Detection of residual microbial biomarkers in bacterial cellulose using laser-induced fluorescence spectroscopy
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Summary
This summary is machine-generated.Laser-induced fluorescence (LIF) spectroscopy rapidly detects microbial contamination in bacterial cellulose (BC). This method offers a faster alternative to traditional purity tests, aiding biotechnological applications.
Area Of Science
- Biomaterials Science
- Analytical Chemistry
- Microbiology
Background
- Bacterial cellulose (BC) is a valuable biomaterial for medical uses.
- Microbial contaminants and residues limit BC's clinical applications.
- Current purity tests for BC are time-consuming and labor-intensive.
Purpose Of The Study
- To investigate laser-induced fluorescence (LIF) spectroscopy for monitoring microbial contamination in BC.
- To assess the effectiveness of different purification protocols on BC contamination.
- To establish a correlation between LIF and laser scanning confocal microscopy (LSM) data.
Main Methods
- Bacterial cellulose (BC) samples from Medusomyces gisevii were purified using alkaline, detergent, and oxidative treatments.
- Laser-induced fluorescence (LIF) spectra were recorded (220-290 nm excitation) and analyzed chemometrically.
- Samples were also examined using laser scanning confocal microscopy (LSM) for comparison.
Main Results
- LIF spectra revealed tryptophan and tyrosine fluorescence, indicating microbial residues in native and treated BC.
- NaOH treatment reduced tryptophan signals; hydrogen peroxide diminished tyrosine signals, but neither eliminated them completely.
- LIF showed strong correlation with LSM data, enabling rapid differentiation of treatment variants and identification of residual contamination.
Conclusions
- LIF spectroscopy is a feasible method for rapid and reliable monitoring of microbial contamination in bacterial cellulose.
- Spectral fingerprints obtained via LIF can indicate the presence and type of residual contamination.
- Standardizing LIF protocols could integrate this technique into biotechnological workflows for efficient contamination assessment.

