Using FTIR to Detect Microbial Contamination during the Production of Microbial Products

Significance of the Topic

Ensuring rapid and accurate detection of microbial contamination during the production of microbial products is critical for product safety and quality. Fourier transform infrared spectroscopy (FTIR) provides a label-free, non-destructive approach to characterize microbial cells by their molecular-vibrational signatures. Integrating diffuse reflectance sampling and chemometric analysis enables high-throughput monitoring and early intervention in biomanufacturing workflows.

Objectives and Study Overview

This study aimed to evaluate an FTIR system (IRXross) combined with a custom DRS-8000 diffuse reflectance accessory for detecting Escherichia coli contamination in cultures of Staphylococcus cohnii. Key goals included:

• Assessing the detection limit of E. coli in mixed cultures over a 10⁰% to 10^–6% range.
• Applying spectral preprocessing, principal component analysis (PCA), and cluster analysis to discriminate contaminated from pure cultures.
• Validating FTIR results against real-time PCR quantification of bacterial composition.

Instrumentation Used

The analytical setup comprised:

• IRXross FTIR spectrophotometer with a DRS-8000 diffuse reflectance accessory (custom model).
• Microfocus 64-well sample plate enabling multi-sample preparation.
• Nitrogen purge to minimize atmospheric water vapor and CO₂ interference.
• Software for automated DRS measurement, Savitzky–Golay second-derivative preprocessing, PCA, and cluster analysis.

Methodology and Sample Preparation

Co-culture mixtures of S. cohnii (product microbe) and E. coli (contaminant) were prepared at initial E. coli concentrations from 1% down to 1×10^–6%. Samples underwent:

1. Centrifugation to pellet cells and removal of supernatant.
2. Wash with physiological saline followed by ultrapure water.
3. Addition of 70% ethanol and homogenization with zirconium beads.
4. Deposition of 5 µL suspension per spot on a microfocus plate and air-drying.

FTIR measurement conditions:

• Resolution: 4 cm^–1, 20-scan averaging, SqrTriangle apodization, DLATGS detector.
• Wavenumber range: 1300–800 cm^–1.
• Preprocessing: second derivative (Savitzky–Golay), unit vector normalization.
• Chemometric evaluation: PCA score plots and Euclidean-distance cluster dendrograms.

Main Results and Discussion

Raw IR spectra of mixed cultures appeared similar, but second-derivative preprocessing revealed subtle differences. PCA and cluster analysis grouped samples into five clusters corresponding to initial E. coli levels:

• Pure E. coli and pure S. cohnii controls.
• Spikes at 1–10^–1% and at 10^–2%, 10^–3–10^–4%, and 10^–5–10^–6%.

Contamination was reliably detected at starting concentrations of 10^–4% or higher. Real-time PCR quantification confirmed that when initial E. coli levels exceeded 10^–2%, the faster-growing contaminant dominated the final culture. FTIR detection corresponded to final E. coli proportions as low as 15–27%.

Benefits and Practical Applications

The combined FTIR-DRS approach offers:

• Rapid analysis (~30 s per sample), supporting timely process control.
• High throughput via multi-well plates and automated measurement.
• Non-destructive, minimal sample preparation without labels or reagents.
• Robust chemometric classification enabling early warning of contamination.

Future Trends and Potential Applications

Emerging directions include:

• Integration of online FTIR probes for real-time bioreactor monitoring.
• Advanced machine-learning models to improve limit-of-detection and classification accuracy.
• Expansion to other microbial systems and complex sample matrices.
• Miniaturized, field-deployable FTIR platforms for point-of-use quality monitoring.

Conclusion

Diffuse reflectance FTIR combined with chemometric analysis enables sensitive, rapid detection of microbial contamination in production cultures. The IRXross-DRS system detected E. coli contamination at initial levels of 10^–4% and above, with results validated by real-time PCR. This methodology supports efficient quality control in microbial biomanufacturing.

Reference

Lasch P, Naumann D “Infrared Spectroscopy in Microbiology.” Encyclopedia of Analytical Chemistry 3:1–32 Mar. 2015.