Application of regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae

Doris Ying Ying Tang; Kit Wayne Chew; Huong-Yong Ting; Yuk-Heng Sia; Francesco G Gentili; Young-Kwon Park; Fawzi Banat; Alvin B Culaba; Zengling Ma; Pau Loke Show

doi:10.1016/j.biortech.2022.128503

Application of regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae

Bioresour Technol. 2023 Feb:370:128503. doi: 10.1016/j.biortech.2022.128503. Epub 2022 Dec 16.

Authors

Affiliations

¹ Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.
² School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459 Singapore.
³ Drone Research and Application Centre, University of Technology Sarawak, Sarawak, Malaysia.
⁴ Department of Forest Biomaterials and Technology (SBT), Swedish University of Agricultural Sciences (SLU), 901 83, Umeå, Sweden.
⁵ School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.
⁶ Department of Chemical Engineering, Khalifa University, P.O Box 127788, Abu Dhabi, United Arab Emirates.
⁷ Department of Mechanical Engineering, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines; Center for Engineering and Sustainable Development Research, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines.
⁸ Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China.
⁹ Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India. Electronic address: PauLoke.Show@nottingham.edu.my.

PMID: 36535615
DOI: 10.1016/j.biortech.2022.128503

Abstract

This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R² of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R² of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.

Keywords: Chlorophyll; Microalgae; Multilayer perceptron; Prediction; Regression.

MeSH terms

Chlorophyll*
Linear Models
Microalgae*
Neural Networks, Computer

Substances

Chlorophyll