Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives

Non-destructive monitoring of microalgae biofilms

Biofilm-based cultivation systems are emerging as a promising technology for microalgae production. However, efficient and non-invasive monitoring routines are still lacking. Here, a protocol to monitor microalgae biofilms based on reflectance indices (RIs) is proposed. This framework was developed using a rotating biofilm system for astaxanthin production by cultivating Haematococcus pluvialis on cotton carriers. Biofilm traits such as biomass, astaxanthin, and chlorophyll were characterized under different light and nutrient regimes. Reflectance spectra were collected to identify the spectral bands and the RIs that correlated the most with those biofilm traits. Robust linear models built on more than 170 spectra were selected and validated on an independent dataset. Astaxanthin content could be precisely predicted over a dynamic range from 0 to 4% of dry weight, regardless of the cultivation conditions. This study demonstrates the strength of reflectance spectroscopy as a non-invasive tool to improve the operational efficiency of microalgae biofilm-based technology.

Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Microalgae provide valuable bio-components with economic and environmental benefits. The monitoring of microalgal production is mostly performed using different sensors and analytical methods that, although very powerful, are limited to qualified users. This study proposes an automated Raman spectroscopy-based sensor for the online monitoring of microalgal production. For this purpose, an in situ system with a sampling station was made of a light-tight optical chamber connected to a Raman probe. Microalgal cultures were routed to this chamber by pipes connected to pumps and valves controlled and programmed by a computer. The developed approach was evaluated on Parachlorella kessleri under different culture conditions at a laboratory and an industrial algal platform. As a result, more than 4000 Raman spectra were generated and analysed by statistical methods. These spectra reflected the physiological state of the cells and demonstrate the ability of the developed sensor to monitor the physiology of microalgal cells and their intracellular molecules of interest in a complex production environment.

Big data and machine learning driven bioprocessing - Recent trends and critical analysis

Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.

Automated cultivation system for microalgae: Growth factors and control

Background:

Microalgae have been a hot research topic due to their various biorefinery applications, particularly microalgae as potential alternative nutraceuticals and supplements have a large and rapidly growing market. However, the commercial production is limited due to high processing cost, low efficiency, and scale up of biomass production.

Objective:

It is important to control the microalgae cultivation system with optimal parameters to maximize the biomass productivity. The growth factors including pH, temperature, light intensity, salinity, and nutrients are discussed as these can significantly affect the cultivation. To monitor and control these in real-time, an automated system incorporating advanced digital technologies like sensors, controllers, artificial intelligence (AI), and Internet of Things (IoT) could be applied.

Conclusion:

This perspective provides insights on the implementation of an automated microalgae cultivation system which improves the productivity, effectiveness, and efficiency.

Biotechnological Enhancement of Probiotics through Co-Cultivation with Algae: Future or a Trend?

The diversity of algal species is a rich source of many different bioactive metabolites. The compounds extracted from algal biomass have various beneficial effects on health. Recently, co-culture systems between microalgae and bacteria have emerged as an interesting solution that can reduce the high contamination risk associated with axenic cultures and, consequently, increase biomass yield and synthesis of active compounds. Probiotic microorganisms also have numerous positive effects on various aspects of health and represent potent co-culture partners. Most studies consider algae as prebiotics that serve as enhancers of probiotics performance. However, the extreme diversity of algal organisms and their ability to produce a plethora of metabolites are leading to new experimental designs in which these organisms are cultivated together to derive maximum benefit from their synergistic interactions. The future success of these studies depends on the precise experimental design of these complex systems. In the last decade, the development of high-throughput approaches has enabled a deeper understanding of global changes in response to interspecies interactions. Several studies have shown that the addition of algae, along with probiotics, can influence the microbiota, and improve gut health and overall yield in fish, shrimp, and mussels aquaculture. In the future, such findings can be further explored and implemented for use as dietary supplements for humans.

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

A review on the enzymes and metabolites identified by mass spectrometry from bacteria and microalgae involved in the degradation of high molecular weight PAHs

High molecular weight PAHs (HMW PAHs) are dangerous pollutants widely distributed in the environment. The use of microorganisms represents an important tool for HMW PAHs bioremediation, so, the understanding of their biochemical pathways facilitates the development of biodegradation strategies. For this reason, the potential role of species of microalgae, bacteria, and microalga-bacteria consortia in the degradation of HMW PAHs is discussed. The identification of their metabolites, mostly by GC-MS and LC-MS, allows a better approach to the enzymes involved in the key steps of the metabolic pathways of HMW PAHs biodegradation. So, this review intends to address the proteomic research on enzyme activities and their involvement in regulating essential biochemical functions that help bacteria and microalgae in the biodegradation processes of HMW PAHs. It is noteworthy that, given that to the best of our knowledge, this is the first review focused on the mass spectrometry identification of the HMW PAHs metabolites; whereby and due to the great concern of the presence of HMW PAHs in the environment, this material could help the urgency of developing new bioremediation methods. The elucidation of the metabolic pathways of persistent pollutant degrading microorganisms should lead to a better knowledge of the enzymes involved, which could contribute to a very ecological route to the control of environmental contamination in the future.

Advances in the technologies for studying consortia of bacteria and cyanobacteria/microalgae in wastewaters

The excessive generation and discharge of wastewaters have been serious concerns worldwide in the recent past. From an environmental friendly perspective, bacteria, cyanobacteria and microalgae, and the consortia have been largely considered for biological treatment of wastewaters. For efficient use of bacteria‒cyanobacteria/microalgae consortia in wastewater treatment, detailed knowledge on their structure, behavior and interaction is essential. In this direction, specific analytical tools and techniques play a significant role in studying these consortia. This review presents a critical perspective on physical, biochemical and molecular techniques such as microscopy, flow cytometry with cell sorting, nanoSIMS and omics approaches used for systematic investigations of the structure and function, particularly nutrient removal potential of bacteria‒cyanobacteria/microalgae consortia. In particular, the use of specific molecular techniques of genomics, transcriptomics, proteomics metabolomics and genetic engineering to develop more stable consortia of bacteria and cyanobacteria/microalgae with their improved biotechnological capabilities in wastewater treatment has been highlighted.

Determination of Cell Abundances and Paralytic Shellfish Toxins in Cultures of the Dinoflagellate Gymnodinium catenatum by Fourier Transform Near Infrared Spectroscopy

Harmful algal blooms are responsible worldwide for the contamination of fishery resources, with potential impacts on seafood safety and public health. Most coastal countries rely on an intense monitoring program for the surveillance of toxic algae occurrence and shellfish contamination. The present study investigates the use of near infrared (NIR) spectroscopy for the rapid in situ determination of cell concentrations of toxic algae in seawater. The paralytic shellfish poisoning (PSP) toxin-producing dinoflagellate Gymnodinium catenatum was selected for this study. The spectral modeling by partial least squares (PLS) regression based on the recorded NIR spectra enabled the building of highly accurate (R2 = 0.92) models for cell abundance. The models also provided a good correlation between toxins measured by the conventional methods (high-performance liquid chromatography with fluorescence detection (HPLC-FLD)) and the levels predicted by the PLS/NIR models. This study represents the first necessary step in investigating the potential of application of NIR spectroscopy for algae bloom detection and alerting.