Glucose and Lactate Monitoring with PTRam

Importance of the Topic

Raman spectroscopy has emerged as a key technique within process analytical technology (PAT) for the pharmaceutical and biopharmaceutical industries, offering non-destructive, real-time monitoring capabilities and seamless integration into online and inline workflows.

Objectives and Overview

This application study demonstrates the feasibility of tracking glucose and lactate concentrations in both static calibration and dynamic simulation experiments using the PTRam process development Raman analyzer, aiming to support cell culture and fermentation process development.

Methodology and Instrumentation

Static calibration was performed with 25 aqueous standards covering glucose (0–4.5 g/L) and lactate (0–4.4 g/L) concentrations. Spectra were acquired with a fiber-optic probe at full laser power (340 mW) and an 18 s integration time. Preprocessing included Savitzky-Golay derivatives and standard normal variate correction. Multivariate PLS models with three factors and four-segment cross-validation were built for each analyte. Dynamic experiments simulated cell metabolic activity over 6.5 h by sequential lactate additions and solution replenishment with high-glucose feed, generating real-time predictions via Vision software.

Used Instrumentation

• PTRam Process Development Raman Analyzer (785 nm laser, 340 mW at sample, 150–2800 cm⁻¹ spectral range)
• Immersible fused silica fiber-optic probe (RIS100-FS)
• Metrohm Vision® software for data acquisition and chemometric modeling

Main Results and Discussion

Calibration models exhibited strong linearity and accuracy for glucose and lactate, with low standard errors of prediction (SEP: 0.20 g/L for glucose, 0.12 g/L for lactate) and minimal bias (−0.13 g/L and −0.08 g/L, respectively). Dynamic monitoring closely followed theoretical concentration profiles, validating the analyzer’s suitability for real-time PAT applications in bioprocess development.

Benefits and Practical Applications

• Non-destructive, inline measurement of critical metabolites
• Rapid feedback for process control in cell culture and fermentation
• Reduced sampling and offline analysis requirements
• Scalability from laboratory to pilot-plant environments

Future Trends and Opportunities

• Integration with advanced chemometric algorithms and machine learning
• Expansion into continuous manufacturing and digital twin frameworks
• Miniaturization for single-use and mobile PAT platforms
• Enhanced regulatory acceptance for real-time release testing

Conclusion

The PTRam analyzer, combined with robust chemometric modeling via Vision software, provides a reliable and high-performance solution for real-time monitoring of glucose and lactate in bioprocess applications, supporting improved process understanding and control in pharmaceutical development.