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Review

Biosensors in Microbial Ecology: Revolutionizing Food Safety and Quality

by
Gajanan A. Bodkhe
1,†,
Vishal Kumar
1,†,
Xingjie Li
2,3,
Shichun Pei
3,4,
Long Ma
2 and
Myunghee Kim
1,*
1
Department of Food Science and Technology, Yeungnam University, Gyeongsan-si 38541, Gyeongsangbuk-do, Republic of Korea
2
State Key Laboratory of Food Nutrition and Safety, Key Laboratory of Industrial Microbiology, Ministry of Education, College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, China
3
Jilin Shuangzheng Diagnostic Monoclonal Antibody Scientist Studio, Surge Medical Inc., Tonghua 134000, China
4
Antibody Development Jilin Province University Enterprise Joint Technology Innovation Labs, Tonghua Normal University, Tonghua 134000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microorganisms 2025, 13(7), 1706; https://doi.org/10.3390/microorganisms13071706
Submission received: 29 May 2025 / Revised: 4 July 2025 / Accepted: 14 July 2025 / Published: 21 July 2025
(This article belongs to the Special Issue Feature Papers in Food Microbiology)
Browse Figures
Versions Notes

Abstract

Microorganisms play a crucial role in food processes, safety, and quality through their dynamic interactions with other organisms. In recent years, biosensors have become essential tools for monitoring these processes in the dairy, meat, and fresh produce industries. This review highlights how microbial diversity, starter cultures, and interactions, such as competition and quorum sensing, shape food ecosystems. Diverse biosensor platforms, including electrochemical, optical, piezoelectric, thermal, field-effect transistor-based, and lateral flow assays, offer distinct advantages tailored to specific food matrices and microbial targets, enabling rapid and sensitive detection. Biosensors have been developed for detecting pathogens in real-time monitoring of fermentation and tracking spoilage. Control strategies, including bacteriocins, probiotics, and biofilm management, support food safety, while decontamination methods provide an additional layer of protection. The integration of new techniques, such as nanotechnology, CRISPR, and artificial intelligence, into Internet of Things systems is enhancing precision, particularly in addressing regional food safety challenges. However, their adoption is still hindered by complex food matrices, high costs, and the growing challenge of antimicrobial resistance. Looking ahead, intelligent systems and wearable sensors may help overcome these barriers. Although gaps in standardization and accessibility remain, biosensors are well-positioned to revolutionize food microbiology, linking ecological insights to practical solutions and paving the way for safer, high-quality food worldwide.

1. Introduction

From a public health perspective, food safety, shelf life, and sensory quality are profoundly influenced by microbial ecology, whereby the dynamic interplay of diverse microbial communities is recognized as a cornerstone of food systems [1,2]. Beneficial microbes, such as Lactococcus spp. and Lactobacillus spp., drive fermentation in dairy and meat products, enhancing flavour and achieving natural preservation [1,2]. Conversely, pathogens like Listeria spp. and Escherichia coli pose significant health risks if undetected [3,4]. Traditional detection methods, including culture-based techniques, polymerase chain reaction (PCR), and microscopy, are effective but time-intensive, often requiring days to yield results, amplifying risks in modern food production [5,6]. Biosensors address these shortcomings by integrating biological recognition elements, such as antibodies or aptamers, with advanced transducers for real-time microbial detection. These deliver speed and precision, unattainable by conventional approaches, detecting E. coli O157:H7 in 20 min using a microelectrode array [7] and identifying Salmonella spp. through nucleic acid-based sensors [8]. Calorimetric detection of Lactobacillus plantarum was achieved in 4.7–18.6 h [9]. Advancements like nanomaterials and IoT connectivity envision intelligent, responsive food safety systems [10]. However, challenges such as high costs and complex food matrix interference persist [11], suggesting broader adoption depends on overcoming these barriers.
This review primarily focuses on the critical role of biosensors in food microbiology, in which microbial ecology and technological innovation have been seamlessly combined to enhance global food safety from a public health perspective. Foundational studies emphasise the importance of biosensors in analyzing complex and dynamic microbial communities, positioning them as essential tools for monitoring food system microbiomes. Applications—from rapid pathogen detection in produce to real-time spoilage tracking in meat via volatile compounds—are highlighted [7,8]. Limitations such as cost and specificity were evaluated, yet bold future pathways, including wearable sensors and antimicrobial resistance (AMR) monitoring, are envisioned as transformative frontiers. Through this lens, biosensors are cast as the vital link whereby ecological insights are translated into actionable safeguards, thereby elevating food quality and public health worldwide.

2. Starter Cultures

Microorganisms selected as starter cultures were deliberately introduced into food matrices to guide fermentation, ensuring consistent quality, improving sensory attributes, and enhancing food safety [Leroy and De Vuyst]. LAB (e.g., Lactococcus and Lactobacillus) and Streptococcus spp. dominated dairy fermentations, while yeasts such as Saccharomyces cerevisiae drive the production of bread and beverages [12]. These cultures excelled when they rapidly acidified the matrix, tolerated diverse processing conditions, and maintained stability across repeated cycles, as emphasised by Liu et al. (2023) [13]. Compatibility among co-cultured strains proved essential, as it prevented antagonism and allowed for harmonious fermentation [14]. Some cultures, such as certain Lactobacillus spp., serve as probiotics, conferring health benefits, although safety, efficacy, and stability are critical [15,16]. Biosensors monitor these cultures in real-time, tracking L. plantarum activity in 4.7–18.6 hours via isothermal microcalorimetry, ensuring fermentation success and probiotic viability [17].

3. Composition of Food Microbial Communities

Food systems host diverse microbial ecosystems that influence food safety and quality. These microbiomes are shaped by numerous factors, including food type, processing, and environmental factors [3]. This section examines microbial diversity across various food categories, the ecological forces that drive these communities, and the tools used to analyse them, highlighting biosensors as crucial for real-time profiling. These devices delivered rapid insights into microbial composition and activity, supporting proactive safety measures and deepening ecological understanding.

3.1. Microbial Diversity Across Food Types

Microbial communities vary widely across food systems, their unique ecological niches dictating safety and quality outcomes. In dairy, lactic acid bacteria (LAB), such as Lactobacillus and Streptococcus, drive fermentation, coexisting with spoilage yeasts and pathogens, including Listeria [2,18]. Meat products harbour LAB alongside spoilage agents, such as Pseudomonas, and pathogens, including Salmonella and Campylobacter, with storage conditions modulating their prevalence [3,19]. Fresh produce supports the growth of E. coli, Salmonella spp., and fungi, posing risks through soil or water contamination [20]. Biosensors swiftly detect this diversity, with surface plasmon resonance (SPR) sensors detecting Salmonella spp. in real time [21] and microelectrode arrays detecting E. coli O157:H7 in 20 min [7].

3.2. Factors Shaping Microbial Ecology

A complex interplay of intrinsic and extrinsic factors governed microbial ecology in food, defining growth and activity [3]. Acidic dairy favours Lactobacillus spp., while low water activity in dried foods supports molds [2]. Refrigeration promotes psychrotrophic bacteria, and modified atmosphere packaging shifts the aerobic–anaerobic balance [22]. Electrochemical biosensors track pH changes and volatile compounds, signalling microbial activity for quality control [23].

3.3. Tools for Microbial Analysis

Analyzing food microbial communities requires a suite of tools that uncover their diversity and hazards with distinct strengths. Culture-based methods miss viable but nonculturable (VBNC) microbes [17]. PCR detects specific [5], 16S rRNA sequencing profiles of broader communities [6], and metagenomics reveals both composition and function [24]. Biosensors complement these with real-time detection, with aptasensors identifying Salmonella spp. [25], Quartz crystal microbalance (QCM) sensors detecting Staphylococcus spp. via mass changes [26,27] and nucleic acid-based sensors tracking Salmonella spp. [8]. Table 1 summarizes the types of microbes, their roles, and examples of biosensor detection.

4. Microbial Interactions and Their Effects

Microbial interactions—encompassing competition, cooperation, and quorum sensing (QS)—have a profound influence on food quality and safety by shaping community dynamics [1]. Biosensors provide critical, real-time insights into these processes, linking ecological complexity to practical outcomes. This section illustrates how these interactions shape food ecosystems and highlights the role of biosensors in monitoring them, offering a window into microbial behavior that enhances food safety and quality control.

4.1. Competition

Within food microbial ecosystems, competition naturally suppresses pathogens, as observed with LAB in cheese production. These bacteria produce bacteriocins and lower pH to outcompete Listeria, bolstering safety and preservation [2]. Electrochemical biosensors detect these antimicrobial metabolites, thereby confirming pathogen suppression, a capability lacking in traditional methods [29]. This real-time monitoring reveals competitive dynamics, strengthening food safety strategies.

4.2. Cooperation

This cooperative interaction mechanism increases fermentation output, which can be seen in the yogurt-making process, where Streptococcus thermophilus and Lactobacillus bulgaricus encourage each other’s growth and metabolism [30]. This synergy ensures a consistent texture and flavor, which is vital for quality. Biosensors monitor lactic acid production in real-time, verifying cooperation [15]. Such tools optimize fermentation, maintaining ecological balance and product standards.

4.3. Quorum Sensing (QS)

QS, a density-dependent communication system, regulates microbial behaviors impacting food safety and shelf life, such as Pseudomonas-driven spoilage in meat or Listeria biofilm formation [1]. SPR biosensors detect QS signalling molecules, and while QCM sensors track the biofilm growth, providing early warnings of spoilage or contamination risks [16,28]. These insights illuminate QS’s ecological role, enabling proactive quality management. These microbial interactions—competition, cooperation, and quorum sensing—are monitored effectively by biosensors, as summarized in Table 2, which outlines key interactions (competition, cooperation, and quorum sensing), corresponding food examples, biosensor types, and detected signals. Electrochemical sensors track bacteriocins in cheese, optical (SPR) sensors detect volatile compounds in yogurt, and mechanical (QCM) sensors measure biofilm mass in meat, illustrating the role of biosensors in ecological management.

4.4. Fermented Food Examples

Fermented foods showcase microbial interactions at work, with biosensors ensuring their success. In fermented sausages, Lactobacillus spp. and Staphylococcus spp. synergistically contribute to preservation and flavor development, a process effectively monitored by volatile-sensing biosensors [22]. In kefir, adenosine triphosphate (ATP)-based sensors maintain yeast-Lactobacillus spp. balance [21]. These cases show how biosensors translate ecological interplay into reliable quality control.

5. Case Studies and Regional Focus

Biosensors can play a significant role in solving real-world challenges in high-risk food categories. This section presents a case study on the effective use of biosensors to address microbial issues with speed and sensitivity in the dairy, meat, and produce industries. Piezoelectric immunosensors detected Campylobacter jejuni in dairy at 102 CFU mL−1 [33], graphene-based sensors identified amines in meat [19], and SPR immunosensors spotted Salmonella spp. at 103 CFU g−1 in powdered milk [16]. These examples demonstrate the transformative potential of biosensors, offering actionable solutions to microbial threats.

5.1. Global and Regional Perspectives

Building on these cases, biosensors enable rapid and sensitive detection across dairy, meat, and produce, addressing universal safety challenges. However, their success depends on adapting to global and regional variations—climate, production practices, and AMR differ starkly across Asia, Africa, and Latin America, requiring tailored strategies. The following examples illustrate these nuances, showing how biosensors enhance food safety worldwide.

5.1.1. Case Studies from Asia

Asia’s environmental and production conditions amplify microbial risks, where biosensors offer critical solutions. High temperatures in Myanmar accelerate the spoilage of meat and dairy products, leading to increased food waste and insecurity [34]. In Cambodia, AMR in livestock has surged, with a 2024 study revealing high E. coli resistance to ampicillin and tetracycline [35,36]. Biosensors detecting spoilage volatiles or resistant strains could transform monitoring, reduce waste, and enhance safety.

5.1.2. Case Studies from Africa

In Ethiopia, 6% Salmonella spp. prevalence in raw milk highlights contamination risks [26]. Rapid detection using biosensors, such as SPR techniques, offers a promising solution, enabling locally tailored responses and strengthening regional food safety. In Nigeria and Kenya, microbial contamination of meat and vegetables in informal markets poses ongoing risks, with Salmonella and E. coli frequently reported [37]. Biosensors could serve as rapid tools to reduce diagnostic delays [38].

5.1.3. Case Studies from Latin America

In Bolivia, governance weaknesses in AMR management necessitate the use of biosensors for resistance tracking [35], specifically for Pseudomonas spp. spoilage in produce and meat necessitates flexible sensors [32].

6. Detection and Monitoring Techniques

From a public health standpoint, biosensor-based detection and monitoring techniques have revolutionized the approach to microbial threats in food systems, outperforming traditional culturing methods and enabling rapid identification, thereby protecting populations from foodborne pathogens. Optical biosensors utilizing surface plasmon resonance were employed to detect Salmonella swiftly via antigen–antibody binding [16]. Electrochemical aptasensors have been developed for targeted detection of Vibrio spp. [27], and Salmonella typhimurium using label-free impedimetric methods [31]. Mechanical QCM sensors detect Staphylococcus spp. by monitoring mass changes on sensor surfaces [28]. Rapid detection of E. coli O157:H7 in 20 min has been achieved using a microelectrode array [7], while isothermal microcalorimetry (IMC) enables identification of Lactobacillus plantarum in 4.7–18.6 h across various cultivation methods [9]. Nucleic acid- and antibody-based biosensors targeting Salmonella spp. achieve sensitivities with limits of detection (LOD) ranging from 1 to 103 CFU mL−1 [8]. These technologies offer high-speed results and portability, reducing the risk of foodborne outbreaks [39]. However, challenges such as matrix interference, elevated manufacturing costs, and lack of standardized protocols limit their widespread adoption [8,11], which highlights the need for refined protocols. These diverse biosensor technologies enable rapid detection across food systems, as summarized in Table 3, which summarizes detection methods, example pathogens, limits of detection (LOD), detection times, and advantages for optical (SPR), electrochemical, QCM, and bioluminescence biosensors. LODs range from 1 to 103 CFU/mL, with detection times as low as 10 min, showcasing rapid, sensitive monitoring across food systems.

6.1. Types of Biosensors by Transduction Mechanism

Biosensors can be classified based on their transduction mechanism. Depending on the applicability, mode of operation, and targeted analyte, they are specifically selected and explored for application potential in food microbiology. Each method offers unique advantages but also has certain limitations, depending on the target analyte, food matrix, and detection environment. A few biosensors are discussed below.

6.1.1. Electrochemical Biosensors

Electrochemical biosensors detect analytes by translating biochemical interactions into measurable electrical signals such as current, voltage, or impedance. They are widely used in food safety due to their simplicity, low cost, and compatibility with portable formats. Recent innovations, such as clustered regularly interspaced short palindromic repeats (CRISPR)-based electrochemical aptasensors, have significantly enhanced sensitivity. A schematic of bacterial detection using direct and alternating current-based electrochemical biosensors is shown in Figure 1. Cui et al. developed a CRISPR-associated protein 12a (Cas12a)– powered biosensor capable of detecting E. coli at the single-cell level in food samples [46]. These platforms are increasingly used to monitor pathogens, mycotoxins, and antibiotic residues [47,48,49].

6.1.2. Optical Biosensors

Optical biosensors operate by detecting changes in light properties—such as fluorescence, absorbance, or refractive index—upon the recognition of a target. Techniques like SPR provide real-time, label-free detection of pathogens, including Salmonella [51,52], while surface-enhanced Raman scattering (SERS) (Figure 2) enables multiplex and ultra-sensitive detection in complex matrices [53].

6.1.3. Piezoelectric Biosensors

Piezoelectric biosensors, including quartz QCM devices (Figure 3) and surface acoustic wave (SAW) devices, measure the mass change that occurs when a target binds to a sensor-coated surface. These systems provide label-free, real-time monitoring and have been successfully applied to detect bacteria such as Staphylococcus and Campylobacter in meat and dairy products [55,56]. Advances in SAW technology have improved robustness and integration with microfluidic systems.

6.1.4. Thermal Biosensors

Thermal biosensors detect temperature changes resulting from biochemical reactions or microbial metabolic activity, as shown in Figure 4. Enzyme-based thermistors have been employed to measure spoilage indicators or pesticide residues in food products. Moreover, calorimetric chips now offer sensitive, real-time detection of microbial contamination by monitoring the heat produced during bacterial growth [58,59].

6.1.5. Field-Effect Transistor (FET)

FET-based biosensors utilize semiconductor interfaces to detect changes in surface potential resulting from the binding of charged analytes, as illustrated in Figure 5. Their miniaturization potential and exceptional sensitivity make them particularly suitable for real-time, label-free detection [59]. Recent developments in graphene- and MoS2-based FETs have enabled the rapid detection of E. coli, Salmonella, aflatoxins, and antibiotic residues with minimal sample preparation [43,61,62,63].

6.1.6. Lateral Flow Assay (LFA)

LFA-based biosensors represent one of the most accessible and widely used platforms for field-based testing. A schematic illustration of the LFA for pathogen detection is shown in Figure 6. Typically paper-based and capillary-driven, LFAs are valued for their speed, low cost, and user-friendliness. Contemporary LFAs incorporate aptamers, quantum dots, or SERS nanotags to enhance sensitivity and allow multiplexing [64]. For example, an aptamer-based LFA for aflatoxin B1 in cereals achieved a detection limit below 0.1 ng.mL−1 [65].
Different types of biosensors offer distinct advantages and are well-suited to various food safety applications. Electrochemical biosensors are among the most cost-effective and portable, often preferred for on-site detection of pathogens. Optical sensors, especially those utilizing SPR or SERS, offer label-free, real-time monitoring with high specificity; however, they typically require more complex instrumentation. Piezoelectric sensors, such as QCM, are highly sensitive and label-free, but they may be susceptible to environmental noise. FET-based biosensors stand out for their ultra-sensitivity and potential for miniaturization, making them suitable for real-time electronic integration. Thermal biosensors and calorimetric devices offer real-time metabolic monitoring but are slower in detection compared to other platforms. Lateral flow assays (LFAs), though less sensitive than lab-based methods, are widely adopted for field use due to their simplicity, speed, and cost-effectiveness. The selection of biosensor type depends on the target analyte, the required detection time, the sample matrix complexity, and resource availability.
In summary, these biosensor technologies offer tailored solutions to meet the diverse detection needs of microbial ecology in food systems. Their strategic deployment enhances early warning capabilities, improves food quality control, and supports safer supply chains.

7. Control Strategies Based on Microbial Ecology

Microbial ecology-based control strategies enhance food safety by using natural microbial interactions. For instance, ATP-based biosensors effectively monitored Lactobacillus spp. during yogurt fermentation, supporting biopreservation and reducing contamination risks [67]. Flexible biosensors detected volatile amines to mitigate meat spoilage [19], while predictive models integrated with biosensor data maintained milk quality by anticipating microbial shifts [68]. IoT-connected biosensors forecast microbial dynamics in sausage production, enhancing safety [10]. Bacteriocins suppressed pathogens, probiotics outcompeted spoilage organisms, and biosensors facilitated the rapid detection of Salmonella spp. detection during contamination events [8]. However, challenges such as matrix interference and standardization gaps reduce reliability [5], and high costs limit accessibility in resource-constrained regions, indicating that broader adoption depends on addressing these barriers.

7.1. Bacteriocins

Bacteriocins, ribosomally synthesized antimicrobial peptides produced by Lactobacillus spp. and other microbes, enhance biopreservation by complementing biosensor-driven strategies [69]. Peptides such as nisin and pediocin inhibit pathogens like Listeria spp. without affecting beneficial microflora, earning Generally Recognized as Safe (GRAS) status [70]. Widely applied in dairy, meat, and vegetable fermentations, they strengthen traditional sanitation efforts. Recent studies have revealed their synergy with biosensors, enabling real-time pathogen monitoring and guided targeted bacteriocin use, thereby reducing additive reliance while ensuring safety [69]. For instance, a high-throughput method using live fluorescent biosensors to identify potential bacteriocin producers from an extensive strain library has been developed, facilitating the selection of effective strains for food preservation [71]. Similarly, electrochemical biosensors are used to detect nisin activity in dairy products, ensuring effective pathogen suppression [72]. Furthermore, electrochemical biosensors have been used to detect lactate produced by lactic acid bacteria, which are often bacteriocin producers, providing an indirect measure of their activity in food matrices [73]. These advancements highlight the potential of biosensors in monitoring and optimizing bacteriocin use for enhanced food safety, aligning ecological principles with practical quality preservation, and minimizing chemical dependence.

7.2. Biofilms

Biofilms, complex microbial communities embedded in extracellular matrices, pose significant challenges by enhancing the resilience of pathogens [13]. Pathogens such as Listeria monocytogenes and Salmonella spp. form biofilms on food contact surfaces, resisting sanitizers and posing a threat to safety [74]. Biosensors detect early biofilm formation through electrochemical or optical signals, enabling timely interventions to prevent the development of mature biofilms [13]. Bacteriocin treatments, combined with physical cleaning, disrupt nascent biofilms [69]. Electrochemical biosensors integrated on microfluidic chips offer high sensitivity and real-time monitoring of microbial biofilms in food processing environments, which is crucial for preventing pathogen persistence [75]. This dual approach—real-time feedback and natural antimicrobial action—anchors biofilm management in microbial ecology, offering proactive safety solutions.

7.3. Probiotics

Probiotics utilize microbial ecology to enhance food safety and quality, offering sustainable benefits that extend beyond fermentation [76]. Defined as live microorganisms conferring health benefits when administered in adequate amounts [77], probiotics include Lactobacillus spp., Bifidobacterium spp., certain Bacillus spp., Enterococcus spp., and yeast species [78]. Probiotics inhibit pathogens through competitive exclusion, produce metabolites to strengthen antimicrobial barriers, and modulate host immunity [76]. Co-fermentation with probiotics shifts microbial communities away from spoilage organisms, extending shelf life [12]. Biosensors can monitor probiotic viability in real-time, ensuring optimal fermentation [67]. Certain probiotic strains also counter biofilms by disrupting adhesion, reducing sanitation costs [78].
Recent studies have demonstrated the use of bacterial biosensors, such as LUX-based and RecA-lux constructs, to evaluate probiotic strains for their antioxidant, DNA-protective, and SOS-suppressing functions, which are particularly relevant under stress or antibiotic challenge [79,80]. For example, Lactobacillus strains tested under intestinal-like conditions using LUX biosensors retained their protective activity, supporting their suitability for functional food applications.
Additionally, engineered probiotics equipped with synthetic biosensors are being developed to sense gastrointestinal markers (e.g., thiosulfate, heme) and trigger controlled therapeutic responses [81]. These “smart microbes” integrate diagnostic and intervention roles, offering new strategies for monitoring food and gut health. Furthermore, biosensor platforms have the potential to detect or modulate biofilm formation through probiotic-derived bacteriocins and adhesion interference, enhancing sanitation and safety [82].
This multifaceted strategy integrates probiotics and biosensors, driving safer, higher-quality products.

8. Decontamination Methods

Decontamination methods complement biosensor-driven detection and control, forming a robust framework for food safety grounded in microbial ecology. Thermal, non-thermal, chemical, and biocontrol techniques each address microbial risks, while biosensors pinpoint these risks, balancing safety, quality, and consumer demand for minimally processed foods. This section explores these approaches, highlighting how real-time monitoring enhances the efficacy of decontamination methods and aligns them with ecological principles.

8.1. Thermal Methods

Thermal processing, encompassing pasteurization and sterilization, stands as a cornerstone of microbial inactivation in food preservation [21]. These methods efficiently eliminate pathogens and spoilage organisms, but they compromise nutritional and sensory qualities—vitamin loss, color shifts, and the presence of undesirable compounds often reduce consumer appeal. Biosensors enhance thermal approaches by precisely identifying contamination risks, ensuring targeted and efficient application, thus mitigating quality trade-offs.

8.2. Non-Thermal Methods

Non-thermal technologies like high-pressure processing (HPP) and pulsed electric fields (PEF) overcome thermal drawbacks, delivering microbial control with minimal quality loss. HPP applies pressures of 100–600 MPa at ambient or chilled temperatures, inactivating microbes by damaging their cell membranes and proteins [83,84]. The application of HPP at 250 MPa reduces E. coli in orange juice without affecting sensory properties [85], while 500 MPa for 60 s effectively controls Salmonella in chicken meat [86]. PEF uses short, high-voltage pulses to disrupt membranes via electroporation, avoiding significant heat [87]. The efficacy of PEF against S. senftenberg has been demonstrated [88,89]. Biosensors optimize these methods by detecting contamination levels, ensuring precision in safety and quality management.

9. Chemical and Biocontrol Agents

Chemical and biocontrol agents add vital layers of protection in microbial management, enhancing physical methods and biosensor insights. Organic acid washes, including lactic, acetic, and citric acids, effectively reduce contamination in meat and produce [74]. Meanwhile, chlorine-based disinfectants, although effective, have sparked concerns over harmful by-products, prompting the development of safer alternatives. Biocontrol strategies, such as bacteriophages and antagonistic microbes, target pathogens in an eco-friendly manner, with CRISPR-based antimicrobials promising a future of precision [90]. Integrated with biosensors, these agents form a holistic approach that eliminates threats identified in real-time, preserving quality and meeting demands for sustainable, safe food.

10. Emerging Technologies in Food Microbiology

Emerging technologies—such as CRISPR-based assays, artificial intelligence (AI) combined with the Internet of Things (IoT), and nanotechnology-enhanced biosensors—propel food microbiology into a proactive field, building on the principles of chemical and biological control. These innovations enhance biosensor accuracy, enable predictive control, and align with this review’s focus on rapid and sensitive solutions, transforming microbial risk management.

10.1. CRISPR-Based Assays

CRISPR-based assays provide high-precision detection and microbial engineering. CRISPR/Cas biosensors achieved rapid detection of Salmonella spp. DNA/RNA in food samples within one hour, surpassing traditional methods [91]. Additionally, CRISPR-mediated modification of Lactobacillus spp. enhanced fermentation safety and quality [92]. This dual role positions CRISPR as a game-changer for preservation.

10.2. AI and IoT Systems

AI-integrated IoT systems enable predictive monitoring of microbial growth. These systems forecast microbial dynamics in meat and dairy using biosensor data [93], while AI-driven pattern recognition identifies Listeria spp. outbreaks [94]. Paired with biosensors, they shift strategies from reactive to proactive.

10.3. Blockchain for Food Traceability

Blockchain technology is being increasingly explored as a complementary tool to biosensor systems for enhancing transparency and traceability in food safety monitoring. By storing biosensor-generated data on a secure, decentralized ledger, blockchain can help ensure that microbial testing results are tamper-proof, time-stamped, and accessible across the entire supply chain. This technology not only enables faster and more informed decisions during contamination events but also supports long-term improvements in accountability and consumer trust. When used in conjunction with AI and IoT-based monitoring platforms, blockchain provides a reliable means of tracking food quality in real-time and enhancing food safety protocols from production to distribution [95].

10.4. Nanotechnology-Enhanced Biosensors

Nanotechnology improves biosensor sensitivity and adaptability. Nanobiosensors detected Pseudomonas spp.-derived amines in meat within minutes [96], and polymer nanoparticles delivered CRISPR-based antimicrobials with high precision [97,98,99]. Continuous monitoring via innovative systems further supports real-time safety control [100]. Together, these technologies tackle AMR and regional spoilage, setting the stage for future innovations. These emerging technologies build on biosensor frameworks, with their development timeline illustrated in Figure 7, highlighting key advances in precision and predictive control. Timeline traces the development of CRISPR-based assays, AI with IoT, and nanotechnology-enhanced biosensors. Milestones include rapid Salmonella detection within an hour and predictive monitoring in meat and dairy, driving precision and proactivity in safety control.

11. Challenges and Future Directions

Biosensors offer immense potential in food microbiology; however, challenges temper their adoption. Complex food matrices interfere with signals [11], high costs limit access for small producers [39], and specificity struggles amid diverse microbes risk false positives [5]. Future advancements promise solutions—smart IoT/AI systems preempt risks [10], and wearable biosensors for food handlers prevent contamination, inspired by Zelada-Guillén et al. (2012) [101]. Addressing AMR remains critical, as evidenced by the detection of chloramphenicol residues using photoelectrochemical aptasensors [102] and the efficient identification of multiple pathogens via multiplexed biosensors [27]. These developments position biosensors to overcome limits and anticipate microbial challenges.

AMR: An Expanding Frontier

AMR poses a growing, underexplored threat in food microbiology, jeopardizing safety worldwide. Salmonella spp. and Campylobacter spp. exhibit increasing resistance to antibiotics, with resistance to fluoroquinolones and cephalosporins in S. enterica documented in poultry and produce, and multidrug-resistant C. jejuni reported in European poultry [103]. The WHO’s Global Antimicrobial Resistance and Use Surveillance (GLASS) report (2025) urged enhanced surveillance, advocating biosensor tracking of resistance patterns [104]. Future biosensors must prioritize detecting AMR determinants, enabling early interventions to protect food systems and align with proactive safety goals.

12. Conclusions

Biosensors are transforming food microbiology by delivering rapid, real-time ecological insights across dairy, meat, and produce, redefining safety and quality control. Integrated into intelligent systems, they offer a proactive edge over traditional methods, leveraging a deeper understanding of microbes for greater impact. As detailed in this review, diverse biosensor types—ranging from electrochemical and optical to piezoelectric, thermal, FET-based, and LFA—offer tailored advantages for different food matrices and microbial targets. Challenges such as cost and matrix interference persist, but nanomaterials, IoT, and multiplexing steadily address them. Wearable biosensors and AMR detection herald a sustainable future, potentially making biosensors indispensable within a decade. This evolution bridges microbial science with global safety needs, fulfilling this review’s vision.

Author Contributions

G.A.B.: conceptualization, methodology, and writing—original draft. V.K.: writing—original draft. X.L.: writing-review and editing. S.P.: writing-review and editing. L.M.: writing-review and editing. M.K.: supervision, project administration, resources, funding acquisition, and writing-review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-00515546).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data are shared in the manuscript.

Conflicts of Interest

Authors Xingjie Li and Shichun Pei were employed by the company Surge Medical Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Bacterial detection using direct and alternating current-based electrochemical biosensors. Reproduced with permission from Ref. [50]. Copyright © 2020, American Chemical Society.
Figure 1. Bacterial detection using direct and alternating current-based electrochemical biosensors. Reproduced with permission from Ref. [50]. Copyright © 2020, American Chemical Society.
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Figure 2. Schematic of SERS-based sensing mechanism. This figure highlights how signal enhancement occurs on nanostructured surfaces for the sensitive detection of foodborne pathogens. Reproduced with permission from Ref. [54]. Copyright © 2024 Elsevier Ltd.
Figure 2. Schematic of SERS-based sensing mechanism. This figure highlights how signal enhancement occurs on nanostructured surfaces for the sensitive detection of foodborne pathogens. Reproduced with permission from Ref. [54]. Copyright © 2024 Elsevier Ltd.
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Figure 3. The principle of a QCM-based gravimetric virus sensor. The change in mass in response to virus binding with the selective receptor is detected as a change in the frequency of the QCM transducer. Reproduced with permission from Ref. [57]. Copyright © 2024 Elsevier Ltd.
Figure 3. The principle of a QCM-based gravimetric virus sensor. The change in mass in response to virus binding with the selective receptor is detected as a change in the frequency of the QCM transducer. Reproduced with permission from Ref. [57]. Copyright © 2024 Elsevier Ltd.
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Figure 4. The standard structure of calorimetric biosensors. Reproduced with permission from Ref. [60]. Copyright © 2001 Elsevier Ltd.
Figure 4. The standard structure of calorimetric biosensors. Reproduced with permission from Ref. [60]. Copyright © 2001 Elsevier Ltd.
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Figure 5. Field effect transistor-based sensor working for the detection of aflatoxin B1 from contaminated food. Reproduced with permission from Ref. [43]. Copyright © 2024 Elsevier Ltd.
Figure 5. Field effect transistor-based sensor working for the detection of aflatoxin B1 from contaminated food. Reproduced with permission from Ref. [43]. Copyright © 2024 Elsevier Ltd.
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Figure 6. Schematic illustration of the LFA for the detection of Escherichia coli O157:H7. TMB = 3,3′,5,5′-tetramethylbenzidine; NC = nitrocellulose; NP = nanoparticles. Reproduced with permission from Ref. [66]. Copyright © 2024 Elsevier Ltd.
Figure 6. Schematic illustration of the LFA for the detection of Escherichia coli O157:H7. TMB = 3,3′,5,5′-tetramethylbenzidine; NC = nitrocellulose; NP = nanoparticles. Reproduced with permission from Ref. [66]. Copyright © 2024 Elsevier Ltd.
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Figure 7. Evolution of emerging technologies in food microbiology [10,91,92,93,94,96,97,100].
Figure 7. Evolution of emerging technologies in food microbiology [10,91,92,93,94,96,97,100].
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Table 1. Predominant microbial types, roles, and biosensor applications in major food categories.
Table 1. Predominant microbial types, roles, and biosensor applications in major food categories.
Food CategoryPredominant Microbial TypesKey RolesBiosensor Detection ExamplesReferences
DairyLAB (Lactobacillus, Lactococcus), yeasts (Saccharomyces), molds (Penicillium)Fermentation, flavor development, preservationpH sensors (LAB), SPR (Listeria)[2,28]
Meat/PoultryLAB, CNS (Staphylococcus), spoilage bacteria (Pseudomonas, Brochothrix), pathogens (Listeria, Salmonella)Fermentation, spoilage, safety risksAmine sensors (Pseudomonas)[7,18]
Fresh ProduceGram-negative bacteria (Pseudomonas, Enterobacteriaceae), fungi, pathogens (Salmonella, E. coli O157:H7)Spoilage, safety risks, plant adaptationSPR (Salmonella)[19,21,22]
Note: LAB: lactic acid bacteria; CNS (coagulase negative Staphylococci).
Table 2. Microbial interactions in food systems.
Table 2. Microbial interactions in food systems.
InteractionFood ExampleBiosensor TypeDetected SignalReferences
CompetitionCheeseElectrochemicalBacteriocins[2,27]
CooperationYogurtOpticalVolatile compounds[9,31]
Quorum sensingMeatQCMBiofilm mass[21,32]
Note: QCM: Quartz Crystal Microbalance.
Table 3. Biosensor detection capabilities in food microbiology.
Table 3. Biosensor detection capabilities in food microbiology.
Biosensor TypeDetection MethodExample PathogenLOD (CFU/mL)Detection TimeAdvantagesReferences
Optical (SPR)Surface plasmon resonanceSalmonella1220 minHigh sensitivity, label-free[40]
ElectrochemicalAmperometric/impedimetricE. coli0.3530 minCost-effective, rapid[41]
QCMMass change detectionE. coli10230 minHigh sensitivity, label-free[42]
FET-basedCharge-sensitive transistor readoutAflatoxin B1, 5.6 ppb~2 minUltra-sensitive, label-free, scalable electronics[43]
LFA (strip-based)Lateral capillary flow, colorimetric/SERSAflatoxin B1, Listeria, Salmonella<1 ng/mL; 103 CFU/mL10–20 minRapid, low-cost, field-deployable[44,45]
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Bodkhe, G.A.; Kumar, V.; Li, X.; Pei, S.; Ma, L.; Kim, M. Biosensors in Microbial Ecology: Revolutionizing Food Safety and Quality. Microorganisms 2025, 13, 1706. https://doi.org/10.3390/microorganisms13071706

AMA Style

Bodkhe GA, Kumar V, Li X, Pei S, Ma L, Kim M. Biosensors in Microbial Ecology: Revolutionizing Food Safety and Quality. Microorganisms. 2025; 13(7):1706. https://doi.org/10.3390/microorganisms13071706

Chicago/Turabian Style

Bodkhe, Gajanan A., Vishal Kumar, Xingjie Li, Shichun Pei, Long Ma, and Myunghee Kim. 2025. "Biosensors in Microbial Ecology: Revolutionizing Food Safety and Quality" Microorganisms 13, no. 7: 1706. https://doi.org/10.3390/microorganisms13071706

APA Style

Bodkhe, G. A., Kumar, V., Li, X., Pei, S., Ma, L., & Kim, M. (2025). Biosensors in Microbial Ecology: Revolutionizing Food Safety and Quality. Microorganisms, 13(7), 1706. https://doi.org/10.3390/microorganisms13071706

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