Application of regression and artificial neural network analysis of Red-Green-Blue image components in prediction of chlorophyll content in microalgae. (February 2023)

• Record Type: Journal Article
• Authors: Ying Ying Tang, Doris; Wayne Chew, Kit; Ting, Huong-Yong; Sia, Yuk-Heng; Gentili, Francesco G.; Park, Young-Kwon; Banat, Fawzi; Culaba, Alvin B.; Ma, Zengling; Loke Show, Pau
• Is Part Of: Bioresource technology. Volume 370(2023)
• Publication Date: 2023-02
• Subjects: Chlorophyll -- Microalgae -- Prediction -- Multilayer perceptron -- Regression; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.128503
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Chlorophyll is important in assessing algae growth and producing nutraceuticals. LR and ANN techniques are used to predict chlorophyll content in microalgae. RMSE, prediction accuracy and R 2 metrics are used as evaluation benchmarks. Both prediction models are superior to conventional spectrophotometry method. ANN with low RMSE is an efficient chlorophyll concentration prediction model. 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 R 2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R 2 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.