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

Comparative Analysis of Laboratory-Based and Spectroscopic Methods Used to Estimate the Algal Density of Chlorella vulgaris

Chlorella vulgaris is of great importance in numerous exploratory or industrial applications (e.g., medicals, food, and feed additives). Rapid quantification of algal biomass is crucial in photobioreactors for the optimization of nutrient management and the estimation of production. The main goal of this study is to provide a simple, rapid, and not-resource-intensive estimation method for determining the algal density of C. vulgaris according to the measured parameters using UV-Vis spectrophotometry. Comparative assessment measurements were conducted with seven different methods (e.g., filtration, evaporation, chlorophyll a extraction, and detection of optical density and fluorescence) to determine algal biomass. By analyzing the entire spectra of diluted algae samples, optimal wavelengths were determined through a stepwise series of linear regression analyses by a novel correlation scanning method, facilitating accurate parameter estimation. Nonlinear formulas for spectrometry-based estimation processes were derived for each parameter. As a result, a general formula for biomass concentration estimation was developed, with recommendations for suitable measuring devices based on algae concentration levels. New values for magnesium content and the average single-cell weight of C. vulgaris were established, in addition to the development of a rapid, semiautomated cell counting method, improving efficiency and accuracy in algae quantification for cultivation and biotechnology applications.

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.

A precise microalgae farming for CO2 sequestration: A critical review and perspectives

Microalgae are great candidates for CO2 sequestration and sustainable production of food, feed, fuels and biochemicals. Light intensity, temperature, carbon supply, and cell physiological state are key factors of photosynthesis, and efficient phototrophic production of microalgal biomass occurs only when all these factors are in their optimal range simultaneously. However, this synergistic state is often not achievable due to the ever-changing environmental factors such as sunlight and temperature, which results in serious waste of sunlight energy and other resources, ultimately leading to high production costs. Most control strategies developed thus far in the bioengineering field actually aim to improve heterotrophic processes, but phototrophic processes face a completely different problem. Hence, an alternative control strategy needs to be developed, and precise microalgal cultivation is a promising strategy in which the production resources are precisely supplied according to the dynamic changes in key factors such as sunlight and temperature. In this work, the development and recent progress of precise microalgal phototrophic cultivation are reviewed. The key environmental and cultivation factors and their dynamic effects on microalgal cultivation are analyzed, including microalgal growth, cultivation costs and energy inputs. Future research for the development of more precise microalgae farming is discussed. This study provides new insight into developing cost-effective and efficient microalgae farming for CO2 sequestration.

An optofluidic platform for cell-counting applications

Cell-counting is critical for a wide range of applications, e.g., life sciences, medicine, or pharmacology. Hemocytometry is a classical method that requires manual counting of cells under a microscope. This methodology is low-cost but manual counting is slow, and the test accuracy is limited by the operator experience. Accuracy and throughput of such application could be improved with the use of automated cell-counting devices. Possessing the ability of recording and processing cell images, devices employing these technologies could dramatically improve the accuracy of the counting results. However, accuracy of these devices still requires further improvement as the counting results rely only on 100-200 cells. Furthermore, the test cost of these devices increases due to the need for a counting chamber or consumables compatible with their hardware settings. Herein, in order to address these drawbacks, we introduced an optofluidic cell-counting platform that could scan more than 2000 cells, which dramatically improves the test accuracy. Our technology could yield an error rate below 1% for cell viability, and below 5% for cell concentration. The platform could deliver the count results within only ∼1 minute, including sample loading, autofocusing, recording images, and image processing. The presented platform also benefits from a built-in fluidic component that eliminates the need for an external counting chamber, and allows fully automated sample loading and self-cleaning modality compatible with any solutions that are typically used for cell-counting tests. Providing an easy-to-use and rapid feature from sample loading to image analyses, our optofluidic platform could be a critical asset for accurate and low cost cell-counting applications.

Robust control designs for microalgae cultivation in continuous photobioreactors

Microalgae are used to produce renewable biofuels and high-value components and in bioremediation and CO2 sequestration tasks. These increasing applications, in conjunction with a desirable constant large-scale productivity, motivate the development and application of practical controllers. This paper addresses the application of robust control schemes for microalgae cultivation in continuous photobioreactors. Due to the model uncertainties and external perturbations, robust control designs are required to guarantee the desired microalgae productivity. Furthermore, simple controller designs are desirable for practical implementation purposes. Therefore, two robust control designs are applied and evaluated in this paper for two relevant case studies of microalgae cultivation in photobioreactors. The first control design is based on an enhanced simple-input output model with uncertain estimation. The second control design is the robust nonlinear model predictive control considering different uncertain scenarios. Numerical simulations of two case studies aimed at lipid production and CO2 capture under different conditions are presented to evaluate the robust closed-loop performance.

Quantifying Microalgae Growth by the Optical Detection of Glucose in the NIR Waveband

Microalgae have become a popular area of research over the past few decades due to their enormous benefits to various sectors, such as pharmaceuticals, biofuels, and food and feed. Nevertheless, the benefits of microalgae cannot be fully exploited without the optimization of their upstream production. The growth of microalgae is commonly measured based on the optical density of the sample. However, the presence of debris in the culture and the optical absorption of the intercellular components affect the accuracy of this measurement. As a solution, this paper introduces the direct optical detection of glucose molecules at 940-960 nm to accurately measure the growth of microalgae. In addition, this paper also discusses the effects of the presence of glucose on the absorption of free water molecules in the culture. The potential of the optical detection of glucose as a complement to the commonly used optical density measurement at 680 nm is discussed in this paper. Lastly, a few recommendations for future works are presented to further verify the credibility of glucose detection for the accurate determination of microalgae's growth.

Robust fractional control based on high gain observers design (RNFC) for a Spirulina maxima culture interfaced with an advanced oxidation process

In this article, the theory of fractional control and state estimation applied to biological science is studied, particularly in hybrid wastewater treatment. For nonlinear systems with stable and known states, an interconnected fractional robust control design with high gain state estimation is proposed to generate a control insensitive to nutritional perturbations originated by an advanced oxidation process in a microalgae culture. An online study is proposed for the mineralization of glyphosate and its feedback in a microalgae cultivation process where through the designed control the light dynamics is manipulated to robustly and automatically regulate the biomass signal provided by an analog sensor and nutrient estimation via state observers. In the literature, there are few results developed with real-time results. This work is a multidisciplinary study with online results where the performance and improvement of the proposed complex process are concluded.

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

Rajput V, Kumar Singh R, Guo Y, Kosolapov A, Usova E, Varduny T, Chalenko E, Yadronova O, Dmitriev P, Zaruba T. Chlorophyll fluorometry in evaluating photosynthetic performance: key limitations, possibilities, perspectives and alternatives

Non-destructive methods for the assessment of photosynthetic parameters of plants are widely applied to evaluate rapidly the photosynthetic performance, plant health, and shifts in plant productivity induced by environmental and cultivation conditions. Most of these methods are based on measurements of chlorophyll fluorescence kinetics, particularly on pulse modulation (PAM) fluorometry. In this paper, fluorescence methods are critically discussed in regard to some their possibilities and limitations inherent to vascular plants and microalgae. Attention is paid to the potential errors related to the underestimation of thylakoidal cyclic electron transport and anoxygenic photosynthesis. PAM-methods are also observed considering the color-addressed measurements. Photoacoustic methods are discussed as an alternative and supplement to fluorometry. Novel Fourier modifications of PAM-fluorometry and photoacoustics are noted as tools allowing simultaneous application of a dual or multi frequency measuring light for one sample.

Machine learning methods for assessing photosynthetic activity: environmental monitoring applications

Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.

Monitoring PHB production in Synechocystis sp. with hyperspectral images

Microalgae wastewater treatment systems have the potential for producing added-value products. More specifically, cyanobacteria are able to accumulate polyhydroxybutyrates (PHBs), which can be extracted and used for bioplastics production. Nonetheless, PHB production requires proper culture conditions and continue monitoring, challenging the state-of-the-art technologies. The aim of this study was to investigate the application of hyperspectral technologies to monitor cyanobacteria population growth and PHB production. We have established a ground-breaking measurement method able to discern spectral reflectance changes from light emitted to cyanobacteria in different phases. All in all, enabling to distinguish between cyanobacteria growth phase and PHB accumulation phase. Furthermore, first tests of classification algorithms used for machine learning and image recognition technologies had been applied to automatically recognize the different cyanobacteria species from a complex microbial community containing cyanobacteria and microalgae cultivated in pilot-scale photobioreactors (PBRs). We have defined three main indicators for monitoring PHB production: (i) cyanobacteria specific-strain density, (ii) differentiate between growth and PHB-accumulation and (iii) chlorosis progression. The results presented in this study represent an interesting alternative for traditional measurements in cyanobacteria PHB production and its application in pilot-scale PBRs. Although not directly determining the amount of PHB production, they would give insights on the undergoing processes.

Chlorophyll fluorescence as a valuable multitool for microalgal biotechnology

Variable fluorescence of chlorophyll (CF) of the photosynthetic apparatus is an ample source of valuable information on physiological condition of photosynthetic organisms. Currently, the most widespread CF-based technique is represented by recording pulse-amplitude modulated (PAM) induction of CF by saturating light. The CF-based monitoring techniques are increasingly employed for characterization of performance and stress resilience of microalgae in microalgal biotechnology. Analysis of CF induction curves reveals the fate of light energy absorbed by photosynthetic apparatus, the proportions of the energy that have been utilized for photochemistry (culture growth), and heat dissipated by photoprotective mechanisms. Hence CF and its derived parameters are an accurate proxy of the metabolic activity of the photosynthetic cell and the engagement of photoprotective mechanisms. This information is a solid foundation for making decisions on the microalgal culture management during the lab-scale and industrial-scale cultivation. Applications of CF and PAM include the monitoring of stressor (high light, nutrient deprivation, extreme temperatures, etc.) effects for assessment of the culture robustness. It also serves as a non-invasive express test for gauging the effect of assorted toxicants in microalgae. This approach is becoming widespread in ecological toxicology and environmental biotechnology, particularly for bioprospecting strains capable of the destruction of dangerous pollutants such as pharmaceuticals. In the review, we discuss the advantages and drawbacks of using CF-based methods for assessment of the culture conditions. Special attention is paid to the potential caveats and applicability of different variations of CF and PAM measurements for solving problems of microalgal biotechnology.