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

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish
Article number128503
JournalBioresource Technology
Volume370
DOIs
StatePublished - Feb 2023

Keywords

  • Chlorophyll
  • Microalgae
  • Multilayer perceptron
  • Prediction
  • Regression

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