Quality Control of fermentation processes

Importance of the Topic

The optimization of ethanol fermentation is critical for reducing production costs and ensuring consistent product quality in the biofuel industry. Rapid, in-line monitoring of key fermentation parameters helps manufacturers adapt to variable feedstock quality and maintain efficient process control.

Study Goals and Overview

This application note evaluates the potential of near-infrared spectroscopy (NIRS) to predict multiple fermentation metrics—ethanol content, sugar concentrations, Brix index, pH, lactic acid, and total solids—in under one minute. A dataset of 206 corn mash samples was used to develop and validate prediction models against conventional laboratory reference methods.

Used Methodology and Instrumentation

Samples of fermentation mash were measured in diffuse reflectance mode on a DS2500 Solid Analyzer across 400–2500 nm. To address sample heterogeneity, each mash was rotated in a large sample cup and spectra from multiple positions were averaged. Model development and spectral data management were performed with Vision Air 2.0 Complete software.

• DS2500 Solid Analyzer (Metrohm 2.922.0010)
• DS2500 Large Sample Cup (Metrohm 6.7402.050)
• Vision Air 2.0 Complete Software (Metrohm 6.6072.208)

Main Results and Discussion

Prediction models showed excellent agreement with reference analyses for major parameters:

• Ethanol content: R² = 0.998, SEC = 0.21 %, SECV = 0.22 %
• Total solids: R² = 0.982, SEC = 0.87 %, SECV = 1.06 %
• Brix index: R² = 0.987, SEC = 0.66, SECV = 0.87
• Total sugar: R² = 0.981, SEC = 1.09 %, SECV = 1.30 %
• Glucose: R² = 0.920, SEC = 0.70 %, SECV = 0.86 %
• Maltotriose: R² = 0.928, SEC = 0.36 %, SECV = 0.42 %
• Dextrin: R² = 0.964, SEC = 0.60 %, SECV = 0.68 %

Moderate correlations were observed for lactic acid (R² = 0.722) and pH (R² = 0.734), indicating potential for further model refinement. Overall, NIRS provided rapid results comparable to HPLC, refractometry, and gravimetric methods.

Benefits and Practical Applications

This single NIR method reduces analysis time to under one minute per sample, consolidating multiple laboratory assays into a single measurement. It enables near real-time monitoring of fermentation, faster process adjustments, and reduced sample handling, improving throughput in both laboratory and production environments.

Future Trends and Potential Applications

Advancements in chemometric algorithms and instrument miniaturization will further enhance accuracy and portability of NIR solutions. Integration with process analytical technology (PAT) frameworks and machine-learning models can support automated, closed-loop control of fermentation. Expanding calibration libraries to include diverse feedstocks and stress conditions will increase robustness.

Conclusion

The DS2500 Solid Analyzer coupled with Vision Air software demonstrates rapid, reliable prediction of key fermentation parameters in corn mash. The approach simplifies quality control by replacing multiple reference methods with a single, nondestructive NIR measurement, facilitating cost-effective and timely process monitoring.

Reference

AN-NIR-093 Quality Control of fermentation processes, Metrohm AG