Sammanfattning

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.

Nyckelord

Chlorophyll; Microalgae; Prediction; Multilayer perceptron; Regression

Publicerad i

Bioresource Technology
2023, Volym: 370, artikelnummer: 128503
Utgivare: ELSEVIER SCI LTD

SLU författare

• Gentili, Francesco

  • Institutionen för skogens biomaterial och teknologi, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Bioinformatik (beräkningsbiologi)
Annan industriell bioteknik


Publikationens identifierare

DOI: https://doi.org/10.1016/j.biortech.2022.128503

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/121161