Recent advances in microfluidic-based spectroscopic approaches for pathogen detection

Abstract

Rapid identification of pathogens with higher sensitivity and specificity plays a significant role in maintaining public health, environmental monitoring, controlling food quality, and clinical diagnostics. Different methods have been widely used in food testing laboratories, quality control departments in food companies, hospitals, and clinical settings to identify pathogens. Some limitations in current pathogens detection methods are time-consuming, expensive, and laborious sample preparation, making it unsuitable for rapid detection. Microfluidics has emerged as a promising technology for biosensing applications due to its ability to precisely manipulate small volumes of fluids. Microfluidics platforms combined with spectroscopic techniques are capable of developing miniaturized devices that can detect and quantify pathogenic samples. The review focuses on the advancements in microfluidic devices integrated with spectroscopic methods for detecting bacterial microbes over the past five years. The review is based on several spectroscopic techniques, including fluorescence detection, surface-enhanced Raman scattering, and dynamic light scattering methods coupled with microfluidic platforms. The key detection principles of different approaches were discussed and summarized. Finally, the future possible directions and challenges in microfluidic-based spectroscopy for isolating and detecting pathogens using the latest innovations were also discussed.

I. INTRODUCTION

Millions of people get infected by pathogenic infections each year due to contaminated food and water. Every year, one out of ten people get infected by foodborne pathogens due to food contamination.^1,2 Foodborne pathogens, including Escherichia coli, Salmonella enterica, and Listeria monocytogenes, are estimated to cause 600 × 10⁶ food-related infections with a mortality rate of 420 000 each year.^3–5 Most microorganisms have the ability to adapt and cultivate under different conditions like higher and lower temperatures, various pH values, pressure, and various concentrations of saline. Survival and growth of pathogens under different conditions allow them to spread infections on a large scale.^6–8 Therefore, there is a dire need to develop technologies capable of detecting pathogens rapidly in remote areas. The rapid detection of pathogens is crucial and key for diagnosing and controlling the spread of infections.

The traditional methods for detecting and identifying bacterial samples include cell culturing, gram staining, and biochemical analysis.^9–11 However, these methods are laborious and take several days, which makes it difficult for rapid detection and deal with emergency incidents. Currently, there are various standard methods applied in clinics and hospitals for the detection of pathogens, including polymerase chain reaction (PCR), flow cytometry, loop-mediated isothermal amplification (LAMP), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and enzyme-linked immunosorbent assay (ELISA). These methods have high sensitivity and specificity, but there are some limitations associated with them. The PCR is a highly sensitive method, but sample contamination may result in misleading results. The used primers could be annealed with similar DNA, resulting in incorrect outcomes.^12,13 Flow cytometry analyzes microorganisms in complex solutions. The pathogen identification is based on optical detection in the form of automated fluorescence microscopy.^14,15 However, the method requires cell suspension, more fluorophores, and the complexity of the instrument also limits its accessibility. LAMP is a molecular detection technique that amplifies DNA using a primer design and forms loop structures.¹⁶ The limitations include the complicated primer design, unsuitable for high-throughput, and challenging assay optimization.^17,18 MALDI-TOF MS is an efficient analytical method based on the ionization and analysis of microbial cells using a laser and mass spectrometry. The detection of pathogens from the sample is performed by analyzing the distinct mass spectra.^19,20 MALDI-TOF MS has some limitations, including the cultivation-derived method, which is dependent on database accuracy, sensitivity issues with low bacterial concentrations, and challenges associated with high cost and maintenance.^21,22 ELISA is an efficient method to detect pathogens even from complex matrices. The method is based on the specific binding of antibodies to the target analyte, and the readout depends on colorimetric changes caused by enzymes attached to antibodies.^23,24 Despite various advantages, several limitations exist, including the number of steps required to perform the assay and the method unsuitable for Point-of-care testing (POCT) sensing.^25,26

Various spectroscopic techniques have been emerging and applied in many applications of biosensing devices.^27–29 The review focuses on biosensing techniques by integrating microfluidic devices with spectroscopic devices, including fluorescence spectroscopy, surface-enhanced Raman spectroscopy, and dynamic light scattering (DLS) to enable highly sensitive and specific detection of pathogens. Microfluidic channels provide a controlled environment for the sample, which enhances the signal-to-noise ratio and reduces interference from background signals.^30–32 Furthermore, microfluidic devices can be designed to perform multiplexed analysis, allowing for the simultaneous detection of multiple analytes in a single assay.^33–35 Optical spectroscopic techniques have shown significance in various biosensing applications, including disease diagnosis, environmental monitoring, and drug discovery. The developed platforms offer the potential for rapid, point-of-care testing and personalized medicine.^36,37 As the field of microfluidics continues to advance, it is expected that microfluidic-based spectroscopic techniques will become more widely used in diagnostic applications. The application of microfluidics has reduced the testing time by avoiding lengthy sample processing and miniaturizing the device size. Cultivating pathogens in the laboratory requires more time because of using large volumes of reagents.^38–40 Reducing the analysis volume tends to shorten the incubation period for detecting pathogens. The droplet incubation in the microfluidic chip requires minimum reaction sample volumes at microliters and below, which has reduced the incubation time from 16 to 20 h to several hours (1–5 h).^41,42 Minimum reaction volumes have an advantage in measuring a high signal-to-background ratio, which helps in rapid sample classification. Various methods have been implemented to apply droplet incubation for high-throughput antibiotic susceptibility analysis in bacteria.^43,44 The sample processing using microfluidic platforms integrated with optical spectroscopic methods tends to produce novel instruments for various biosensing applications.

In this review, various optical spectroscopic methods integrated with microfluidic chips are discussed for the identification and detection of pathogens. Identification and detection of pathogens are two related but different processes. The detection method determines the presence or absence of a pathogen in a sample. Beyond this, the identification method involves verifying the pathogen's presence and accurately identifying its type or species using more advanced techniques. First, fluorescence-based spectroscopic methods are discussed and categorized into signals and imaging classification. Microdroplets have been widely used to detect and classify the generated fluorescence for pathogen detection. Then, microfluidic devices coupled with various Surface-Enhanced Raman Spectroscopy (SERS) approaches were discussed. In particular, different functionalized SERS tags and their fabrication on the chip were mentioned, as well as the particular isolation and methods to concentrate the target sample at the SERS detection region. Next, the advances of microfluidic devices coupled with light scattering detection modules to detect and classify pathogens were mentioned. The section also focuses on the advancements in applying optical fiber photosensors in microfluidic platforms to detect light scattering. Finally, envision the future perspective of spectroscopic techniques integrated with microfluidic systems in enhancing the rapid performance for isolation and detection of pathogens. Figure 1 illustrates the main research topics focused in the review on integrating microfluidic platforms with various spectroscopic techniques for pathogens detection.

FIG. 1.

Schematic illustration of the various spectroscopic strategies integrated with microfluidics for identifying pathogens.

II. MICROFLUIDIC METHODS INCORPORATED WITH PATHOGENS DETECTION TECHNIQUES

A. Fluorescence-based microfluidic system

In microfluidic devices, fluorescence-based detection offers several significant benefits for effective detection of pathogens. The higher sensitivity of fluorescence-based methods makes it possible to identify infections at low concentrations, which increases the efficacy of the procedure.⁴⁵ The sample processing may be completed quickly and effectively due to the fine control that microfluidic systems provide over fluid flow and sample sizes. Furthermore, fluorescent materials offer a flexible means of multiplexing, enabling the simultaneous identification of multiple pathogens in a single platform. The ability to follow temporal changes in pathogen concentrations through dynamic monitoring is made possible by the real-time nature of fluorescence signals in microfluidics.^46,47 Fluorescent-based biosensing methods have several advantages over electrochemical biosensing systems. Fluorescence biosensing techniques have various applications for pathogen detection, including higher sensitivity, multiplexing, signal amplification, and system stability. Fluorescent techniques allow the detection of pathogens based on visualization and quantification of targeted microbes in complex samples. However, the electrochemical techniques offer lower sensitivity and can be prone to interference from other substances, leading to inaccurate results. The surface of an electrochemical sensor can be contaminated by biological components, which can affect the performance of sensor over time.^48,49 The detection of microorganisms using fluorescence is categorized into two types: signal-based and imaging-based. The fluorescence can be acquired from the micro-organism using different reagents and labels.^50–52 In microfluidics, the fluorescence is usually detected from the testing sample using miniature photodetectors or optical sensors. Differences in the fluorescence acquired from various micro-organisms can be classified based on the variations in the signal. Similarly, imaging-based fluorescence detection can classify pathogens using image segmentation and classification approaches. Imaging classification by artificial intelligence (AI) algorithms is emerging and widely applied in biosensing applications.^53–55

1. Microfluidic immunofluorescence assays (MIFAs)

MIFAs are a novel and convincing method in the field of pathogen identification. MIFAs offer an effective means for the quick and accurate identification of infections by combining the accuracy of microfluidic devices with the specificity of immunofluorescence methods. Immunofluorescence uses antibodies that have been fluorescently tagged to identify certain antigens in biological materials. Selectively selected antibodies bind to antigens on the target, and the signal is enhanced by the addition of fluorescent secondary antibodies.^56,57 The combination of immunofluorescence with microfluidics yields benefits, including multiplexed analysis, lower sample sizes, and improved sensitivity.

Wang et al. designed an integrated microfluidic device for the automatic detection of Mycobacterium tuberculosis. The device can also differentiate between dead and live microbes. The clinically isolated samples were cultured and used to test the developed system. A heparin-binding hemagglutinin antibody is used to capture the bacteria within 10 min. The system captures and releases extracellular vesicles using magnetic beads, allowing for high throughput and an automated system. The system uses a syringe pump, a motorized pipettor arm, a 96-well plate, and a magnetic tweezer to manipulate magnetic beads. Bacterial detection is performed within 90 min and can detect 100 CFU of live M. tuberculosis.⁵⁸ Another microfluidic platform coupled with aptasensor uses hybridization chain reaction amplification for rapid detection and to determine the concentration of E. coli by naked eye. The tested pathogens were cultivated in laboratory and spiked into milk for the detection purpose. The sample was incubated for an hour after adding aptamer to the reaction wells of the chip. The device achieved a rapid detection time of 75 min. The technique can detect pathogens with a detection limit of 250 and 400 CFU/ml using buffered testing and milk samples, respectively.⁵⁹ Phage-based bioluminescence assay incorporated in the microfluidic chip for detecting E. coli using real-world drinking water samples [Fig. 2(a)]. The detection technique uses the combination of membrane filtration and selective enrichment to detect E. coli with a concentration of 4.1 CFU per 100 ml of water within 5.5 h.⁶⁰

FIG. 2.

Fluorescence-based microfluidic methods to detect pathogens. (a) Phage-based portable microfluidic device to detect pathogens from water samples.⁶⁰ Reproduced with permission from Alonzo et al., Lab Chip 22(11), 2155 (2022). Copyright 2022 Author(s), licensed under a Creative Commons Attribution License. (b) An integrated droplet platform to access bacterial growth detection and antimicrobial susceptibility assessment.⁶¹ Reproduced with permission from Kaushik et al., Biosens. Bioelectron. 97, 260 (2017). Copyright 2017 Elsevier. (c) Scheme of the microfluidic setup to detect pathogens using computer vision-based DNAzyme method.⁶² Reproduced with permission from Rauf et al., Biosensors 12(1), 34 (2022). Copyright 2022 Author(s), licensed under a Creative Commons Attribution License.

2. Fluorescence in situ hybridization microfluidics

Fluorescence In Situ Hybridization (FISH) microfluidics is a novel method in the field of pathogen identification. FISH is a molecular biology method used to visually identify and locate specific DNA or RNA sequences, enabling highly specific identification of target pathogen. FISH is a useful, culture-independent method that uses fluorescently labeled probes that hybridize with intracellular ribosomal RNA to quickly and selectively identify target bacteria.^63,64 Pathogens may be identified rapidly and sensitively by combining the FISH with the microfluidic devices. Various methodologies have been developed to integrate the FISH technique with microfluidics to develop simple and user-friendly methods for detecting pathogens.⁶⁵ Barbosa et al. combined FISH with a microfluidic chip to identify Candida spp. (C. tropicalis). The microfluidic chip comprises hydrodynamic trapping and microbarriers for isolating target cells from the sample. The FISH method was applied to the trapped C. tropicalis cells in the microfluidic platform using a specific peptide nucleic acid (PNA) probe. The C. tropicalis was detected from lab-cultured samples and artificially contaminated urine using a PNA probe by combining FISH with microfluidics. The proposed technique can detect C. tropicalis with a concentration of 10⁵ cells/ml in 6 h.⁶⁶ A multiplexed microfluidic fluorescence in situ hybridization method (μFISH) was developed to detect E. coli from both saline and whole blood samples from patients infected with E. coli. The microbes were isolated from the blood samples using magnetic nanoparticle (MNP) conjugated with recombinant human mannose-binding lectin. The method uses a unique 16S rRNA sequences probe to identify and quantify pathogens using a magnetically confined microfluidic channel sample. The μFISH device requires 3 h diagnosing time with lower concentrations (<1–5 cells/ml) in a buffer solution.⁶⁷ Yamaguchi and Goto developed an in-liquid-fluorescence in situ hybridization assay (liq-FISH) to detect E. coli based on optimized processes of fixation and hybridization. A simple method was devised based on the specific rRNA sequences of E. coli. The FISH reactions were performed in the microfluidic chip and flowed through the microfluidic channel to observe the fluorescence immobilization of the cells. A microfluidic-based microscopic counting system designed to detect pathogens using hybridized cells with a concentration of <10⁴ cells/ml in 5 h.⁶⁸ One-pot wash-free FISH assay was demonstrated for detecting and quantifying pathogens in droplets. The method enables the mixture of samples and reagents in the microfluidic chip to create the droplet to encapsulate the bacteria, followed by in situ permeabilization, hybridization, and signal detection. The assay can quantify E. coli, K. pneumonia, and P. mirabilis with a detection limit of 3 × 10³ bacteria/ml and detection time of 1.5 h.⁶⁹

3. Droplet-based fluorescence sensing

Analyzing microdroplets in microfluidic chips has been widely applied in fluorescence-based spectroscopic techniques. Droplet-based microfluidic devices are designed to generate droplets within the range of picoliter to nanoliter.^70,71 The microorganisms encapsulated in the droplet create a controlled microenvironment. The microorganisms or biological material in the droplets interacts with specific reagents or detection agents, which change the optical properties of droplets, such as color or fluorescence.^72–74 Optical spectroscopic techniques based on imaging and signal detection are applied to detect the variations in the characteristics of the droplets. Microdroplets have several advantages: requiring minimum testing sample, parallel processing, lower cell cultivation time due to minimal sample, risk of sample contamination, and precise control over experimental conditions.^75,76

The microfluidic chip platform “DropFAST” was developed to monitor bacterial growth and evaluate the antibiotic resistance for the pathogens. DropFAST uses a resazurin-based fluorescence assay to monitor the bacterial growth in 20 pl droplets after an incubation of 1 h. The pathogens detection is based on fluorescence using droplet incubation, which shortens the cultivation time [Fig. 2(b)]. The platform was employed to test the antimicrobial susceptibility of E. coli with a concentration of 10⁷ CFU/ml.⁶¹ A miniature integrated microfluidic chip was developed for label-free microbial screening using fluorescence-activated droplet sorting. The technique works by detecting differences in the light properties of droplets as they pass through a detection area. The recorded optical signal change was analyzed to count and sort droplets based on absorbance intensity. The testing was performed by overnight incubation of droplets containing E. coli within a short time. The generated bacterial droplets with a concentration of 3 × 10⁹ CFU/ml were diluted with LB media for testing. The system's detection limit is 400 CFU/droplet.⁷⁷ Another research showed the detection and quantification of microbes by incubating droplets and detecting the fluorescent droplets. The method is based on estimating the shrinkage index value using fluorescent values to estimate the growth and quantification of bacteria. The cultivation duration for the growth of bacteria in the droplet was minimized to 2 h.⁷⁸ The research can be extended further to design and apply imaging or signal-based systems to detect bacterial microbes automatically.

4. Nucleic acid-based fluorescence

The combination of microfluidic technologies and nucleic acid-based detection constitutes a potent synergy in pathogen detection. The method offers an effective solution for quick and sensitive pathogen detection by combining the accuracy of microfluidic platforms with the specificity of nucleic acid probes that target pathogen DNA or RNA sequences.^79,80 Yanchen et al. introduced a droplet DNAzyme-coupled rolling circle amplification system for selective detection of bacteria from clinical urine samples with higher sensitivity. The method uses DNAzyme as a recognition element for bacterial detection using incubated droplets. The technique has a high sensitivity of single cell with 1.5 h detection time.⁸¹ A computer vision-based DNAzyme sensor in a microfluidic chip was developed for the isolation and specific detection of E. coli from the water sample. The E. coli was separated by encapsulating it in a droplet containing DNAzyme. The algorithm was developed by analyzing the videos taken during the experiments and tagging the fluorescent droplets for counting. The droplets were incubated to generate a fluorescence signal due to the reaction of DNAzyme interacting with microbes [Fig. 2(c)]. The platform was tested with a bacterial concentration of 8 × 10⁷ CFU/ml.⁶² Droplet microfluidic and LAMP combined to create a sensitive biosensor for amplifying the extracted RNA for detecting S. typhimurium. The technique was applied to the milk samples artificially contaminated with S. typhimurium, S. flexneri, and S. aureus cultures. The LAMP-assisted amplification reactions were performed on the pico-sized droplets, and pathogen detection was performed using a mathematical model and fluorescence imaging. The droplets were incubated at an isothermal condition for 30 min, and results showed a minimum detection limit of 5 × 10⁵ CFU/ml of S. typhimurium in the milk sample.⁸² Another automated device was developed using a microfluidic chip integrating rotary valve-assisted and recombinase polymerase amplification T7-Cas13a for single-step nucleic acid detection. The microfluidic chip can be adjusted to process samples and acquire fluorescence in the detection platform. The Group B streptococci DNA was detected with a detection sensitivity of 8 copies/reaction with 100% accuracy. The entire detection process can be performed automatically in 30 min..⁸³ A comparison of the pathogen detection performances using fluorescence methods in microfluidics is shown in Table I.

TABLE I.

Comparison of different microfluidic techniques based on fluorescence to detect pathogens.

Detection technique	Performance	Testing sample	Detection time	Detection limit	Reference
Magnetic beads, heparin-binding hemagglutinin	M. tuberculosis, Automated system, high throughput	Clinical samples, Tryptic soy broth	90 min	100 CFU/ml	58
Aptasensor based chip	E. coli, hybridization chain reaction amplification, naked eye detection, 75 min detection time	Buffered testing, spiked milk samples	75 min	250 CFU/ml	59
Phage-based bioluminescence assay	E. coli, membrane filtration, selective enrichment, testing within 5.5 h	Real-world drinking water samples	5.5 h	4.1 CFU/100 ml	60
FISH	C. tropicalis, PNA probe	Pure culture and artificially contaminated urine	6 h	105 cells/ml	66
Multiplexed μFISH	E. coli, S. aureus, magnetic separation, 16S rRNA sequences probe	Clinical blood and saline sample	3 h	<1–5 cells/ml	67
Liq-FISH	E. coli, on-chip process of processes of fixation and hybridization	Cultured using LB medium	5 h	<104 cells/ml	68
Digital droplet FISH	E. coli, K. pneumonia, and P. mirabilis, droplet-based FISH, in situ permeabilization, and in situ hybridization	Cultured using LB medium	1.5 h	3 × 103 bacteria/ml	69
Resazurin-based fluorescence assay	Antibiotic resistance E. coli, tested the antibacterial effect of gentamicin, droplet incubation-based testing	Cultured with Mueller–Hinton broth	1 h	107 CFU/ml	61
Fluorescence-activated droplet analysis	E. coli, optical signal detection, miniature device	Cultured with LB	Overnight incubation	400 CFU/droplet	77
Shrinkage index value estimation	E. coli, Pseudomonas fluorescens, fluorescence intensity based on bacterial growth, droplet analysis	Cultured with Bacto agar	2 h	NA	78
DNAzyme-coupled rolling circle amplification	E. coli, high sensitivity with single-cell detection, droplet incubation	Clinical urine sample, lab spiked sample	1.5 h	Single-cell detection	81
DNAzyme-based sensor	E. coli, imaging-based droplet counting, fluorescent detection	Water spiked sample	30 min	8 × 107 CFU/ml	62
LAMP-assisted amplification	S. typhimurium, droplet analysis, fluorescence imaging	Milk spiked sample	30 min	5 × 105 CFU/ml	82
Rotary valve-assisted and recombinase polymerase amplification	Group B streptococci, 100% accuracy, automated device	Lab cultured	30 min	8 copies/reaction	83

B. Raman scattering-based microfluidic system

Raman scattering is the inelastic scattering of photons when light interacts with a sample. The Raman scattering signal contains information about the vibrational modes of molecules in the sample.^84–86 The nature of Raman scattering is generally weak and challenging to detect the signal. Therefore, the Surface-Enhanced Raman Spectroscopy (SERS) technique usually uses metallic substrates with nano-sized thickness or nanoparticles to amplify the Raman signals. Raman scattering offers highly specific molecular fingerprinting, enabling the detection and identification of pathogens by analyzing their distinct vibrational spectra. Raman scattering-based biosensing provides several advantages over electrochemical systems, including a wide range of complex and turbid samples, high specificity, and accessibility.^87–89 The SERS technique has been coupled in microfluidic chips using various strategies. The critical part of integrating microfluidic devices with SERS is to filter or concentrate the sample at the target region.

1. Dynamic microfluidic control for SERS

Integrating dynamic microfluidic control into Surface-Enhanced Raman Spectroscopy (SERS) is an innovative advancement in pathogen identification. The strategy maximizes the interaction between pathogenic analytes and plasmonic surfaces by dynamically adjusting fluid parameters within microchannels, such as flow rates and reaction conditions. The integrated technique offers a dynamic strategy to improve SERS signal quality, sensitivity, and specificity in real-time analysis, significantly improving pathogen identification.^90,91 A microfluidic system is designed to perform different operations, including bacterial enrichment, metabolite collection, and SERS measurement, to test the antibiotic resistance of bacterial samples. The chip is integrated with the electrically controlled system to maintain the sample flow [Fig. 3(a)]. The system is capable of determining the antimicrobial susceptibility of E. coli and detecting E. coli with a detection limit of 10³ CFU/ml. However, the testing duration requires a single day.⁹² The work was further improved by developing a microwell-based microfluidic chip. An automated controlled microfluidic platform designed to detect the antimicrobial susceptibility of pathogens using SERS. The small microwells were fabricated and coated with an antibiotic solution to create drug-resistant bacteria. The microwells were filled with testing bacteria and cultivated in microfluidic chips. After incubation, the measurement of SERS signals was taken and compared with existing data of the bacterial samples [Fig. 3(d)]. Isolates of ampicillin susceptible and resistant E. coli were tested with a concentration of 10⁸ CFU/ml within a duration of 3.5 h.⁹⁵ Another capillary-driven microfluidic device was constructed using SERS detection to detect E. coli. The surface of the microchannel was functionalized with antibody-modified magnetic nanoparticles (MNPs) to capture E. coli from milk samples. The immunomagnetic separation (IMS) isolated the magnetic nanoparticles linked with pathogens, which were transferred to another chamber labeled with gold nanorods and used as the Raman probe. The device can detect E. coli 10¹–10⁷ CFU/ml from milk samples in less than 1 h.⁹⁶ Shang et al. combined the label-free SERS and optical tweezer method to develop a microfluidic testing platform for detecting Lactobacillus fermentum. The stability of the SERS spectra was much improved by using optical tweezers to hold the bacteria in place. The acquired SERS spectra were analyzed using support vector machine (SVM) and XGBoost machine learning methods for identification, and the classification accuracy for all tested lactic acid bacteria exceeded 95%. Furthermore, the synthetic mixture of bacteria samples was used to verify the detection results.⁹⁷

FIG. 3.

SERS-based techniques combined with microfluidics for identifying microbes. (a) Microfluidic system integrated with membrane filtration and the SERS-active substrate to detect antibiotic susceptibility.⁹² Reproduced with permission from Chang et al., Anal. Chem. 91(17), 10988 (2019). Copyright 2017 Elsevier. (b) A nanoporous membrane-based microfluidic chip for capturing and detecting pathogens using SERS.⁹³ Reproduced with permission from Krafft et al., Electrophoresis 42(1–2), 86 (2021). Copyright 2021 Author(s), licensed under a Creative Commons Attribution License. (c) AI-assisted Raman scattering from the microdroplets to detect microbes.⁹⁴ Reproduced with permission from Safir et al., Nano Lett. 23(6), 2065 (2023). Copyright 2022 Author(s), licensed under a Creative Commons Attribution License. (d) Microfluidic chip with microwells with an automatic control system for the antimicrobial susceptibility testing of microbes.⁹⁵ Reproduced with permission from Liao et al., Biosens. Bioelectron. 191, 113483 (2021). Copyright 2021 Elsevier.

2. Multiplexed SERS detection in microfluidics

Combining multiplexed SERS with microfluidic technologies brings about an essential advancement in pathogen identification. The method merges the sensitivity of SERS with the accuracy of microfluidics, allowing for the simultaneous identification of several pathogens in a single system.^98,99 The antibody-functionalized SERS-labeled silver nanoparticles were prepared to capture various bacterial microbes. The developed particles were mixed with the testing sample containing several bacterial species with functionalized saline solution or human blood. The mixture was pumped into the microfluidic channel, and the SERS signal generated by Raman-active molecules was analyzed to classify targeted bacteria. The technique can detect and quantify P. aeruginosa, S. aureus, E. coli, and S. agalactiae with sensitivity ranges from units to tens of CFU/ml.¹⁰⁰ A three-dimensional microfluidic platform fabricated with nanoporous membrane to enrich bacteria and serves as the target area for SERS detection. The fabricated microfluidic platform is useful for detecting and quantifying the concentration of bacteria in drinking water. The enrichment is based on the electrokinetic flow of the testing sample across the porous membrane, where the bacterial microbes and AgNP clusters were trapped to enrich the testing area [Fig. 3(b)]. The SERS spectra obtained from the spiked tap water samples containing common pathogens Escherichia coli and Pseudomonas taiwanensis showed significant Raman shift.⁹³ Fareeha et al. devised a system by processing droplets containing gold nanorods and microbial cells, which were incubated and identified with SERS and machine learning [Fig. 3(c)]. The system identified S. epidermidis and E. coli from blood sample samples with classification accuracy of ≥90% using droplets with pathogens. The study was performed with a sample concentration of 1 × 10⁹ cells/ml.⁹⁴ Asgari et al. developed a SERS optofluidic sensor coupled with an immunoassay to detect Escherichia coli and Salmonella. The technique consists of three stages: initially, enhancing sensitivity by enrichment; subsequently, separating and labeling target bacteria with SERS nanotags carrying specific antibodies; and finally, detecting the labeled bacterial cells in a hydrodynamic flow-focusing SERS optofluidic device, which is capable of detecting low bacterial cell counts in a sample stream resembling a thin film. The method showed a lower detection limit of 10 CFU/200 g of testing sample with a total analysis and detection time of 2 h.¹⁰¹

3. Functionalized SERS tags in microfluidics

The application of functionalized SERS tags in microfluidic platforms is an innovative approach to detect pathogens. By integrating customized SERS-active nanomaterials with specific binding functions, these tags improve the precision and accuracy of pathogen detection. Functionalized SERS tags in microfluidics provide precise control of interactions, enhancing binding affinity to specific pathogens.^102,103 Wang et al. fabricated a microfluidic chip with integrated NaYF₄:Yb,Er@SiO₂@Au under near-infrared laser excitation for the detection of E. coli using SERS. The developed substrate was integrated into a microfluidic chip to detect R6G and E. coli using SERS under near-infrared excitation. The experiments were performed with a bacterial concentration of 10⁵ CFU/ml under 785 nm laser excitation in the microfluidic chip.¹⁰⁴ Another microfluidic chip was designed and utilized to acquire the SERS signal by SERS-tagged gold nanostars functionalized with monoclonal-specific antibodies. The prepared gold nanostars were used to isolate pathogens from the testing sample. After separation, the remaining were incubated to cultivate the bacteria. The cultivated sample was then reinjected into the microfluidic chip to acquire the SERS signal for 30 min. The developed technique can detect and identify L. monocytogenes with a 10⁵ CFU/ml concentration in real-time with continuous flow in the microfluidic chip. The signal classification time is about 100 s to classify L. monocytogenes in the presence of Listeria innocua.¹⁰⁵ Sara et al. developed a microfluidic immunosensor to separate and detect E. coli in romaine lettuce. SERS nanoprobes comprised gold nanoparticles and specific antibodies for isolating E. coli from lettuce samples. The separated sample flowed through the microfluidic device by hydrodynamic flow to detect the Raman scattering. The bacterial microbe was detected within 1 h with a detection limit of 0.5 CFU/ml. The developed SERS-based microfluidic device can detect pathogens in complex food matrix.¹⁰⁶

4. Raman scattering-based advanced techniques

Combining Surface-Enhanced Raman Spectroscopy (SERS) with advanced detection methods is a significant approach for accurately and effectively identifying pathogens. Integrating SERS with additional techniques, such as microfluidics, molecular biology, or artificial intelligence, amplifies the effectiveness of pathogen detection strategies. There are several advancements in the applications of SERS using microfluidic devices. A paper-based analytical method was devised by integrating CRISPR/Cas12a and SERS to detect pathogens from food samples. The Salmonella typhimurium was detected with a detection limit of 3–4 CFU/ml using spiked samples with a dynamic detection range from 1 to 10⁸ CFU/ml. The sample testing was performed in 45 min to detect the pathogens from food samples.¹⁰⁷ However, the system requires complicated manual sampling processing at various stages of the experimental setup. Processing samples in the microfluidic chip requires bacterial enrichment to acquire the SERS signal. A droplet-based optofluidic system was designed by combining SERS combined with LAMP in a microfluidic chip to detect pathogens. The system uses multifunctional gold nanoparticles for the amplification of DNA by LAMP. The indirect SERS detection method using multifunctional AuNPs allows for the detection of Listeria monocytogenes in a buffer and in a food matrix (ultra-high temperature milk). The LAMP reaction was performed for 1 h with a detection limit of 3.6 × 10² CFU/ml.¹⁰⁸ The research in developing advanced materials to attain efficient SERS signals is crucial. Raman scattering analysis is improved by deep learning, specifically convolutional neural networks, which can identify complicated spectrum patterns, categorize bacteria, and enhance signal-to-noise ratios. Pathogen identification is further improved by real-time detection and transfer learning. Lu et al. introduced a technology based on artificial intelligence to examine a biological Raman spectrum and accurately identify microorganisms at a single-cell level. Integrating a convolutional neural network (ConvNet) architecture with Raman spectroscopy enables the identification and classification of individual microbial cells based on their distinct spectral properties. The Raman spectrum of the 14 microbial cells with distinct species was analyzed to train an optimal ConvNet model. The results showed a classification accuracy of 95.64 ± 5.46%.¹⁰⁹

The combination of stable isotope probing (SIP) and Raman microspectroscopy is a new method that has not yet been widely explored. The SIP technique is used in microbial ecology to detect the active microbial populations engaged in certain metabolic activities within diverse microbial communities. Raman Stable Isotope Probing (R-SIP) can be applied to measure growth rates of heterotrophic bacteria at the single-cell level. The stable isotopic composition of biomolecules can be used to identify and characterize microbes that incorporate the labeled substrates into their biomass.^110,111 Matanfack et al. demonstrated that R-SIP in the deep-UV range can overcome the limitations of the VIS-range. The spectral shift of red region wavelength was analyzed for E. coli cells. The technique has the potential for the identification of live bacterial cells.¹¹² Another automated Raman-based optofluidic platform was introduced for sorting stable-isotope-probing-labeled microbes using microfluidics, optical tweezing, and Raman microspectroscopy. The system was applied for sorting two intestinal bacteria, one soil bacterium, and one marine bacterium with a high sorting accuracy of 98.3 ± 1.7%.¹¹³ Table II presents a comparative analysis of pathogen detection using SERS methods in microfluidics.

TABLE II.

Comparison of different microfluidic techniques based on SERS to detect pathogens.

Detection technique	Performance	Testing sample	Detection time	Detection limit	Reference
Membrane filtration SERS	E. coli and antibiotic resistance E. coli, single day testing duration, electrically controlled system	Cultured with Mueller−Hinton broth	1–2 days	103 CFU/ml	92
Micro-well based chip	Antibiotic resistance E. coli, automated controlled platform	Cultured with Mueller−Hinton broth	3.5 h	108 CFU/ml	95
Capillary-driven microfluidic chip	E. coli, sample isolation using IMS, gold nanorods as Raman probe	Lab-cultured, milk spiked sample	1 h	7 CFU/ml	96
Optical tweezers	Lactobacillus fermentum, machine learning classification, identification accuracy 95%	Lab cultured	Real-time detection (2 s)	NA	97
Functionalized gold nanostars	L. monocytogenes, SERS tags, inflow detection	Cultured with nutrient broth	SERS signal 30 min, classification time 100 s	105 CFU/ml	105
Functionalized silver NPs	P. aeruginosa, S. aureus, E. coli, and S. agalactiae	Saline solution or human-spiked blood	Screening time 10 min/ml	units to tens of CFU/ml	100
Nanoporous structure and electrokinetic flow	Escherichia coli and Pseudomonas taiwanensis, disposable and low-cost devices using nanoporous membrane, AgNP clusters	Spiked tap water	<1 min	E. coli (124 × 106 cells/ml) P. taiwanensis (19 × 106 cells/ml)	93
Droplet-based SERS	S. epidermidis and E. coli, gold nanorods applied, droplets incubation, machine learning for classification	Lab-cultured samples and mouse blood samples	NA	1 × 109 cells/ml	94
SERS optofluidic device	Escherichia coli and Salmonella, specific antibody-based enrichment, SERS nanotags	Food samples (lettuce and packed salad)	2 h	10 CFU/200 g	101
Near-infrared laser excitation	E. coli, advanced material fabrication in chip, 785 nm laser excitation	Lab cultured	NA	105 CFU/ml	104
SERS-microfluidic immunosensor	E. coli, nanoprobes consisted of gold nanoparticles and specific antibodies	Lettuce sample	1 h	0.5 CFU/ml	106
Integrating CRISPR/Cas12a and SERS	S. typhimurium, microfluidic paper-based analytical device	Real food samples	45 min	3–4 CFU/ml	107
SERS assisted LAMP	L. monocytogenes, multifunctional gold nanoparticles, droplets incubation, real-time identification	Buffer and milk sample	1 h	3.6 × 102 CFU/ml	108
Laser tweezers Raman spectroscopy	14 different microbial species, deep learning classification, classification accuracy 95.64 ± 5.46%	Lab cultured with various growth conditions	NA	Single-cell analysis	109

C. Dynamic light scattering-based microfluidics

Dynamic light scattering (DLS) is a technique based on analyzing the fluctuations in scattered light intensity over time. DLS is a method used to quantify changes in scattered light intensity from particles in a solution caused by Brownian motion. DLS provides information on diffusion coefficients and particle size distribution.^114–116 The technique is helpful for real-time analysis of particles passing through a channel when used in microfluidics. Light scattering is a phenomenon in which the incident light deviates from the original path when interacting with the particles.^117–119 The fluctuations and variations in the light intensity depend on the size, shape, and characteristics of the particles. Therefore, the distinction of the light intensity contributed to identifying and classifying the particles.^120–123 DLS biosensing provides notable advantages over electrochemical biosensors, including rapid detection, label-free analysis, and multiplexing. DLS technique has been employed in various applications to characterize particles including biological materials.^124,125

1. Multiplexed pathogen detection

The combination of DLS with microfluidic devices offers a novel method for pathogen identification that can analyze many pathogens simultaneously. DLS, a well-known technique for evaluating particle size and distribution, can be easily incorporated into microfluidic systems to improve pathogen detection accuracy and efficiency. The dynamic union provides a flexible tool for rapidly detecting various pathogens by enabling real-time monitoring of the size fluctuations in microbes within microscale settings.¹²⁶ Yang et al. developed a real-time spectrometer on microfluidic for detecting and collecting airborne pathogens. The prototype was designed to combine microfluidics with flow cytometry in which the air was pumped into the microfluidic channel to identify aerosol pathogens using optical detection. The detection of pathogens is based on dynamic transmission analysis, principal component analysis, and partial least squares discriminant analysis. Three different pathogenic particles, including rice smut spores, Aspergillus niger spores, and Aspergillus charcoal spores, were identified with classification accuracy 94.23%, 96.52%, and 92.86%, respectively. The dynamic transmission spectroscopy in microfluidic platform attained a high classification accuracy of 93.75% with high precision.¹²⁷ Another optofluidic prototype was designed to detect pathogens using various optical parameters of absorbance, fluorescence, and scattered light. An E. coli and Streptomyces hygroscopicus were used for testing with a final concentration of 5 × 10⁷ spores/ml for droplet generation. The microfluidic platform consists of a microfluidic chip with microchannels to flow the droplets. Optical fibers are integrated with the Polydimethylsiloxane (PDMS) fabricated chip to pass the incident light through the droplets and acquire the three different optical signals of fluorescence, scattering, and light absorbance. The generated droplets were incubated and passed through a microfluidic channel to obtain optical signals. By simultaneously measuring absorbance, scattered light, and fluorescence signals, the three populations of droplets (with antibiotic and inhibited growth, without antibiotic and proliferating cells, and empty control droplets) were classified into three clusters. The significance variance with R² value of 0.98 was observed for scattered light with an Optical Density (OD) range of 0.5–12.¹²⁸ The system can be further extended to use the statistical values for classifying pathogens in real-time analysis. Huang et al. designed and fabricated an integrated microfluidic multi-angle laser scattering system for rapid and label-free detection of waterborne pathogenic parasites. The system consisted of two chips; the first chip was used to control the concentration of the sample and then injected into the second chip to collect the multi-angle scattering patterns of the flowing microbes. The principal component analysis is used to extract the Zernike moment features from the scattered patterns, and the linear discriminator analysis technique was applied to classify the scattered patterns. The Cryptosporidium parvum oocysts and Giardia lamblia cysts spiked samples were tested with a higher average classification accuracy of 98%. The system can detect various concentrations of samples with lower concentration of 10 (oo)cysts/10 ml in 50 min, including the time required for sample pre-concentration and pattern recognition.¹²⁹

2. DLS-enhanced biosensors

The combination of advanced methods with DLS significantly increases the effectiveness of pathogen detection in microfluidic systems. The efficiency of pathogen detection can be enhanced when integrated with functionalized nanoparticles, PCR, and optofluidic system. Combining size analysis with genetic identification in a simplified way offers a potent tool for pathogen detection.^130,131 Qian et al. proposed a strategy for detecting Salmonella in milk using nucleic acid amplification and dynamic light scattering. The method uses PCR to amplify the invA gene from the pathogenic sample. The streptavidin-modified gold nanoparticles probe was added to measure the change in size of the particles using DLS. A significant change in the diameter of the particles was detected from 145.5 to 181.6 nm. The method detected Salmonella in cultured and spiked samples with a detection limit of 10⁰ CFU/ml.¹³² Another immunosensor using gold nanoflower-enhanced dynamic light scattering was designed for detecting E. coli in milk samples. The optimum conditions were evaluated to enhance the DLS signal using gold nanoflower particles. Under optimum conditions, the sensor can identify E. coli within the detection range of 6 × 10⁰–6 × 10⁴ CFU/ml and a detection limit of 2.7 CFU/ml. The developed method is very sensitive to detect a single bacterial microbe in 1 ml of testing sample.¹³³ A microfluidic-based electro-optical biosensor designed for rapid and low-cost detection of pathogens. The detection approach combines evanescent-field sensing with a dielectrophoretic cell-collecting methodology. The sensor can assemble the analyte particles to the target surface of the sensor by applying the phenomenon of dielectrophoresis [Fig. 4(b)]. The scattered light signal was observed, and E. coli was detected in 10 min with a detection limit of 10² CFU/ml from prepared mimic urine sample.¹³⁵ An optofluidic flow cytometer was developed using a 3D in-plane spherical mirror to enhance the optical scattering. The system is based on a simultaneous collection of fluorescence and scattering signals using an optical fiber. The collected data were processed to discriminate the particles or pathogenic samples using different optical properties [Fig. 4(c)].¹³⁶ Table III presents a comparative analysis of pathogen detection in microfluidics using DLS approaches.

FIG. 4.

Light scattering based microfluidic platform to detect microbes. (a) Microfluidic platform with on-chip droplet incubation and optical-based detection of microbes.¹³⁴ Reproduced with permission from Hussain et al., Spectrochim. Acta A: Mol. Biomol. Spectrosc. 288, 122206 (2023). Copyright 2023 Elsevier. (b) An integrated microfluidic electro-optical biosensor system for label-free detection of pathogens.¹³⁵ Reproduced with permission from Petrovszki et al., Microelectron. Eng. 239, 111523 (2021). Copyright 2021 Elsevier. (c) An integrated optofluidic flow cytometer platform to detect pathogens.¹³⁶ Reproduced with permission from Zorzi et al., Sensors 23, 9191 (2023). Copyright 2023 Author(s), licensed under a Creative Commons Attribution License.

TABLE III.

Comparison of different microfluidic techniques based on DLS to detect pathogens.

Detection technique	Performance	Testing sample	Detection time	Detection limit	Reference
Real-time spectrometer	Rice smut spores, Aspergillus niger spores, and Aspergillus charcoal spores, detect airborne pathogens, classification accuracy of 93.75% with high precision	Lab-prepared aerosol pathogens	Real-time detection	NA	127
Optofluidic multi-parametric analysis	E. coli and Streptomyces hygroscopicus, droplet incubation, multi-parameters analysis	Culture media TB and MMM (Minimal Media for Microorganisms)	NA	5 × 107 spores/ml (droplet generation)	128
Multi-angle scattering	Cryptosporidium parvum oocysts and Giardia lamblia cysts, Zernike moment features	Spiked water sample	50 min	10(oo)cysts/10 ml	129
Nucleic acid amplification and DLS	Salmonella, streptavidin-modified gold nanoparticles probe, diameter of the particles detected from 145.5 to 181.6 nm	Pure culture and spiked milk sample	NA	100 CFU/ml	132
Gold nanoflower-enhanced DLS	E. coli, detection range of 6 × 100–6 × 104 CFU/ml, highly sensitive, capable of detecting single microbe/ml	Spiked milk sample	30 min	2.7 CFU/ml	133
Electro-optical biosensor	E. coli, dielectrophoretic-based sample collection	Mimic urine sample	10 min	102 CFU/ml	135
Optofluidic Flow Cytometer	E. coli, integrated 3D in-plane spherical mirror for enhanced optical signal collection	Lab cultured with LB	NA	NA	136
Multiple light scattering	Gram-positive cocci and Escherichia coli, user-friendly, machine learning for data classification, clinical urine samples tested	Artificial urine	30 min	103 CFU/ml	137
Multi-angle dynamic light scattering	P. aeruginosa, classification accuracy of 97.9%, machine learning	Lab-prepared nutrient-broth medium	25 min	102 CFU/ml	138
Droplet-based multi-angle dynamic light scattering	P. aeruginosa, droplet incubation, classification accuracy of 95.6%, machine learning	Lab-cultured media	6 h	105 CFU/ml	134

3. DLS combined with machine learning

Combining machine learning with DLS represents an innovative approach to detect pathogens. This dynamic combination improves the system's ability to identify complex patterns indicative of different pathogens while enhancing the analysis of small variations in pathogen dynamics. The combination of DLS and machine learning offers great potential for the quick, precise, and multi-parametric identification of pathogens in various biological samples. Kwang et al. developed a system named “Bacometer” to rapidly detect pathogens from urine samples. The system is based on detecting an optical scattered light signal from the bacteria and then classifying it using machine learning. Bacometer’s basic principle is to use a scattering surface to produce multiple light scattering without labor-intensive bacterial cultivation. The device makes use of multiple light scattering to optimize the interaction between the light and the target microorganisms. The clinical positive urine samples were tested with a concentration of 10³ CFU/ml with a classification accuracy of 90.9% in 30 min.¹³⁷ Multi-angle dynamic light scattering (MDLS) utilizes several photodetectors arranged at various angles to measure the scattering light from the testing sample containing pathogens or biological samples.^139,140 The MDLS approach uses detailed and accurate information from the scattered light based on the size, shape, and characteristics of the particles. Various prototypes have been developed based on MDLS to acquire the scattering of light from the testing sample at multiple angles.^141,142 The developed devices were optimized using microfluidics and by reducing the number of photodetectors. The technique was further simplified by creating a microfluidic platform with two optical sensors and laser light. The optical fibers were embedded in the chip linked to optical sensors and a laser source. The device was validated by detecting and classifying the microparticles with sizes of 2 and 5 μm. The validated microfluidic platform was utilized to detect pathogens by combining other techniques, including immunomagnetic separation and droplet incubation. The pathogens were detected using a microfluidic chip based on immunomagnetic separation to isolate the target pathogens, and then the separated sample flowed through the microfluidic channel. The scattered light was collected from the pair of optical sensors, and the features acquired from the obtained data were further classified using machine learning approaches. The SVM classifier with a sigmoid kernel showed a higher classification accuracy of 97.9% compared with other machine learning classifiers. The technique can detect P. aeruginosa with a detection limit of 10² CFU/ml and a total detection time of 25 min.¹³⁸ A similar platform was applied for the detection of pathogens using droplet incubation. Initially, the sample containing pathogens was used to create droplets. The generated droplets were collected and incubated in the incubation chamber. The droplets containing pathogens have higher density after incubation due to the growth of pathogens.¹⁴³ The optimum conditions showed that the testing time was 6 h, including the culturing time of 5 and 1 h for sample preparation, testing procedure, and data classification. The incubated droplets were passed through an optical module of the microfluidic chip to detect light scattering. The method was applied to classify P. aeruginosa with a classification accuracy of 95.6% using the k-nearest neighbors (KNNs) classifier [Fig. 4(a)].¹³⁴ The method has several limitations, including the sample's specificity, inability to differentiate different types of pathogens, and requires laborious work to transfer the sample from chip to the incubation chamber and then reinject the incubated droplets to the microfluidic chip. However, the research can be extended to develop a single miniature device for generating and cultivating pathogens in the microfluidic chip, integrating it with an optical module to detect the pathogens using optical spectroscopy.

III. FUTURE PERSPECTIVES

The prospects for revolutionary advancements in microfluidic-based spectroscopic methods for identifying bacterial microorganisms are fascinating. Researchers are progressing in developing novel approaches for the detection of pathogens. Several essential aspects must be considered in designing novel methods for pathogens detection, including sample purification, minimal sample consumption, label-free testing, automated sample processing, and rapid detection. The isolation of targeted biological molecules in microfluidic devices or channels is a significant challenge. Several methods can be adapted to isolate and separate the pathogens that can be used for further classification. Microfluidic-based spectroscopic approaches foresee a paradigm change in microbial detection as these technologies advance. The emerging techniques are anticipated to solve the present issues with high sensitivity, specificity, and speed and open the door for revolutionary applications across various industries. These advancements can revolutionize healthcare diagnostics and improve environmental monitoring by making speedy, accurate, and efficient bacterial identification more widely accessible and ubiquitous in the future.

A single microfluidic chip capable of isolating and detecting pathogens is essential to avoid contamination and reduce the detection time. A microfluidic device was developed for isolating extracellular vesicles with droplets with an antibody-free magnetic bead strategy. The detection principle is based on capillary electrophoresis coupled with laser-induced fluorescent detection. The excitation wavelength used was 488 nm, and the emission wavelength was 520 nm. The method was used to confirm the identity of isolated extracellular vesicles. The significantly lower concertation of 1.05 × 10¹⁰ particles/ml was recovered using a pony serum sample.¹⁴⁴ The studies on extracting exosomes and extracellular vesicles provide proof-of-concept for using a microfluidic droplet platform and pave the way for further optimization and application of techniques in diagnostics. The findings of these studies have important implications for developing new diagnostic and therapeutic approaches based on pathogens detection. The functionalization of aptamers and antibodies on the surface of microfluidic chips and channels has been applied through various techniques. As templates, the aptamers against E. coli were linked to the PDMS microfluidic channel surface via the immobilized 7-polyamidoamine dendrimers. The modified microchannels with dendrimer-aptamer conjugates provide multiple binding sites for enhanced target capturing at high-throughput rates. The approach was applied to capture and detect E. coli, showing a lower detection limit of 10² cells/ml. The microfluidic system with G7-polyamidoamine dendrimers showed enhanced detection signals, improved target capturing efficiencies, and higher throughput than G4-polyamidoamine dendrimers.¹⁴⁵ These approaches can be applied to fabricate microfluidic chips with functionalized channels to capture and isolate different targeted pathogens. The functionalized surface can be applied to develop optical detection modules for detecting optical signals. Different optical spectroscopic techniques, such as SERS, fluorescence, and light scattering, can be used as optical detection modules for pathogen identification.

Microfluidic platforms based on droplet analyses have emerged, which can cultivate microbes in the droplets to reduce the cultivation time with minimum sample volume. The research can be extended to create a single chip to generate droplets and cultivate pathogens encapsulated in droplets using a microfluidic chip. The incubated droplets can be analyzed using light scattering techniques or imaging classification. Microfluidic chips are easy to fabricate and economical to develop on a larger scale. However, in the future, microfluidics can be used as disposable chips, and the data can be analyzed or classified after injecting samples into the chips. Such platforms will be economical and practically applicable in remote settings. Litti et al. printed a 3D microfluidic device using Janus magnetic/plasmonic Fe3O4/Au nanostars (JMNSs) as a SERS substrate. The substrate with a combination of various materials enables the attachment at the microfluidic channel when a magnetic field is applied; it enhances the SERS signals. The developed system is economical, uses a minimal sample of 100 μl, and the detection time is within 40 min. The designed system is capable and practically applicable to detect pathogens.¹⁴⁶ It is anticipated that the trend toward mobility and reduction in size of the microfluidic platforms will continue, allowing several advanced methods to be used in various contexts. Several research areas involved in microfluidic platforms can be improved to develop novel approaches. 3D printing has been applied to create reusable and disposable microfluidic chips, making the fabrication process very economical and easy to use.

Coupling the spectroscopic devices and instruments in the microfluidic chips is complicated when using spectroscopic-based approaches. One crucial area of study that requires attention is the integration of electrical components and stability maintenance during the detecting process. Integrating machine learning and artificial intelligence (AI) with microfluidics and spectroscopy is one of the main areas. By facilitating intelligent data analysis, pattern recognition, and real-time decision-making, this combination seeks to improve analytical abilities and ultimately lead to more rapid and precise bacterial identification. Using new materials and surface modifications in microfluidic channels is expected to be critical to technological progress.

IV. CONCLUSIONS

The rapid and accurate identification of pathogens is essential to control the food quality in industry. The early detection of pathogens helps in curing the diseases caused by pathogens at an early stage. Microfluidics techniques are emerging for screening and detecting various biological materials in biomedical sciences and engineering. Microfluidic devices have emerged as an innovative method in POCT diagnostics with rapid, sensitive, and accurate testing for detecting various infections. Microfluidic biosensors offer several advantages over traditional biosensing methods, such as rapid analysis, low sample consumption, and high throughput. Plasmonic nanoparticles, functionalized surfaces, and sophisticated coatings may boost signal amplification and increase the sensitivity of bacterial detection. Applying on-chip spectroscopic methods has emerged as a prominent area of research within the lab-on-chip domain. Overall, making an integrated chip to process and isolate the sample and then applying a spectroscopic technique to the chip is challenging and ideal. Therefore, several research areas are yet to be investigated for developing a single microfluidic platform capable of isolating samples and automatically processing for pathogens detection and identification. As the field of microfluidics continues to advance, more innovative biosensing applications are expected to emerge.

AUTHOR DECLARATIONS

Conflict of Interest

The authors declare no conflict of interest.

Author Contributions

Mubashir Hussain: Formal analysis (equal); Writing – original draft (equal). Xu He: Software (equal). Chao Wang: Resources (equal). Yichuan Wang: Investigation (equal). Jingjing Wang: Validation (equal). Mingyue Chen: Visualization (equal). Haiquan Kang: Validation (equal). Na Yang: Resources (equal). Xinye Ni: Supervision (equal). Jianqing Li: Supervision (equal). Xiuping Zhou: Data curation (equal). Bin Liu: Project administration (equal).

DATA AVAILABILITY

Data sharing is not applicable to this article as no new data were created or analyzed in this study.