A number of techniques are available for bacterial detection in clinical settings, but each one has its own scope and limitations. Some of the most common techniques used for bacterial detection and identification have been summarized in the following Table 1, along with their scope and limitations, which are discussed in detail in the subsequent section.

The culture-based method has been considered to be the most established and reliable method for bacterial identification. The culture-based method involves bacterial sample culturing in a laboratory setting using growth media and identification of bacteria on the basis of their morphology, Gram staining, and biochemical testing [47]. There are several advantages to culture-based methods, including the ability to detect a wide range of bacterial species, their adaptability, and their availability in most clinical laboratories. When used appropriately, this method can provide accurate and reliable identification results and also allow for antibiotic susceptibility testing [48]. Several factors are responsible for accurate identification results in culture-based methods, such as media type, quality of the sample, and handling skills. However, the main limitation of the culture-based method is its time-consuming nature, taking up to several days to obtain results, which may delay diagnosis and treatment, leading to poor patient outcomes [49]. Moreover, culture-based methods have limited sensitivity, and some bacterial species may be difficult to culture, leading to false-negative results. Most of the pathogenic bacteria can grow within the time period of 24 h, but some of them can take up to days for their visible growth on the culture plates [50]. Another factor is cost, which may increase when specialized media and equipment are needed for specific bacteria. The culture-based method is also susceptible to contamination, leading to false-positive results. A recent meta-analysis was conducted to compare the clinical outcomes of culture-positive and culture-negative patients, out of which only about 40.1% of the patients having sepsis or septic shock showed a positive blood culture. A similar mortality rate was observed in both culture-positive and culture-negative patients, which demonstrated the unreliability of blood cultures [51]. Antimicrobial therapy before the blood culture can also result in a negative blood culture, which decreases the bacterial identification probability [52]. The future challenges of culture-based methods include limitations in detecting fastidious or slow-growing bacteria, the increasing demand for rapid diagnostic techniques, and the emergence of antibiotic-resistant strains that may not grow on standard culture media.

PCR is a technique used for the amplification of a unique DNA segment from a complex mixture of genetic material. PCR has been widely used in bacterial identification, especially in clinical and research settings. The principle of PCR in bacterial identification relies on bacterial DNA amplification by using specific primers that target conserved regions of the bacterial genome [53]. The PCR should be optimized to minimize non-specific amplification, which can otherwise lead to false positives and decreased sensitivity. PCR has several advantages, including high sensitivity, specificity, and speed. The bacterial DNA can be quantified by real-time PCR during the amplification process, where the amplification of the targeted amplicon is directly proportional to the fluorescent emission of a dye that generally binds to the amplicon. This allows the detection of bacterial infections at a very early stage. The amplified product of target DNA can be identified using gel electrophoresis at the end or in real time by a fluorescence signal indicating the presence or absence of DNA fragments of the target [54]. Multiplex PCR offers simultaneous detection of multiple organisms in one go by using different primers for the amplification, which increases the speed and efficiency of bacterial identification [55]. Despite several advantages, the PCR technique also presents some limitations in this field. Potential for contamination is one of the major limitations of this technique, which can occur at any stage of sample preparation, the amplification process, or the analysis step, leading to decreased sensitivity or false positives. Another limitation of PCR is the possibility of false positives due to the presence of non-specific amplification products. The complexity of bacterial genome sequences can be a challenge for PCR identification, particularly in cases where the target sequence is highly conserved and present in multiple bacterial species [56]. In such cases, the primers designed for PCR amplification may not be specific enough to differentiate between closely related bacterial species or strains. Another challenge in PCR identification of bacteria is the presence of multiple copies of the target sequence in the bacterial genome, which can result in false positives or overestimations of bacterial load [57]. Many modifications to the PCR have been conducted to date to improve its identification performance. Septifast is an approved multiplex real-time PCR system developed by Roche Diagnostics, but even in this identification system, the sensitivity and specificity are not so accurate [58]. A comparison of diagnostic performance has been conducted among blood culture samples and SeptiFast, out of which 87.8% of culture-positive cases were detected by both blood culture and PCR. In a systematic review, a meta-analysis was performed, and a specificity of 0.86 and a sensitivity of 0.68 were observed [59]. Magicplex^TM is another type of multiplex real-time PCR developed by Seegene that can detect more than 90 microorganisms at their gene level, but its low sensitivity (29%) and specificity (95%) can limit its use in clinical applications [60]. Future challenges of PCR include the need for more standardized protocols, improvements in sensitivity and specificity, and addressing the limitations posed by complex and diverse microbial communities.

Mass spectrometry (MS) rapidly identifies bacteria in patient blood by detecting the mass-to-charge ratio (m/z) of ionized biological molecules, such as bacterial proteins [61,62]. The matrix-assisted laser desorption time of flight mass spectrometry (MALDI-TOF-MS) method is approved by the US FDA for identifying bacteria [63]. MALDI-TOF-MS basically detects bacteria on the basis of housekeeping genes and ribosomal proteins, which revolutionized bacteria identification in clinical laboratories [64,65]. It is possible to identify bacteria accurately and quickly, separating methicillin-resistant staph (MRSA) Staphylococcus aureus from methicillin-sensitive Staphylococcus aureus [66]. MALDI-TOF-MS can identify pathogens in less than an hour from purified bacterial pellets and commercialized kits such as the MALDI Biotyper Sepsityper^TM Kit [67]. The bacterial sample can be obtained from various sources, such as clinical samples (urine, blood, cerebrospinal fluid) or culture plates. Sepsityper^TM is an easy-to-use sample preparation kit for the rapid identification of bacteria from positive blood cultures. It is designed to improve accuracy and simplify the process of sample preparation. The sample is mixed with a matrix to create a dried spot on a MALDI plate, which allows for the ionization of the bacterial sample during mass spectrometry [68,69].

A number of studies are also going on to combat the problem of antimicrobial resistance using MALDI-TOF-MS. The presence of β-lactam resistance can be conferred by MALDI-TOF-MS as it gives key spectral peaks corresponding to enzymatic modifications conferring antimicrobial resistance [70]. The identification of antibiotic resistance using MALDI-TOF-MS was observed through β-lactam ring hydrolysis after exposing the antibiotics to β-lactamase-producing bacteria, which revealed a decrease in the mass spectral peak of the antibiotic and the appearance of new peaks corresponding to its hydrolysis products [71]. There are some limitations to the technique. The coverage of the database is limited, as it may not contain all strains and species of bacteria. This can lead to misidentification [72,73]. A second issue could be interference during sample preparation. This can lead to inaccurate identification of contaminants or microorganisms in the sample [74]. MALDI-TOF-MS can also have trouble distinguishing closely related species or strains with similar mass spectra. To address this, it is possible to expand reference databases by including more strains and species of bacteria, develop new sample preparation techniques to reduce contamination, and use complementary methods, such as DNA sequences or phenotypic analysis, to confirm or resolve inconclusive results. MALDI-TOF-MS can be improved by developing new matrix formulations, improving instrumentation and data analysis algorithms, and integrating with other technologies.

The use of nanomaterials for bacteria detection has attracted more attention in recent years. Nanoparticles can be used to detect particular bacteria based on their changed properties [75]. Nanoparticle-based detection methods could offer a number of advantages over traditional methods for bacteria detection in terms of sensitivity, specificity, and time for detection [76]. SPR is a unique optical property of noble metal nanoparticles that is a consequence of resonance due to the interaction of the collective oscillation of conduction band electrons of metal nanoparticles with incident light [77]. SPR of metallic nanoparticles can be fine-tuned over a broad spectral range (ultraviolet (UV) to near infrared (NIR)) and depends strongly on the particle shape, size, composition, and surrounding medium [78]. Plasmon-enhanced spectroscopy, such as surface-enhanced Raman scattering (SERS), is a result of the amplification of the local electromagnetic field due to SPR excitation [79]. Plasmonic metal nanoparticles have become important in biosensing as a result of advancements in nanofabrication techniques. Various platforms for bacteria detection could be developed using metallic nanoparticles, such as colorimetric and fluorescent detection platforms [80].

Among the metallic nanoparticles, gold nanoparticles (AuNPs) are the most widely utilized for colorimetric detection due to various advantages such as controlled synthesis, excellent solubility, and easy surface modification [81]. Target-induced colorimetric change is generally visible to the naked eye for qualitative detection or quantifiable by UV–visible spectroscopy. Priyadarshi et al. have demonstrated the impact of the size of AuNPs in colorimetric bacterial sensing where smaller AuNPs (20 nm) showed more sensitivity as compared to large sized AuNPs (40 nm) [33]. In another paper, Miranda et al. have reported a colorimetric assay using an enzyme-nanoparticle conjugate system for E. coli detection [82]. In this work, AuNPs were functionalized with quaternary amines electrostatically bound to β-galactosidase, inhibiting its activity. The enzyme activity was restored after its release from the nanocomplex, following AuNPs binding with E. coli, leading to an enhanced colorimetric readout. In this assay, 10² bacteria/mL was the limit of detection in solution and 10⁴ bacteria/mL on a test strip. Similarly, Peng et al. demonstrated the detection strategy for various bacterial species on the basis of interactions between bacteria and phages [83]. The phage’s attachment to the bacterial surface and subsequent AuNP aggregation on the capsid resulted in a clear colorimetric change with a detection limit of 100 cells. Li et al. developed a colorimetric sensor array for the identification of 12 bacteria and 3 fungi, where four different types of functionalized AuNPs have been used as sensing elements [84]. The rapid color change was observed within 5 s due to the interaction between AuNPs and bacteria, which gave a unique color shift pattern. Fluorescence-based methods are more sensitive (up to 1000 times) as compared to colorimetric methods [85]. The amount of emitted light is directly proportional to the target analyte concentration in the sample, which can detect even low concentrations of analyte. Yin et al. used upconversion fluorescent nanoparticles for simultaneous detection of seven bacteria [86]. The construct was based on guanidium-functionalized upconversion fluorescent nanoparticles, hydrogen peroxide, and tannic acid, which quantify bacteria in a non-specific manner as bacterial presence effectively strengthens the nanoparticles’ luminescence. The proposed strategy was time saving, highly sensitive, and cost effective compared to the traditional approach. In another report, Phillips et al. developed a fluorescence-based biosensor for bacteria sensing using polymer-conjugated AuNP constructs, where polymers and AuNPs are used as flares and quenchers, respectively [87]. The anionic polymer conjugated with cationic AuNPs acted as fluorescence-quenched complexes. Upon bacteria addition, the polymer is released due to interaction between the anionic bacterial surface and cationic AuNPs, which results in fluorescence recovery. Another study by Zheng et al. utilized a silica-quantum dot-based fluorescent lateral flow immunoassay for simultaneous detection of E. coli and S. typhimurium [88]. The nanotags were directly mixed with the sample and loaded on the test strip, which showed a detection limit of 50 cells/mL within 15 min. Similarly, Yu et al. used vancomycin-functionalized gold nanoclusters and aptamer-functionalized AuNPs as energy donors and acceptors, respectively, for S. aureus detection [89]. This strategy showed a detection limit of 10 CFU/mL. Apart from bacterial detection at early stages, bacterial clearance is also important in sepsis patients. Various magnetic nanoparticle-based approaches have been developed recently for this purpose [90,91]. Lee et al. have developed synthetic ligand (zinc-coordinated bis(dipicolylamine))-modified magnetic nanoparticles for the removal of bacteria and their endotoxins from whole blood using a microfluidic device [92]. The ligand forms complexes with specific lipids present on the bacterial outer membrane, providing high binding affinity to pathogenic bacteria in the blood. The bacteria bound to modified magnetic nanoparticles were removed using magnetic microfluidic devices, resulting in 100% bacteria removal from bovine whole blood. Similarly, Shi et al. reported hemocompatible magnetic nanoparticles that bind and remove bacteria and their endotoxins circulating in blood without significantly affecting blood cells [93]. The nanoparticles were made hemocompatible using polydopamine coating and modified with an imidazolium-based ionic liquid, which possesses anti-bacterial activity. These studies provide a new platform for pathogen removal from blood that can be further improved for clinical use. The development of various nanosystems for bacteria detection is currently an active and growing area of research. Despite a lot of progress in recent years, there are still some challenges, such as their stability and toxicity, that need to be addressed before using nanoparticles in real-time clinical settings. The non-specific aggregation in complex biological systems can give false negative or positive results, which could be further improved by making the nanosystems more specific towards their target using different biorecognition molecules.

Raman scattering is vibrational spectroscopy based on the interaction of light with matter, resulting in a unique spectral fingerprint that can identify the bacterial species [94]. Raman signal intensity could be greatly amplified by the phenomenon called SERS. For SERS, it is essentially required to use plasmonic nanomaterials with diverse geometries [95,96,97]. SERS could offer several benefits for bacteria identification, such as high sensitivity, specificity, multiplexing capability, and rapid analysis [98]. However, the spectral patterns are so complex that they cannot easily distinguish the bacteria species; therefore, algorithm-based analysis has been incorporated for accurate bacterial identification [99,100]. Deep-learning algorithms can accurately process complex patterns of spectra data, which can distinguish bacterial strains with high reliability [101,102]. In a recent study, a Raman spectra dataset with deep learning accurately identified 30 bacterial pathogens, differentiated antibiotic-susceptible bacteria with 89% accuracy, and achieved 99.7% accuracy for treatment identification [103]. Another study achieved 95–100% accuracy in identifying 12 different microbes with a deep-learning platform using the Raman dataset [104]. Ding et al. used SERS spectra of three Salmonella serovars to train a convolutional neural network, which enabled accurate identification of the three serovars with individual accuracy rates of 98.68%, 95.35%, and 96.17%, respectively [105]. Bacterial cellulose nanocrystals conjugated with Concanavalin A lectin were used for E. coli identification. A set of supervised learning models called support vector machines (SVM) was used for classification in this study and achieved 87.7% accuracy in discriminating 19 bacterial strains [106]. Another study showed that SERS and machine learning can rapidly identify bacterial susceptibility to antibiotics with over 99% accuracy in 10 min. Bayesian Gaussian mixture analysis offers a promising approach toward practical, rapid antimicrobial susceptibility testing [107]. The technique demonstrated strong 97.8% accuracy, with the k-nearest neighbors algorithm exhibiting superior performance [108]. A new deep-learning model called the dual-branch wide kernel network has been used to boost the efficacy of SERS for detecting E. coli and S. epidermidis in media without the need for separation procedures. This technique has shown classification accuracies of up to 98%, making it a fast and effective method [109].

Label-based SERS methods use tags with Raman reporter molecules that bind to target bacteria, providing sensitive and unique signals. Particle labeling significantly improves bacterial identification using SERS. A study demonstrated that using solution state gold silver core–shell nanodumbbells with target nucleic acid significantly improved the signal reproducibility of SERS for bacterial identification, achieving a higher sensitivity of 4.5 CFU/mL compared to culture-based assays and conventional PCR [110]. SERS can help identify bacteria in complex samples with low pathogen concentrations. Magnetically assisted SERS based on aptamer recognition identified Staphylococcus aureus at 10 cells/mL [111]. Similarly, gold nanoflowers were used to design a self-calibrating SERS system for identifying bacterial phenotypes via specific DNA sequences, achieving a detection limit of around 5 fM for S. aureus DNA identification [112]. The incorporation of magnetic microparticles with SERS showed excellent sensitivity for bacterial identification compared to fluorescence by minimizing the non-specific binding of NPs during the target binding step [113]. Combining magnetic material and SERS label for immunoassay was reported for bacterial identification with limits of 10–25 cells/mL for E. coli, L. monocytogenes, and S. typhimurium [114]. Simultaneous bacterial analysis and eradication via photothermal treatment were also reported by Gao et al. They demonstrated the SERS platform for offering potential anti-bacterial applications [115]. SERS spectra limitations have led to the development of electrochemical surface-enhanced Raman spectroscopy, allowing for more efficient differentiation of bacterial species in a biologically relevant electric field environment [116].

These recent results collectively demonstrate that SERS-based bacterial identification is a promising platform for accurately identifying target bacteria. With the continued advancements in nanotechnology and machine-learning algorithms, SERS is expected to be a reliable and rapid tool for the identification of bacteria, particularly in clinical settings where rapid and accurate diagnosis is crucial for patient outcomes.