APA (7th ed.) Citation

Du, R., Bu, T., Huang, Z., Morley, J. D., Xing, Y., Li, M., . . . Jin, Z. (2025). 3D excitation-emission matrix (EEM) fluorescence-based efficient detection of cosmetics health risks: Comparative AI algorithm and eXtreme Gradient Boosting (XGBoost) implementations. Journal of cleaner production, 533, 146996. https://doi.org/10.1016/j.jclepro.2025.146996

Chicago Style (17th ed.) Citation

Du, Runbao, et al. "3D Excitation-emission Matrix (EEM) Fluorescence-based Efficient Detection of Cosmetics Health Risks: Comparative AI Algorithm and EXtreme Gradient Boosting (XGBoost) Implementations." Journal of Cleaner Production 533 (2025): 146996. https://doi.org/10.1016/j.jclepro.2025.146996.

MLA (9th ed.) Citation

Du, Runbao, et al. "3D Excitation-emission Matrix (EEM) Fluorescence-based Efficient Detection of Cosmetics Health Risks: Comparative AI Algorithm and EXtreme Gradient Boosting (XGBoost) Implementations." Journal of Cleaner Production, vol. 533, 2025, p. 146996, https://doi.org/10.1016/j.jclepro.2025.146996.