Statistical modeling for estimating glucosinolate content in Chinese cabbage by growth conditions
| Autor: | Milon Chowdhury, Do-Gyun Kim, Myung-Jun Ko, Wang-Hee Lee, Joon-Yong Shim, Sun-Ok Chung |
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| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
0301 basic medicine Nutrition and Dietetics biology Brassica Plant factory Statistical model Regression analysis biology.organism_classification 01 natural sciences 03 medical and health sciences Human health chemistry.chemical_compound 030104 developmental biology chemistry Glucosinolate Food science Food quality Agronomy and Crop Science 010606 plant biology & botany Food Science Biotechnology Mathematics |
| Zdroj: | Journal of the Science of Food and Agriculture. 98:3580-3587 |
| ISSN: | 0022-5142 |
| Popis: | Glucosinolate in Chinese cabbage (Brassica campestris L. ssp. pekinensis (Lour.) Rupr) has potential benefits for human health, and its content is affected by growth conditions. In this study, we used a statistical model to identify the relationship between glucosinolate content and growth conditions, and to predict glucosinolate content in Chinese cabbage.; Result: Multiple regression analysis was employed to develop the model's growth condition parameters of growing period, temperature, humidity and glucosinolate content measured in Chinese cabbage grown in a plant factory. The developed model was represented by a second-order multi-polynomial equation with two independent parameters: growth duration and temperature (adjusted R2 = 0.81), and accurately predicted glucosinolate content after 14 days of seeding.; Conclusion: To our knowledge, this study presents the first statistical model for evaluating glucosinolate content, suggesting a useful methodology for designing glucosinolate-related experiments, and optimizing glucosinolate content in Chinese cabbage cultivation. © 2018 Society of Chemical Industry.; © 2018 Society of Chemical Industry. |
| Databáze: | OpenAIRE |
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