Statistical modeling for estimating glucosinolate content in Chinese cabbage by growth conditions

Kim, Do‐Gyun; Shim, Joon‐Yong; Ko, Myung‐Jun; Chung, Sun‐Ok; Chowdhury, Milon; Lee, Wang‐Hee

Statistical modeling for estimating glucosinolate content in Chinese cabbage by growth conditions

2018

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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