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On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses: History
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Subjects: Biotechnology & Applied Microbiology
Contributor: Ivo Havlik

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. 

  • microalgal cultivations
  • on-line monitoring
  • optical sensors
  • biological variables
  • software sensors

1. Introduction

Microalgal biomass contains significant amounts of valuable components including lipids, proteins, carbohydrates, pigments and vitamins that can be separated and upgraded to various products in the biofuel, food, fodder, cosmetic and pharmaceutical industries [1][2]. Long chain polyunsaturated fatty acids produced by microalgae play a significant role as health food supplements [3].
Efficient medium- and large-scale microalgal cultivations require on-line monitoring methods as the bases for process control, not only of standard process variables such as temperature and pH but of products in the biological phase as well. On-line sensing has important advantages over classical off-line analytical methods: no sampling and sample processing is necessary and measurement results can be transmitted to controllers in real time or with only a slight delay. Minimizing the measurement delay is of high importance for process control, and thus, on-line and in-situ sensors are preferred that employ, in closed photobioreactor cultivations, non-invasive measurement principles to avoid contamination. Optical sensors thus occupy a central position as they can be constructed to be non-invasive.
The terms on-line, in-line, at-line, off-line and in situ define the manner of measurement and sensor placement in the process. Definitions of the terms on-line, in-line, at-line, off-line and in situ differ slightly between different industrial branches [4][5][6][7]. In an on-line measurement setup, either the sample is drawn from the process and not returned to the process stream [6] or the sensor is placed in a continuous bypass [7]; in an in-line setup the sample is analyzed within the continuous stream flow and not removed from it; in an at-line setup the sample is removed from the process, isolated from and analyzed in close spatial proximity to it; in an off-line setup measurements are carried out in a separate lab, utilizing a discrete sample; and in an in-situ setup the sensor is placed in the reactor vessel itself and is continuously in contact with the content [6]. Generally, on-line and in-line methods differ from the off-line and at-line methods in the time in which information about process or material properties is obtained compared with the time in which these properties change [5]. With on-line and in-line methods, the measurement process is faster than the change in the system properties, while with at-line and off-line methods, the measurement process can be slower than those changes. It is also easier to automate on- and in-line methods, so these analyses permit continuous process control. At-line and off-line analyses are characterized by manual or automated sampling followed by discontinuous sample processing, measurement and evaluation. In general, continuous process control with at-line or off-line sensing is not possible.
Process variables requiring monitoring in microalgal cultivations can be divided into three groups [8]:
  • Physical: light energy supply, temperature, mixing intensity, light frequency within the culture;
  • Chemical: pH, pO2, pCO2, N, P, other nutrients, extracellular products, chemical contaminants;
  • Biological: biomass concentration and composition (presence of intracellular products, mostly lipids and pigments), presence of other biological species, physiological state, photosynthetic efficiency (PE) (from which biomass yield on light energy can be derived), cell morphology.
Certain monitoring requirements in microalgal cultivations are distinct from those in most biotechnological processes:
  • Monitoring of light in phototrophic microalgal cultivations; and
  • Biological variables in microalgal cultivations are almost always intracellular products (lipids, carbohydrates, proteins) that are produced mostly in the stationary phase of the cultivation. Furthermore, there is a need to monitor harmful or competing biological contaminants (algae, pathogens, herbivores) in outdoor cultivations in open photobioreactors.
On-line monitoring of physicochemical variables in microalgal cultivations (temperature, pH, pO2, pCO2, inorganic nutrients, light intensity) is already available using standard sensors employed in the chemical and bioprocess industry: thermoelements, electrodes (pH, dissolved O2 and CO2, inorganic nutrients), gas analyzers (gaseous O2, CO2) and, specifically for phototrophic microalgal cultivations, measurement of light intensity with quantum sensors or dosimeters [9][10]. What remains is the monitoring of biological parameters/the biological phase, which is much more difficult because almost all the desired products in microalgal cultivations are intracellular [11]. In contrast to sensors measuring physicochemical variables, on-line sensors measuring biological variables as concentrations of lipids, carbohydrates and proteins or physiological variables as photosynthetic efficiency are in an earlier developmental stage.
In the last decade, evolution in the implementation of existing and established methods (e.g., optical spectroscopy) to the monitoring of the biological phase in microalgal cultivations has occurred and some new approaches (implementation in microfluidic devices, laser reflectance, hyperspectral imaging) have been developed and examined. The support in signal processing using chemometric models and machine learning has also grown. In this entry, these developments are summarized with focus on monitoring the biological phase (biomass concentration and composition, physiological state, morphology, biological contaminants) using non-destructive, real-time, on-line, or in-line methods that avoid contamination of the running cultivation in closed photobioreactor systems, minimize measurement delays and thus supply the information required for successful process control without physically affecting the cultivation. Optical methods, including microscopy, spectroscopy of absorption, reflectance and scattering, and multispectral/hyperspectral imaging form the core of available methods. Non-optical methods, including nuclear magnetic resonance spectroscopy (NMR) and measurement of capacitance, impedance, or dielectric effects, have been also used for on-line monitoring of biomass components. Physical sensors built on these physical principles supply measurement signals that are further processed by software sensors, i.e., chemometric methods, image processing, various mathematical models, and artificial neural networks, or a combination of those, to yield meaningful process values enabling to estimate concentrations of desired cultivation products. The massive low-cost computing power available today provides fast data acquisition even when the raw hardware sensor data must be extensively processed.

2. Measurement Methods Used for On-Line Monitoring of Biological Variables

In this section, on-line measurement methods are listed, with several exceptions where an off-line measurement method is mentioned, mostly because of the possible future on-line implementation. Monitoring methods, the corresponding monitored variable(s), and the sensor type are summarized in Table 1. The reported accuracy of these methods, together with the biological variable measured, measurement method and measurement conditions and limitations, is shown and compared in Table 2. Biomass concentration and cell count can be monitored using optical density, color analysis, fluorometry, spectral methods or permittivity with accuracy from about R2 = 0.90 to R2 = 0.99. Biomass components as lipids, pigments and proteins can be monitored using NMR, PAM (quantum yield), spectral methods, fluorometry and ISM with comparable accuracy with that of biomass and cell count but with more complex and more expensive instrumentation, especially lipids with NMR and PAM and fatty acids with ISM.
Table 1. Monitoring method of biological variables in microalgal cultivations implemented on-line or with on-line potential. (PAM: pulse amplitude modulation, a fluorescence technique; ANN: artificial neural networks; ISM: In-Situ Microscopy).
Monitoring Method Monitored Variable
(Concentration)		 On-Line/Off-Line Sensor Type Comment References
OD, turbidity (single wavelength) Biomass On Self-constructed 560 nm
Amphenol TSD-10 730 nm
Commercial 880 nm		 Flow-through cell (1) [12][13][14][15]
OD (multiple wavelength) Biomass
Growth phase
Chlorophyll		 On
Off		 (1) OD self-constructed
LED 400, 750, 850 nm
Laser 650, 685, 780 nm
(2) 550, 665, 750 nm		 (1) Flow-through cell (1) [16][17]
(2) [18]
Reflectance Contamination
Biomass
Cell count		 On
Off		 (1) Spectrometer
(2) (a) Reflectance probe
(2) (b) Spectroradiometer		 Contamination on-line (1) [19]
(2) (a) [20][21]
(2) (b) [22]
Color analysis (RGB) Biomass On
Off		 (1) Commercial color sensor
(2) CCD camera, Webcam		 (1) Flow-through cell (1) [23]
(2) [12][24][25][26]
Hyperspectral (Absorbance/transmittance spectrum) Biomass
Cell count
Lipids
Carotenoids		 Off (2) (a, c) Spectral camera
(2) (b) Spectroradiometer
(2) (d) Spectrometer		   (2) (a) [27]
(2) (b) [22]
(2) (c) [28]
(2) (d)[29]
ISM Cell morphology
Lipids		 On
Off		 (1) In-Situ Microscope
(2) In-Situ Microscope, holographic microscope		   (1) [30]
(2) [31]
Chlorophyll fluorometry Protein
Biomass
Contamination		 On
Off		 (1) LEDs/Photodiode
(2) (a) Fluorometer
(2) (b) 2D-Fluorometer		 Single/multiple excitation
ANN
Chemometric model		 (1) [32]
(2) [33][34][35]
(2) (a) [36]
(2) (b) [37][38]
PAM fluorometry Quantum yield
(Photosynthetic efficiency)
Contamination		 On
Off		 PAM fluorometer Stress detection
(1) Light adapted except [39]
(2) Dark adapted		 (1) [39][40][41][42][43]
(2) [44]
2D-fluorometry Biomass
Nitrate
Cell count
Cell viability
Fatty acids
Lipids
Pigments		 Off 2D fluorometer with a cuvette or with a fiber optics probe Chemometric models (2) [37][38][45][46][47]
NMR Lipids On Benchtop NMR in a bypass Expensive instruments (1) [48][49][50][51][52]
Dielectric spectroscopy, dielectrophoresis, capacitance, impedance, permittivity Viable cell concentration
Lipids		 On (1) Commercial probe
(2) Microfluidic device		   (1) [12][53]
(2) [54]
Microfluidic implementation Lipids On
Off		 (2) (a) PAM fluorometer
(2) (b) Permittivity		   (2) (a) [55]
(2) (b) [54]
Mass spectrometry Contamination On TOF mass spectrometer Grazer detection
Expensive instruments		 (1) [56]
(1) On-line implementation. (2) On-line implementation possible.
Table 2. Accuracy of the reviewed methods where available in the original article. Included are only methods not using software sensors; these are shown in Section 4. (DWC: dry weight concentration (of biomass); OD: optical density; Vout: sensor output voltage; NTU: nephelometric turbidity unit; r, g, b: intensities of the red, green and blue components; T751, T676: transmittance at 751 and 676 nm; Chl a: chlorophyll a; CC: cell count; HR: hyperspectral reflectance; EC: extinction coefficient; OD560: optical density at 560 nm; Δε: change in permittivity; QY: quantum yield; ISM: In-Situ Microscope; DHA: docosahexaenoic acid; R2: coefficient of determination; r: Pearson correlation coefficient).
Biological Variable Measurement Method Method Accuracy Limitations/Conditions Reference
Biomass OD, turbidity OD/DWC: R2 = 0.81–0.96 [16] PBR bypass [16]
[13]
[15]
[17]
Vout/OD: R2 = 0.95 [13]
NTU/DWC: R2 = 0.88–0.93 [15]
OD/DWC: R2 = 0.88–0.92 [15]
OD/DWC: R2 = 0.99 [17]
Biomass Color analysis (RGB) OD/DWC: R2 = 0.998 [23] PBR bypass [23] [23]
[25]
[26]
(r,g,b)/DWC: R2 = 0.97–0.99 [25] Open container/biofilm, suspension [25]
(r,b)/DWC: R2 = 0.90–0.96 [26] Open container/suspension [26]
Biomass Transmittance spectrum DWC/T751/T676: r = 0.51–0.93 [27] Microwells [27]
Biomass Chlorophyll fluorometry DWC/Chl a fluorescence: r = 0.95 [32] Fiber probe in PBR bypass [32]
[36]
Cell count Transmittance spectrum
Hyperspectral reflectance, EC
Permittivity
Chlorophyll fluorometry		 CC/T751/T676: r = 0.85–0.96 [27] Microwells [27] [27]
[22]
[53]
[32]
CC/HR,EC: R2 = 0.99 [22] Open container [22]
CC/OD560: R2 = 0.992–0.999 [53] Flask bypass [53]
CC/Chl a fluorescence: r = 0.92 [32] Fiber probe in PBR bypass [32]
Viable cell count Permittivity (ε) OD560/Δε: R2 = 0.99 (calibration) Commercial probe [53]
OD560/Δε: R2 = 0.77 (cultivation)
Lipids NMR
Quantum yield (ΔF′/Fm′)
NIR spectrum		 Algal lipids/NMR signal: R2 > 0.99 [52] PBR bypass [52] [52]
[49]
[43]
[28]
Algal lipids/NMR signal: R2 = 0.99 [49] Bleed [49]
Lipids as %DW/QY(ΔF′/Fm′): r = −0.96 [43] In-situ fiber [43]
Lipids predicted/observed: R2 = 0.94 [28] Sampling [28]
Fatty acids ISM/Image recognition DHA/cell diameter: R2 = 0.98 (calibration) PBR in-situ probe [31]
Protein Chlorophyll fluorometry Protein/Chl a fluorescence: r = 0.92 Fiber probe in PBR bypass [32]
Carotenoids (C) VIS/NIR spectrum Predicted/observed C: r = 0.96 Fiber in sample [29]
The most often encountered process variable estimated in microalgal cultivations is the biomass concentration, determined using several physical measurement signals: turbidity, absorbance (optical density) at a predefined wavelength or as a spectrum, reflectance, color analysis (red–green–blue; RGB), IR spectroscopy and fluorescence, sometimes combined with a software sensor (observer). Other estimated variables are the pigment and lipid content, and for their estimation, physical measurements of cell count, nitrate and glucose concentration are employed, complemented by process signals obtained by various methods as measurement of turbidity, IR spectrum and fluorescence, RGB imaging and transmission spectra, NMR spectroscopy, fluorescence, hyperspectral or RGB imaging, and dielectrophoresis.

3. High-Throughput Methods for Monitoring of Biological Variables

Although this entry is focused on on-line sensors, monitoring of biological variables in microalgal cultivations by using off-line methods capable of mass processing of drawn samples in a fast and reliable manner (hence the name “high-throughput methods”) deserves a short mention here because some of these methods, mostly those using some type of optical sensing, could be adapted for on-line, in-situ use.
High-throughput methods present a special category of rapid analytical methods used for mass analyses not only in microalgal cultivations but in microbial cultivations in general. They are optimized mostly for parallel analysis of many cultivation samples or small volume cultivations performed in well plates. With microalgae, a typical application is screening for neutral lipids using fluorescent stains, mostly Nile Red or BODIPY [57][58][59]. The fluorescence-based staining methods fight a problem of the uniform dye penetration into cells which must be facilitated by different solvents [58][59][60]. A fast method toolbox has been presented by Palmer et al. [61] for screening for phycobiliproteins, chlorophylls, carotenoids, proteins, carbohydrates, and lipids using simple colorimetric methods with the purpose of strain selection and optimization. An estimation of carotenoid concentration in Spirulina using VIS/NIR transmission spectra was investigated in [29] with spectra obtained by an Ocean Insights (USA) fiber optic probe immersed in a test tube. This method could be also adapted for on-line use.

4. Computer-Aided Monitoring and Software Sensors

In all biotechnological processes, one can find variables that cannot be measured directly in real time without substantial effort, human or instrumental, because of a lack of suitable sensors. Available sensors are too inaccurate, unstable, expensive, or there are no sensors available for the variable at all. Then, process control theory allows us to use so-called state observers that use signals of available sensors and convert them, through help of a mathematical model of the process, into indirect measurement of the desired variable. Pattern recognition in the form of ANNs or sophisticated multiple regression methods as used in chemometric models is another way to the same end, obtaining values of process variables that cannot be measured directly. These estimators are usually called “software sensors” to stress that they provide values of process variables as if they were provided by physical sensors, to be used for monitoring and control purposes. The first estimator type, the model-driven estimator, uses mostly mass and energy balance process models and kinetic models, implemented in observers (Luenberger observer), adaptive observers, interval observers or statistical filters of the Kalman–Bucy type [62]. In model-driven estimators, quality of the software sensor depends decisively on the quality of the underlying mathematical model of the process. The second estimator type, the data-driven estimator, is represented by some type of multivariable correlations between measured and target variables, as in chemometric models [4][63][64], or ANNs [65] performing pattern recognition between measured and target variables. Combinations of all these approaches are widely used in hybrid models [62]. In a certain sense, practically all modern sensors implementing complex sensing methods are software sensors because the primary signals must be less or more processed by some software to provide the value of the desired process variable, either in real time or with some delay. A good example is NMR spectroscopy, which requires formidable computing power implemented in the hardware instrument to perform multistep data processing and deliver a single current value of lipid concentration [52]. Sometimes it is rather difficult to decide if the indirect measurement should be classified as a software sensor, e.g., a nonlinear regression model—as in chemometrics—using as input absorption measured at multiple wavelengths. However, this question is much more academic than practical.
Various process variables are estimated in the following overview of software sensors used in microalgal cultivations: biomass, cell count, cell viability, concentration of lipids, carbohydrates, glucose, sulfur, nitrate, chlorophylls a and b, carotenoids, total fatty acids and EPA, and contamination of a single-strain cultivation by other microalgal strains. Process variables measured with a hardware sensor or method either on-line or off-line employed as estimator inputs are cell count by a particle counter, concentration of nitrate and glucose, temperature, OD, output flow O2, output flow CO2, turbidity, air injection flow, CO2 injection flow, irradiance, fluorescence spectra, hyperspectral OD, reflectance and 2D fluorescence in the form of EEMs.
Most model-based estimators belong to one of two categories. In the first category there are observers that correct the values generated by the process model using some readily measurable process value with a constant gain selected during the observer design. The constant gain can also be continuously adapted by some available technique [66], and interval observers can limit the state trajectory based on the known intervals of uncertain model parameters [67]. The second category are variants of the Kalman filter which alters the correction gain in each iteration recursively using the comparison between noisy state estimations and noisy measurements, using covariance matrices of the system and measurement noises whose selection is critical for the proper functioning of the filter [68].
The most often encountered process variable estimated by software sensors in microalgal cultivations is the biomass concentration using several physical measurement signals: turbidity, outlet gas composition, pO2, pH, irradiation intensity, fluorescence and reflectance. It is followed by the lipid content based on cell count, nitrate, turbidity, glucose (off-line), IR spectra (off-line with ATR-FTIR) and 2D fluorometry spectra (off-line, but adaptable to on-line). Further process variables estimated by means of software sensors include carbohydrates and proteins, intracellular nitrate quota, substrate (glucose), sulfur, cell count, cell viability, concentration of chlorophyll a and b, carotenoids, total fatty acids and EPA. Software sensors measuring biological parameters in microalgal cultivations reported in recent years are summarized in Table 3.
Table 3. Software sensors monitoring biologically important variables in microalgal cultivations.
Variables Monitored by the Software Sensor Input Variables (On-Line When Not Otherwise Stated) Software Sensor Type References
Lipids
Carbohydrates		 Particle counter
Nitrate (assumed measured on-line)		 Adaptive interval observer [67][69]
1. Extracellular nitrate, intracellular nitrate quota
2. Intracellular nitrate quota		 1. OD (biomass)
2. OD (biomass), extracellular nitrate		 Luenberger observer [66]
Biomass
Glucose		 Turbidity Robust nonlinear observer [14]
Biomass
Sulfur		 Outlet gas (O2, CO2) by MS EKF [70]
Lipids Turbidity
Glucose (off-line)		 EKF, UKF, PF [71][72]
Biomass pO2
pH,
air flow
CO2 flow
solar radiation		 EKF [68]
Cell count Fluorescence spectrum (off-line) ANN [36]
Contamination Multispectral absorption (off-line) ANN [73]
Biomass Reflectance (off-line) ANN [20]
Biomass Reflectance (off-line) SVR, RF regression [21]
Protein, lipids, carbohydrates IR spectrum (ATR-FTIR) off-line Chemometrics [74]
Cell count
Cell viability
Nitrate concentration
Chlorophyll a, b concentration
Carotenoids
Total fatty acids
EPA		 2D fluorometry (EEM)
(off-line, adaptable to on-line)		 Chemometrics [37][38][45][46][47]
Biomass (X)
Fucoxanthin (Fx)		 2D fluorometry (EEM) Chemometrics [75]
Carotenoids Transmission spectrum Chemometrics [29]
The accuracy of the individual software sensors reviewed here and listed in Table 3, together with the estimated biological variable, software sensor type and measurement conditions and limitations is shown and compared in Table 4. Numerically expressed accuracy is reported only with chemometric models. Results produced by observers, Kalman filters and ANNs are, as a rule, reported in original publications only in graphical form depicting the comparison of measured and estimated data during the cultivation. That reflects the fact that these estimators work dynamically, providing step-by-step estimation of process state variables based on measured process outputs. Several software sensors listed in Table 4 are implemented on-line, some work with experimental data supplied off-line but could be adapted to a true on-line implementation, and some are implemented in an off-line mode.
Table 4. Accuracy of software sensor methods where numerically available. In most cases, only graphs comparing the time course of variables’ estimation vs. their measurement are shown in the original article (denoted here as “graphic comparison”).
Biological Variable SW Sensor Type Method Accuracy Limitations/Conditions Reference
Lipids
Carbohydrates		 Adaptive interval observer Graphic comparison 35 days
Graphic comparison 35 days		 Tested with experimental data, adaptable to on-line [69]
[67]
Extracellular nitrate, intracellular nitrate quota
Intracellular nitrate quota		 Luenberger observer Graphic comparison 4–6 days Tested with experimental data, adaptable to on-line [66]
Biomass Glucose Robust nonlinear observer Graphic comparison 18 days On-line implementation [14]
Biomass Sulfur EKF Graphic comparison 8–10 days On-line implementation [70]
Lipids EKF, UKF, PF Graphic comparison 300 h
Graphic comparison 300 h		 On-line implementation [72]
[71]
Biomass EKF Graphic comparison within 1 day On-line implementation [68]
Cell count ANN Graphic comparison 10 days Adaptable to on-line [36]
Contamination ANN Identification of 4 pure species
Accuracy > 98.7%		 Measured in samples [73]
Biomass (X) ANN Predicted/observed X: R2 = 0.92 Measured in samples [20]
Biomass (X) SV regression
RF regression		 Predicted/observed X: R2 = 0.87
Predicted/observed X: R2 = 0.81		 Measured in samples [21]
Protein (P)
Lipids (L)
Carbohydrates(C)
Ratio carbohydrates/proteins		 Chemometric models Predicted/observed P: R2 = 0.88/0.92/0.85
Predicted/observed L: R2 = 0.82/0.90/0.77
Predicted/observed C: R2 = 0.65/0.77/0.63
Predicted/observed C/P: R2 = 0.84		 Freeze-dried samples [74]
Cell count (CC)
Cell viability (CV)
Nitrate concentration (N)
Chlorophyll a, b concn. (Chl)
Carotenoids (C)
Total fatty acids (TFA)
EPA fraction in TAG		 Chemometric models Predicted/observed CC: R2 = 0.66–0.97
Predicted/observed CV: R2 = 0.69
Predicted/observed N: R2 = 0.80
Predicted/observed Chl: R2 = 0.75–0.85
Predicted/observed C: R2 = 0.72–0.89
Predicted/observed TFA: R2 = 0.78
Predicted/observed EPA: R2 = 0.87		 Adaptable to on-line CC, CV: [37]
CC, CV, N: [38]
CC, Chl, TFA: [45]
EPA: [46]
Chl, C: [47]
Biomass (X)
Fucoxanthin (Fx)		 Chemometric model Validation X: R2 = 0.93–0.96
Validation Fx: R2 = 0.63–0.77		 Measured in samples [75]
Carotenoids (C) Chemometric model Predicted/observed C: r = 0.96 Measured in samples [29]

This entry is adapted from the peer-reviewed paper 10.3390/en15030875

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