Title: Preferred Embodiments of an Online Spectroscopic–Electrochemical Coupled Monitoring System and Method for Microbial Fermentation, Enabling Real-Time Metabolic Flux Estimation and Adaptive Control
Technical Field
The embodiments relate to bioprocess monitoring and control, and more specifically to an integrated, sterilizable, online spectroscopic–electrochemical system and associated methods for estimating metabolic fluxes in real time and adaptively tuning process parameters to enhance product yield and batch-to-batch consistency across multiple microbial strains and culture media.
Definitions
- Spectroscopic module means a non-invasive or minimally invasive optical measurement subsystem configured to acquire, in situ or via a sterile bypass loop, spectra such as Raman, near-infrared (NIR), mid-infrared (MIR), or ultraviolet-visible (UV-Vis).
- Electrochemical module means a set of sensors or analytical electrochemical cells measuring, without limitation, pH (potentiometric), dissolved oxygen (amperometric/galvanic or optical with electrochemical reference), oxidation–reduction potential (ORP), conductivity, and electrochemical impedance spectroscopy (EIS) or voltammetric responses related to metabolites or substrates.
- Metabolic flux estimation refers to the computation of intracellular or extracellular reaction rates consistent with a stoichiometric model, constrained by online measurements and mass balances, optionally solved via optimization or Bayesian data assimilation.
- Adaptive control means a feedback or model predictive control policy that updates control parameters (e.g., feed rate, agitation, aeration, pH setpoint, temperature) based on the estimated fluxes and state variables, with online model adaptation.
- Bioprocess unit includes a fermenter or bioreactor, typically sterilizable-in-place (SIP) and cleanable-in-place (CIP).
Overview of the Invention
In preferred embodiments, the system comprises: (i) a sanitary mechanical interface and flow path permitting in situ or bypass measurements while maintaining sterility; (ii) an optical spectroscopic module configured to collect high signal-to-noise spectra over preselected bands that are informative for substrates, products, and key metabolic intermediates; (iii) an electrochemical module providing orthogonal measurements of physicochemical parameters and electroactive analytes; (iv) a data acquisition and control unit implementing multivariate chemometrics, sensor fusion, and a constrained optimization engine for real-time metabolic flux estimation; and (v) an adaptive control supervisor that adjusts process actuators to meet production objectives under safety and quality constraints. The system is designed to be compatible with multiple microbial strains and culture media via a modular model library and automated model selection/calibration procedures.
Preferred Embodiments—Device
1. Process Interface and Sanitary Construction
- A probe housing (100) in 316L stainless steel with pharmaceutical-grade surface finish (Ra ≤ 0.8 μm), utilizing sanitary tri-clamp or DIN flanges, integrates optical windows (110) and electrochemical sensor ports (120).
- The optical windows (110) are formed of sapphire or fused quartz with anti-fouling coatings, rated for SIP cycles at 121–135°C and pressures up to 3 bar(g). Double O-ring seals (EPDM or PTFE) provide redundancy and steam barrier design.
- A bypass measurement loop (130) constitutes an alternative to in situ immersion. It is constructed with sanitary tubing, an aseptic peristaltic or magnetically coupled pump (140), and an isothermal flow cell (150) hosting both optical and electrochemical interfaces. The loop includes sterile filters and steamable valves (160) for isolation during SIP/CIP.
- An optical access module (170) positions fiber-optic probes with kinematic mounts to ensure repeatable coupling while preventing laser leakage to the environment.
- Anti-fouling features include: (a) a wiper assembly (180) with sterile bellows for periodic window cleaning; (b) a backflush port (190) for pulsed rinsing with sterile medium; and/or (c) optional low-power ultrasonic transducers (195) bonded to the window flange to mitigate biofilm accumulation.
2. Spectroscopic Module
- In a first preferred embodiment, a Raman subsystem comprises a stabilized laser (200) at 785 nm (or 532/830 nm as alternatives) with adjustable output power under interlocks; a fiber-coupled probe (210) with non-fouling window; a spectrograph (220) with spectral range 200–3,200 cm−1 and resolution ≤ 8 cm−1; and on-probe temperature sensing for drift compensation.
- In a second preferred embodiment, an NIR subsystem employs a diode-array or FT-NIR analyzer (230) covering 900–2,500 nm, with transflectance or interactance probe geometry (240) optimized for turbid fermentations, pathlength 1–4 mm via adjustable spacer.
- In a third preferred embodiment, a UV-Vis subsystem (250) with 200–800 nm range quantifies chromophoric species (e.g., cofactors, pigments) and turbidimetry for biomass.
- Optical safety is ensured by enclosure interlocks; laser key control; and beam dumps. Modules are housed in IP-rated enclosures suitable for cleanroom use.
3. Electrochemical Module
- pH sensor (300) is a sterilizable, gel-filled or steam-sterilizable glass electrode with integrated temperature sensor and reference junction designed for low drift in high-protein media.
- Dissolved oxygen sensor (310) is amperometric (Clark-type) or galvanic with membrane resistant to fouling; an optical DO sensor may serve as redundancy for cross-validation.
- ORP sensor (320) provides redox state monitoring relevant to cofactor balance.
- Conductivity and permittivity sensors (330) provide ionic strength and biomass-related capacitance signals.
- An electrochemical analytical cell (340) with microfabricated working, reference, and counter electrodes (e.g., gold, platinum, glassy carbon) enables EIS, chronoamperometry, cyclic voltammetry, or square-wave voltammetry to quantify electroactive substrates/products (e.g., organic acids, redox mediators) and probe membrane integrity. The cell is integrated into the bypass flow cell (150) to maintain sterile conditions.
- Automatic polarization cleaning routines apply controlled pulses to mitigate biofouling of electrode surfaces.
4. Data Acquisition, Processing, and Control
- A controller (400) includes: high-precision ADC/DAC; galvanic isolation; synchronized sampling across optical and electrochemical inputs; and time-stamped data logging with audit trails compliant with electronic records/electronic signatures requirements for regulated environments.
- Preprocessing pipelines (410) perform dark/white referencing, baseline correction, cosmic-ray removal (Raman), scatter correction (e.g., SNV), smoothing, and wavelength alignment; electrochemical data are filtered and temperature-compensated.
- Chemometric models (420) employ principal component analysis (PCA), partial least squares (PLS), multivariate curve resolution, and/or nonlinear learners to estimate concentrations of key extracellular metabolites/substrates/biomass proxies from spectra and electrochemical features. Models are validated with cross-validation and independent sets; transfer learning compensates for strain/media changes.
- Sensor fusion (430) combines estimates via Bayesian filters (e.g., extended/unscented Kalman filters) or constrained optimization to yield consistent state estimates with uncertainty quantification.
- Flux estimation engine (440) solves, at each cycle, a constrained optimization problem: find flux vector v minimizing deviation from priors subject to S·v = r (dynamic mass balances), bounds informed by physiology, and equality/inequality constraints (e.g., uptake capacity). r is computed from online concentration/accumulation rates and known dilution/aeration. Regularization terms penalize abrupt changes to stabilize solutions.
- Adaptive control supervisor (450) implements model predictive control (MPC) with a receding horizon objective to maximize production rate or yield while enforcing safety constraints (e.g., oxygen limitation avoidance, foaming limits). Control moves include substrate feed rate, co-substrate/cofactor feeds, agitation, aeration/oxygen enrichment, pH titration, temperature, and antifoam dosing. A gain-scheduling or online parameter adaptation module (455) updates kinetic/stoichiometric parameters as inferred from fluxes.
- Fault detection and diagnostics (460) employ residual analysis, Hotelling’s T2/Q statistics, and parity-space checks to detect sensor drift, fouling, or model mismatch. Automatic reversion to safe fallback control is provided.
5. Sterilization, Cleanability, and Compliance
- Materials and seals are selected for repeated SIP/CIP cycles; steam traps and condensate drains ensure full sterilant contact. Ports and dead-leg lengths comply with sanitary design practice.
- The control system integrates user authentication, access control, time-stamped audit logs, configuration/version control, and secure data export. Network communication uses encrypted protocols.
- Electrical safety and enclosure ratings adhere to applicable industrial and laboratory standards. Where explosive atmospheres are possible, components are selected in accordance with applicable explosion safety directives.
Preferred Embodiments—Method
1. Calibration and Model Commissioning
- Prior to production, a design of experiments (DOE) is conducted across relevant ranges of strains, media, temperatures, and feed regimes to build calibration sets. Offline reference assays (e.g., HPLC for sugars/organic acids, gravimetric/optical biomass, enzymatic assays) provide ground truth.
- Chemometric models are constructed for each spectral modality and fused with electrochemical features. Variable selection and wavelength region optimization reduce collinearity and media interference. Cross-validated performance metrics and uncertainty bounds are recorded.
- A library of organism- and pathway-specific stoichiometric models S is prepared, with exchange flux bounds reflecting physiology and medium composition. Where genome-scale models exist, reduced-order models are derived for real-time tractability.
2. Startup and Baseline
- The system components are SIP’d with the fermenter. Post-sterilization, baseline spectra and electrochemical baselines are recorded under sterile medium circulation to capture matrix signatures.
- Self-check routines verify sensor health, laser power, spectral alignment, and electrode polarization response. Baselines and references are updated accordingly.
3. Online Monitoring and Flux Estimation
- During fermentation, spectra and electrochemical signals are acquired at 1–60 s intervals. Preprocessing is applied as above.
- Concentration estimates of substrates (e.g., glucose, glycerol), products (e.g., ethanol, organic acids, amino acids), dissolved gases (via DO dynamics), and biomass proxies are computed in real time with uncertainty.
- Finite-difference or filtering methods compute rates of change. Together with known feed compositions and off-gas data if available, extracellular mass balances are closed. The flux estimation engine solves for intracellular/exchange fluxes consistent with S·v = r, enforcing physiological bounds (e.g., ATP maintenance). A probabilistic formulation may propagate measurement uncertainties to obtain confidence intervals on v.
4. Adaptive Control and Parameter Tuning
- The MPC computes control trajectories over a finite horizon (e.g., 10–30 min) to optimize a cost function such as: maximize instantaneous or terminal product formation flux, maintain specific growth rate within a band, and minimize substrate overflow metabolism, all subject to process constraints (oxygen transfer limits, maximum agitation, pH bounds, foam alarm).
- The controller updates feed rate profiles (e.g., exponential feeding to target specific growth rate inferred from fluxes), aeration and oxygen enrichment to maintain DO setpoints linked to respiratory fluxes, and temperature or pH setpoints to steer cofactor balance per redox flux indicators.
- Online parameter adaptation adjusts kinetic surrogates and yield coefficients using recursive estimation to maintain controller-model alignment. Safe-guard logic enforces monotone limits and rate-of-change caps to prevent shocks.
5. Multi-Strain and Multi-Medium Compatibility
- A classifier selects the appropriate model set from the library based on early spectral fingerprints and metadata (strain, medium). If unknown, a hybrid model initializes from the nearest neighbor and adapts online within validated bounds.
- Transfer learning updates chemometric and flux models to account for medium-specific background spectra and matrix effects, using on-the-fly spiking or feed composition changes as calibration points.
- Plug-and-play templates map standard actuators/sensors across vessel scales, enabling deployment from bench to pilot and production with scale-appropriate tuning.
6. Quality Assurance and Fault Handling
- Continuous monitoring of model residuals triggers alarms and corrective actions: schedule window cleaning/backflush, recalibrate a sensor, or switch to redundancy. The system maintains data integrity via immutable logs and versioned model archives.
- At batch end, the system generates a report compiling flux trajectories, control actions, deviations, and key performance indicators to support batch release and process characterization.
Illustrative Parameters and Ranges (Non-Limiting)
- Laser power at probe: 50–300 mW (Raman), subject to sample heating limits.
- Spectral acquisition time: 0.5–10 s per spectrum; ensemble averaging configurable.
- Electrochemical EIS frequency sweep: 100 kHz to 1 Hz, logarithmic spacing; amplitude 5–10 mV rms.
- DO control range: 10–60% saturation, strain dependent.
- Feed control update period: 5–30 s; feed flow resolution ≤ 0.1% of full scale.
- Temperature control resolution: ±0.1°C; pH control resolution: ±0.02 pH.
Alternative Embodiments
- A purely in situ configuration without bypass loop using flushable optic windows and retractable electrochemical assemblies with sanitary steam-block retractors.
- Replacement or augmentation of Raman/NIR with MIR using attenuated total reflectance (ATR) crystals where fouling can be controlled by shear or wipers.
- Integration with off-gas analyzers (e.g., CO2, O2) and soft sensors to refine carbon and oxygen balances informing flux constraints.
- Use of reinforcement learning layered atop MPC for long-horizon optimization, with strict safety shields enforcing validated operating envelopes.
Industrial Applicability
The embodiments are applicable to aerobic and anaerobic fermentations for biochemicals, biologics, and food applications. The integrated design supports enhanced yield and improved batch-to-batch consistency by enabling actionable, real-time insight into metabolic state and by closing the loop with adaptive control, while maintaining sterility and compliance in regulated manufacturing environments.
Legal Notes
The foregoing description sets forth preferred embodiments and non-limiting alternatives to enable a person skilled in the art to practice the invention. Variations, substitutions, and equivalents that fall within the spirit and scope of these embodiments would be understood by such persons and are intended to be encompassed by the claims in a corresponding patent application. The inclusion of specific materials, vendors, standards, or parameters is illustrative and not restrictive, and no admission is made regarding the state of the art or any necessity to combine features except as recited in claims.
