AUTHOR=Manochkumar Janani , Jonnalagadda Annapurna , Cherukuri Aswani Kumar , Vannier Brigitte , Janjaroen Dao , Chandrasekaran Rajasekaran , Ramamoorthy Siva TITLE=Machine learning-based prediction models unleash the enhanced production of fucoxanthin in Isochrysis galbana JOURNAL=Frontiers in Plant Science VOLUME=Volume 15 - 2024 YEAR=2024 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1461610 DOI=10.3389/fpls.2024.1461610 ISSN=1664-462X ABSTRACT=Introduction

The marine microalga Isochrysis galbana is prolific producer of fucoxanthin, which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. This study presented a novel integrated experimental approach coupled with machine learning (ML) models to predict the fucoxanthin content in I. galbana by altering the type and concentration of phytohormone supplementation, thus overcoming the multiple methodological limitations of conventional fucoxanthin quantification.

Methods

A novel integrated experimental approach was developed, analyzing the effect of varying phytohormone types and concentrations on fucoxanthin production in I. galbana. Morphological analysis was conducted to assess changes in microalgal structure, while growth rate and fucoxanthin yield correlations were explored using statistical analysis and machine learning models. Several ML models were employed to predict fucoxanthin content, with and without hormone descriptors as variables.

Results

The findings revealed that the Random Forest (RF) model was highly significant with a high R2 of 0.809 and RMSE of 0.776 when hormone descriptors were excluded, and the inclusion of hormone descriptors further improved prediction accuracy to R2 of 0.839, making it a useful tool for predicting the fucoxanthin yield. The model that fitted the experimental data indicated methyl jasmonate (0.2 mg/L) as an effective phytohormone. The combined experimental and ML approach demonstrated rapid, reliable, and cost-efficient prediction of fucoxanthin yield.

Discussion

This study highlights the potential of machine learning models, particularly Random Forest, to optimize parameters influencing microalgal growth and fucoxanthin production. This approach offers a more efficient alternative to conventional methods, providing valuable insights into improving fucoxanthin production in microalgal cultivation. The findings suggest that leveraging diverse ML models can enhance the predictability and efficiency of fucoxanthin production, making it a promising tool for industrial applications.