APA (7th ed.) Citation

Liu, T., Jiang, H., & Chen, Q. (2022). Input features and parameters optimization improved the prediction accuracy of support vector regression models based on colorimetric sensor data for detection of aflatoxin B1 in corn. Microchemical journal, 178, 107407. https://doi.org/10.1016/j.microc.2022.107407

Chicago Style (17th ed.) Citation

Liu, Tao, Hui Jiang, and Quansheng Chen. "Input Features and Parameters Optimization Improved the Prediction Accuracy of Support Vector Regression Models Based on Colorimetric Sensor Data for Detection of Aflatoxin B1 in Corn." Microchemical Journal 178 (2022): 107407. https://doi.org/10.1016/j.microc.2022.107407.

MLA (9th ed.) Citation

Liu, Tao, et al. "Input Features and Parameters Optimization Improved the Prediction Accuracy of Support Vector Regression Models Based on Colorimetric Sensor Data for Detection of Aflatoxin B1 in Corn." Microchemical Journal, vol. 178, 2022, p. 107407, https://doi.org/10.1016/j.microc.2022.107407.