Statistical modeling for estimating glucosinolate content in Chinese cabbage by growth conditions
2018
Kim, Do‐Gyun | Shim, Joon‐Yong | Ko, Myung‐Jun | Chung, Sun‐Ok | Chowdhury, Milon | Lee, Wang‐Hee
BACKGROUND: 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
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