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

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.