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Review

Surface-Enhanced Raman Scattering of Bioaerosol: Where Are We Now? A Systematic Review

by
Stefano Fornasaro
*,
Sabrina Semeraro
,
Sabina Licen
and
Pierluigi Barbieri
Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Giorgieri 1, 34127 Trieste, Italy
*
Author to whom correspondence should be addressed.
Chemosensors 2025, 13(3), 86; https://doi.org/10.3390/chemosensors13030086
Submission received: 17 December 2024 / Revised: 18 February 2025 / Accepted: 24 February 2025 / Published: 3 March 2025
(This article belongs to the Special Issue Surface-Enhanced Raman Scattering (SERS) Spectroscopy as a Tool for Analytical and Bioanalytical Analyses)
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Abstract

:
Surface-enhanced Raman scattering (SERS) spectroscopy has grown in popularity as a bioaerosol monitoring method due to its high sensitivity and specificity, as well as its ability to be performed in complex biological mixtures using portable and relatively inexpensive devices. However, due to a lack of standardised methodologies, SERS sensing of bioaerosols remains difficult. Full-length peer-reviewed journal articles related to the application of SERS spectroscopy to examine bioaerosols were systematically searched in PubMed, Scopus, and Web of Science databases using the PRISMA guidelines. A total of 13 studies met the inclusion criteria for our systematic literature search. A critical evaluation of the experimental aspects involved in the collection of bioaerosols for SERS analysis is presented, as well as the elective applicability and weaknesses of various experimental setups, helping to provide a solid foundation for real-time bioaerosol characterisation using SERS spectroscopy.

1. Introduction

The term “bioaerosol” refers to primary biological aerosol particles (PBAPs), which are airborne particles originating from biological sources, including bacteria, viruses, fungi, protozoa, plants, and animals [1]. These particles encompass a variety of viable and non-viable entities, comprising single and/or clustered organisms or spores, fragments and/or residues of organisms, or organisms’ metabolites such as endotoxins or mycotoxins [2].
Bioaerosols play an important role in a variety of fields, including public health, environmental science, and occupational safety [3,4,5]. They span a wide range of sizes, from less than 0.1 μm in diameter (viruses) to 100 or more μm in diameter (fungal spores), and their monitoring in workplace environments is used for assessing indoor air quality, infectious disease outbreaks and rapid transmission, agricultural exposures, and occupational health [6]. Furthermore, they play a significant role in ecological processes and climate dynamics, making their characterisation and analysis critical for both scientific understanding and practical interventions [7].
Despite their importance, bioaerosols pose significant analytical challenges due to their complex and heterogeneous nature and often low concentrations in ambient air. Considering the absence of comparative standards, the above-mentioned information must be especially taken into account when designing the sampling protocol and interpreting the experimental results [8].
Conventional approaches for bioaerosol analysis, including culture-based methods and molecular methods, yield significant insights but are constrained by various limitations [9]. Culture-based methods necessitate long incubation periods and can solely detect viable organisms, neglecting non-culturable yet biologically significant components. Although extremely sensitive, molecular methods that extract DNA, proteins or other biological material and assess their properties, abundance, or diversity, such as next generation sequencing (e.g., metagenomics or metabarcoding) or quantitative Polymerase Chain Reaction (qPCR) are restricted in their ability to detect unknown or novel bioaerosols due to the dependence on prior knowledge of the target organism’s genetic material [10]. Moreover, these methods have lengthy processing times, limited portability, and frequently lack the capacity for real-time analysis, which is essential for applications including environmental monitoring (e.g., forecasting aeroallergens), biosecurity and public health early-warning systems, and climate research (e.g., cloud glaciation) [11].
Recently, surface-enhanced Raman scattering (SERS) spectroscopy has attracted interest as a feasible method for detecting airborne pathogens at environmentally relevant concentrations and sensing trace environmental contaminants, among many other potential applications [11,12,13,14,15]. SERS exploits the phenomenon of Raman signal amplification that occurs when molecules are adsorbed onto, or in close proximity to (distance < 10 nm) a plasmonic active surface (e.g., silver or gold-based nanoparticles or other nanostructures commonly referred to as SERS substrates) [16]. Highly active areas on the substrate (hot spots) are responsible for the large vibrational signal enhancement. These areas are generated where the local electric field is large due to the collective excitation of conducting electrons within the small metallic structure (i.e., the surface plasmon) [17,18]. This enhancement significantly improves the detection of low-concentration analytes with high molecular specificity. A more detailed description of the SERS enhancement mechanisms as well as its experimental and theoretical foundations can be found elsewhere [19,20].
The application of SERS spectroscopy in bioaerosol analysis may offer several advantages. Unlike spontaneous Raman scattering, SERS spectroscopy has a short assay time and requires far smaller concentrations of analytes, making it particularly suited for bioaerosol studies. The possibility of a direct (also known as “label free”) detection can reduce the need for complex sample preparation, while the availability of portable instrumentation can facilitate on-site and real-time (RT) monitoring [12,21]. Moreover, SERS spectra can reveal the specific molecular structure and relative abundance of multiple analytes within the same sample. Advanced signal processing techniques, grounded in chemometrics and machine learning, are routinely employed to optimise the extraction of structural and chemical information from a single SERS spectrum, making it an effective instrument for characterising complex bioaerosol mixtures [22]. Despite these advantages, the application of SERS in this field is still evolving, with ongoing research addressing challenges such as the reproducibility, specificity, and standardisation of protocols. However, to the best of our knowledge, there are no reports specifically aimed in summarising the advances of SERS in this field.
Therefore, we conducted a systematic literature review (SLR) on MEDLINE-PubMed, Web of Science (Wos) and Scopus to evaluate the use of SERS spectroscopy for continuous RT or near-RT analysis of bioaerosols and the underlying molecular events, focusing on both proof-of-principle and real-life application studies. By examining recent developments, challenges, and future directions, this SLR seeks to provide a detailed overview of the current state of the art, identify gaps in knowledge, and highlight opportunities for further research and the practical implementation of SERS spectroscopy for bioaerosol analysis and characterisation in various environmental and clinical settings.

2. Methodology

The present SLR was conducted according to the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) Statement [23], due to its potential to improve the reliability and reproducibility of reviews, its standardised nature, and its widespread application in bibliometric studies. We read the scientific literature to seek information and address a series of specific questions, such as the following:
  • Has any research been conducted in a controlled and standardised system? The aim was to offer an overview of the available knowledge about how bioaerosol generation and sampling technologies may affect SERS analysis.
  • Are there any studies performed on real samples? In which environment? The aim was to survey investigations on real-life applications.
  • What nature of PBAPs can be detected by SERS? How? The aim was to determine what types of bioaerosol were tested.
  • Which biological/environmental processes have been investigated? The aim was to survey the possible practical applications.

2.1. Search Strategy

An extensive electronic search was conducted in the MEDLINE-PubMed, SCOPUS, and WoS databases for peer-reviewed journal articles published by the end of 14 October 2024, using the following key terms: “Surface enhanced Raman”, “airborne bacteria”, “airborne virus”, “airborne pathogen*”, “pollen”, “bioaerosol*”, and “endotoxin*”. The Boolean terms “AND” and “OR” were used to combine keywords. Only scientific articles in English were considered. Zotero software was used to store articles retrieved from databases which were screened for duplicates. Studies that appeared in more than one database, or appeared more than once in the same database, were considered only once. Titles and abstracts were screened by one reviewer to identify relevant publications that met the inclusion criteria. Full-text review was applied to determine the eligibility of articles.

2.2. Inclusion and Exclusion Criteria

Articles were eligible for inclusion if they demonstrated use of SERS for the detection and/or characterisation of bioaerosol and PBAPs. Articles were excluded based on the following criteria: (a) the full paper was not accessible; (b) articles were conference proceedings, opinion articles, commentaries, or review papers; (c) air was not sampled as analytical matrix; and (d) only SERS from non-biological particles (e.g., VOCs) was reported.
Due to the scarcity of the literature on the proposed theme, no criteria of temporal restriction about the publication date of the chosen articles were applied.

2.3. Bibliometric Analysis

A bibliometric analysis of the scientific production was performed within the R software environment for statistical computing and graphics (R version 4.4.0 “Puppy Cup”), building on the package bibliometrix [24]. Initially, the records were separated according to the year of publication in various journals to determine the level of interest in the topic over time. Subsequently, a co-occurrence network and a thematic map were generated to find out the main keywords used in the analysed studies, as well the overall conceptual structure within the extracted literature. Finally, once the scientific production was quantitatively examined, the content of the articles examined was reviewed.

2.4. Data Extraction

Two reviewers (S.F. and S.L.) independently conducted separate screenings of the included articles to assess the quality of each study. Any discrepancies were resolved via discussion until a consensus was reached. Eligible articles were subjected to data extraction based on the following criteria: (a) bioaerosol origin; (b) bioaerosol collection protocol; (c) SERS operating procedures; (d) detection limit; and (e) the overall time of analysis. When multiple analytical techniques were evaluated within a study, results were commented based on the SERS technique only.

3. Results and Discussion

The bibliographic search using keyword combinations identified 60 articles on Scopus, 59 articles on WoS and 22 articles on PubMed, resulting in a total of 141 scientific articles. In the first phase, duplicate articles were removed. Titles and abstracts of the remaining 69 papers were reviewed, and 36 of them were excluded as they were not relevant to the inclusion criteria. The remaining 33 full-text articles were assessed for eligibility, and 20 were finally excluded. The reasons for excluding the latter full-text articles were because they did not sample air as an analytical matrix (n = 9), they did not study biological particles (n = 10), or it was not an original document (n = 1). Finally, 13 papers were deemed to be fully consistent with the review topic and were included in this SLR (Figure 1).
The 13 publications were from 12 sources, with 80 authors. The timespan was 2005–2024 with an annual growth rate of 6%. The average number of citations per document was 22. The number of author keywords was 49. The international co-authorship was 39%, 7 co-authors per document, with 613 references. The average age of the publications was 6 with 22 average citations per publication. A co-occurrence network map (Figure S1) and a thematic map (Figure S2) were generated based on the identified 214 keywords, plus to contextualise and understand the overall conceptual structure between various concepts within the 13 publications.
Various target samples and methods for bioaerosol generation and sampling have been used to study bioaerosols with SERS spectroscopy. However, no common standardised protocols have been used; thus, a direct comparison of the results is not possible, since each study showed different aims, environmental conditions, sampling and laboratory resources. In addition, considering SERS analysis, differences at the level of the characteristics of the laser light used for excitation, the way the analyte is put into contact with the metal surface, and the type of approaches used for data processing were found. All the above-mentioned aspects contributed to increase the discrepancy among results.
Concerning the lack of detail on fundamental parameters among the selected studies, 6 studies out of 13 had no information on the dimensional characterisation of the considered bioaerosol, and 6 studies had no information on the sampling time and/or flow rate, preventing the direct comparison of the total volume analysed.
The fundamental characteristics of the studies in relation to the review scope are described in Table 1 and Table 2.

3.1. Bioaerosol Generation

When referring to the generation of bioaerosol, it is easy to distinguish between two main categories. Particles can be naturally generated (by natural or anthropogenic systems) or artificially produced for experimental purposes to mimic aspects of natural production in a controlled and/or simplified setting [38].
No study has yet examined the utility of SERS spectroscopy for the study of bioaerosol generation dynamics in the environment, with most of the research devoted to laboratory tests. Partial exceptions can be considered: two studies documenting SERS analysis of pollen directly collected from pollinating trees [28,31], and three works analysing human-produced liquid bioaerosols during breathing, coughing, or sneezing. Bandarenka et al. employed SERS spectroscopy to investigate the composition of wash swabs from Cu-TiO2/TiO2-modified filters with disinfecting ability [33]. The filters were embedded in facial masks worn by a volunteer for 4 h. Although the study primarily aimed to conduct a comparative analysis of the microbial abatement associated with Cu-TiO2 photocatalytic activity, the authors accomplished the identification of bands typical of airborne bacteria in the SERS spectra. Han et al. collected real-world airborne SARS-CoV-2 particles from the indoor air of a COVID-19 positive patient’s dormitory, using bioaerosol collected from the rooftop of the Fudan University as negative control [35]. Similarly, Tahir et al. directly sampled ambient bioaerosol at the cafeteria of Fudan University and compared the obtained SERS profiles with the ones from bioaerosol collected from the air-flow chamber after ultraviolet (UV) light airborne microorganism inactivation [24].
With respect to the eight studies investigating bioaerosols in laboratory settings, there are many aspects to consider. The experimental bioaerosol generation can be split into different steps, each affecting data variability and interpretation. Each step, from biological material preparation and storage prior to aerosolisation to the actual aerosolisation mechanism, poses several challenges [39]. Regarding the PBAP type, seven studies focused on bacteria, with E. coli as the most often chosen microorganism model, and three studies were related to SARS-CoV-2. Reproducible particle concentrations in laboratory tests were often higher than those naturally observed, to facilitate detection and statistical analysis. Furthermore, most bioaerosol generated in laboratory tests was produced from pure culture suspensions, which are never found in natural environments.
A further issue is how to reproduce as faithfully as possible the natural behaviour of bioaerosols, in terms of aerosol source composition, particle size, relative humidity, temperature and residence time. First, aerosol containment chambers are essential for conducting bioaerosol investigations. Unfortunately, such test chambers are seldom commercially available, and all the chambers documented in the selected papers were constructed by the researchers themselves.
Several bioaerosol generators have been developed and documented in the literature, each employing a distinct methodology to aerosolise biological particles. However, it remains uncertain which device can generate high bioaerosol concentrations while preserving the viability and structure of microorganisms [40].
Unfortunately, full technical details on sample preparation process were largely absent or unreferenced in most of the selected studies, with a vaguely defined “nebuliser” as the most frequently reported bioaerosol generator (n = 3), followed by a medical-grade nebuliser (n = 2), and a Collison nebuliser (n = 1) (Table 1). It is important to note that all these devices were modified versions of those intended for the aerosolisation of non-biological material (e.g., using pneumatic nebulisation) and were therefore not tailored for bioaerosol research. The advantages of this type of nebulisers are user-friendliness, the minimal volume of material required, good reproducibility and high particle output. However, pneumatic nebulisation generates droplets via physical shearing and impaction, and it has been shown how this can injure and fragment microorganisms [38].
The work conducted by Su et al. was the only one mentioning the use of ultrasonic atomisation for aerosol generation [32]. This makes it very difficult to afford proper comparison and highlights the still-developing stage of bioaerosol SERS analysis.

3.2. Bioaerosol Sampling

In bioaerosol studies, the selection of a sampling method is usually determined by the intended downstream analysis together with the taxa being targeted for investigation [41]. Relative humidity, temperature, the chemical composition of the air, and time spent in the aerosol state are key environmental factors that influence microbial and viral integrity, also guiding the choice of a sampling strategy. The fundamental concern is always having enough molecules in the final sample.
In the case of SERS spectroscopy, given that signal enhancement is an optical near-field phenomenon and its efficacy is theoretically correlated with the number of molecules close to the substrate’s surface, the passive accumulation of enough molecules for detecting them typically takes too long [42]. For this reason, high-volume active sampling methods were usually preferred. Figure 2 summarises the basic principles of the most widespread methods for bioaerosol sampling. More details can be found in recent reviews [43,44,45].
In the selected studies, filtration was the most common method used (n = 4), followed by impaction (n = 3) and cyclonic/impinger methods (n = 2/1). Interestingly, no consistency regarding the choice of sampling strategy was found in the selected studies, not even when driven by a common goal (Table 1).
Filter-based sampling was specifically investigated for personal bioaerosol sampling, i.e., by embedding a flexible plasmonic substrate into a face mask. In this case, the same substrate simultaneously served as a PBAPs sampler and as a SERS enhancer. In a study conducted by Hwang et al., a Au-TiO2 chip was incorporated inside a face mask to preconcentrate low-volume respiratory aerosols and detect SERS spectra from SARS-CoV-2 spike proteins [37]. Similarly, Ryu et al. also attached a flexible plasmonic substrate inside a face mask to detect SARS-CoV-2 [36]. Tahir et al. placed the SERS substrate directly into an eight-stage Andersen cascade sampler. This strategy allowed for high-volume sampling, resulting in large amounts of material for SERS detection and revealing the changes in the SERS signal during different cell growth phases as well as the number of single live and dead bacteria [29].
The direct collection of bioaerosols into a liquid medium is considered preferable in comparison to collecting on a solid support to prevent compromising bioaerosol viability [44]. Wet sampling processes, such as liquid impaction, wet cyclone, and swirling systems have been used for collecting airborne particles into a liquid medium. However, the transfer of suspended samples to the SERS substrate continues to be a limitation, highlighting the importance of specific conditions to maximise the chances of observing an intense SERS spectrum from a resuspended PBAP. Interestingly, the substantial number of customised systems described to solve this issue is most likely owing to the fact that technical advancement in the collection of bioaerosols is less established and standardised, when compared to atmospheric chemistry and the collection of gases and aerosols [14]. On one hand, at very low PBAP concentrations, there are often not enough molecules present in the collected liquid to reach the substrate’s surface by diffusion on a reasonable timescale. On the other hand, the fast emission velocity of bioaerosols (e.g., 2–10 m/s for respiratory aerosols [46]) can substantially prevent effective direct adsorption onto a substrate’s surface [47]. For those reasons, the direct transfer of the collected fluid sample onto the surfaces of SERS substrates in a microfluidic device was the most often used approach to facilitate the interaction of molecules with hotspots and ensure reproducible conditions. Sengupta et al. developed a custom impactor/collector system for sampling different bioaerosols and either transferring them into a cuvette containing a suspension of AgNPs or directly transferring them to a circulating stream of the AgNPs using a micropump. A major advantage of the micropump system is a rapid detection (few minutes) [25,26]. Schwarzmeier et al. combined a Coriolis µ wet cyclone impactor with an antibody-based microarray readout system for the analysis of airborne E. coli [27]. The collection fluid of the Coriolis µ sampler was pumped through the flow cell and mixed with AgNPs immediately after the sample collection. The entire detection process took less than 3 h, but the selectivity was higher than that of antibody–antigen interactions with a limit of detection of 144 particles/cm3. Choi et al. developed a micro-optofluidic SERS platform (Figure 3), which ensures the collection of airborne particles by inertia and drag forces and simultaneously mixes them with AgNPs in a single microfluidic channel [30]. The reported LOD was approximately 102 CFU/mL, and the total bacterial aerosol concentration was determined in real time based on the ratio of sampled air to SERS colloid, making this study the first to feature simultaneous bioaerosol sampling and SERS analysis in real time.

3.3. SERS Substrates

The performance of SERS spectroscopy as an analytical technique is highly dependent on the plasmonic properties of SERS substrates [48]. Since the SERS response is the result of a complex interaction between the analyte, the matrix, and the metal substrate, every analytical problem requires a SERS substrate with its own set of appropriate characteristics. Metallic nanostructures made of gold and silver are widely used because of their adaptability and ease of synthesis. These nanostructures can be tailored into various shapes and sizes allowing their properties to be modified. Moreover, they can be deposited on solid supports, using various bottom-up approaches, to prepare solid SERS substrates [49].
The type of nanostructures varied in the selected studies, as well as how they were incorporated in the final substrate (Table 2). Colloidal silver nanoparticles (AgNPs) were the most common and were used in five studies while gold nanoparticles (AuNPs) were used in three studies. Three studies used nanoparticles incorporated in a composite solid substrate, such as gold–titanium dioxide (Au-TiO2), gold-porous anodic alumina (Au-PAA), and silver/nickel/macroporous silicon (Ag/Ni/macropsia). Su et al. used AgNPs added on the top of the copper electrodes of the developed biochip to obtain SERS spectra [32].
Top-down substrates with nano-sized gaps produce a homogeneous SERS signal, good repeatability from batch to batch and sample to sample, making them easier to standardise and commercialise. The Klarite® inverted square pyramidal SERS substrate, used in a study conducted by Tahir et al. [29], was one of the first commercial substrates by Renishaw Diagnostics Ltd. It is a good example of an expensive and time-consuming production substrate, and it is no longer available on the market.
Chemosensors 13 00086 g004
Figure 4. Conceptual differences between the detection strategies used in bioanalytical SERS. Direct detection seeks analyte-specific bands, whereas indirect detection seeks bands of a different, well-characterised molecule bounded to the analyte, sometimes referred to as a “SERS reporter”, “SERS tag”, or “SERS label”. Reproduced from Ref. [50] with permission from the Royal Society of Chemistry.
Figure 4. Conceptual differences between the detection strategies used in bioanalytical SERS. Direct detection seeks analyte-specific bands, whereas indirect detection seeks bands of a different, well-characterised molecule bounded to the analyte, sometimes referred to as a “SERS reporter”, “SERS tag”, or “SERS label”. Reproduced from Ref. [50] with permission from the Royal Society of Chemistry.
Chemosensors 13 00086 g004
Notably, none of the studies indicated whether the choice of the type of metal nanostructure and/or the wavelength were correlated. Ag for instance, can display a SERS effect when excited with lasers having wavelengths in the blue/green region (e.g., 514/ 532 nm, but also with 405/413 nm), but yields intense SERS spectra also upon near infrared excitation (785 nm). On the other hand, Au substrates may work well upon red-near IR region (e.g., 633, 785 or 830 nm) excitation, depending on their surface characteristics. The wavelength of 785 nm was chosen for sample excitation in almost half of the studies (n = 6), followed by 514/532 nm (n = 3), and 633 nm (n = 3). Bandarenka et al. was the only group to apply a laser source of 473 nm for spectral analysis [33]. The major reason for the success of 785 nm as the excitation wavelength is probably the ability of 785 lasers to minimise intrinsic fluorescence (autofluorescence) of PBAPs, which arises from the presence of fluorescent molecular components within the sampled particles. Furthermore, lasers at this wavelength provide substantial signal enhancements with all metals commonly used for SERS substrate preparation because they match their localised surface plasmon resonances, a critical requirement for the SERS effect to take place.

3.4. SERS Analysis

The detection and quantification of PBAPs with SERS can be accomplished through two strategies: direct and indirect (Figure 4). Direct detection was the preferred detection scheme with 12 studies, while only 1 used indirect detection employing malachite green isothiocyanate (MGITC) as the SERS reporter selectively coupled to the target via specific antibodies as the recognition element [36]. Direct SERS detection presents the most unique advantages of SERS spectroscopy, in which it can provide molecular fingerprint information for both identification and detection without the presence of reporter molecules [18]. However, to fully exploit the capabilities of direct SERS for PBAPs identification, it is necessary to understand the molecular origin of their SERS spectra; that is, the SERS bands observed in the spectrum must be interpreted in terms of specific molecular species (e.g., a metabolite, a protein, or a nucleic acid). It is advisable to collect a comprehensive array of control SERS spectra under diverse acquisition settings throughout the development phase, including signals from pure metabolites, pure SERS substrates, and pure matrices devoid of analyte molecules. Preferably, the signals of the most significant interferences should be collected under the experimental conditions designated for the specific bioaerosol detection study. From a strictly analytical and metrological perspective, this form of controls is crucial for accurately determining the method’s figures of merit (FoMs), including the limit of detection (LOD). In most of the publications examined in this SLR, FoMs are frequently incompletely reported and/or inadequately defined. The LOD is often determined for a particular SERS substrate using analytes recognised for their strong and well-characterised Raman activity, despite having no relation to the PBAP and matrix intended for the proposed SERS method. Among the studies analysed, only one utilised an official definition for the LOD calculation [51], while 6 out of 13 papers did not specify the exact LOD for the method (Table 2). Regarding the SERS spectra obtained from cultured bacteria, it is now fully recognised that the purine metabolites (e.g., adenine, hypoxanthine, xanthine, guanine, and so on) secreted from microbial cells and present near the outer cell wall area mainly contribute to the SERS features of microbes [17,52]. This consideration is extremely important for bioaerosol studies, since the type and concentration of purines are dependent on microbe-specific enzymes and metabolic activity [53]. This indicates that bacteria should (have enough time to) release enough metabolites into the collection liquid to be detected by SERS. Slight variations in the parameters affecting this behaviour, such as the presence of different bacteria or the total available surface area of the substrate, may thus alter the SERS spectrum, leading to erroneous conclusions. Figure 5 shows the comparison between the SERS spectra of several strains of E coli, as obtained in different bioaerosol studies. Despite significant overlap for the most important bands in the SERS spectra, discernible variations are evident. These variations are most likely caused by the different bacterial growth conditions and spectral acquisition methods used in each study (Table 2).
Although this occurrence may not provide a risk in laboratory tests, it could significantly limit the interpretability of data derived from environmental sampling, as numerous parameters remain uncontrollable. The relevant bands in the spectra (as emerging from conventional spectral deconvolution or peak finding methods) can in principle be due to any biological molecule present in the sampled bioaerosol and adsorbed on the substrate’s surface to a sufficient amount. In this case, advanced chemometrics and machine learning approaches can help in analysing the complex spectral profile, unravelling the contributions from different sources [55]. Examples are evident in the research conducted by Kneipp’s group, which used hierarchical cluster analysis (HCA), principal component analysis (PCA), and artificial neural networks (ANNs) to classify pollen samples from the Coniferales and Fagales plant orders based on SERS data [28]. The ANN effectively extracted taxonomically relevant information from the data, achieving a high accuracy of 97% in identifying various pollen species, including closely related species that were not well differentiated by HCA or PCA. The same group explored the feasibility of integrating SERS with various spectroscopic and spectrometric techniques, including FTIR, Raman, and MALDI-TOF MS, in a data fusion framework to analyse minor biochemical variations among pollen samples [31]. Consensus Principal Component Analysis (CPCA) facilitated the integration of data from various methods, allowing for the exploration of the underlying global information. Another interesting approach was the one reported by Hwang et al. [37]. Machine learning was utilised to analyse SERS spectra through a deep learning-based autoencoder algorithm. The autoencoder effectively classified SERS signals of SARS-CoV-2 lysates in respiratory aerosols, optimising hyperparameters and achieving the classification of concentrations with over 98% accuracy. The algorithm employed a modified loss function and ablation of non-discriminant SERS features to improve classification accuracy.

4. Conclusions and Practical Implications

4.1. Conclusive Remarks

With this systematic review, we aimed at bringing to light the actual state-of-the-art research exploring SERS spectroscopy as a tool for bioaerosol analysis, providing some answer to the initially defined questions, as follows:
  • Are there any studies performed in a controlled and standardised system? SERS spectroscopy can be used to detect PBAPs and their chemical composition in the laboratory. However, the road to full deployment is still long, and more research is still required in this area.
  • Are there any studies performed on real samples? In which environments? Some proof-of-concept studies for the detection of airborne pollen and bacteria using SERS were performed in the environment where these bioaerosols were present. The identification of pollen extracts was carried out using real pollen samples from different plant species. The detection of SARS-CoV-2 was also conducted on real breath aerosol samples.
  • What nature of airborne bioanalytes/microorganism can be detected by SERS? How? SERS spectroscopy was used to detect various airborne bioanalytes, including bacteria, pollen, and viruses. This detection was achieved by adsorbing the microorganisms onto a substrate, typically silver or gold nanoparticles, which enhance the Raman signals and allow for the identification and characterisation of the bioanalytes based on their distinct spectral fingerprints.
  • Which biological/environmental processes have been investigated? The research papers discussed the investigation of various biological and environmental processes related to bioaerosols, including the following: (i) the characterisation of airborne pollen and bacteria; (ii) the detection of bacterial contamination in indoor air; (iii) the characterisation and detection of aerosolised bacteria; (iv) the disinfection of airborne contaminants on mask filters; and (v) the detection of SARS-CoV-2 variants.

4.2. Road Map for Standardisation

Improving the efficiency of SERS spectroscopy in bioaerosol monitoring and ensuring reliable and reproducible results across various applications necessitate the standardisation of analytical procedures, which include sampling, sample preparation and pre-treatment, signal acquisition, and data interpretation. Based on the findings of this systematic literature review, we propose a possible roadmap for standardising procedures for biosensing bioaerosols with SERS spectroscopy (Figure 6). This roadmap offers a foundational framework. The details of each step must be customised to align with the distinct needs and priorities of the field.
The very first step involves developing Standard Operating Procedures (SOPs) for the collection and handling of bioaerosol samples for SERS analysis. This includes specifications for sampling equipment, procedures for sample collection, and conditions for sample storage. A single, comprehensive approach is not feasible. Distinct SOPs are required for different contexts, such as outdoor and indoor settings, to preserve target PBAPs and ensure the collection of a representative sample from the specific environment. The sampling duration is critical and dependent upon the location and flow rates. Active sampling is more suitable for environments with anticipated low concentrations, such as hospital settings, as it produces larger volumes and biomass. Passive sampling is suitable for scenarios involving prolonged exposure, such as direct sampling from facial masks.
The next step must address SOPs to enhance the likelihood of detecting a signal from the collected sample, in other words, how to properly prepare bioaerosol samples prior to SERS analysis. Integrating processes such as filtering, separation, or preconcentration into compact, SERS-compatible microfluidic devices is highly achievable and constitutes an effective approach for advancing automated real-time bioaerosol monitoring. Miniaturising functional components such as mixers and pumps and ensuring cost-effectiveness are crucial to ensure the broad commercial application of these platforms.
The third step is to define SOPs for optimising SERS substrates (material selection, surface modification, and quality control) as well as SERS instrumentation (laser parameters, spectral acquisition settings, and data storage formats). This would ensure the reproducibility and comparability of SERS measurements across laboratories, as well as the minimisation of variability caused by differences in instrumentation and experimental conditions.
The final step should include SOPs for analysing and interpreting SERS data, including background correction, band assignment, and target PBAP quantification. This would require the implementation of algorithms and software tools for automated data analysis. Moreover, the establishment of comprehensive guidelines for interpreting SERS spectra will enhance the consistent identification of bioaerosol components across various studies, thus facilitating the quick dissemination of information concerning bioaerosol concentrations to pertinent stakeholders.
Finally, fostering collaboration among researchers, regulatory bodies, and industry stakeholders is essential for the successful implementation of standardised procedures. Establishing a regulatory framework that encompasses the best practises for the biosensing of bioaerosols will facilitate the adoption of standardised methods across various sectors, including healthcare, environmental monitoring, and public safety [56,57]. By addressing these key areas, the field can advance towards more reliable and effective biosensing methodologies that strengthenpublic health and environmental safety [56,58,59,60,61].

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/chemosensors13030086/s1, Figure S1: Co-occurrence network; Figure S2: Thematic map; Table S1: Comparison on different detection methods for PBAP.

Author Contributions

Conceptualisation, S.F.; methodology, S.F.; software, S.F.; validation, S.L.; investigation, S.F. and S.S.; data curation, S.F.; writing—original draft preparation, S.F.; writing—review and editing, all authors; visualisation, S.F.; supervision, S.L.; project administration, S.F.; funding acquisition, S.F. and P.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Union, NextGenerationEU project PNRR iNEST CUP J43C22000320006. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union nor the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Data Availability Statement

No new data were created or analysed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Després, V.R.; Huffman, J.A.; Burrows, S.M.; Hoose, C.; Safatov, A.S.; Buryak, G.; Fröhlich-Nowoisky, J.; Elbert, W.; Andreae, M.O.; Pöschl, U.; et al. Primary Biological Aerosol Particles in the Atmosphere: A Review. Tellus B Chem. Phys. Meteorol. 2012, 64, 15598. [Google Scholar] [CrossRef]
  2. Cox, J.; Mbareche, H.; Lindsley, W.G.; Duchaine, C. Field Sampling of Indoor Bioaerosols. Aerosol Sci. Technol. 2020, 54, 572–584. [Google Scholar] [CrossRef]
  3. Delort, A.-M.; Amato, P. (Eds.) . Microbiology of Aerosols; John Wiley & Sons: Hoboken, NJ, USA, 2017; ISBN 978-1-119-13229-5. [Google Scholar]
  4. Tian, J.; Yan, C.; Alcega, S.G.; Hassard, F.; Tyrrel, S.; Coulon, F.; Nasir, Z.A. Detection and Characterization of Bioaerosol Emissions from Wastewater Treatment Plants: Challenges and Opportunities. Front. Microbiol. 2022, 13, 958514. [Google Scholar] [CrossRef] [PubMed]
  5. Duchaine, C. Indoor Bioaerosols: Progress and Challenges in the Study of the Invisible Entities That Changed Our World. In Proceedings of the 5th International Conference on Building Energy and Environment; Wang, L.L., Ge, H., Zhai, Z.J., Qi, D., Ouf, M., Sun, C., Wang, D., Eds.; Environmental Science and Engineering; Springer Nature: Singapore, 2023; pp. 2239–2241. ISBN 978-981-19-9821-8. [Google Scholar]
  6. Zhang, C.; Dai, X.; Gebrezgiabhier, T.; Wang, Y.; Yang, M.; Wang, L.; Wang, W.; Man, Z.; Meng, Y.; Tong, L.; et al. Navigating the Aerosolized Frontier: A Comprehensive Review of Bioaerosol Research Post-COVID-19. Atmosphere 2024, 15, 404. [Google Scholar] [CrossRef]
  7. Xie, W.; Li, Y.; Bai, W.; Hou, J.; Ma, T.; Zeng, X.; Zhang, L.; An, T. The Source and Transport of Bioaerosols in the Air: A Review. Front. Environ. Sci. Eng. 2021, 15, 44. [Google Scholar] [CrossRef] [PubMed]
  8. Huang, Z.; Yu, X.; Liu, Q.; Maki, T.; Alam, K.; Wang, Y.; Xue, F.; Tang, S.; Du, P.; Dong, Q.; et al. Bioaerosols in the Atmosphere: A Comprehensive Review on Detection Methods, Concentration and Influencing Factors. Sci. Total Environ. 2024, 912, 168818. [Google Scholar] [CrossRef]
  9. King, M.D.; Lacey, R.E.; Pak, H.; Fearing, A.; Ramos, G.; Baig, T.; Smith, B.; Koustova, A. Assays and Enumeration of Bioaerosols-Traditional Approaches to Modern Practices. Aerosol Sci. Technol. 2020, 54, 611–633. [Google Scholar] [CrossRef]
  10. Franchitti, E.; Pascale, E.; Fea, E.; Anedda, E.; Traversi, D. Methods for Bioaerosol Characterization: Limits and Perspectives for Human Health Risk Assessment in Organic Waste Treatment. Atmosphere 2020, 11, 452. [Google Scholar] [CrossRef]
  11. Huffman, J.A.; Perring, A.E.; Savage, N.J.; Clot, B.; Crouzy, B.; Tummon, F.; Shoshanim, O.; Damit, B.; Schneider, J.; Sivaprakasam, V.; et al. Real-Time Sensing of Bioaerosols: Review and Current Perspectives. Aerosol Sci. Technol. 2020, 54, 465–495. [Google Scholar] [CrossRef]
  12. Sivaprakasam, V.; Hart, M.B. Surface-Enhanced Raman Spectroscopy for Environmental Monitoring of Aerosols. ACS Omega 2021, 6, 10150–10159. [Google Scholar] [CrossRef]
  13. Fu, Y.; Kuppe, C.; Valev, V.K.; Fu, H.; Zhang, L.; Chen, J. Surface-Enhanced Raman Spectroscopy: A Facile and Rapid Method for the Chemical Component Study of Individual Atmospheric Aerosol. Environ. Sci. Technol. 2017, 51, 6260–6267. [Google Scholar] [CrossRef]
  14. Estefany, C.; Sun, Z.; Hong, Z.; Du, J. Raman Spectroscopy for Profiling Physical and Chemical Properties of Atmospheric Aerosol Particles: A Review. Ecotoxicol. Environ. Saf. 2023, 249, 114405. [Google Scholar] [CrossRef] [PubMed]
  15. Félix-Rivera, H.; Hernández-Rivera, S.P. Raman Spectroscopy Techniques for the Detection of Biological Samples in Suspensions and as Aerosol Particles: A Review. Sens. Imaging 2012, 13, 1–25. [Google Scholar] [CrossRef]
  16. Wang, X.; Huang, S.-C.; Hu, S.; Yan, S.; Ren, B. Fundamental Understanding and Applications of Plasmon-Enhanced Raman Spectroscopy. Nat. Rev. Phys. 2020, 2, 253–271. [Google Scholar] [CrossRef]
  17. Premasiri, W.R.; Moir, D.T.; Klempner, M.S.; Krieger, N.; Jones, G.; Ziegler, L.D. Characterization of the Surface Enhanced Raman Scattering (SERS) of Bacteria. J. Phys. Chem. B 2005, 109, 312–320. [Google Scholar] [CrossRef]
  18. Zong, C.; Xu, M.; Xu, L.-J.; Wei, T.; Ma, X.; Zheng, X.-S.; Hu, R.; Ren, B. Surface-Enhanced Raman Spectroscopy for Bioanalysis: Reliability and Challenges. Chem. Rev. 2018, 118, 4946–4980. [Google Scholar] [CrossRef] [PubMed]
  19. Han, X.X.; Rodriguez, R.S.; Haynes, C.L.; Ozaki, Y.; Zhao, B. Surface-Enhanced Raman Spectroscopy. Nat. Rev. Methods Primers 2022, 1, 87. [Google Scholar] [CrossRef]
  20. Yi, J.; You, E.-M.; Hu, R.; Wu, D.-Y.; Liu, G.-K.; Yang, Z.-L.; Zhang, H.; Gu, Y.; Wang, Y.-H.; Wang, X.; et al. Surface-Enhanced Raman Spectroscopy: A Half-Century Historical Perspective. Chem. Soc. Rev. 2025, 54, 1453–1551. [Google Scholar] [CrossRef]
  21. Piorek, B.D.; Andreou, C.; Moskovits, M.; Meinhart, C.D. Discrete Free-Surface Millifluidics for Rapid Capture and Analysis of Airborne Molecules Using Surface-Enhanced Raman Spectroscopy. Anal. Chem. 2014, 86, 1061–1066. [Google Scholar] [CrossRef]
  22. Dos Santos, D.P.; Sena, M.M.; Almeida, M.R.; Mazali, I.O.; Olivieri, A.C.; Villa, J.E.L. Unraveling Surface-Enhanced Raman Spectroscopy Results through Chemometrics and Machine Learning: Principles, Progress, and Trends. Anal. Bioanal. Chem. 2023, 415, 3945–3966. [Google Scholar] [CrossRef]
  23. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  24. Aria, M.; Cuccurullo, C. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
  25. Sengupta, A.; Laucks, M.L.; Dildine, N.; Drapala, E.; Davis, E.J. Bioaerosol Characterization by Surface-Enhanced Raman Spectroscopy (SERS). J. Aerosol Sci. 2005, 36, 651–664. [Google Scholar] [CrossRef]
  26. Sengupta, A.; Brar, N.; Davis, E.J. Bioaerosol Detection and Characterization by Surface-Enhanced Raman Spectroscopy. J. Colloid Interface Sci. 2007, 309, 36–43. [Google Scholar] [CrossRef]
  27. Schwarzmeier, K.; Knauer, M.; Ivleva, N.P.; Niessner, R.; Haisch, C. Bioaerosol Analysis Based on a Label-Free Microarray Readout Method Using Surface-Enhanced Raman Scattering. Anal. Bioanal. Chem. 2013, 405, 5387–5392. [Google Scholar] [CrossRef]
  28. Seifert, S.; Merk, V.; Kneipp, J. Identification of Aqueous Pollen Extracts Using Surface Enhanced Raman Scattering (SERS) and Pattern Recognition Methods. J. Biophotonics 2016, 9, 181–189. [Google Scholar] [CrossRef]
  29. Tahir, M.A.; Zhang, X.; Cheng, H.; Xu, D.; Feng, Y.; Sui, G.; Fu, H.; Valev, V.K.; Zhang, L.; Chen, J. Klarite as a Label-Free SERS-Based Assay: A Promising Approach for Atmospheric Bioaerosol Detection. Analyst 2020, 145, 277–285. [Google Scholar] [CrossRef]
  30. Choi, J.; Lee, J.; Jung, J.H. Fully Integrated Optofluidic SERS Platform for Real-Time and Continuous Characterization of Airborne Microorganisms. Biosens. Bioelectron. 2020, 169, 112611. [Google Scholar] [CrossRef]
  31. Diehn, S.; Zimmermann, B.; Tafintseva, V.; Seifert, S.; Bağcıoğlu, M.; Ohlson, M.; Weidner, S.; Fjellheim, S.; Kohler, A.; Kneipp, J. Combining Chemical Information From Grass Pollen in Multimodal Characterization. Front. Plant Sci. 2020, 10, 1788. [Google Scholar] [CrossRef]
  32. Su, X.; Ren, R.; Wu, Y.; Li, S.; Ge, C.; Liu, L.; Xu, Y. Study of Biochip Integrated with Microelectrodes Modified by Poly-Dopamine-Co-Chitosan Composite Gel for Separation, Enrichment and Detection of Microbes in the Aerosol. Biosens. Bioelectron. 2021, 176, 112931. [Google Scholar] [CrossRef]
  33. Bandarenka, H.; Burko, A.; Girel, K.; Laputsko, D.; Orel, E.; Mizgailo, A.; Sharopov, U.; Podelinska, A.; Shapel, U.; Pankratov, V.; et al. Improvement of Heat Dissipation in Ag/Ni Substrates for Testing Cu-TiO2/TiO2-Modified Filters Using SERS Spectroscopy. Crystals 2023, 13, 749. [Google Scholar] [CrossRef]
  34. Liu, J.; Liu, Y.; Zhang, Y.; Ruan, X.; Cheng, H.; Ge, Q.; Zhang, L. Efficient V-Shaped Substrate for Surface and Volume Enhanced Raman Spectroscopic Analysis of Bioaerosol: Prevention from Potential Health Risk. Environ. Health 2024, 2, 212–220. [Google Scholar] [CrossRef]
  35. Han, J.; Zhang, X.; Jones, R.R.; Sui, G.; Valev, V.K.; Zhang, L. Dual-Functional Au-Porous Anodic Alumina (PAA) Sensors for Enrichment and Label-Free Detection of Airborne Virus with Surface-Enhanced Raman Scattering. Build. Environ. 2024, 257, 111484. [Google Scholar] [CrossRef]
  36. Ryu, J.S.; Lee, S.H.; Kim, H.; Kang, H.; Li, P.; Lee, J.H.; Jang, H.; Kim, S.; Kwon, H.; Jung, H.S.; et al. Advancing SARS-CoV-2 Variant Detection with High Affinity Monoclonal Antibodies and Plasmonic Nanostructure. Adv. Funct. Mater. 2024, 34, 2405340. [Google Scholar] [CrossRef]
  37. Hwang, C.S.H.; Lee, S.; Lee, S.; Kim, H.; Kang, T.; Lee, D.; Jeong, K.-H. Highly Adsorptive Au-TiO2 Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols. ACS Appl. Mater. Interfaces 2022, 14, 54550–54557. [Google Scholar] [CrossRef]
  38. Alsved, M.; Bourouiba, L.; Duchaine, C.; Löndahl, J.; Marr, L.C.; Parker, S.T.; Prussin, A.J.; Thomas, R.J. Natural Sources and Experimental Generation of Bioaerosols: Challenges and Perspectives. Aerosol Sci. Technol. 2020, 54, 547–571. [Google Scholar] [CrossRef]
  39. Santarpia, J.L.; Ratnesar-Shumate, S.; Haddrell, A. Laboratory Study of Bioaerosols: Traditional Test Systems, Modern Approaches, and Environmental Control. Aerosol Sci. Technol. 2020, 54, 585–600. [Google Scholar] [CrossRef]
  40. Zhen, H.; Han, T.; Fennell, D.E.; Mainelis, G. A Systematic Comparison of Four Bioaerosol Generators: Affect on Culturability and Cell Membrane Integrity When Aerosolizing Escherichia Coli Bacteria. J. Aerosol Sci. 2014, 70, 67–79. [Google Scholar] [CrossRef]
  41. Ferguson, R.M.W.; Garcia-Alcega, S.; Coulon, F.; Dumbrell, A.J.; Whitby, C.; Colbeck, I. Bioaerosol Biomonitoring: Sampling Optimization for Molecular Microbial Ecology. Mol. Ecol. Resour. 2019, 19, 672–690. [Google Scholar] [CrossRef]
  42. Langer, J.; Jimenez de Aberasturi, D.; Aizpurua, J.; Alvarez-Puebla, R.A.; Auguié, B.; Baumberg, J.J.; Bazan, G.C.; Bell, S.E.J.; Boisen, A.; Brolo, A.G.; et al. Present and Future of Surface-Enhanced Raman Scattering. ACS Nano 2020, 14, 28–117. [Google Scholar] [CrossRef]
  43. Ghosh, B.; Lal, H.; Srivastava, A. Review of Bioaerosols in Indoor Environment with Special Reference to Sampling, Analysis and Control Mechanisms. Environ. Int. 2015, 85, 254–272. [Google Scholar] [CrossRef] [PubMed]
  44. Mainelis, G. Bioaerosol Sampling: Classical Approaches, Advances, and Perspectives. Aerosol Sci. Technol. 2020, 54, 496–519. [Google Scholar] [CrossRef] [PubMed]
  45. Lee, I.; Jeon, E.; Lee, J. On-Site Bioaerosol Sampling and Detection in Microfluidic Platforms. TrAC Trends Anal. Chem. 2023, 158, 116880. [Google Scholar] [CrossRef]
  46. Morawska, L.; Buonanno, G.; Mikszewski, A.; Stabile, L. The Physics of Respiratory Particle Generation, Fate in the Air, and Inhalation. Nat. Rev. Phys. 2022, 4, 723–734. [Google Scholar] [CrossRef]
  47. Caviezel, D.; Narayanan, C.; Lakehal, D. Adherence and Bouncing of Liquid Droplets Impacting on Dry Surfaces. Microfluid. Nanofluid 2008, 5, 469–478. [Google Scholar] [CrossRef]
  48. Bonifacio, A. Nanostrucured Substrates for Surface-Enhanced Raman Scattering Spectroscopy. In Nanomaterials for Theranostics and Tissue Engineering; Elsevier: Amsterdam, The Netherlands, 2020; pp. 137–174. ISBN 978-0-12-817838-6. [Google Scholar]
  49. Cialla-May, D.; Bonifacio, A.; Markin, A.; Markina, N.; Fornasaro, S.; Dwivedi, A.; Dib, T.; Farnesi, E.; Liu, C.; Ghosh, A.; et al. Recent Advances of Surface Enhanced Raman Spectroscopy (SERS) in Optical Biosensing. TrAC Trends Anal. Chem. 2024, 181, 117990. [Google Scholar] [CrossRef]
  50. Cialla-May, D.; Bonifacio, A.; Bocklitz, T.; Markin, A.; Markina, N.; Fornasaro, S.; Dwivedi, A.; Dib, T.; Farnesi, E.; Liu, C.; et al. Biomedical SERS—The Current State and Future Trends. Chem. Soc. Rev. 2024, 53, 8957–8979. [Google Scholar] [CrossRef] [PubMed]
  51. Magnusson, B.; Örnemark, U. Eurachem Guide: The Fitness for Purpose of Analytical Methods—A Laboratory Guide to Method Validation and Related Topics, 2nd ed.; Eurachem: Uppsala, Sweden, 2014; ISBN 978-91-87461-59-0. [Google Scholar]
  52. Premasiri, W.R.; Lee, J.C.; Sauer-Budge, A.; Théberge, R.; Costello, C.E.; Ziegler, L.D. The Biochemical Origins of the Surface-Enhanced Raman Spectra of Bacteria: A Metabolomics Profiling by SERS. Anal. Bioanal. Chem. 2016, 408, 4631–4647. [Google Scholar] [CrossRef]
  53. Chien, J.-Y.; Gu, Y.-C.; Chien, C.-C.; Chang, C.-L.; Cheng, H.-W.; Chiu, S.W.-Y.; Nee, Y.-J.; Tsai, H.-M.; Chu, F.-Y.; Tang, H.-F.; et al. Unraveling RNA Contribution to the Molecular Origins of Bacterial Surface-Enhanced Raman Spectroscopy (SERS) Signals. Sci. Rep. 2024, 14, 19505. [Google Scholar] [CrossRef]
  54. Rohatgi, A. Webplotdigitizer (4.5). Computer Software. 2021. Available online: https://automeris.io/WebPlotDigitizer (accessed on 18 February 2025).
  55. Horta-Velázquez, A.; Arce, F.; Rodríguez-Sevilla, E.; Morales-Narváez, E. Toward Smart Diagnostics via Artificial Intelligence-Assisted Surface-Enhanced Raman Spectroscopy. TrAC Trends Anal. Chem. 2023, 169, 117378. [Google Scholar] [CrossRef]
  56. Li, X.; Li, L.; Zhuo, Z.; Zhang, G.; Du, X.; Li, X.; Huang, Z.; Zhou, Z.; Cheng, Z. Bioaerosol Identification by Wide Particle Size Range Single Particle Mass Spectrometry. Atmosphere 2023, 14, 1017. [Google Scholar] [CrossRef]
  57. Kabir, E.; Azzouz, A.; Raza, N.; Bhardwaj, S.K.; Kim, K.-H.; Tabatabaei, M.; Kukkar, D. Recent Advances in Monitoring, Sampling, and Sensing Techniques for Bioaerosols in the Atmosphere. ACS Sens. 2020, 5, 1254–1267. [Google Scholar] [CrossRef]
  58. Liu, T.; Zhang, J.; Cao, J.; Zheng, H.; Zhan, C.; Liu, H.; Zhang, L.; Xiao, K.; Liu, S.; Xiang, D.; et al. Identification of Coexistence of Biological and Non-Biological Aerosol Particles with DAPI (4′,6-Diamidino-2-Phenylindole) Stain. Particuology 2023, 72, 49–57. [Google Scholar] [CrossRef]
  59. Harshitha, R.; Arunraj, D.R. Real-time Quantitative PCR: A Tool for Absolute and Relative Quantification. Biochem. Mol. Biol. Educ. 2021, 49, 800–812. [Google Scholar] [CrossRef]
  60. Sharma Ghimire, P.; Tripathee, L.; Chen, P.; Kang, S. Linking the Conventional and Emerging Detection Techniques for Ambient Bioaerosols: A Review. Rev. Environ. Sci. Biotechnol. 2019, 18, 495–523. [Google Scholar] [CrossRef]
  61. Fennelly, M.; Sewell, G.; Prentice, M.; O’Connor, D.; Sodeau, J. Review: The Use of Real-Time Fluorescence Instrumentation to Monitor Ambient Primary Biological Aerosol Particles (PBAP). Atmosphere 2017, 9, 1. [Google Scholar] [CrossRef]
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Figure 1. PRISMA 2020 flow diagram detailing the study selection process. The diagram includes the number of records identified, screened, assessed for eligibility, and included in the systematic literature review.
Figure 1. PRISMA 2020 flow diagram detailing the study selection process. The diagram includes the number of records identified, screened, assessed for eligibility, and included in the systematic literature review.
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Figure 2. Schematic illustration of bioaerosol sampling methods: (a) filtration; (b) impingement; (c) cyclone; and (d) cascade impaction. In filtration, the filter collects PBAPs through interception, inertial impaction, and diffusion. On the impinger system, PBAPs are sampled by a vacuum pump as they shoot through the equipment’s nozzle at high speed, impacting a collector liquid. When the bioaerosol current enters the cyclone, air flows by the inside curved wall into a spiral structure. PBAPs larger than the fixed diameter clash with the cyclone wall and deposit at its bottom or in the collection liquid. A vacuum pump sucks air out of the impactor, which flows through small orifices or slits to the impact surface, where inertia separates the particles. On the cascade impactor, by decreasing nozzle sizes, particles are categorised by their inertia and gathered by size in different stages. The impaction surface usually consists of a lubricated tape or plate, filter material, or growth medium (agar).
Figure 2. Schematic illustration of bioaerosol sampling methods: (a) filtration; (b) impingement; (c) cyclone; and (d) cascade impaction. In filtration, the filter collects PBAPs through interception, inertial impaction, and diffusion. On the impinger system, PBAPs are sampled by a vacuum pump as they shoot through the equipment’s nozzle at high speed, impacting a collector liquid. When the bioaerosol current enters the cyclone, air flows by the inside curved wall into a spiral structure. PBAPs larger than the fixed diameter clash with the cyclone wall and deposit at its bottom or in the collection liquid. A vacuum pump sucks air out of the impactor, which flows through small orifices or slits to the impact surface, where inertia separates the particles. On the cascade impactor, by decreasing nozzle sizes, particles are categorised by their inertia and gathered by size in different stages. The impaction surface usually consists of a lubricated tape or plate, filter material, or growth medium (agar).
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Figure 3. Design of a continuous optofluidic surface-enhanced Raman spectroscopy (SERS) system for detecting airborne biological particles: (a) Schematic of the optofluidic platform. The inertia of airborne particles drives them into a silver nanoparticle (AgNP) colloid in the curved flow stream. The fluid flow rates are precisely controlled to form a stable two-phase stratified flow. Particles that are impacted into the liquid film pass through a serpentine mixing channel into the SERS detection volume. (b) A schematic diagram illustrates continuous particle collection in the μ-sampler. (c) The optical setup is shown for continuous SERS. Collected particles with AgNPs are analysed using an in-house custom Raman system. (d) A photograph shows the SERS setup and the optofluidic platform. Reprinted with permission of [Elsevier Science & Technology Journals] from Ref [30].
Figure 3. Design of a continuous optofluidic surface-enhanced Raman spectroscopy (SERS) system for detecting airborne biological particles: (a) Schematic of the optofluidic platform. The inertia of airborne particles drives them into a silver nanoparticle (AgNP) colloid in the curved flow stream. The fluid flow rates are precisely controlled to form a stable two-phase stratified flow. Particles that are impacted into the liquid film pass through a serpentine mixing channel into the SERS detection volume. (b) A schematic diagram illustrates continuous particle collection in the μ-sampler. (c) The optical setup is shown for continuous SERS. Collected particles with AgNPs are analysed using an in-house custom Raman system. (d) A photograph shows the SERS setup and the optofluidic platform. Reprinted with permission of [Elsevier Science & Technology Journals] from Ref [30].
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Figure 5. Example of how characteristic SERS bands (highlighted in light blue areas) are observed in bioaerosol studies using different strains of E. coli: a, BL21 [25]; b, BL21 [26]; and c, DH5α [29,30,32,34]. All spectra are obtained using Ag nanoparticles as substrates, except for c, obtained with Klarite® gold substrate. See Table 2 for more details. Spectral data are obtained from Refs. [25,26,29,30,32,34], respectively, by using the open software WebPlotDigitizer® (Version 4.5) [54].
Figure 5. Example of how characteristic SERS bands (highlighted in light blue areas) are observed in bioaerosol studies using different strains of E. coli: a, BL21 [25]; b, BL21 [26]; and c, DH5α [29,30,32,34]. All spectra are obtained using Ag nanoparticles as substrates, except for c, obtained with Klarite® gold substrate. See Table 2 for more details. Spectral data are obtained from Refs. [25,26,29,30,32,34], respectively, by using the open software WebPlotDigitizer® (Version 4.5) [54].
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Figure 6. A roadmap for standardisation.
Figure 6. A roadmap for standardisation.
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Table 1. Strategies for bioaerosol generation and sampling described in the studies included in this SLR.
Table 1. Strategies for bioaerosol generation and sampling described in the studies included in this SLR.
PBAP TypeAerosol
Generator		Generation
Conditions		Sampling
Method		Sampling
Conditions		Refs
E. coli
P aeruginosa
S. enterica		nebuliser
(Omron NE-C21)		2.1 μm size;
5 min		impaction
(custom collector)		na[25]
E. coli
Populus deltoides
Sequoia sempervirens		nebuliser
(Omron NE-C21)		9 minimpaction
(custom collector)		9 min[26]
E. colino name
(custom for study)		0.8–14.5 μm size;
4 L/min		wet cyclone
(Coriolis μ)		150 mL/min;
10 min;
5 mL PBS		[27]
Pollen (14 species) nananrnr[28]
E. colinr0.5–20 μm size;
10 min		impaction
(Andersen cascade)		28.3 L/min;
10 min		[29]
S. epidermidis
M. luteus
E. hirae
B. subtilis
E. coli		one-jet Collison
nebuliser		1 L/mincontinuous
optofluidic platform		1 L/min;
5 min		[30]
Poa alpinanananrnr[31]
S. aureus
E. coli
Candida albicans		ultrasonic atomiser (BSW-2A)2 mL of microbial suspension (106 cfu/mL) for 5 minfiltration
(biochip)		1 L/min;
20 min		[32]
not specifiedhuman breath4 h breathingfiltration4 h[33]
E. coli
S. epidermidis		no name
(custom for study)		1–5 μm size;
30–45 mL/h;
30 min		wet cyclone 265 L/min
10 min;
2 mL DW		[34]
SARS-CoV-2 pseudovirusnebuliser0.1 ng/mL;
5 min		swirling5 min;
(filtration)		[35]
SARS-CoV-2nebuliser200 μL of viral lysate nebulised onto the face mask and dried for 1 h at 25 °Cfiltrationna[36]
SARS-CoV-2nebuliser<10 μm size;
10 s		filtrationna[37]
AgNPs, silver colloidal nanoparticles; AuNPs, gold colloidal nanoparticles; DW, deionised water; na, not applicable; nr, not reported; PBS, phosphate-buffered saline solution; and ss, solid substrate.
Table 2. Analytical details for the SERS analysis included in this SLR.
Table 2. Analytical details for the SERS analysis included in this SLR.
SubstrateLaser Line (nm)Laser
Power		Exposure
Time (s)		LODOverall Time (min)Refs
AgNPs514 250 mW60nr5[20]
AgNPs514100 mW120~102 CFU/mL~12[21]
AgNPs (immunoassay)6337 mW6.25144 particles/cm380[22]
AuNPs7851.4 × 106 W/cm2500nr~9[23]
Klarite®78510 mW100109 CFU/mL~12[24]
AgNPs532nr60~102 CFU/mL15[25]
AuNPs7852.9 × 105 W/cm21000nr~20[26]
AgNPs78511.8 mW51.263 CFU/m3~40[27]
Ag/Ni/macroPSi ss473nr1nr~15[28]
AgNPs on AAO7851 mW150103 cells/mL,
104 cells/mL		~12[29]
AuNPs-PAA ss785nr300nr~7[30]
AuNPs-MGITC (immunoassay)633nrnr2.30 pfu/mL~120[31]
Au-TiO26335 mW1nr~60[32]
AAO, anodic aluminium oxide; AgNPs, silver colloidal nanoparticles; AuNPs, gold colloidal nanoparticles; CFU, colony-forming unit; macroPSi, macroporous silicon; LOD, limit of detection; MGITC, malachite green isothiocyanate; na, not applicable; Ni, Nickel; nr, not reported; PAA, porous anodic alumina; pfu, plaque-forming unit; and ss, solid substrate. Overall time refers to bioaerosol collection + SERS analysis.
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Fornasaro, S.; Semeraro, S.; Licen, S.; Barbieri, P. Surface-Enhanced Raman Scattering of Bioaerosol: Where Are We Now? A Systematic Review. Chemosensors 2025, 13, 86. https://doi.org/10.3390/chemosensors13030086

AMA Style

Fornasaro S, Semeraro S, Licen S, Barbieri P. Surface-Enhanced Raman Scattering of Bioaerosol: Where Are We Now? A Systematic Review. Chemosensors. 2025; 13(3):86. https://doi.org/10.3390/chemosensors13030086

Chicago/Turabian Style

Fornasaro, Stefano, Sabrina Semeraro, Sabina Licen, and Pierluigi Barbieri. 2025. "Surface-Enhanced Raman Scattering of Bioaerosol: Where Are We Now? A Systematic Review" Chemosensors 13, no. 3: 86. https://doi.org/10.3390/chemosensors13030086

APA Style

Fornasaro, S., Semeraro, S., Licen, S., & Barbieri, P. (2025). Surface-Enhanced Raman Scattering of Bioaerosol: Where Are We Now? A Systematic Review. Chemosensors, 13(3), 86. https://doi.org/10.3390/chemosensors13030086

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