Machine learning-based prediction models unleash the enhanced production of fucoxanthin in Isochrysis galbana - Manochkumar, Janani; Jonnalagadda, Annapurna; Cherukuri, Aswani Kumar; Vannier, Brigitte; Janjaroen, Dao; Chandrasekaran, Rajasekaran; Ramamoorthy, Siva (Frontiers in Plant Science, 2024)
The marine microalgae, Isochrysis galbana is a 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 Isochrysis galbana by altering the type and concentration of phytohormone supplementation, thus overcoming the multiple methodological limitations of conventional fucoxanthin quantification. Morphological analysis of microalgal structure revealed the influence of type and concentration of phytohormones and the correlation between growth rate and fucoxanthin yield was further evidenced by statistical and ML models. The findings revealed that the Random Forest (RF) model was highly significant with a high 𝑅 2 of 0.809 and 𝑅𝑀𝑆𝐸 of 0.776 when hormone descriptors were excluded and the inclusion of hormone descriptors further improved prediction accuracy to 𝑅 2 of 0.839 making it a useful tool for predicting the fucoxanthin yield. The model which fitted to the experimental data indicated methyl jasmonate (0.2 mg l -1 ) as an effective phytohormone. In short, the estimation of fucoxanthin yield using prediction models is rapid, reliable, more efficient, and less expensive. This research highlights the potential of utilizing diverse ML models to optimize the parameters affecting microalgal growth, offering valuable insights to improve the fucoxanthin production efficiency in microalgae cultivation.