2D Fluorescence spectroscopy for monitoring Dunaliella salina concentration and integrity during membrane harvesting

Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition

The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method's prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.

Rapid measurement of brown tide algae using Zernike moments and ensemble learning based on excitation-emission matrix fluorescence

Accurate real-time prediction of microalgae density has great practical significance for taking countermeasures before the advent of Harmful algal blooms (HABs), and the non-destructive and sensitive property of excitation-emission matrix fluorescence (EEMF) spectroscopy makes it applicable to online monitoring and control. In this study, an efficient image preprocessing algorithm based on Zernike moments (ZMs) was proposed to extract compelling features from EEM intensities images. The determination of the highest order of ZMs considered both reconstruction error and computational cost, then the optimal subset of preliminarily extracted 36 ZMs was screened via the BorutaShap algorithm. Aureococcus anophagefferens concentration prediction models were developed by combining BorutaShap and ensemble learning models (random forest (RF), gradient boosting decision tree (GBDT), and XGBoost). The experimental results show that BorutaShap_GBDT preserved the superior subset of ZMs, and the integration of BorutaShap_GBDT and XGBoost achieved the highest prediction accuracy. This research provides a new and promising strategy for rapidly measuring microalgae cell density.

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.

A rapid fluorescence approach on differentiation of typical dinoflagellate of East China Sea

Detecting the marine phytoplankton by the means of absorption or fluorescence spectra were successfully deployed in the past decades, however, the differentiation are mainly limited in levels of class, such as bacillariophytas, dinophytas, raphidophytes, chlorophytes, cyanobacteria, etc. which are characterized by their specific composition of photosynthetic pigments. To further differentiate the typical dinoflagellate Prorocentrum donghaiense, Amphidinium carterae, Scrippsiella trochoidea, Karenia mikimotoi out of the common diatom Skeletonema costatum and haptonema Phaeocystis globosa at East China Sea, a rapid 3D-fluorescence method equipped with CHEMTAX model were conducted. Initial fluorescence excitation spectra of each algal species (under variable environmental conditions) were captured by 3D-fluorometer first. Then fingerprints of each algae were characterized by ten-point discrete excitation spectrum with the excitation wavelengths of 405, 420, 435, 470, 490, 505, 535, 555, 570 and 590 nm, which closely reflecting the difference of photosynthetic pigments. By equipping with CHEMTAX model, the standard spectra and norm spectra were constructed for FS-CHEMTAX (Fluorescence spectra-CHEMTAX) model to further identify the algal species and estimate the cell density. The developed method performed a better way of identifying the toxic species Amphidinium carterae, Phaeocystis globosa, and Karenia mikimotoi out of the non-toxic ones, with the identification accuracy rates of 83.3%, 90% and 100%, in monocultures, and 77.8%, 90% and 100%, in the bi-mixed cultures, respectively. Meanwhile, the detection limits for the three toxic species were found as low as 250, 1,400 and 120 cells/mL. The concentrations estimated are in good agreement with the microscopic cell counts for all the algae groups (correlation coefficients (R²) exceed 0.8). The relative error of predict concentration was lowest for small cells, i.e., Phaeocystis globosa (10.0%) and Amphidinium carterae (21.1%), but the highest for big cells, i.e. Karenia mikimotoi (41.8%) when the target algae become the dominant species. The overall concentration detection error was no more than one order of magnitude, indicating that this method could provide an important technical support for monitoring the related harmful algal blooms.

Perspectives of fluorescence spectroscopy for online monitoring in microalgae industry

Microalgae industrial production is viewed as a solution for alternative production of nutraceuticals, cosmetics, biofertilizers, and biopolymers. Throughout the years, several technological advances have been implemented, increasing the competitiveness of microalgae industry. However, online monitoring and real-time process control of a microalgae production factory still require further development. In this mini-review, non-destructive tools for online monitoring of cellular agriculture applications are described. Still, the focus is on the use of fluorescence spectroscopy to monitor several parameters (cell concentration, pigments, and lipids) in the microalgae industry. The development presented makes it the most promising solution for monitoring up-and downstream processes, different biological parameters simultaneously, and different microalgae species. The improvements needed for industrial application of this technology are also discussed.

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.

From Black Box to Machine Learning: A Journey through Membrane Process Modelling

Membrane processes are complex systems, often comprising several physicochemical phenomena, as well as biological reactions, depending on the systems studied. Therefore, process modelling is a requirement to simulate (and predict) process and membrane performance, to infer about optimal process conditions, to assess fouling development, and ultimately, for process monitoring and control. Despite the actual dissemination of terms such as Machine Learning, the use of such computational tools to model membrane processes was regarded by many in the past as not useful from a scientific point-of-view, not contributing to the understanding of the phenomena involved. Despite the controversy, in the last 25 years, data driven, non-mechanistic modelling is being applied to describe different membrane processes and in the development of new modelling and monitoring approaches. Thus, this work aims at providing a personal perspective of the use of non-mechanistic modelling in membrane processes, reviewing the evolution supported in our own experience, gained as research group working in the field of membrane processes. Additionally, some guidelines are provided for the application of advanced mathematical tools to model membrane processes.

Production and monitoring of biomass and fucoxanthin with brown microalgae under outdoor conditions

The effect of light on biomass and fucoxanthin (Fx) productivities was studied in two microalgae, Tisochrysis lutea and Phaeodactylum tricornutum. High and low biomass concentrations (1.1 and 0.4 g L-1 ) were tested in outdoor pilot-scale flat-panel photobioreactors at semi-continuous cultivation mode. Fluorescence spectroscopy coupled with chemometric modeling was used to develop prediction models for Fx content and for biomass concentration to be applied for both microalgae species. Prediction models showed high R2 for cell concentration (.93) and Fx content (.77). Biomass productivity was lower for high biomass concentration than low biomass concentration, for both microalgae (1.1 g L-1 : 75.66 and 98.14 mg L-1  d-1 , for T. lutea and P. tricornutum, respectively; 0.4 g L-1 : 129.9 and 158.47 mg L-1  d-1 , T. lutea and P. tricornutum). The same trend was observed in Fx productivity (1.1 g L-1 : 1.14 and 1.41 mg L-1 d-1 , T. lutea and P. tricornutum; 0.4 g L-1 : 2.09 and 1.73 mg L-1  d-1 , T. lutea and P. tricornutum). These results show that biomass and Fx productivities can be set by controlling biomass concentration under outdoor conditions and can be predicted using fluorescence spectroscopy. This monitoring tool opens new possibilities for online process control and optimization.

Front-face fluorescence excitation-emission matrix (FF-EEM) for direct analysis of flocculated suspension without sample preparation in coagulation-ultrafiltration for wastewater reclamation

Fluorescence spectroscopy has been suggested as a promising online monitoring technique in water and wastewater treatment processes due to its high sensitivity and selectivity. However, a pre-filtration is still indispensable in fluorescence measurement for removing ubiquitous particles and flocs in real samples to eliminate the strong light scattering that could attenuate fluorescence detection significantly. This study proposed a front-face fluorescence spectroscopy, which could characterize the liquid sample with suspended solids directly without pre-filtration. Front-face excitation-emission matrix (FF-EEM) coupled with parallel factor (PARAFAC) analysis was used for analyzing fluorescence components and to probe coagulation of secondary effluent and fouling in the subsequent ultrafiltration (UF), and conventional right-angle fluorescence EEM (RA-EEM) was also compared. The results showed that FF-EEM was less susceptible to turbidity (induced by standard particles) in the secondary effluent compared to RA-EEM. FF-EEM could successfully measure dissolved fluorophores in coagulated suspension without pre-filtration, while conventional RA-EEM was undermined significantly due to the existing flocs. FF-EEM coupled with PARAFAC could accurately probe dissolved organic matter and fouling in coagulation- UF wastewater reclamation processes. Therefore, it was demonstrated that this front-face fluorescence without any sample preparation step might be highly promising in real-time online fluorescence monitoring in multi water and wastewater treatment processes.

Fluorescence spectroscopy and chemometrics for simultaneous monitoring of cell concentration, chlorophyll and fatty acids in Nannochloropsis oceanica

Online monitoring of algal biotechnological processes still requires development to support economic sustainability. In this work, fluorescence spectroscopy coupled with chemometric modelling is studied to monitor simultaneously several compounds of interest, such as chlorophyll and fatty acids, but also the biomass as a whole (cell concentration). Fluorescence excitation-emission matrices (EEM) were acquired in experiments where different environmental growing parameters were tested, namely light regime, temperature and nitrogen (replete or deplete medium). The prediction models developed have a high R² for the validation data set for all five parameters monitored, specifically cell concentration (0.66), chlorophyll (0.78), and fatty acid as total (0.78), saturated (0.81) and unsaturated (0.74). Regression coefficient maps of the models show the importance of the pigment region for all outputs studied, and the protein-like fluorescence region for the cell concentration. These results demonstrate for the first time the potential of fluorescence spectroscopy for in vivo and real-time monitoring of these key performance parameters during Nannochloropsis oceanica cultivation.

Characterization of fluorescence foulants on ultrafiltration membrane using front-face excitation-emission matrix (FF-EEM) spectroscopy: Fouling evolution and mechanism analysis

The understanding of fouling behavior and mechanism is critical for fouling control in membrane processes. This study adopted a novel fluorescence front-face excitation-emission matrix (FF-EEM) approach to characterize the fluorescence foulants deposited on membrane surface. Methods for quantifying protein and humic substances deposited on ultrafiltration (UF) membrane were established. Foulants deposited on the membrane surface during the UF of model foulants (bovine serum albumin (BSA) and humic acids (HA)) and wastewater effluent organic matter (EfOM) were quantified using the FF-EEM and liquid EEM coupled with mass balance calculation. The foulants mass data obtained by FF-EEM were further used to analyze fouling mechanism involved in UF. The FF-EEM based method was more accurate than the liquid EEM based method, as the problems associated with liquid EEM based method (such as the error propagation in the mass balance calculation and the ineffectiveness of inner filter correction) were avoided in FF-EEM based method. The fouling resistance did not correlate well with the amount of foulants, as the major fouling mechanism instead of the mass of foulants mainly determined the extent of fouling. This work demonstrated FF-EEM could be a powerful tool for investigating fouling evolution and fouling mechanism in UF process.