Unravelling the impact of light spectra on microalgal growth and biochemical composition using principal component analysis and artificial neural network models

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Esteves, AF (Author) FEUP; Pardilho, S (Author) Other; Gonsalves, AL (Author) Other; Vitor Vilar (Author) FEUP; Pires, JCM (Author) Other

Journal

Title: Algal Research

Vol. 85

ISSN: 2211-9264

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-017-GTZ

DOI: 10.1016/j.algal.2024.103820

Abstract (EN): Optimising cultivation conditions is essential for making the large-scale production of microalgae-based products economically and environmentally viable. However, the species-specific responses of microalgae to light spectra remain underexplored, particularly regarding the interconnected effects on growth, nutrient uptake, photosynthetic performance, and biochemical composition. This study presents the impact of different light spectra on Chlorella vulgaris growth and nutrient uptake. The photosynthetic activity and biomass biochemical composition were also assessed at two different growth stages (late-exponential and stationary phases). Principal component analysis (PCA) was also conducted to uncover the relationships between key variables, including light wavelength, exposure time, biomass concentration, nutrients availability, N:P ratio and biomass composition. Additionally, genetic algorithm-optimised artificial neural networks (GA-ANN) models were developed to predict biochemical composition based on environmental variables. The results demonstrated that red light promoted high growth rates (0.509 f 0.004 d- 1 ) and biomass productivity (103 f 6 mg L- 1 d- 1 ), whereas blue light worsened growth-related results (0.424 f 0.003 d- 1 ). NO3-N uptake was enhanced by white light (21.6 f 0.4 %) and orange light in the stationary phase (32 f 1 %). Meanwhile, PO4-P uptake was boosted by red light in the late-exponential phase (67 f 2 %). Regarding the biomass composition, blue light enhanced protein production (33.8-39.0 % DCW), whereas red light increased carbohydrate accumulation (20.2-23.5 % DCW). The lipid (21.6-24.5 % DCW) and photosynthetic pigment contents were boosted by white light. The GA-ANN models demonstrated strong predictive accuracy, with protein content showing the highest performance (R2: 0.991, RMSE: 0.006 % DCW), followed by carotenoids and chlorophyll content. The outcomes of this study are useful for improving microalgal production techniques, bioremediation strategies and target compound accumulation methods.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11