Fourier Transform Infrared Spectroscopy for Molecular Analysis of Microbial Cells
Bibliographic record
Abstract
A rapid and inexpensive method to characterise chemical cell properties and identify the functional groups present in the cell wall is Fourier transform infrared spectroscopy (FTIR). Infrared spectroscopy is a well-established technique to identify functional groups in organic molecules based on their vibration modes at different infrared wave numbers. The presence or absence of functional groups, their protonation states, or any changes due to new interactions can be monitored by analysing the position and intensity of the different infrared absorption bands. Additionally, infrared spectroscopy is non-destructive and can be used to monitor the chemistry of living cells. Despite the complexity of the spectra, the elucidation of functional groups on Gram-negative and Gram-positive bacteria has been already well documented in the literature. Recent advances in detector sensitivity have allowed the use of micro-FTIR spectroscopy as an important analytical tool to analyse biofilm samples without the need of previous treatment. Using FTIR spectroscopy, the infrared bands corresponding to proteins, lipids, polysaccharides, polyphosphate groups, and other carbohydrate functional groups on the bacterial cells can now be identified and compared along different conditions. Despite some differences in FTIR spectra among bacterial strains, experimental conditions, or changes in microbiological parameters, the IR absorption bands between approximately 4,000 and 400 cm(-1) are mainly due to fundamental vibrational modes and can often be assigned to the same particular functional groups. In this chapter, an overview covering the different sample preparation protocols for infrared analysis of bacterial cells is given, alongside the basic principles of the technique, the procedures for calculating vibrational frequencies based on simple harmonic motion, and the advantages and disadvantages of FTIR spectroscopy for the analysis of microorganisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".