1. Introduction

Fourier Transform-Infrared (FTIR) spectroscopy has emerged as an exceptionally versatile and indispensable tool, revolutionizing molecular analysis across a spectrum of scientific domains, including microbiology (Beekes et al., 2007; Baker et al., 2014; Zarnowiec et al., 2015; Nandiyanto et al., 2019; He et al., 2022; Koczoń et al., 2023). This analytical technique operates on the principle that molecules absorb specific frequencies of infrared light, which correspond to the vibrational frequencies of their chemical bonds (Griffiths, 1983; Nandiyanto et al., 2019; He et al., 2022; Lilo et al., 2022; Weber et al., 2023). The absorption produces a characteristic spectrum that can be used to identify the functional groups and molecular structures present in a sample.

FTIR spectroscopy has been a valuable tool in various biological studies across different scientific areas. In the field of microbiology, it has significantly aided in the rapid and accurate identification of microorganisms (Helm et al., 1991; Wenning and Scherer, 2013; Corte et al., 2014; Câmara et al., 2020; Feng et al., 2020; Brito and Lourenço, 2021; Smirnova et al., 2022; Kamnev et al., 2023; Tata et al., 2023; Tessaro et al., 2023), contributing to timely infection diagnosis and the implementation of appropriate treatment methods. This technique has also allowed for in-depth analyses of microbial structures, metabolic activities, and responses to environmental changes, leading to a better understanding of microbial physiology and behavior (Corte et al., 2014; Câmara et al., 2020; Smirnova et al., 2022; Tessaro et al., 2023). Moreover, FTIR spectroscopy has played a crucial role in monitoring and evaluating the dynamics of microbial communities in different environments (Kamnev et al., 2023; Marques et al., 2023). This has contributed to the management of water quality, assessment of ecosystem health, and the detection of microbial pollution. In the context of metal-pollutant bioremediation, FTIR has enabled the comprehensive analysis of how microorganisms interact with metal contaminants (Pagnucco et al., 2023). This has helped in understanding the mechanisms involved in metal sequestration, transformation, and detoxification. Furthermore, in the domain of organic pollutant bioremediation, FTIR has served as a valuable tool in investigating the interactions between microorganisms and organic contaminants. It has provided insights into the biochemical transformations and degradation pathways involved in the bioremediation process (Aziz et al., 2020).

This review explores the diverse applications of FT-IR spectroscopy in microbiology, with a particular emphasis on its use in microbial cell biology and environmental microbiology. While previous reviews on FT-IR have provided valuable insights into the general principles and applications of the technology, our study seeks to differentiate itself by focusing on its applications in microbial cell biology and environmental microbiology. Additionally, this review addresses the limitations of FT-IR in these disciplines and the challenges that researchers encounter when practically employing FT-IR technology. The review also outlines potential avenues for future research and development, laying the groundwork for further advancements in the field of microbiology and FT-IR technology.

2. Basic principles of FTIR

FT-IR spectroscopy is based on the principle that molecules absorb specific frequencies of infrared light, corresponding to the vibrational frequencies of their chemical bonds. When a sample is exposed to infrared light, the molecules within the sample absorb this light at characteristic frequencies, causing the bonds within the molecules to vibrate. Different types of bonds, such as C-H, O-H, and N-H bonds, have distinct vibrational frequencies, leading to unique patterns in the absorption of infrared light. By measuring the intensity of the absorbed light at various wavelengths, an FT-IR spectrometer produces a characteristic spectrum for the sample. This spectrum can be used to identify the functional groups and molecular structures present in the sample, essentially providing a “fingerprint” that can be used for qualitative and quantitative analysis. FT-IR spectroscopy can be used to study a diverse array of specimens, including solids, liquids, and gases, and it has applications across various scientific domains, including chemistry, physics, materials science, and biology (Griffiths, 1983; Koczoń et al., 2023).

The range of IR radiation encompasses electromagnetic radiation with frequencies between 14,300 and 20 cm−1, with the most significant vibrational frequencies for most molecules falling within the mid IR spectrum, ranging between 4,000 and 400 cm−1 (Griffiths, 1983). Within this specific range, there are four different regions: (1) the single bond region (2,500–4,000 cm−1), (2) the triple bond region (2,000–2,500 cm−1), (3) the double bond region (1,500–2,000 cm−1), and (4) the fingerprint region (600–1,500 cm−1) (Figure 1). Table 1 shows some common peaks observed in FT-IR spectra, along with their corresponding functional groups and reference wavenumbers. Far-and near-IR ranges are less frequently employed, primarily because these regions register overtone (secondary) vibrations and combination vibrations, making them analytically challenging to study and interpret (Nandiyanto et al., 2019; Pirutin et al., 2023).

Figure 1. Typical infrared values for various types of bonds. The region 500–1,500 cm−1, which is in the mid-IR region, is called the fingerprint region and provides molecular fingerprints unique to specific compounds. Reproduced from Master Organic Chemistry (https://www.masterorganicchemistry.com/2016/11/23/quick_analysis_of_ir_spectra/) (accessed on 28 September 2023), with kind permission from James Ashenhurst.

Table 1. Possible assignment of some bands frequently found in microbial IR spectra (peak frequencies have been obtained from the second derivative spectra).

Within the mid IR spectrum, researchers commonly use five distinct spectral windows (Figure 1). The first window, ranging from 3,000 to 2,800 cm−1, is primarily influenced by specific functional groups like membrane fatty acids and certain amino acid side-chain vibrations, dominated by C-H stretching vibrations of CH₃ and CH₂ functional groups. The second window, between 1,800 and 1,500 cm−1, is affected by amide I and amide II groups in proteins and peptides, showcasing intense peaks providing comprehensive insights into protein structure, along with vibrations of ester functional groups in lipids and nucleic acid absorptions. The third window, spanning 1,500 to 1,200 cm−1, represents a mixed region influenced by proteins, fatty acids, and compounds with phosphate groups, affected by CH₂ and CH₃ bending modes. The fourth window, from 1,200 to 900 cm−1, is characterized by symmetric stretching vibrations of PO₂− groups in nucleic acids, along with vibrations related to carbohydrates, polysaccharides, and nucleic acids. Lastly, the fifth window, between 900 and 700 cm−1, known as the true fingerprint region, demonstrates subtle spectral patterns arising from vibrations of aromatic rings in specific amino acids and nucleotides.

The spectrum’s peaks serve as identifiers for functional groups in both organic and inorganic compounds, utilizing their characteristic absorption bands within the infrared region of the electromagnetic spectrum (Griffiths, 1983; El-Azazy et al., 2021). These functional groups, linked to specific vibrational modes resulting from atomic movements within the groups (Griffiths, 1983; Smith, 1998; El-Azazy et al., 2021), encompass stretching, bending, and combination bands. Expressing the frequencies of these vibrations in wavenumbers (cm−1) enables the identification of particular functional groups. For instance, the C=O stretching vibration of a carbonyl group, typically found in ketones and aldehydes, appears around 1,700–1,750 cm−1 (Table 1). Meanwhile, the O-H stretching vibration of alcohols typically appears around 3,200–3,600 cm−1 (Table 1). By comparing the absorption bands’ positions and intensities in the sample’s spectrum to known reference spectra, analysts can determine the presence of specific functional groups in the sample (Silverstein et al., 2014). Notably, complex and distinctive patterns within the ~1,500–400 cm−1 region are highly specific to the compound’s molecular structure. However, peak positions may vary due to factors such as the molecular environment, sample preparation, and instrument settings, underscoring the importance of consulting reliable databases and literature for accurate peak assignments (Griffiths, 1983; Silverstein et al., 2014; He et al., 2022).

3. Types of FT-IR used in microbiology

In microbiology, various forms of Fourier Transform Infrared (FT-IR) spectroscopy serve different research purposes. Transmission FT-IR involves transmitting infrared light through a sample to measure the transmitted light and absorption spectrum, proving valuable for analyzing solid samples like microbial cell walls or biofilm components. Attenuated Total Reflection (ATR) FT-IR is used for liquid and solid samples unsuitable for transmission FT-IR, enabling analysis without extensive sample preparation by measuring the sample’s infrared spectrum in contact with an ATR crystal. Micro-FTIR Spectroscopy allows microscopic analysis of small sample areas, providing high spatial resolution and detailed information about the molecular composition of specific microorganisms or microbial components within a sample. Diffuse Reflectance Fourier Transform Infrared (DRIFT) spectroscopy is ideal for analyzing powdered or granulated samples, including microbial samples, without demanding extensive sample preparation.

In the case of ATR FT-IR, the sample is situated on a densely refractive crystal, usually of higher refractive index (Figure 2A). This method requires minimal or no sample preparation. The IR beam reflects off the crystal’s inner surface, giving rise to an evanescent wave that extends beyond the crystal’s surface and interacts with the sample in intimate contact with the ATR crystal (Baker et al., 2014; Bottum et al., 2023). The sample absorbs some of the evanescent wave’s energy, and the resulting reflected radiation reaches the IR spectrometer’s detector upon exiting the crystal. DRIFT involves directing the IR beam into the sample, where it is reflected, scattered, and transmitted through the sample material (Figure 2B). The IR light that becomes diffusely scattered within the sample and returns to the detector optics is termed diffuse reflection. Lastly, FT-IR micro-spectroscopy is an innovative technique that combines an FT-IR spectrometer with a microscope (Figure 2C). This approach enables the examination of limited areas on surfaces, such as agar plates, and facilitates the acquisition of reflectance or transmittance spectra from samples consisting of a few hundred cells, like microcolonies that develop within 6 to 10 h (Bhargava, 2012; Watanabe et al., 2023).

Figure 2. Schematic representation of methods used to characterize molecular composition of microorganisms: (A) FTIR-attenuated total reflectance, (B) diffuse reflectance FT-IR (DRIFT), and (C) FTIR-microspectroscopy.

When subjecting a sample of a substance to continuous infrared (IR) light radiation, a distinctive resonance absorption band with intricate, fingerprint-like characteristics is obtained. The intensity of these absorption bands arises from scanning before and after the IR beam traverses the substance. Instead of isolated peaks, the frequencies and intensities of the IR bands exhibit distinctive, broad, and intricate profiles. These profiles serve as signatures that can be harnessed for the identification, characterization, and quantification of the sample. Upon radiating a sample containing bacterial cells, the resulting IR spectrum encapsulates the overall chemical composition of the sample. This spectrum holds the capability to provide insights into taxonomic variations due to inherent chemical distinctions or to identify chemical alterations resulting from exposure to challenging environments.

4. Applications of FT-IR in microbial cell biology

FT-IR spectroscopy has found numerous applications in the field of microbiology due to its ability to provide rapid and non-destructive information about the molecular composition of microorganisms. It is also used to investigate bacterial cell wall composition, studies biofilm formation and antibiotic resistance, monitor microbial growth and metabolic activity, and explore microbial community dynamics in complex environments. Furthermore, FT-IR spectroscopy provides insights into bacterial interactions with other microorganisms, host cells, and environmental factors, offering valuable information about microbial physiology and behavior. Thus, this technique finds widespread applications in microbiology, providing valuable insights into bacterial physiology, ecology, and interactions (Maquelin et al., 2000; Tiquia-Arashiro, 2019a; Jansson et al., 2023). Below reviews key applications of FT-IR in microbiology:

4.1. Microbial identification

FT-IR spectroscopy has been used for the identification of microorganisms based on their unique spectral fingerprints (Table 2). The FT-IR spectra of microbial cells, including bacteria, yeast, and fungi, contain characteristic peaks that reflect the presence of specific biomolecules (lipids, proteins, carbohydrates) and their functional groups. These spectral patterns can be compared to reference spectra in databases to identify the microorganisms. Microorganisms contain diverse biomolecules like proteins, lipids, nucleic acids, and carbohydrates, which exhibit characteristic absorption bands in the infrared spectrum. For bacterial identification, the FT-IR spectra of bacterial samples can be compared to spectral databases or reference spectra, researchers can identify bacterial species or strains based on their unique spectral features (Lin et al., 1998; Naumann et al., 2001; Dziuba et al., 2007; Rebuffo-Scheer et al., 2008; Novais et al., 2019; Feng et al., 2020; Brito and Lourenço, 2021). The ability of FT-IR spectroscopy to detect bacteria relies on the fact that the chemical composition and structure of bacterial biomolecules vary among different species and strains, resulting in distinct infrared absorption patterns. These patterns enable differences and identification. To enhance the accuracy and efficiency of bacterial detection and classification using FT-IR spectroscopy, researchers employ advanced statistical analysis methods like Principal Component Analysis (PCA) (Lasch et al., 2004; Bombalska et al., 2011), hierarchical clustering analysis (HCA) (Lasch et al., 2004; Dziuba et al., 2007; Bombalska et al., 2011; Feng et al., 2020), linear discriminant analysis (LDA) (Brito and Lourenço, 2021), stepwise discriminant analysis (SDA) (Lasch et al., 2004), and Ward’s algorithm (Dziuba et al., 2007; Table 2). By developing spectral libraries or databases that encompass a wide range of bacterial species, robust and reliable models for bacterial identification can be established (Martak et al., 2019; Tessaro et al., 2023; Yang et al., 2023). In bacterial identification, most analyses of bacterial samples have been observed to fall within the mid-IR region (4,000 to 600 cm−1) mainly because absorption patterns of functional groups in biological molecules are observed in this particular region as sharp fundamental vibrations, rather than broad overtones or harmonics which are found in the near-IR (AlMasoud et al., 2021).

Table 2. Applications of FT-IR in microbial identification.

One of the key challenges in the use of FT-IR for microbial identification is the complexity of microbial samples, leading to the potential for overlapping spectral signals and difficulties in accurate identification (Wenning and Scherer, 2013). Moreover, the lack of standardized protocols and extensive databases can impede consistent and reliable microbial identification, particularly for less studied or newly discovered microorganisms (Franco-Duarte et al., 2019; Feng et al., 2020). In certain cases, the resolution of infrared spectroscopy might not be adequate to distinguish between closely related microbial species or strains, limiting its efficacy in precise microbial identification (AlMasoud et al., 2021). Microbial species exhibit considerable diversity, and within each species, there can be substantial variability due to genetic, environmental, and phenotypic factors (Feng et al., 2020).

Capturing this diversity in an FT-IR library would necessitate the inclusion of a wide range of strains and conditions, requiring extensive sampling and data collection efforts. Ensuring the standardization of sample preparation, data acquisition, and analytical protocols across different laboratories is crucial for the reproducibility and comparability of spectral data. Variations in experimental procedures can lead to inconsistencies in spectral profiles, making it challenging to build a reliable and consistent FT-IR library. Moreover, analyzing and interpreting complex spectral data from diverse microbial species requires advanced computational tools and expertise. Developing robust algorithms and software for efficient data processing, feature extraction, and classification of microbial spectra is essential for effective library management and utilization. However, despite these limitations, infrared spectroscopy remains instrumental in the rapid and non-destructive identification of microorganisms, aiding in the timely diagnosis of infections and the implementation of appropriate treatment strategies. Furthermore, it contributes to environmental monitoring and various biotechnological applications, demonstrating its significant role in the field of microbial identification despite its shortcomings.

4.2. Bacterial typing

FT-IR can differentiate bacterial strains and classify them into different groups or species based on their spectral profiles. This aids in bacterial typing, strain characterization, and epidemiological studies (Goodacre et al., 2000; Perkins et al., 2005; Yu, 2005; Wang et al., 2019; Feng et al., 2020; AlMasoud et al., 2021; Table 3). Whole living cells can be analyzed non-destructively, which allows in vivo investigations. As an example, diffuse reflectance infrared spectroscopy (DRIFT) was used to discriminate among 36 strains of vegetative Bacillus cells and their spores (Goodacre et al., 2000; Table 3). Different serovars of Salmonella enterica have been discriminated by mid-FTIR in attenuated total reflection (ATR) mode applying soft independent modeling of class analogy (SIMCA modeling) (Perkins et al., 2005; Glassford et al., 2013). Discrimination of endospores by mid-FTIR in ATR mode followed by the application of PCA (Goodacre et al., 2000; Perkins et al., 2005; Yu, 2005; Wang et al., 2019; Feng et al., 2020), hierarchical cluster analysis (HCA) (Perkins et al., 2005; Wang et al., 2019; Feng et al., 2020), canonical variates analysis (CVA/DFC) (Goodacre et al., 2000), correlation analysis (Feng et al., 2020), and SIMCA (Perkins et al., 2005) remained possible even after autoclaving of the samples (Perkins et al., 2005; Table 3). Libraries have been developed to relate spectral absorbance peaks of key functional groups present in proteins, carbohydrates, lipids, or nucleic acids (Yu and Irudayaraj, 2005; Fadlelmoula et al., 2022). Spectra of biological samples can be divided into different regions or windows. The typical fingerprint region for microorganisms is between wavenumbers of 650 cm−1 and 1,800 cm−1 originating from cellular carbohydrate compounds and proteins.

Table 3. Applications of FT-IR in bacterial typing.

Discrimination of vegetative cells and spores of Bacillus circulans was possible using FT-IR and subsequent chemometrical analysis of the spectra (Fadlelmoula et al., 2022). By FT-IR spectroscopy, spores of Bacillus thuringiensis, B. subtilis, and B. megaterium were easily distinguished. In some cases, however, IR fingerprints obtained by chemometrical analysis of spores of B. atrophaeus, B. brevis, B. circulans, and B. lentus clustered close together making discrimination difficult. The distance trees resulting from HCA based on FT-IR investigations of pure cultures agreed to phylogenetic trees derived from classical molecular methods based on 16S rRNA gene sequences (Zhao et al., 2006). Brandes and Brandl (2011) showed that spores originating from different Bacillus species can be discriminated against by applying FT-IR and subsequent multi-scaling chemometrical data treatment (Brandes and Brandl, 2011). A study carried out by Wang et al. (2019) proved the ability of FT-IR to distinguish between 16 types of foodborne pathogenic bacterial strains and was supported by multivariate analysis such as PCA and HCA of FT-IR data. In this study the authors found that a specific spectral region from 1,300 to 1,000 cm−1 which corresponds to phosphate and polysaccharide vibrations was successfully employed to discriminate bacterial strains (Smith, 2011; Wang et al., 2019).

One of the primary limitations in bacterial typing is related to the need for high resolution to differentiate between closely related strains or species, which may not always be achievable with standard infrared spectroscopy (AlMasoud et al., 2021). Moreover, the technique might struggle with complex microbial samples, leading to overlapping signals and difficulties in accurately distinguishing between different bacterial types (Perkins et al., 2005; AlMasoud et al., 2021). To address this challenge of achieving high resolution and differentiating closely related bacterial strains or species in bacterial typing using infrared spectroscopy, the integration of advanced data processing techniques such as machine learning algorithms and multivariate analysis could be beneficial (AlMasoud et al., 2021). These methods can enhance the spectral analysis by enabling the identification of subtle differences and patterns within complex microbial samples, thereby improving the accuracy of bacterial classification. Leveraging these computational tools alongside infrared spectroscopy can provide a comprehensive and robust framework for precise bacterial typing, allowing for the differentiation of closely related strains with greater resolution and reliability.

4.3. Microbial growth phases monitoring

FT-IR can monitor changes in microbial growth phases by tracking alterations in the spectral profiles of cells during different growth stages (Portenier et al., 2005; AlQadiri et al., 2008; Corte et al., 2011; Grace et al., 2020; Kochan et al., 2020; Spain and Funk, 2022; Table 4). This helps in understanding microbial physiology and metabolism (Tiquia-Arashiro, 2014). In batch cultures, bacterial growth is usually accompanied by four distinct stages which are clearly visible on the growth curve: (1) lag, (2) exponential (log), (3) stationary and (4) death phase (Cooper, 1991; Tiquia et al., 2004, 2008; Rolfe et al., 2012; Wang et al., 2015; Kochan et al., 2020). Knowledge of the bacterial growth stage, bacterial numbers and growth kinetics is needed in research and commercial applications. The basis of the traditional methods to determine growth phase is cell numbers which can be established using standard plate counting or by optical density (Stuart, 2004; Tiquia, 2010a; Nguyen et al., 2013; Plecha et al., 2013; Patel et al., 2019). However, any biochemical changes that could reflect microbial physiology cannot be described by these methods (Stuart, 2004; Tiquia, 2010a; Oest et al., 2018).

Table 4. Applications of FT-IR in monitoring microbial growth phases.

FT-IR spectroscopy detected significant changes in the chemical composition of bacteria through its different growth stages (Stuart, 2004; Spain and Funk, 2022). For the lag and log phases, these changes were mainly related to the various relative amounts of nucleic acid, accompanied by changes in protein composition (Kochan et al., 2020; Semeraro et al., 2023). The dominant spectral differences were associated with relative nucleic acid content, which reached its highest level after 60 and 90 min (Stuart, 2004; Kochan et al., 2020). This was expressed by bands at 1,215, 1,085, and 965 cm−1 in ATR. Further to that, an alteration in the protein composition (Amide II/Amide I) and substantial changes in relative carbohydrate content, visible via bands at 1,035 cm−1 (ATR) were observed. These changes may reflect the cellular activity aimed at adaptation to a new environment or in preparation for division (Stuart, 2004; AlQadiri et al., 2008; Spain and Funk, 2022). The results demonstrate the possibilities offered by multimodal vibrational spectroscopies (e.g., ATR-FTIR) toward providing a biochemical characterization, enabling the study of microbial physiology even given low bacterial numbers. Such biochemical probing opens a new door toward studying lag-phase related events (Stuart, 2004; Kochan et al., 2020; Spain and Funk, 2022). So, FT-IR demonstrates changes in the overall chemical composition of bacterial populations during growth (Zeroual et al., 1994; Stuart, 2004; Corte et al., 2011; Kochan et al., 2020; Tata et al., 2023).

FT-IR spectroscopy has been proven essential in elucidating bacterial chemical changes and understanding microbial physiology during different growth stages. However, its limited ability to capture subtle cellular composition changes and challenges in data interpretation pose constraints for comprehensive monitoring of microbial growth phases. Spectral overlap and diverse biochemical compositions across microbial species further complicate the establishment of a universal standard for interpreting FT-IR spectra. Additionally, the technique’s incapacity to capture rapid changes in cellular composition restricts its effectiveness for real-time analysis of microbial growth in dynamic environments. To overcome the challenges associated with using FT-IR spectroscopy to monitor microbial growth phases, the integration of complementary techniques and advanced data analysis methods can enhance the depth and accuracy of the analysis. Coupling FT-IR with high-resolution microscopy and flow cytometry can provide additional spatial and temporal information, enabling a more comprehensive understanding of cellular changes during different growth stages. Leveraging multivariate data analysis approaches, such as PCA and partial least squares regression (PLSR), can facilitate the deconvolution of complex spectral data and enable the identification and quantification of individual biochemical constituents, addressing the challenge of spectral overlap and variability across microbial species. Furthermore, the development of standardized protocols and reference databases specific to different microbial growth phases can aid in the interpretation and comparison of FT-IR spectra, fostering a more consistent and reliable analysis framework. Integration of these strategies can enhance the utility of FT-IR spectroscopy in monitoring microbial growth phases, providing a more holistic and detailed understanding of microbial physiology and metabolism.

4.4. Characterization of microbial cell wall components

FT-IR has been used to analyze the composition of microbial cell walls (Table 5), to study changes in cell phenotype (Galichet et al., 2001), elucidate changes in functional groups among Gram-positive and Gram-negative bacteria (Jiang et al., 2004; Tang et al., 2013; Zhang et al., 2023), determine the interactions with nanoparticles with the cell wall (Nadtochenko et al., 2005; Huang et al., 2017), monitor drug interactions with bacteria cells (Zlotnikov et al., 2023), characterize the functional role of extracellular polysaccharides and lipopolysaccharide (LPS) extracted from endophytic Pseudomonas putida against rice blast (Ashajyothi et al., 2023), and compare structural components of the cell wall of different algal strains logarithmic and stationary growth phases (Spain and Funk, 2022; Table 5). Changes in these structural components (e.g., peptidoglycan, lipopolysaccharides) can indicate bacterial responses to various conditions.

Table 5. Applications of FT-IR in characterization of microbial cell wall components.

One study utilized FT-IR and other spectroscopic methods to investigate the cell surface properties of Aquabacterium commune (Ojeda et al., 2008). The FT-IR signals revealed the presence of carbon, phosphorus, and nitrogen atoms in the bacterial cell wall, with specific signals demonstrating variations in response to changes in pH. These variations were linked to acid–base reactive carboxyl, phosphoryl, and amine functional groups, suggesting their involvement in the acid–base exchange reactivity observed during titration experiments. In a separate study, FT-IR spectroscopy was employed to analyze the isolated cell wall material of four algal strains (Chlorella vulgaris, Coelastrella sp., Scendesmus sp. B2-2, and Haematococcus pluvialis) during logarithmic or stationary growth phases (Spain and Funk, 2022). The spectral shape indicated typical carbohydrate (960–1,180 cm−1) and protein (amide II; 1,475–1,620 cm−1 and amide I; 1,620–1,710 cm−1) regions, while the lipid fraction (around 1,740 cm−1) showed minimal absorption, suggesting a low presence of fatty acids in the cell walls. For strain comparison, the FTIR spectra were normalized to the amide I band (indicating protein content) (Spain and Funk, 2022).

Another study (Huang et al., 2017) utilized two-dimensional Correlation Fourier Transformation Infrared spectroscopy (2D-FTIR-COS) to investigate the interaction between TiO2 nanoparticles and bacterial cell membranes using bacterial ghosts (BGs), which are non-living bacterial cell envelopes. The results suggested that the proteins in BGs exhibited a strong preference for interacting with TiO2 nanoparticles, while the interaction with characteristic functionalities in polysaccharides (C-OH) and phospholipids (P=O) was minimal. This observation was further confirmed by the settlement of TiO2 nanoparticles in the presence of specific biomolecules such as bovine serum albumin (BSA), alginate, and phosphatidylethanolamine (PE). The asynchronous map of 2D-FTIR-COS indicated a sequential bonding order of COO- > aromatic C=C stretching > NH, amide II > C=O, ketone, shedding light on the interaction between TiO2 nanoparticles and bacterial cell membranes in aquatic environments.

Zhang et al. (2023) determined the impact of reactive oxygen species on cell activity and structural integrity of Gram-positive and Gram-negative bacteria in electrochemical disinfection system. Around 70% of the cell wall mass is composed of -CH3- and -CH2-vibrational stretching bands, with changes in their positions suggesting alterations in the cell wall layers. Notably, Staphylococcus aureus (a Gram-positive bacterium) exhibited a pronounced blue shift trend in the -CH2 and -CH3 peaks, indicating increased fluidity in the cell wall bilayer, potentially induced by peroxidation. Conversely, E. coli (A Gram-negative bacterium) showed no significant dependency in peak positions despite disorder in cell wall components. Analysis within the 2,000–1,000 cm−1 region identified five prominent peaks, including amides, nucleic acids, lipid, and protein vibrations, PO2-profiles, and C–O–P symmetric stretches from oligo/polysaccharides. Changes in peak positions and intensities of amide and C–O–P suggested modifications in cell wall proteins and oligosaccharides. While the C–O–P band weakening in E. coli indicated potential damage due to electrochemical oxidation, S. aureus exhibited slight changes after 60 min. Damage to the C–O–P band, a vital component in lipopolysaccharides (LPS), could lead to LPS degradation and subsequent cell structure failure. The decrease in C–O–P intensity served as an indicator of cell damage, corroborated by inactivation efficiency results. Furthermore, changes in the PO2-band near 1,245 cm−1 suggested the disruption of the hydrogen phosphate group during the electrochemical oxidation process. However, the detection of specific C=O peaks representing peroxidation products might require extended treatment time for accurate identification.

Fourier-transform infrared (FT-IR) spectroscopy has been widely used to investigate microbial cell walls, yet its application faces notable limitations. Interpreting FT-IR data is complex and demands specialized expertise, posing a significant challenge. Sample preparation sensitivity can affect spectra quality, with issues like sample thickness and uniformity leading to potential distortions. Limited spatial resolution restricts the observation of microscopic cell wall variations. Overlapping peaks within spectra can complicate the identification of specific functional groups, potentially leading to misinterpretations. Dependence on reference spectra, particularly for scarce data, may hinder compound characterization. FT-IR spectroscopy’s sensitivity to environmental factors, such as temperature and humidity, can introduce variability in results. While offering insights into cell wall chemical composition, it may lack the resolution required for understanding intricate cell wall interactions, limiting its use in certain research contexts. Considering these limitations is crucial for ensuring accurate and meaningful interpretation of FT-IR results in cell wall analysis. Standardizing sample preparation protocols, including precise control of sample thickness, uniformity, and purity, can minimize variability and ensure the reproducibility of spectral results. Building comprehensive reference databases for microbial cell wall components can facilitate more accurate comparison and interpretation of FT-IR results. Implementing rigorous environmental controls during data acquisition, such as temperature and humidity regulation, can help mitigate variations in spectral data. Furthermore, combining FT-IR with other high-resolution structural analysis techniques, such as electron microscopy and atomic force microscopy, can provide a more comprehensive understanding of the complex interactions within microbial cell walls.

5. Use of FT-IR spectroscopy in environmental microbiology

The study of microbial ecology has undergone transformative advancements due to innovative analytical techniques. Among these, Fourier Transform-Infrared (FT-IR) spectroscopy has emerged as a pivotal tool, enabling researchers to delve into the world of microorganisms with unprecedented precision and depth. FT-IR spectroscopy holds the potential to unravel the complexities of microbial communities, shedding light on their composition, metabolic activities, and responses to environmental changes. It also offers insights into the molecular signatures that characterize microorganisms, paving the way for a comprehensive understanding of microbial ecosystems and their vital roles in various habitats. This exploration into the utilization of FT-IR spectroscopy in microbial ecology opens new avenues for deciphering the hidden dynamics that govern microbial interactions and their impact on broader ecological systems.

5.1. Monitoring microbial biofilms

Microbial biofilms, complex communities of microorganisms adhering to surfaces and encased in self-produced extracellular polymeric substances (EPS), have garnered significant attention due to their diverse roles and impact on various fields, including medicine, industry, and environmental science (Schmitt et al., 1995; Schmitt and Flemming, 1998; Bosch et al., 2006; Mukherjee et al., 2011; Quilès et al., 2016; Tugarova et al., 2017; Di Martino, 2018; Miao et al., 2019; Gieroba et al., 2020; Cheah and Chan, 2022).

The use of Fourier Transform-Infrared (FT-IR) spectroscopy has proven to be a valuable and versatile approach in monitoring bacterial (Bosch et al., 2006; Elzinga et al., 2012; Quilès et al., 2016; Tugarova et al., 2017; Singhalage et al., 2018; Cheeseman et al., 2021; Kamnev et al., 2023), fungal (Singhalage et al., 2018; Cheeseman et al., 2021; Dimopoulou et al., 2021), algal (Tong and Derek, 2021; Wang et al., 2021) and microbial community (Schmitt and Flemming, 1998; Oberbeckmann et al., 2014; Gong et al., 2019; Miao et al., 2019) biofilms (Table 6). As an analytical tool, the attenuated total reflection (ATR) offers a further possibility to directly investigate the chemical composition of smooth surfaces of various materials (Smith, 1998; Elzinga et al., 2012; Cheeseman et al., 2021; Tong and Derek, 2021; Watanabe et al., 2023). This can be achieved practically without any sample preparation. With respect to biofilm research, this offers the significant advantage that the sample can be investigated in a relatively undisturbed state. Especially for membrane and polymer investigations, ATR is a helpful analytical tool. A further advantage of FTIR-ATR-spectroscopy in biofilm research is the possibility of measuring in aqueous media as well as investigating the development of a biofilm in situ, non-destructively and in real time directly at the substratum/liquid interface (Schmitt et al., 1995; Schmitt and Flemming, 1998; Oberbeckmann et al., 2014; Miao et al., 2019; Jansson et al., 2023). The major advantage of the ATR method is that the biofilm can be observed non-destructively, directly, on-line and in real time (Schmitt and Flemming, 1998; Tiquia-Arashiro, 2012). FT-IR can provide useful information on the functional groups of EPS that play an adhesive and cohesive role in maintaining biofilms (Geoghegan et al., 2008; Di Martino, 2018). The EPS matrix plays a pivotal role in biofilm formation, stability, and protection. FT-IR spectroscopy assists in elucidating the composition of EPS, which includes polysaccharides, proteins, and other biomolecules. By studying changes in specific bands related to carbohydrate and protein vibrations, researchers can assess the dynamic nature of EPS as biofilms develop, mature, and respond to environmental cues (Bosch et al., 2006; Ojeda et al., 2008; Mukherjee et al., 2011; Bowman et al., 2018; Cheeseman et al., 2021; Cheah and Chan, 2022).

Table 6. Applications of FT-IR in monitoring microbial biofilms.

The complex composition of biofilms can lead to challenges in identifying specific components due to overlapping spectral bands and the heterogeneous mixture of biomolecules and extracellular polymeric substances (EPS). Advanced spectral deconvolution techniques are necessary for accurate analysis. The inherent heterogeneity of biofilm structures introduces variability in FT-IR signals, making it difficult to establish consistent baseline data for comparison and identify subtle changes during biofilm development and responses to environmental stimuli. Environmental influences on biofilm formation and EPS composition, such as temperature and nutrient availability, require careful consideration and control during experimental design and data interpretation. Background signals and contaminants can interfere with the identification of specific biomolecules and functional groups within the biofilm, affecting the reliability of FT-IR analysis. The limited spatial resolution of traditional FT-IR spectroscopy may hinder precise characterization of localized changes within the biofilm matrix, emphasizing the need for complementary imaging techniques and high-resolution FT-IR imaging systems to enable more accurate spatial analysis of microbial biofilms. Employing advanced data processing and statistical modeling approaches, such as multivariate analysis and machine learning algorithms, can aid in interpreting complex FT-IR spectra and discerning subtle changes in biofilm development and response to environmental stimuli. Integration of complementary imaging techniques, such as confocal microscopy and atomic force microscopy, can provide spatially resolved information, complementing FT-IR spectroscopy and enabling more comprehensive characterization of localized biofilm structures and molecular distributions. Furthermore, the development of high-resolution FT-IR imaging systems and the incorporation of advanced data visualization tools can enhance spatial resolution and facilitate detailed mapping of biofilm composition and dynamics at the microscale level (Kamnev et al., 2023).

5.2. Functional profiling of microbial communities

Microbial communities within natural environments carry out a plethora of functions essential for nutrient cycling, organic matter decomposition, and ecosystem stability (Tiquia et al., 2006a,b; Tiquia, 2008, 2010b; Igisu et al., 2012; Mckindles and Tiquia-Arashiro, 2012; Flood et al., 2015; Tiquia-Arashiro and Grube, 2019; Duncan and Petrou, 2022; Naylor et al., 2022). The metabolic activities and functional profiles of the microbial communities are influenced by environmental factors, including temperature fluctuations, nutrient availability, and pollutant exposure (Liu et al., 2003; Wu et al., 2005; Gentile et al., 2006; Tiquia, 2010a, 2011; Dong et al., 2017; Qin et al., 2018; Tiquia-Arashiro, 2019b; Huang et al., 2020; Cabugao et al., 2022; Duncan and Petrou, 2022).

FT-IR spectroscopy has been crucial in comprehending the functional characteristics of microbial communities (Table 7). This method is commonly combined with genetic techniques like metagenomics and qPCR (Edwards et al., 2014; Lin et al., 2014; Andrei et al., 2015; Miao et al., 2019; Wang et al., 2021; Wilson et al., 2021 and Zada et al., 2021). Additionally, it is utilized alongside genetic fingerprinting techniques (Edwards et al., 2014), radioisotope labeling (Andrei et al., 2015; Wang et al., 2021; Wilson et al., 2021; Duncan and Petrou, 2022), as well as biochemical assays like community level physiological profiles (CLPP) (Artz et al., 2006) and enzyme assays (Miao et al., 2019). Other supporting analytical and imaging techniques include excitation emission matrix spectroscopy (EEMS) (Wilson et al., 2021) and scanning electron microscopy (SEM) (Miao et al., 2019).

Table 7. Applications of FT-IR in functional profiling of microbial communities.

The FT-IR protocol follows a structured process that involves sample preparation, data acquisition, and advanced data analysis techniques (Figure 3). To ensure accurate representation of microbial components, diverse samples are collected from various environments and undergo preparation steps such as cell lysis and extraction (Figure 3A). FT-IR spectroscopy generates unique spectral signatures revealing the biomolecular composition of the microbial samples, providing insights into lipids, proteins, carbohydrates, and nucleic acids (Figure 3B). The data analysis stage utilizes sophisticated tools like PCA and HCA and SIMCA (Figure 3C), enabling comparisons with reference databases to identify microbial taxa and their functional attributes (Figure 3D).

Figure 3. FT-IR protocol for microbial functional analysis. (A) Sample preparation, (B) FT-IR assay and data processing, (C) multivariate statistical analyses, and (D) functional profiling of the microbial communities.

While FT-IR spectroscopy has proven to be a valuable tool for studying microbial functions in natural environments, it has encountered challenges in interpreting the complex data it generates. The acquired spectra demand a profound grasp of both spectroscopy and microbiology. To ensure meaningful comparisons and reliable findings across studies, standardized protocols and data analysis methods are imperative, given the diversity and variability in natural environments and microbial communities. Uniform protocols and robust analytical frameworks are essential for maintaining consistency and coherence in result interpretation, thereby bolstering the credibility and reproducibility of findings. Despite challenges, FT-IR spectroscopy serves as a bridge between molecular insights and ecosystem processes. By utilizing multivariate statistical tools such as PCA and cluster analysis, researchers can identify functional patterns and understand the roles of microorganisms in environmental dynamics. The integration of FTIR spectroscopy with other advanced techniques, including genetic analysis and stable isotope probing (Wang et al., 2023), holds promise for deeper insights into microbial functions, but requires careful consideration and methodological refinement to effectively merge these approaches.

5.3. Microbial stress and viability

The use of FT-IR spectroscopy for microbial stress and viability has emerged as a valuable approach for understanding the responses of microorganisms to various stressors (Table 8). Microorganisms encounter a multitude of stressors, including changes in nutrient availability, temperature fluctuations, pH variations, exposure to toxins, and other environmental challenges. Traditional methods for assessing microbial stress and viability often involve time-consuming culturing procedures and may not accurately represent the real-time physiological state of microorganisms (Kumar and Ghosh, 2019). FT-IR spectroscopy offers a rapid and comprehensive alternative for studying these aspects. When microorganisms undergo stress or changes in their physiological state, alterations occur in their biochemical composition, resulting in shifts in FT-IR spectra. Researchers can analyze these changes to assess the stress levels and viability of microorganisms (Moen et al., 2005, 2009; Alvarez-Ordóñez et al., 2011; Câmara et al., 2020). Monitoring these shifts allows researchers to infer stress-induced modifications within cellular components, including protein denaturation, lipid peroxidation, and changes in carbohydrate content, offering insights into microbial adaptation mechanisms (Moen et al., 2005, 2009; Alvarez-Ordóñez et al., 2011; Câmara et al., 2020). Employing various modes of FT-IR spectroscopy, including transmission, DRIFT, and ATR, researchers have examined changes in the composition and structure of whole bacterial cells in response to specific stress factors and plant-derived signals (Kamnev, 2008; Meyvisch et al., 2022). Investigations using FT-IR spectroscopy have revealed distinctive metabolic variations between epiphytic and endophytic strains of the Azospirillum brasilense species under conditions of heavy-metal stress (Kamnev, 2008). Furthermore, FT-IR spectroscopy has been utilized to study the metabolomic stress responses triggered by N-alkylpyridinium bromide surfactants in yeast strains Saccharomyces cerevisiae and Candida albicans, demonstrating its utility in probing stress-induced metabolic alterations in various microorganisms (Corte et al., 2014). Studies involving FT-IR spectroscopy have highlighted its sensitivity in detecting stressful conditions or pathological states, ranging from bacterial reactions to antibiotics, responses to starvation and environmental stressors, to encounters with pollutants and gene mutations, underscoring its potential in diverse research contexts (Melin et al., 2001; Kamnev et al., 2005; Portenier et al., 2005; Stehfest et al., 2005; El-Bialy et al., 2019; Câmara et al., 2020; Ribeiro da Cunha et al., 2020; Scarsini et al., 2021; Soares et al., 2022; Table 8).

Table 8. Applications of FT-IR in microbial stress and viability assessment.

Microbial viability assessment is central to understanding the functional status of microbial populations in diverse environments (Braissant et al., 2020). It provides critical information about the metabolic activity, reproductive potential, and overall health of microorganisms (Nguyen et al., 2013; Pomaranski and Tiquia-Arashiro, 2016; Braissant et al., 2020). In microbial ecology, assessing viability aids in deciphering community responses to environmental fluctuations, resource availability, and biotic interactions (Tiquia et al., 2002; Cho et al., 2012; Berninger et al., 2018). Additionally, viability assessments are pivotal for monitoring the efficacy of antimicrobial treatments, studying microbial survival strategies, and predicting ecosystem responses to perturbations.

FT-IR spectroscopy has practical applications in discerning viable microbial cells and distinguishing between viable and non-viable ones (Davis et al., 2012; Sundaram et al., 2012; Meneghel et al., 2020; Table 8). Meneghel et al. (2020) developed two distinct FT-IR systems for analyzing the infrared spectra of bacteria in water, operating at different spatial resolutions (Figure 4). The first system integrated a custom-built attenuated total reflection inverted microscope with a synchrotron-based FT-IR spectrometer (Figure 4A), enabling the acquisition of spectra at the individual-cell level within the 1,800–1,300 cm−1 range. The second system utilized a transmission FT-IR microscope with a specially designed sample holder for liquid samples (Figure 4B), allowing the examination of viable cells across the entire mid-IR region by regulating the optical path length. These approaches helped identify potential cellular markers of bacterial populations, including the secondary structure of proteins, particularly the proportions of α-helix and β-sheet structures, as well as cell envelope components, specifically polar head groups of membrane phospholipids and complex sugars of the peptidoglycan cell wall. These components, already utilized for microbial identification, were also implicated in cryo-resistance mechanisms. Multivariate analysis of the spectra revealed that cryo-sensitive cells exhibited the highest cell heterogeneity and the highest content of proteins with the α-helix structure. Moreover, cluster analysis of bacterial cells highlighted phosphate and peptidoglycan vibrational bands associated with the cell envelope as potential markers of resistance to environmental conditions (Meneghel et al., 2020).

Figure 4. Two types of FT-IR developed by Meneghel et al. (2020). (A) Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) inverted microscope for the analysis of individual bacterial cells in an aqueous environment using synchrotron radiation. An enlargement diagram of the liquid sampling area showing the ATR principle. (B) Thermal source-powered FTIR microscope for probing clusters of a few thousands of live cells in the mid-IR region (4,000–975 cm−1). Diagram shows the demountable liquid micro-chamber.

Sundaram et al. (2012) conducted research indicating the effectiveness of FT-IR in distinguishing between live and deceased cells of Salmonella typhimurium and Salmonella enteritidis. They discovered significant spectral variations in components such as the cell wall, cell membrane, cytoplasm, polysaccharides, proteins, peptides, amide bands, and nucleic acids. The Mahalanobis distance and SIMCA analysis yielded a perfect 100% accuracy in classifying live and dead cells of both Salmonella serotypes. However, the authors emphasized the need for further investigations to develop a comprehensive spectral library for different Salmonella serotypes and validate the method’s sensitivity and selectivity using standard microbiological procedures. Davis et al. (2012) also utilized FT-IR spectroscopy to differentiate live and dead Escherichia coli O157:H7 cells and those treated with various inactivation methods (heat, salt, UV, antibiotics, and alcohol). The study demonstrated the efficacy of partial least squares analysis and canonical variate analysis (CVA) in rapidly distinguishing between live and dead cells. This approach shows promise in reducing detection time compared to other techniques like fluorescent microscopy and EMA-qPCR, which necessitate lengthier preparation and detection times. The findings suggest potential applications of this FT-IR approach in analyzing food samples subjected to different inactivation treatments.

In these studies, change