Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques.
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| Title: | Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques. |
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| Authors: | Suratanee A; Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.; Intelligent and Nonlinear Dynamic Innovations Research Center, Science and Technology Research Institute, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand., Chutimanukul P; National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Klong Luang, Thailand., Saelao T; Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand., Chadchawan S; Center of Excellence in Environment and Plant Physiology (CEEPP), Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.; Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand., Buaboocha T; Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.; Center of Excellence in Molecular Crop, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand., Plaimas K; Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand.; Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand. |
| Source: | PloS one [PLoS One] 2024 Oct 02; Vol. 19 (10), pp. e0309132. Date of Electronic Publication: 2024 Oct 02 (Print Publication: 2024). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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