Using an FTIR Spectrometer to Analyze How a Bacterial Culture is Affected by Differences between Culture Medium Ingredients (Peptones and Yeast Extracts)

Significance of the Topic

Reliable control of culture media ingredients is critical for consistent microbial production. Natural ingredients such as peptones and yeast extracts show lot-to-lot and vendor-to-vendor variability that can alter growth, metabolism, and product quality. Non-destructive, high-throughput analytical approaches that can rapidly detect biochemical changes in cultures are therefore valuable for quality control, equivalence testing when changing suppliers or lots, and process monitoring in biomanufacturing.

Objectives and Study Overview

The study evaluated whether Fourier transform infrared (FTIR) spectroscopy, combined with chemometric analysis, can detect differences in the biochemical state of Staphylococcus cohnii cultures caused by variations in peptone and yeast extract ingredients. Multiple commercially available and in-house media formulations were tested to assess instrument sensitivity to ingredient-driven metabolic changes and to demonstrate FTIR utility for ingredient acceptance testing and process monitoring.

Methodology

The experimental workflow comprised media preparation, culture under fixed conditions, sample processing to obtain dried cell deposits, FTIR measurement, spectral preprocessing, and multivariate analysis.

• Microorganism: Staphylococcus cohnii ATCC 29974.
• Media: A commercial soybean-based peptone medium (PA) and five in-house soybean-based peptone media (PB–PF). Yeast extract sets included a commercial medium (YA), in-house media using extracts from multiple companies, different grades (YB_STD, YB_HQ), different lots (YD_lot1, YD_lot2), and other manufacturers (YC, YE).
• Culturing: 37 °C for 24 hours, identical incubation conditions for all media.

Sample Preparation

Samples were prepared as follows (three replicate spots per sample):

1. Centrifuge culture and discard supernatant.
2. Wash pellet with physiological saline and centrifuge.
3. Resuspend pellet in ultrapure water, add ethanol to 70%.
4. Homogenize with zirconium beads to form a suspension.
5. Deposit 5 µL aliquots onto a 64-well microfocus plate and dry prior to FTIR measurement.

Instrumental Conditions and Data Processing

Instrumentation and acquisition details were:

• Instrument: IRXross FTIR spectrometer with custom DRS-8000 diffuse reflectance accessory.
• Detector: DLATGS.
• Resolution: 4 cm-1; Number of averaged scans: 20; Apodization: Square Triangle.
• Purge: Nitrogen to minimize atmospheric H2O and CO2 interference.
• Measurement mode: Diffuse reflectance spectroscopy (DRS) for multi-sample and easy cleaning.

Spectral preprocessing and chemometrics:

• Second-derivative transformation using a Savitzky–Golay filter to enhance subtle band features.
• Spectral window selected: 1300–800 cm-1 (region rich in carbohydrate, polysaccharide, and phosphodiester vibrations relevant to cellular composition).
• Unit vector normalization to compensate for intensity differences.
• Multivariate analysis: Principal component analysis (PCA) and hierarchical cluster analysis (averages, Euclidean distance).

Main Results and Discussion

Peptone comparisons:

• Raw IR spectra of cultures appeared broadly similar, but second-derivative spectra revealed distinct differences near ~1050 cm-1 and ~900 cm-1, bands commonly associated with carbohydrate and glycosidic or phosphate-related vibrations in cellular material.
• The commercial peptone medium (PA) and a plant-derived in-house peptone (PF) produced spectra that separated from the cluster formed by other in-house peptones (PB–PE).
• PF produced the most divergent spectra and corresponded to poor microbial growth, suggesting the spectral differences primarily reflect altered biomass composition and metabolic state due to suboptimal peptone quality.
• PCA and dendrograms grouped samples into three broad clusters, highlighting FTIR sensitivity to ingredient-driven changes in culture state.

Yeast extract comparisons:

• Second-derivative spectra again showed discriminating features around 1050 and 900 cm-1. Yeast extracts YD_lot1, YD_lot2, and YE produced spectra distinct from other yeast-extract-supplemented cultures.
• YD_lot1 and YD_lot2 clustered together, indicating limited lot-to-lot variation for that supplier in this test.
• YE yielded the most irregular spectra and correlated with poor microbial growth, analogous to the PF peptone case.
• Different grades from the same supplier (YB_STD vs YB_HQ) produced detectable differences, indicating FTIR can sense quality/grade-related effects on culture metabolism.

The combined observations indicate FTIR spectroscopy—with appropriate preprocessing and chemometrics—can detect culture-state changes arising from media ingredient variability, flagging both gross growth failure and more subtle metabolic shifts caused by ingredient grade or vendor differences.

Practical Benefits and Applications

Key practical advantages demonstrated:

• Rapid, non-destructive screening of culture samples to detect ingredient-induced metabolic alterations.
• High sample throughput using DRS and microplate deposition, favorable for routine QC workflows.
• Ability to perform equivalence testing when switching suppliers or lots, supporting change control in biomanufacturing.
• Early detection of abnormal cultures that could impact yield or product quality, thereby reducing downstream risks.

Instrumentation Used

Instrumentation and accessories referenced in the study:

• IRXross Fourier transform infrared spectrometer (Shimadzu).
• DRS-8000 diffuse reflectance accessory (custom model).
• DLATGS detector; nitrogen purge for atmospheric control.

Future Trends and Potential Uses

Prospective developments and expanded applications include:

• Integration of FTIR monitoring into routine QC pipelines with standardized spectral libraries for common media and organisms.
• Real-time or at-line FTIR sampling strategies during fermentation to enable dynamic process monitoring and control.
• Enhanced chemometrics (supervised classification, machine learning models) to predict specific metabolic endpoints or product-relevant quality attributes from spectra.
• Combining FTIR with complementary sensors (e.g., Raman, mass spectrometry) to increase specificity for particular metabolites or macromolecular classes.
• Pursuit of regulatory acceptance and method validation pathways for using FTIR in supplier qualification and batch release processes.

Conclusion

FTIR spectroscopy, when combined with robust preprocessing and multivariate analysis, provides a practical and sensitive approach to monitor the effects of culture medium ingredient variability on microbial cultures. The method can differentiate cultures affected by poor-quality or distinct ingredients, detect lot and grade differences, and support QC and change-control activities in microbial product manufacturing.

References

• Lasch, P.; Naumann, D. Infrared Spectroscopy in Microbiology. Encyclopedia of Analytical Chemistry, 2015, vol. 3, pp. 1–32.
• Shimadzu Corporation and JSR Corporation. Application News: Using an FTIR Spectrometer to Analyze How a Bacterial Culture is Affected by Differences between Culture Medium Ingredients (Peptones and Yeast Extracts). First Edition Sep. 2026. (Joint research report; IRXross instrument).