenomic approaches to estimate harvesting time of Artemisia argyi and classifying its quality based on flavonoids, essential oil and leaf weight ratio. Plant Biotechnol Rep 17, 459–469 (2023). https://doi.org/10.1007/s11816-023-00846-5
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DOI: https://doi.org/10.1007/s11816-023-00846-5