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.
Keywords: Chinese cabbage; glucosinolate; growth conditions; multiple regression analysis; statistical modeling.
© 2018 Society of Chemical Industry.