Why and How to Monitor Microalgal Biofilms?

By David Pereira

Microalgae are an excellent source of valuable compounds for diverse industries, including food, aquaculture, and nutraceuticals. Traditionally, microalgae are cultivated in a planktonic state within photobioreactors (PBRs) and raceways. Recent advancements have led to the development of biofilm-based technologies, which utilize microalgae’s ability to form biofilms and grow on substrates. This technique reduces the required water volume and simplifies the harvesting process, resulting in higher biomass yields and enhanced energy and cost efficiency.

However, the industrial production of these valuable molecules can only be optimized if we carefully monitor biological features like biomass and cell traits. In both traditional and biofilm-based cultivation methods, there are currently no standard, easy-to-use systems for online or inline analysis. Monitoring is predominantly done destructively by taking samples for offline analysis, which is both time-consuming and poses a contamination risk. Specifically, in biofilm systems, this method removes a significant amount of the final product as biomass is highly concentrated. Thus, the development of non-destructive techniques to improve productivity and preserve the integrity of the biofilms is highly desirable.

Innovations in the monitoring field have introduced optical methods for in situ monitoring, using techniques like UV-Vis, fluorescence, and vibrational spectroscopy (FTIR and Raman). Yet, for microalgal biofilms, which often develop on opaque carriers like textile fabrics, a new non-invasive monitoring tool is necessary. The opacity of these carriers makes reflectance-based methods more suitable and effective than transmission-based ones.

To address this gap, techniques already in use in fields such as precision agriculture and environmental science can be adapted for microalgal biofilms. Reflectance technology, for instance, has been successfully used for characterizing natural biofilm communities and for measuring primary production in oceans. From a commercial perspective, this sensing technique has proven effective in agriculture for applications such as plant phenotyping and monitoring plant growth and nutritional status. This is accomplished by computing reflectance indices (RIs) based on measurements in the visible (400-700 nm) and near-infrared (700-1000 nm) spectral regions, which are then utilized to develop correlations with the targeted features.

At CentraleSupélec in Paris, our research is dedicated to addressing these monitoring challenges. Haematococcus pluvialis was chosen as the primary experimental microalgae not only because it is the leading producer of astaxanthin – a carotenoid known for its potent antioxidant properties and numerous health benefits – but also because it undergoes significant changes in cell pigments, structure, and composition during its life cycle. These changes make it an ideal candidate for evaluating the robustness and adaptability of a monitoring sensor under varying conditions.

Therefore, we explored the dynamics of biomass and astaxanthin production in H. pluvialis within a rotating biofilm system. This study involved characterizing key biofilm traits for process optimization, including biomass accumulation, astaxanthin content, and chlorophyll levels under various light and nutrient conditions (Morgado et al., 2023). Subsequently, in the monitoring domain, we developed a protocol using reflectance spectroscopy and specifically tailored reflectance indices that correlated strongly with those key biofilm traits in a non-destructive manner (Morgado et al., 2024).

Figure 1. Workflow depicting the protocol utilized to develop and evaluate the regression models based on reflectance spectra.

The workflow of that protocol, summarized in Figure 1, involved:

  1. Acquiring reflectance spectra simultaneously with each biofilm trait measurement.
  2. Identifying relevant bands by applying pre-processing techniques such as the Savitzky–Golay filter and Pearson’s correlation, and constructing reflectance indices in three different formats: A-B, A/B, and (A-B)/(A+B).
  3. Computing all possible combinations, and selecting the best indices for each biofilm trait. These indices were then used to develop and calibrate linear models.
  4. Validating the models on an independent dataset to evaluate their predictive performance and reliability.

Our findings confirmed that the relationships between biofilm traits and reflectance indices (RIs) can be accurately described using linear regression models, demonstrating their strong predictive performance and adaptability under various operational conditions. The implementation of this monitoring technique could lead to an efficient and smarter way of microalgae biofilm farming making it an excellent candidate for scaling up to industrial applications. Looking forward, the focus will shift towards integrating this framework into an online monitoring system, combined with mathematical modeling and control strategies, to further boost the productivity and efficiency of microalgal biofilm-based technologies.

References

Morgado, D., Fanesi, A., Martin, T., Tebbani, S., Bernard, O., & Lopes, F. (2023). Exploring the dynamics of astaxanthin production in Haematococcus pluvialis biofilms using a rotating biofilm-based system. Biotechnology and Bioengineering, 1–14. https://doi.org/10.1002/bit.28624

Morgado, D., Fanesi, A., Martin, T., Tebbani, S., Bernard, O., & Lopes, F. (2024). Non-destructive monitoring of microalgae biofilms. Bioresource Technology, 398, 130520. https://doi.org/10.1016/j.biortech.2024.130520