Portable Electrochemical Sensing Platform for Aflatoxin B1 Detection in Food Matrices

Portable Electrochemical Sensing Platform for Aflatoxin B1 Detection in Food Matrices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Portable Electrochemical Sensing Platform for Aflatoxin B1 Detection in Food Matrices Kundan Kumar Mishra, Krupa M Thakkar, Vikram Narayanan Dhamu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7583817/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Aflatoxin B1 (AFB1) is one of the most hazardous foodborne toxins, posing a major risk to food safety and human health worldwide. Traditional detection techniques are often limited by lengthy procedures, high costs, and insufficient sensitivity for on-site applications. To overcome these challenges, we developed a portable, non-faradaic electrochemical impedance spectroscopy (EIS) platform designed for rapid and highly sensitive detection of AFB1 in overnight-soaked corn samples. The working electrode was modified with a semiconducting-gold composite layer, followed by DTSSP crosslinker chemistry and antibody immobilization, which provided enhanced surface activity and stable biofunctionalization. This configuration enabled fast detection within 5 minutes, achieving an impressive detection limit of 0.005 ng/mL and a wide dynamic range of 0.01–40.96 ng/mL. The sensor demonstrated excellent reproducibility, with intra- and inter-study %CV consistently below 20%. Validation against laboratory benchtop systems showed a strong correlation (Pearson r = 0.977). Diagnostic evaluation further confirmed its robustness, yielding 90.6% accuracy and an AUC of 0.83. These results highlight the combined benefits of nanocomposite surface engineering and non-faradaic EIS detection in achieving highly sensitive performance. Compact, user-friendly, and reliable, this sensing platform represents a promising solution for on-site toxin detection in food supply chains, thereby contributing to improved food safety monitoring and reduced public health risks associated with AFB1 exposure. Electrochemical Impedance Spectroscopy toxins Aflatoxin immunosensor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Mycotoxin contamination in agricultural products has raised significant global concern due to its adverse effects on human and animal health, along with its substantially high mortality rates. Mycotoxins are extremely noxious secondary metabolites produced from capillaceous fungi that commonly thrive between 25 ℃ to 35 ℃, as well as a moist, humid environment. Furthermore, mycotoxins are prone to toxicate crops during cultivation, harvest, preservation, and treatment. Aflatoxins (AFs), produced by Aspergillus spp, primarily Aspergillus flavus and Aspergillus parasiticus, present to be the most pernicious compared to other mycotoxins. AFs are categorized into B1, B2, G1, or G2 by their unique fluorescent properties under ultra-violet (UV) light, specifically at 365 nm, and their ability to mobilize into different structures. To illustrate, B-group AFs will present as a cyclopentane ring with a blue glow, while G-group AFs will show a lactone ring with a yellowish-green glow. In addition, M-group AFs (AFM1 and AFM2) are produced by the hydroxylation, the addition of a hydroxyl group, of B-group AFs (AFB1 and AFB2), respectively. In the 1960s, the fatalness of AFs was initially discovered across several farms in England due to the sudden deaths of turkeys. Thereafter, examiners found that the Brazilian groundnut meal, a common feed given to turkeys, proved to be tremendously poisonous due to the accumulation of metabolites of Aspergillus flavus. AFs are typically found in a variety of grains, nuts, spices, dairy products, etc [ 1 ]. Precisely, Aflatoxin B1 (AFB1) is the most pestilential out of all the AFs due to its ability to cause cancer, disrupt the development of an embryo or fetus, and induce mutations in an organism's DNA [ 2 ], [ 3 ]. Considerable amounts of AFB1 can lead to hepatocellular carcinoma, a type of cancer that forms directly in the hepatocytes. Additionally, AFB1 poisoning can lead to immunosuppression and malnourishment, as well as severe damage to organs in the cardiorespiratory, gastrointestinal, nervous, and reproductive systems. AFB1 is categorized as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC). The deleterious nature of AFB1 stems from its robust ability to decompose the cell membrane and disrupt the DNA situated in the cell. As a result, strict limitations have been established by the Federal Food and Drug Administration (FDA) in the United States and the European Union (EU) on safe amounts of AFB1 present in certain crops and food items sold to the community, as described in Supplementary Table S1 For instance, the FDA has set a maximum limit of 20 ppb for human food; at the same time, the EU placed a maximum limit of 2 ppb for human consumption, after processing [ 4 ], [ 5 ], [ 6 ]. Numerous detection methods have been created to ensure the safety of humans and animals regarding AFB1 contamination in food and feed products. Current techniques in AFB1 detection consist of customary methods such as polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA) and chromatographic methods, such as high-performance liquid chromatography (HPLC), thin layer chromatography (TLC), and gas chromatography (GS) [ 7 ],[ 8 ], [ 9 ], [ 10 ], [ 11 ]. Nevertheless, these methods have known consequences as being laborious and extensive, requiring costly and tailored lab-equipment, and involving trained lab professionals to carry out experimentation and elucidate the results. Although ELISA is acknowledged as providing expeditious results compared to the other methods listed, it fails to reach adequate sensitivity requirements. Correspondingly, PCR has a possibility of providing false positives. In addition, both ELISA and PCR requires separation of the pathogen from the sample, which can be extremely difficult when deploying on the field [ 7 ], [ 12 ], [ 13 ], [ 14 ], [ 15 ]. Even though these methods are valued for their reliability, there are several disadvantages as being exceedingly tiresome, having difficulty with on-field deployment, demanding highly priced equipment, requiring qualified and proficient lab technicians, and not possessing proper sensitivity to lower concentrations. Supplementary Table S2 presents a comparative overview of various immunosensors, emphasizing that many existing label-free approaches lack direct sensitivity toward Aflatoxin B1 . Although techniques such as bioluminescence assays, fluorescence-based ELISA, and electrochemical methods like chronoamperometry and cyclic voltammetry have been explored for Aflatoxin B1 detection, they often come with significant limitations[ 16 ], [ 17 ], [ 18 ], [ 19 ], [ 20 ], [ 21 ]. These include the need for complex sample preparation, reliance on labeling agents, extended assay times, and the requirement for skilled personnel. Furthermore, most of these methods are designed for single-analyte detection, making them less efficient and more costly when screening for multiple toxins. These challenges highlight the need for more practical, sensitive, and user-friendly electrochemical platforms tailored specifically for Aflatoxin B1 detection. To overcome the potential disadvantages associated with traditional and modern analytical methods, there is a growing need for a detection technique that is not only rapid and highly sensitive but also user-friendly and suitable for on-site applications. An ideal sensing approach should eliminate the dependence on sophisticated, bulky, and expensive laboratory instruments, while maintaining a durable and stable structure that ensures reliability and consistency during real-world use. In light of these requirements, we present a novel electrochemical sensing platform that leverages Electrochemical Impedance Spectroscopy (EIS) for the detection of foodborne toxins[ 22 ], [ 23 ], [ 24 ], [ 25 ]. This platform incorporates antibody-functionalized electrodes using a bifunctional crosslinker, creating a stable and specific interface for target binding. EIS, particularly in the non-faradaic mode, allows for label-free, real-time monitoring of antigen-antibody interactions through changes in interfacial capacitance, offering a simple yet effective method for detecting the presence of toxins (as illustrated in Fig. 1 ). Our sensor demonstrates an impressively low limit of detection (LoD), confirming its exceptional sensitivity for Aflatoxin B1 detection. Notably, this high performance is achieved without complex preprocessing, labeling agents, or reliance on highly skilled personnel overcoming major barriers associated with conventional analytical methods. The key novelty of this work lies in its use of a frequency-tuned, non-Faradaic impedance platform, where the electrode–analyte interaction is specifically optimized at 200 Hz based on differences in effective surface charge, as supported by zeta potential analysis. This allows the system to function as a uniquely tuned Randles circuit, capable of selectively distinguishing Aflatoxin B1 from structurally similar toxins like Aflatoxin M1 , even within complex sample matrices. Furthermore, the sensor retains its performance across corn-based matrices, underscoring the robustness and scalability of the detection mechanism. As a result, our platform not only addresses critical challenges related to cost, complexity, and portability, but also offers a novel, label-free, circuit-level strategy for field-deployable, real-time toxin detection—paving the way for practical applications in food safety and public health surveillance. 2. Material and Methods 2. 1 Materials and Reagents The Aflatoxin B1 (AFB1) antibody used for sensor modification, obtained from Invitrogen (USA). Phosphate-buffered saline (PBS) with a pH of 7.4 and the crosslinker DTSSP (3,3′dithiobis(sulfosuccinimidyl propionate)) were acquired from Thermo Fisher Scientific Inc. (Waltham, MA, USA). The samples were then distributed into minute portions, which were maintained at -20°C for later use. To proceed with experimentation, the samples were then adjusted to room-temperature and centrifuged carefully. To avoid further purification processes, only laboratory-grade, purified chemicals were used. 2.2 ATR-IR spectroscopy setup The Nicolet iS-50 FTIR Spectrometer, provided by Thermo Scientific Inc., in Attenuated Total Reflectance mode, was used to record infrared spectra (IR). Specifically, the sensor incorporated deuterated triglycine sulfate (DTGS), while the window component utilized KBr. In addition, a germanium crystal was deployed for spectrum collection, delivering a strong resolution of 4 cm-1 covering a wavelength range of 4000 cm-1 to 400 cm-1, encompassing a total of 256 scans. Au was thoroughly covered on the surface of the glass substrates, following with ZnO afterwards, while maintaining uniform parameters that were utilized for the deposition of the sensor substrate. Moving on, DTSSP was used to modify and immobilize the electrodes, in accordance with the immunoassay procedure expressed in next section. Subsequently, AFB1 antibodies were incubated on the surface of the electrodes in 100% PBS. 2.3 Sensor design The biosensor is built on a three-electrode configuration, consisting of a gold working electrode (WE), a gold reference electrode (RE), and a carbon counter electrode (CE). To improve the surface-to-volume ratio of the working electrode and enhance biomolecule immobilization, a thin semiconducting layer of zinc oxide (ZnO) was deposited. This ZnO layer was applied using sputter deposition, a physical vapor deposition method well known for producing uniform, adherent, and stable coatings on metallic substrates. Electrochemical impedance spectroscopy (EIS) was employed as the primary analytical technique to monitor antigen–antibody interactions on the modified electrode surface. The sensor array includes 16 independent WE, RE, and CE enabling simultaneous and multiplexed impedance measurements. To ensure reproducibility and uniform electrochemical behavior across all electrodes, the ZnO coating parameters were kept constant, with a controlled deposition duration of 60 minutes. 2.4 Modification of electrode sensing platform To ensure effective surface modification and optimal analytical performance, each step in the electrode preparation process was carefully optimized. Initially, the gold electrode surface was thoroughly rinsed with phosphate-buffered saline (PBS) to eliminate any interfering contaminants. A crosslinking mixture containing 6 mM DTSSP (3,3′-dithiobis(sulfosuccinimidyl propionate)) and 10 µg/mL AFB1 antibody was freshly prepared and incubated at 4°C for 30 minutes to protect antibody functionality and promote stable conjugation. This mixture was then uniformly applied to the electrode surface and incubated at room temperature for another 30 minutes in the dark, allowing adequate time for the immobilization of the antibody–crosslinker complex onto the ZnO-coated electrode. After incubation, the electrodes were gently washed with deionized water to remove unbound materials. A commercial blocking buffer (Superblock) was applied next and incubated for 5 minutes at room temperature in dark conditions to minimize nonspecific interactions. Following removal of the blocking buffer and a DI water rinse, the functionalized electrodes were preserved using lyophilization by freezing at − 18°C and applying vacuum pressure (5 pascals) for 25 minutes to enhance shelf-life and stability. During the testing phase, 5 µL of AFB1 antigen was applied to the electrodes and allowed to incubate for 5 minutes, which was identified as the optimal time for effective antigen–antibody binding based on impedance response. Electrochemical impedance spectroscopy (EIS) was subsequently conducted using a 10 mV AC bias over a frequency range of 1000 Hz to 80 Hz. These conditions were systematically optimized to ensure robust signal generation and reproducibility, confirming that parameters such as antibody concentration, pH, and incubation times were adequately tuned for sensitive and specific detection of AFB1. 2.5 Statistical Analysis The data reported incorporates mean values accompanied with the standard error of the mean (SEM) to measure the accuracy and precision of collected data. Additionally, the collected data is acquired from three duplicate sensors (N = 3). To comply with the Clinical and Laboratory Standards Institute (CLSI) rules and regulations, both inter-assay and intra-assay variations remain no more than 10%. GraphPad Prism (Version 8.01), created by GraphPad Software (La Jolla, CA, USA), successfully executed the statistical analysis of the collected data. In addition, subsequent evaluation of the data was obtained from Origin 2023. The ZView Software (Version 4) was utilized for data modeling and equivalent circuit study. BioRender (BioRender.com) was used to design the several graphics of the reaction mechanisms presented in this study. 3. Result and Discussion 3.1 Comprehensive Characterization of an Aflatoxin B1 Sensor Platform The characterization of the developed AFB1 sensor platform was conducted using Zeta Potential and Dynamic Light Scattering (DLS) measurements to investigate the interactions between Aflatoxin B1 (AFB1) and its specific antibodies in solution. Both techniques provide complementary insights into electrostatic properties, particle size distribution, and colloidal stability, which are essential for understanding antibody–antigen interactions prior to electrode immobilization. Zeta potential measurements revealed that PBS alone exhibited a potential of − 10.7 mV, which became more negative upon the addition of antibodies (–14.13 mV) and further decreased with the antibody–AFB1 mixture (–14.56 mV). This shift toward increased negativity suggests that antibody adsorption imparts additional surface charge, thereby enhancing electrostatic repulsion among particles[ 26 ]. Although the absolute magnitude of the zeta potential remains below 30 mV, implying only moderate colloidal stability, the observed trend confirms that antibody–AFB1 interactions influence the electrostatic environment, which can contribute to sensor performance. Dynamic Light Scattering (DLS) was then employed to quantify the hydrodynamic diameter of particles in suspension by analyzing the fluctuations of scattered light due to Brownian motion. In this study, baseline PBS exhibited an apparent particle size of 535.7 nm, likely arising from trace background particles or micro-aggregates present in the solution rather than individual AFB1 molecules, given that AFB1 is a small hapten with a molecular weight of ~ 312 Da and low aqueous solubility. The addition of AFB1 to PBS increased the apparent particle size to 713.03 nm, indicating the formation of small aggregates or precipitated toxin clusters. Upon introduction of the anti-AFB1 antibody, the average particle size further increased to 973.2 nm. This substantial size increase reflects the formation of antibody–AFB1 complexes and clustering effects, analogous to DLS-based immunoassays where antigen binding to antibody-coated particles produces measurable aggregation. Although AFB1 is monovalent and cannot directly cross-link antibodies, aggregation likely occurs due to multiple antibody molecules binding to clustered AFB1 precipitates, resulting in larger immune complexes in solution. These results indicate that antibody binding significantly alters the colloidal state of AFB1-containing suspensions, supporting the feasibility of antibody–toxin recognition on the sensor surface. This SEM image (shown in Supplementary Fig. S1 ) demonstrates the morphological changes that occur upon antigen–antibody binding on the sensor surface. The spherical structures visible correspond to antibody-functionalized ZnO surfaces, while the attached and aggregated regions indicate AFB1 antigen binding, forming larger immune complexes[ 27 ]. The distinct clustering and coverage pattern confirm successful antigen recognition and immobilization by the antibody layer, validating the sensor design. The polydispersity index (PDI) of PBS was measured to be 0.106, reflecting a monodisperse baseline solution. When overnight socked corn was added, the PDI increased to 0.65, indicating a highly polydisperse system due to the presence of large particles such as starch granules and protein aggregates. These data highlight the effect of complex matrices on particle dispersion and underscore the importance of understanding particle behavior for biosensor application in real samples. Collectively, the zeta potential and DLS results provide qualitative evidence of antibody–AFB1 binding in solution, demonstrating changes in surface charge and particle size consistent with immune complex formation. While these measurements are conducted in bulk solution rather than directly on the electrode, they offer indirect confirmation that the antibodies can recognize and bind AFB1, which is critical for the functionality of the immobilized sensor. These findings align with previously reported strategies where zeta potential and DLS have been used to verify antigen–antibody interactions and aggregation prior to sensor fabrication. The combination of zeta potential and DLS analyses reveals that antibody incorporation significantly alters surface charge and particle size in AFB1 suspensions, confirming the formation of immune complexes. This information not only validates the molecular recognition mechanism of the sensor but also provides insight into colloidal behavior that can influence sensor stability, selectivity, and reproducibility in real sample analysis. Supplementary Fig. S2 allegorizes the Open Circuit Potential (OCP) of the sensor platform, which emphasizes its solidity and secureness throughout a period of 1200 seconds for AFB1. Additionally, the sensor exhibits minor variations in voltage. The potential change for AFB1 is 0.4 mV. By these means, this consistency and stability, as described above, is highly crucial for real-world use. The strength of the sensor, showcased by its minute potential differences and Zeta Potential and DLS data, confirms the validity and accuracy in the detection of dangerous toxins in common food supplies[ 28 ], [ 29 ]. To proceed, our research utilizes affinity-based detection as the principal sensing mechanism. As summarized in Section 2.3 , Fourier-Transform Infrared Spectroscopy (FTIR) was used to authenticate the binding of the crosslinker DTSSP (3,3′dithiobis(sulfosuccinimidyl propionate)) [ 30 ] to the surface of the sensor and the immobilization of the AFB1 antibody to DTSSP. On the glass surface, a thin sheet of Zinc Oxide (ZnO) was delicately placed using vapor deposition and efficiently functionalized with the corresponding antibody to depict the electrode. The presence of ZnO helps ameliorate the activity on the sensor surface, which results in increased sensitivity that can assist in efficient detection. Continuing, the FTIR spectra of the manufactured sensor validates antibody existence on the sensor platform. A notable peak at 1742 cm⁻¹ was noticed to subsequent surface modification with DTSSP. Next, Fig. 2 B features the C-N-C stretch of N-hydroxysuccinimide (NHS) at 1232 cm⁻¹ and the N-O-C esters peak at 1042 cm⁻¹. Afterwards, the passing of the peaks at 1742 cm⁻¹, 1232 cm⁻¹, and 1042 cm⁻¹ strongly proves favorable antibody immobilization onto the ZnO surface through amide bond formation modified by DTSSP [ 31 ], [ 32 ], [ 33 ]. The data reported above confirmed the sensor’s sensitivity, practicality, and effectiveness in the detection of AFB1. This approves day-to-day utilization as well [ 34 ], [ 35 ]. 3.2 Electrochemical Signal Response on the Modified Sensor Platform. Electrochemical Impedance Spectroscopy (EIS) served as the primary technique to probe interfacial electrochemical changes on the sensor during Aflatoxin B1 (AFB1) detection [ 13 ]. Measurements were recorded using a 10 mV AC excitation [ 30 ] [ 13 ], [ 14 ], allowing sensitive monitoring of alterations in the electrical double layer produced when antigens bind to antibodies immobilized on the electrode. Repeated impedance recordings were combined to build Calibrated Dose Response (CDR) curves that map impedance changes to known antigen concentrations. Performance evaluation used overnight-soaked corn samples spiked with AFB1 across the 0.01–40.96 ng/mL range. The Nyquist analysis provides an in-depth evaluation of dose-dependent impedance spectra for Aflatoxin B1 (AFB1), as shown in Supplementary Fig. S3. These plots illustrate the relationship between the imaginary component (Zimg) and the real component (Zreal) across all the AFB1 concentrations. A clear shift toward the imaginary axis was observed with increasing toxin levels, indicating enhanced antigen–antibody binding on the electrode surface. This behavior reflects modulation of the electrical double-layer charge, which was systematically examined across a wide frequency range to determine the optimal conditions for sensor operation. Dose-dependent impedance data (Fig. 3 A) show a clear, nearly linear increase in percentage impedance change as AFB1 concentration rises, consistent with progressive modulation of the double-layer charge. The CDR exhibited strong linearity (R² = 0.956), supporting the sensor’s quantitative capability. Frequency-sweep analysis identified 200 Hz as the most favorable frequency for these measurements, providing the best signal-to-noise performance [ 10 ], [ 11 ]. Specificity controls were performed using unmodified (bare) electrodes and electrodes treated only with blocking solution. As presented in Supplementary Figures S4A and S4B, both controls produced less than 25% maximum impedance change, with the bare electrodes’ response saturating after the second dose—evidence of negligible specific binding and effective suppression of nonspecific adsorption by the blocking layer. These controls confirm that antibody functionalization is essential for selective AFB1 capture. To interpret the electrochemical processes, impedance spectra were fitted with a Randles equivalent circuit (Fig. 3 B), including solution resistance (R s ), charge-transfer resistance (R ct ), and a constant phase element (CPE) to account for non-ideal capacitive behavior arising from surface heterogeneity [ 36 ]. Extracted CPE values increased with higher AFB1 concentrations (Fig. 3 C), reflecting sensitivity to antigen–antibody interactions at the interface. Collectively, these analyses demonstrate the platform’s precision, reproducibility, and robustness for quantitative AFB1 sensing in realistic food matrices. 3.3 Spike-and-Recovery Validation of the Sensor Spike-and-recovery testing was conducted to validate the sensor’s accuracy and precision. Defined amounts of AFB1 were spiked into overnight-soaked corn samples and measured concentrations were determined from the Calibrated Dose Response (CDR). Comparing the introduced (expected) concentrations with the sensor-read values quantifies any systematic bias or measurement error and provides a rigorous assessment of method reliability. Three AFB1 levels were randomly introduced into the matrix—0.045 ng/mL (low), 2.88 ng/mL (mid), and 22.96 ng/mL (high)—with N = 8 replicates per level. The limit of detection (LoD) was estimated using a signal-to-noise criterion: the specific signal threshold (SST) was set as the baseline mean plus three times its standard deviation. By this approach the sensor achieved an LoD of 0.005 ng/mL and a limit of quantification (LOQ) of 0.01 ng/mL, demonstrating its capability to resolve trace AFB1 concentrations. Figure 3 D displays the percentage-recovery results for the overnight-soaked corn matrix, with recoveries consistently exceeding 80% across the tested range [ 30 ], further corroborating the assay’s robustness and analytical accuracy [ 31 ]. 3.4 Fingerprinting of Aflatoxin B1 : Cross-reactivity and Specificity Evaluation To establish the molecular fingerprinting capability of the developed electrochemical sensor towards Aflatoxin B1 , a systematic cross-reactivity study was performed using a representative toxin cocktail containing Aflatoxin B1 (AFB1) and Aflatoxin M1 (AFM1). The schematic illustration of this experimental strategy is presented in Fig. 4 A, where the sensor was selectively modified with AFB1-specific antibodies via a DTSSP linker, and exposed to varying concentrations of AFB1-AFM1 mixtures (low: 0.045 ng/mL, mid: 2.88 ng/mL, and high: 22.96 ng/mL for each toxin). The Nyquist plots generated from electrochemical impedance spectroscopy (EIS) for the cocktail exposures are shown in Fig. 4 B. A clear progressive shift in the impedance was observed with increasing concentrations, signifying an enhanced capacitive response. This shift is indicative of an increased surface dielectric polarization attributed to the antigen–antibody interactions at the electrode-electrolyte interface. The increase in capacitive behavior reflects the formation of a well-packed biomolecular layer on the sensor surface, which disrupts ion transport and modulates the interfacial charge dynamics. AFM1 produced only a minor impedance change, with a slight shift at 400 Hz suggesting weak nonspecific binding. However, this response was significantly lower than that of AFB1, confirming the sensor’s frequency-dependent selectivity and its ability to discriminate structurally similar toxins. To validate the molecular recognition fidelity in real samples, recovery studies were conducted using a corn flour matrix spiked with AFB1 and AFM1 at the same concentrations (0.045, 2.88, and 22.96 ng/mL). The results presented in Fig. 4 C demonstrate that the recovery rates for AFB1 consistently exceeded 80% across all concentration levels, confirming the robustness of the antibody–antigen affinity under complex matrix interference. This high recovery further emphasizes the sensor’s practical applicability for AFB1 detection in food-grade matrices. To interrogate potential cross-reactivity with AFM1, frequency-resolved impedance signatures were analyzed. At a fixed frequency of 400 Hz, where impedance behavior is sensitive to dielectric modulation, the AFM1 recovery appeared elevated relative to other frequencies; however, quantitative analysis revealed that the % recovery of AFM1 remained below 60% (see Supplementary Fig. S5). This suggests that although AFM1 may induce partial binding or nonspecific surface interactions at this frequency, the extent of signal overlap remains significantly lower compared to AFB1, thereby confirming partial discrimination capability. A more decisive specificity assessment was performed at 200 Hz, a frequency regime dominated by resistive contributions arising from strong molecular binding events. As shown in Fig. 4 D shows that the % recovery of AFB1 was consistently above 85%, whereas the corresponding recovery for AFM1 fell below 20%. This result strongly supports that the sensor exhibits minimal cross-reactivity to AFM1 under optimal detection conditions. The pronounced differential in recovery further validates that the AFB1 antibody-functionalized sensor leverages conformational and epitope-specific interactions that are absent in AFM1 due to structural divergence, particularly in the terminal furan ring and lactone moiety. Similarly, Zearalenone (ZEA) exhibited recovery values below 20%, indicating low binding affinity and potential matrix interference during detection. The structural differences between ZEA and AFB1, including variations in the lactone ring and overall steric conformation, likely limit its interaction with the AFB1-specific antibodies. Collectively, these data underscore the fingerprinting capacity of the sensor for selective AFB1 detection while highlighting the minimal cross-reactivity toward AFM1 and ZEA. The concerted analysis across different frequencies and toxin combinations not only confirms the functional specificity of the immobilized antibodies but also highlights the importance of frequency-domain analysis in deciphering cross-reactivity mechanisms in multi-analyte systems. 3.5 Validation of Sensor Performance: Reproducibility, Repeatability, and Device Correlation To evaluate the practical applicability of the developed electrochemical biosensor for food safety monitoring, we conducted a comprehensive validation of its analytical performance in detecting trace levels of Aflatoxin B1 (AFB1) in real-world matrices. Specifically, overnight-soaked corn samples—selected for their relevance in mycotoxin contamination studies—were spiked with known concentrations of AFB1, and tested for analytical precision and reproducibility under both intra-assay and inter-assay conditions. As depicted in Fig. 5 A, the sensor exhibited high precision across the concentration range of 0.045 ng/mL, 2.88 ng/mL, and 22.96 ng/mL of AFB1, with coefficient of variation (%CV) values consistently below the critical threshold of 20%. This level of precision meets the performance benchmarks stipulated by the Clinical and Laboratory Standards Institute (CLSI) guidelines, underscoring the robustness of the sensing platform for trace-level quantification in complex food matrices [ 37 ], [ 38 ]. Notably, the low %CV at the femtomolar-to-nanomolar range further demonstrates the stability of the electrode functionalization and the reliability of the impedance-based transduction mechanism, even in the presence of sample matrix components that often introduce variability in biosensing assays. To explore the feasibility of deploying this sensor in resource-limited or field settings, we developed and tested a portable version of the detection platform. The aim was to determine whether the sensor's electrochemical signature could be faithfully reproduced using a miniaturized, battery-operated potentiostat, simulating a real-time, on-site detection environment. The portable device was subjected to the same analytical protocol as the benchtop laboratory instrument, using identical spiked corn samples for direct performance comparison. To statistically validate the agreement between the two systems, a Pearson correlation analysis was performed on the impedance response data obtained from both platforms. The resulting correlation coefficient (Pearson r = 0.965), shown in Fig. 5 B, reflects a strong linear relationship between the laboratory-grade potentiostat and the portable unit. This high degree of correlation confirms that the portable sensor retains analytical fidelity comparable to its benchtop counterpart, with no significant loss of sensitivity or specificity during miniaturization. Together, these findings establish the platform’s capability for reproducible, accurate detection of AFB1 in a high-moisture corn matrix. Moreover, the successful translation of the sensing mechanism to a compact format underscores its potential as a deployable tool for point-of-need toxin screening. This opens avenues for rapid food safety diagnostics in agricultural supply chains, grain storage facilities, and rural inspection sites where conventional laboratory infrastructure may be inaccessible. 3.6 Performance Evaluation of the AFB1 Classifier Model The classifier model developed in this study demonstrated high accuracy in distinguishing between safe and unsafe levels of Aflatoxin B1 in 48 overnight soaked corn samples. A threshold-based classification approach was implemented, where samples were categorized as "Safe" if their scores exceeded 85 and "Unsafe" otherwise. The model's performance was evaluated using a confusion matrix, which revealed an accuracy of 90.6% for Aflatoxin B1 , confirming its ability to correctly classify most samples (Supplementary Fig. S6). To further assess the model's effectiveness, the Receiver Operating Characteristic (ROC) curve was analyzed, highlighting the trade-off between sensitivity (true positive rate) and specificity. The Area Under the Curve (AUC) value for Aflatoxin B1 was 0.83, indicating a strong ability to differentiate between safe and unsafe concentrations (Fig. 5 C). The high AUC score reinforces the model's robustness, confirming its suitability for real-time toxin detection. This validation underscores the classifier's potential for on-site food safety monitoring, providing a reliable tool for detecting Aflatoxin B1 contamination and mitigating public health risks. In a Fig. 5 D, the x-axis represents the mean impedance value for each concentration of Aflatoxin B1 in the overnight-soaked corn sample, comparing measurements between the lab benchtop potentiostat and the portable device. The y-axis denotes the difference in impedance values between the two methods for each concentration, highlighting the variation in readings across different Aflatoxin B1 levels. The system demonstrates a mean bias of 4.06, suggesting no significant variation in measurement cycles between the two devices, particularly in the upper concentration range. Additionally, all test samples fall within ± 1.96 times the standard deviation, forming the upper and lower 95% confidence interval, which confirms strong agreement between both methods. The balanced distribution of data points above and below the mean bias line further indicates the absence of systematic over- or underestimation, ensuring consistent and unbiased detection of Aflatoxin B1 levels across different concentrations. 4. Conclusion We developed a portable, electrochemical impedance-based sensor for the rapid detection of Aflatoxin B1 in overnight-soaked corn samples, achieving a detection limit of 0.005 ng/mL using just 5 µL of sample with results in under 5 minutes. The sensor integrates specific antibodies for high selectivity and consistent performance across a wide concentration range in both lab and field settings. By optimizing the sensing response at 200 Hz, the platform leverages frequency-resolved interfacial charge behavior to discriminate Aflatoxin B1 from structurally similar toxins like Aflatoxin M1 , even in complex food matrices. This frequency-specific signal tuning, validated through cross-reactivity studies, enables highly selective detection without preprocessing or labeling. With its sensitivity, speed, and ease of use, the platform shows strong potential for on-site food safety monitoring.In future studies, efforts will be directed toward improving long-term sensor stability, exploring real-time measurements in complex food matrices, and validating performance across a broader range of environmental conditions to further enhance field applicability. Declarations Author Contributions Conceptualization, K.K.M.; methodology, K.K.M.; software, K.K.M.; validation, K.K.M. and V.N.D.; formal analysis, K.K.M.; investigation, V.N.D., S.M. and S.P.; resources, K.K.M. and S.P.; data curation, K.K.M. and K.M.T.; writing—original draft preparation, K.K.M., K.M.T.; writing—review and editing, K.K.M.; visualization, K.K.M. and S.P.; supervision, S.P.; project administration, K.K.M. and S.P.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript. Funding This work was partially supported by funding from EnLiSense LLC. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Conflicts of Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Drs. Shalini Prasad and Sriram Muthukumar have a significant interest in Enlisense LLC, a company that may have a commercial interest in the results of this research and technology. The potential individual conflict of interest has been reviewed and managed by The University of Texas at Dallas, and played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report, or in the decision to submit the report for publication. 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Muthukumar, and S. Prasad, “Quick and Sensitive Two‐Plex Electrochemical Platform for Pathogen Detection in Water,” Nano Select , Apr. 2025, doi: 10.1002/nano.70017. Additional Declarations No competing interests reported. Supplementary Files 03SupplementaryInformation.docx GraphicalAbstract.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 27 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 10 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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12:48:39","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112280,"visible":true,"origin":"","legend":"","description":"","filename":"5ebd06b1fbab4c41af93ac82e9057dcb1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/ac620e929906ee493f3cab41.xml"},{"id":92175635,"identity":"eade3c20-cec1-41d0-862e-4ade014d72f8","added_by":"auto","created_at":"2025-09-25 12:48:39","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121872,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/6f2b96a1107c6797a0131fae.html"},{"id":92176986,"identity":"0164d294-d7fb-4294-99c1-52c40dfc6fa1","added_by":"auto","created_at":"2025-09-25 12:56:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":339925,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the sample matrix preparation of corn matrix and electrochemical detection of \u003cem\u003eAflatoxin B1\u003c/em\u003e on the modified sensor platform.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/e9591c386d29801dbcdef705.png"},{"id":92177353,"identity":"49ee4bfe-0e84-40e2-a9b6-647bd696c4f6","added_by":"auto","created_at":"2025-09-25 13:04:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147514,"visible":true,"origin":"","legend":"\u003cp\u003e(A) present the results of the Dynamic Light Scattering (DLS) study, and Zeta potential study (B) A FTIR spectra of (i) DTSSP functionalized on ZnO surface (ii) Conjugation of \u003cem\u003eAflatoxin B1\u003c/em\u003e antibody and DTSSP.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/39d80bc51f6c0ec10bc054d2.png"},{"id":92177354,"identity":"d4886f6e-27d4-4927-85dc-59bdd4164047","added_by":"auto","created_at":"2025-09-25 13:04:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":347490,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The Calibration Dose Response (CDR) plot for \u003cem\u003eAflatoxin B1\u003c/em\u003e on the sensor platform represents concentrations ranging from 0.01 to 40.96 ng/mL in an overnight-soaked corn sample. (B) The equivalent circuits for the corresponding sensor platform (C) Represents electrical double-layer capacitance variation with different concentrations of \u003cem\u003eAflatoxin B1\u003c/em\u003e spiked in corn sample. (D) The recovery graph shows the system’s performance in detecting \u003cem\u003eAflatoxin B1\u003c/em\u003eat different concentrations.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/08f519d2ee5bdb090e68413d.png"},{"id":92175603,"identity":"827daae7-f42c-41bb-97b0-3f1926af3e14","added_by":"auto","created_at":"2025-09-25 12:48:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156851,"visible":true,"origin":"","legend":"\u003cp\u003e(A) provides a schematic depiction of the experimental workflow designed to evaluate the sensor's performance. (B) shows the impedance response of the sensor at 200 Hz, as captured through EIS following interaction with the \u003cem\u003eAflatoxin B1\u003c/em\u003e and \u003cem\u003eAflatoxin M1\u003c/em\u003ecocktail. (C) The recovery graph shows the system’s performance in cocktail of \u003cem\u003eAflatoxin B1\u003c/em\u003e and Aflatoxin\u003cem\u003e M1\u003c/em\u003e at different concentrations. (D) Results of the specificity study showcasing the sensor response to increasing concentrations of Aflatoxin\u003cem\u003e M1\u003c/em\u003e and \u003cem\u003eZearalenone\u003c/em\u003e compared to \u003cem\u003eAflatoxin B1\u003c/em\u003e concentration at 200 Hz.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/b5f34860f230422e41857e04.png"},{"id":92175609,"identity":"6ee56609-231d-40aa-90e7-63d19db038b7","added_by":"auto","created_at":"2025-09-25 12:48:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":206734,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The reproducibility and repeatability of the sensor platform were assessed through statistical analysis, as depicted in (\u003cstrong\u003eC\u003c/strong\u003e) for intra-assay variation (N = 8) and inter-assay variation (n = 3), showing consistent performance confirmed by %CV. (B) Correlation analysis of readings from portable and benchtop devices across five concentrations, illustrating a linear relationship. \u0026nbsp;(C) The ROC curve, generated using the pROC package, illustrated the trade-off between sensitivity and 1-specificity at various thresholds, with high AUC values indicating the model's robustness and reliability in distinguishing between safe and unsafe toxin concentrations. (D) Bland–Altman plots to evaluate the level of correlation showing a mean bias of 4.06 with a 95% confidence limit\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/14f79d77164c8d3dff44f2d7.png"},{"id":92178597,"identity":"3eb06891-75f7-40ca-ae49-1294bad6a450","added_by":"auto","created_at":"2025-09-25 13:12:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1909704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/1c91a13f-736b-4c69-8f50-bbb0306f0b66.pdf"},{"id":92175605,"identity":"b446f951-b4d2-4106-a7d9-cb71360a8dd9","added_by":"auto","created_at":"2025-09-25 12:48:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1072786,"visible":true,"origin":"","legend":"","description":"","filename":"03SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/20606fe58dc1cbb23541a622.docx"},{"id":92175606,"identity":"1d7248ea-b0fe-4a1a-9852-71a5d8445c04","added_by":"auto","created_at":"2025-09-25 12:48:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":239665,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-7583817/v1/c24ff7a82c0b5e1d89587661.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Portable Electrochemical Sensing Platform for Aflatoxin B1 Detection in Food Matrices","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMycotoxin contamination in agricultural products has raised significant global concern due to its adverse effects on human and animal health, along with its substantially high mortality rates. Mycotoxins are extremely noxious secondary metabolites produced from capillaceous fungi that commonly thrive between 25 ℃ to 35 ℃, as well as a moist, humid environment. Furthermore, mycotoxins are prone to toxicate crops during cultivation, harvest, preservation, and treatment. \u003cem\u003eAflatoxins\u003c/em\u003e (AFs), produced by Aspergillus spp, primarily Aspergillus flavus and Aspergillus parasiticus, present to be the most pernicious compared to other mycotoxins. AFs are categorized into B1, B2, G1, or G2 by their unique fluorescent properties under ultra-violet (UV) light, specifically at 365 nm, and their ability to mobilize into different structures. To illustrate, B-group AFs will present as a cyclopentane ring with a blue glow, while G-group AFs will show a lactone ring with a yellowish-green glow. In addition, M-group AFs (AFM1 and AFM2) are produced by the hydroxylation, the addition of a hydroxyl group, of B-group AFs (AFB1 and AFB2), respectively. In the 1960s, the fatalness of AFs was initially discovered across several farms in England due to the sudden deaths of turkeys. Thereafter, examiners found that the Brazilian groundnut meal, a common feed given to turkeys, proved to be tremendously poisonous due to the accumulation of metabolites of Aspergillus flavus. AFs are typically found in a variety of grains, nuts, spices, dairy products, etc [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrecisely, \u003cem\u003eAflatoxin B1\u003c/em\u003e (AFB1) is the most pestilential out of all the AFs due to its ability to cause cancer, disrupt the development of an embryo or fetus, and induce mutations in an organism's DNA [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Considerable amounts of AFB1 can lead to hepatocellular carcinoma, a type of cancer that forms directly in the hepatocytes. Additionally, AFB1 poisoning can lead to immunosuppression and malnourishment, as well as severe damage to organs in the cardiorespiratory, gastrointestinal, nervous, and reproductive systems. AFB1 is categorized as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC). The deleterious nature of AFB1 stems from its robust ability to decompose the cell membrane and disrupt the DNA situated in the cell. As a result, strict limitations have been established by the Federal Food and Drug Administration (FDA) in the United States and the European Union (EU) on safe amounts of AFB1 present in certain crops and food items sold to the community, as described in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e For instance, the FDA has set a maximum limit of 20 ppb for human food; at the same time, the EU placed a maximum limit of 2 ppb for human consumption, after processing [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNumerous detection methods have been created to ensure the safety of humans and animals regarding AFB1 contamination in food and feed products. Current techniques in AFB1 detection consist of customary methods such as polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA) and chromatographic methods, such as high-performance liquid chromatography (HPLC), thin layer chromatography (TLC), and gas chromatography (GS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e],[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, these methods have known consequences as being laborious and extensive, requiring costly and tailored lab-equipment, and involving trained lab professionals to carry out experimentation and elucidate the results. Although ELISA is acknowledged as providing expeditious results compared to the other methods listed, it fails to reach adequate sensitivity requirements. Correspondingly, PCR has a possibility of providing false positives. In addition, both ELISA and PCR requires separation of the pathogen from the sample, which can be extremely difficult when deploying on the field [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Even though these methods are valued for their reliability, there are several disadvantages as being exceedingly tiresome, having difficulty with on-field deployment, demanding highly priced equipment, requiring qualified and proficient lab technicians, and not possessing proper sensitivity to lower concentrations. Supplementary Table S2 presents a comparative overview of various immunosensors, emphasizing that many existing label-free approaches lack direct sensitivity toward \u003cem\u003eAflatoxin B1\u003c/em\u003e. Although techniques such as bioluminescence assays, fluorescence-based ELISA, and electrochemical methods like chronoamperometry and cyclic voltammetry have been explored for \u003cem\u003eAflatoxin B1\u003c/em\u003e detection, they often come with significant limitations[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These include the need for complex sample preparation, reliance on labeling agents, extended assay times, and the requirement for skilled personnel. Furthermore, most of these methods are designed for single-analyte detection, making them less efficient and more costly when screening for multiple toxins. These challenges highlight the need for more practical, sensitive, and user-friendly electrochemical platforms tailored specifically for \u003cem\u003eAflatoxin B1\u003c/em\u003e detection.\u003c/p\u003e\u003cp\u003eTo overcome the potential disadvantages associated with traditional and modern analytical methods, there is a growing need for a detection technique that is not only rapid and highly sensitive but also user-friendly and suitable for on-site applications. An ideal sensing approach should eliminate the dependence on sophisticated, bulky, and expensive laboratory instruments, while maintaining a durable and stable structure that ensures reliability and consistency during real-world use. In light of these requirements, we present a novel electrochemical sensing platform that leverages Electrochemical Impedance Spectroscopy (EIS) for the detection of foodborne toxins[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This platform incorporates antibody-functionalized electrodes using a bifunctional crosslinker, creating a stable and specific interface for target binding. EIS, particularly in the non-faradaic mode, allows for label-free, real-time monitoring of antigen-antibody interactions through changes in interfacial capacitance, offering a simple yet effective method for detecting the presence of toxins (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our sensor demonstrates an impressively low limit of detection (LoD), confirming its exceptional sensitivity for \u003cem\u003eAflatoxin B1\u003c/em\u003e detection. Notably, this high performance is achieved without complex preprocessing, labeling agents, or reliance on highly skilled personnel overcoming major barriers associated with conventional analytical methods. The key novelty of this work lies in its use of a frequency-tuned, non-Faradaic impedance platform, where the electrode\u0026ndash;analyte interaction is specifically optimized at 200 Hz based on differences in effective surface charge, as supported by zeta potential analysis. This allows the system to function as a uniquely tuned Randles circuit, capable of selectively distinguishing \u003cem\u003eAflatoxin B1\u003c/em\u003e from structurally similar toxins like \u003cem\u003eAflatoxin M1\u003c/em\u003e, even within complex sample matrices. Furthermore, the sensor retains its performance across corn-based matrices, underscoring the robustness and scalability of the detection mechanism. As a result, our platform not only addresses critical challenges related to cost, complexity, and portability, but also offers a novel, label-free, circuit-level strategy for field-deployable, real-time toxin detection\u0026mdash;paving the way for practical applications in food safety and public health surveillance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003e2. 1 Materials and Reagents\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eAflatoxin B1\u003c/em\u003e (AFB1) antibody used for sensor modification, obtained from Invitrogen (USA). Phosphate-buffered saline (PBS) with a pH of 7.4 and the crosslinker DTSSP (3,3\u0026prime;dithiobis(sulfosuccinimidyl propionate)) were acquired from Thermo Fisher Scientific Inc. (Waltham, MA, USA). The samples were then distributed into minute portions, which were maintained at -20\u0026deg;C for later use. To proceed with experimentation, the samples were then adjusted to room-temperature and centrifuged carefully. To avoid further purification processes, only laboratory-grade, purified chemicals were used.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 ATR-IR spectroscopy setup\u003c/h2\u003e\u003cp\u003eThe Nicolet iS-50 FTIR Spectrometer, provided by Thermo Scientific Inc., in Attenuated Total Reflectance mode, was used to record infrared spectra (IR). Specifically, the sensor incorporated deuterated triglycine sulfate (DTGS), while the window component utilized KBr. In addition, a germanium crystal was deployed for spectrum collection, delivering a strong resolution of 4 cm-1 covering a wavelength range of 4000 cm-1 to 400 cm-1, encompassing a total of 256 scans. Au was thoroughly covered on the surface of the glass substrates, following with ZnO afterwards, while maintaining uniform parameters that were utilized for the deposition of the sensor substrate. Moving on, DTSSP was used to modify and immobilize the electrodes, in accordance with the immunoassay procedure expressed in next section. Subsequently, AFB1 antibodies were incubated on the surface of the electrodes in 100% PBS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sensor design\u003c/h2\u003e\u003cp\u003eThe biosensor is built on a three-electrode configuration, consisting of a gold working electrode (WE), a gold reference electrode (RE), and a carbon counter electrode (CE). To improve the surface-to-volume ratio of the working electrode and enhance biomolecule immobilization, a thin semiconducting layer of zinc oxide (ZnO) was deposited. This ZnO layer was applied using sputter deposition, a physical vapor deposition method well known for producing uniform, adherent, and stable coatings on metallic substrates. Electrochemical impedance spectroscopy (EIS) was employed as the primary analytical technique to monitor antigen\u0026ndash;antibody interactions on the modified electrode surface. The sensor array includes 16 independent WE, RE, and CE enabling simultaneous and multiplexed impedance measurements. To ensure reproducibility and uniform electrochemical behavior across all electrodes, the ZnO coating parameters were kept constant, with a controlled deposition duration of 60 minutes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Modification of electrode sensing platform\u003c/h2\u003e\u003cp\u003eTo ensure effective surface modification and optimal analytical performance, each step in the electrode preparation process was carefully optimized. Initially, the gold electrode surface was thoroughly rinsed with phosphate-buffered saline (PBS) to eliminate any interfering contaminants. A crosslinking mixture containing 6 mM DTSSP (3,3\u0026prime;-dithiobis(sulfosuccinimidyl propionate)) and 10 \u0026micro;g/mL AFB1 antibody was freshly prepared and incubated at 4\u0026deg;C for 30 minutes to protect antibody functionality and promote stable conjugation. This mixture was then uniformly applied to the electrode surface and incubated at room temperature for another 30 minutes in the dark, allowing adequate time for the immobilization of the antibody\u0026ndash;crosslinker complex onto the ZnO-coated electrode. After incubation, the electrodes were gently washed with deionized water to remove unbound materials. A commercial blocking buffer (Superblock) was applied next and incubated for 5 minutes at room temperature in dark conditions to minimize nonspecific interactions. Following removal of the blocking buffer and a DI water rinse, the functionalized electrodes were preserved using lyophilization by freezing at \u0026minus;\u0026thinsp;18\u0026deg;C and applying vacuum pressure (5 pascals) for 25 minutes to enhance shelf-life and stability. During the testing phase, 5 \u0026micro;L of AFB1 antigen was applied to the electrodes and allowed to incubate for 5 minutes, which was identified as the optimal time for effective antigen\u0026ndash;antibody binding based on impedance response. Electrochemical impedance spectroscopy (EIS) was subsequently conducted using a 10 mV AC bias over a frequency range of 1000 Hz to 80 Hz. These conditions were systematically optimized to ensure robust signal generation and reproducibility, confirming that parameters such as antibody concentration, pH, and incubation times were adequately tuned for sensitive and specific detection of AFB1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe data reported incorporates mean values accompanied with the standard error of the mean (SEM) to measure the accuracy and precision of collected data. Additionally, the collected data is acquired from three duplicate sensors (N\u0026thinsp;=\u0026thinsp;3). To comply with the Clinical and Laboratory Standards Institute (CLSI) rules and regulations, both inter-assay and intra-assay variations remain no more than 10%. GraphPad Prism (Version 8.01), created by GraphPad Software (La Jolla, CA, USA), successfully executed the statistical analysis of the collected data. In addition, subsequent evaluation of the data was obtained from Origin 2023. The ZView Software (Version 4) was utilized for data modeling and equivalent circuit study. BioRender (BioRender.com) was used to design the several graphics of the reaction mechanisms presented in this study.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Comprehensive Characterization of an \u003cem\u003eAflatoxin B1\u003c/em\u003e Sensor Platform\u003c/h2\u003e\u003cp\u003eThe characterization of the developed AFB1 sensor platform was conducted using Zeta Potential and Dynamic Light Scattering (DLS) measurements to investigate the interactions between \u003cem\u003eAflatoxin B1\u003c/em\u003e (AFB1) and its specific antibodies in solution. Both techniques provide complementary insights into electrostatic properties, particle size distribution, and colloidal stability, which are essential for understanding antibody\u0026ndash;antigen interactions prior to electrode immobilization.\u003c/p\u003e\u003cp\u003eZeta potential measurements revealed that PBS alone exhibited a potential of \u0026minus;\u0026thinsp;10.7 mV, which became more negative upon the addition of antibodies (\u0026ndash;14.13 mV) and further decreased with the antibody\u0026ndash;AFB1 mixture (\u0026ndash;14.56 mV). This shift toward increased negativity suggests that antibody adsorption imparts additional surface charge, thereby enhancing electrostatic repulsion among particles[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although the absolute magnitude of the zeta potential remains below 30 mV, implying only moderate colloidal stability, the observed trend confirms that antibody\u0026ndash;AFB1 interactions influence the electrostatic environment, which can contribute to sensor performance.\u003c/p\u003e\u003cp\u003eDynamic Light Scattering (DLS) was then employed to quantify the hydrodynamic diameter of particles in suspension by analyzing the fluctuations of scattered light due to Brownian motion. In this study, baseline PBS exhibited an apparent particle size of 535.7 nm, likely arising from trace background particles or micro-aggregates present in the solution rather than individual AFB1 molecules, given that AFB1 is a small hapten with a molecular weight of ~\u0026thinsp;312 Da and low aqueous solubility. The addition of AFB1 to PBS increased the apparent particle size to 713.03 nm, indicating the formation of small aggregates or precipitated toxin clusters. Upon introduction of the anti-AFB1 antibody, the average particle size further increased to 973.2 nm. This substantial size increase reflects the formation of antibody\u0026ndash;AFB1 complexes and clustering effects, analogous to DLS-based immunoassays where antigen binding to antibody-coated particles produces measurable aggregation. Although AFB1 is monovalent and cannot directly cross-link antibodies, aggregation likely occurs due to multiple antibody molecules binding to clustered AFB1 precipitates, resulting in larger immune complexes in solution. These results indicate that antibody binding significantly alters the colloidal state of AFB1-containing suspensions, supporting the feasibility of antibody\u0026ndash;toxin recognition on the sensor surface. This SEM image (shown in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) demonstrates the morphological changes that occur upon antigen\u0026ndash;antibody binding on the sensor surface. The spherical structures visible correspond to antibody-functionalized ZnO surfaces, while the attached and aggregated regions indicate AFB1 antigen binding, forming larger immune complexes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The distinct clustering and coverage pattern confirm successful antigen recognition and immobilization by the antibody layer, validating the sensor design.\u003c/p\u003e\u003cp\u003eThe polydispersity index (PDI) of PBS was measured to be 0.106, reflecting a monodisperse baseline solution. When overnight socked corn was added, the PDI increased to 0.65, indicating a highly polydisperse system due to the presence of large particles such as starch granules and protein aggregates. These data highlight the effect of complex matrices on particle dispersion and underscore the importance of understanding particle behavior for biosensor application in real samples. Collectively, the zeta potential and DLS results provide qualitative evidence of antibody\u0026ndash;AFB1 binding in solution, demonstrating changes in surface charge and particle size consistent with immune complex formation. While these measurements are conducted in bulk solution rather than directly on the electrode, they offer indirect confirmation that the antibodies can recognize and bind AFB1, which is critical for the functionality of the immobilized sensor. These findings align with previously reported strategies where zeta potential and DLS have been used to verify antigen\u0026ndash;antibody interactions and aggregation prior to sensor fabrication. The combination of zeta potential and DLS analyses reveals that antibody incorporation significantly alters surface charge and particle size in AFB1 suspensions, confirming the formation of immune complexes. This information not only validates the molecular recognition mechanism of the sensor but also provides insight into colloidal behavior that can influence sensor stability, selectivity, and reproducibility in real sample analysis. Supplementary Fig. S2 allegorizes the Open Circuit Potential (OCP) of the sensor platform, which emphasizes its solidity and secureness throughout a period of 1200 seconds for AFB1. Additionally, the sensor exhibits minor variations in voltage. The potential change for AFB1 is 0.4 mV. By these means, this consistency and stability, as described above, is highly crucial for real-world use. The strength of the sensor, showcased by its minute potential differences and Zeta Potential and DLS data, confirms the validity and accuracy in the detection of dangerous toxins in common food supplies[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo proceed, our research utilizes affinity-based detection as the principal sensing mechanism. As summarized in Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e, Fourier-Transform Infrared Spectroscopy (FTIR) was used to authenticate the binding of the crosslinker DTSSP (3,3\u0026prime;dithiobis(sulfosuccinimidyl propionate)) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to the surface of the sensor and the immobilization of the AFB1 antibody to DTSSP. On the glass surface, a thin sheet of Zinc Oxide (ZnO) was delicately placed using vapor deposition and efficiently functionalized with the corresponding antibody to depict the electrode. The presence of ZnO helps ameliorate the activity on the sensor surface, which results in increased sensitivity that can assist in efficient detection. Continuing, the FTIR spectra of the manufactured sensor validates antibody existence on the sensor platform. A notable peak at 1742 cm⁻\u0026sup1; was noticed to subsequent surface modification with DTSSP. Next, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB features the C-N-C stretch of N-hydroxysuccinimide (NHS) at 1232 cm⁻\u0026sup1; and the N-O-C esters peak at 1042 cm⁻\u0026sup1;. Afterwards, the passing of the peaks at 1742 cm⁻\u0026sup1;, 1232 cm⁻\u0026sup1;, and 1042 cm⁻\u0026sup1; strongly proves favorable antibody immobilization onto the ZnO surface through amide bond formation modified by DTSSP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The data reported above confirmed the sensor\u0026rsquo;s sensitivity, practicality, and effectiveness in the detection of AFB1. This approves day-to-day utilization as well [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Electrochemical Signal Response on the Modified Sensor Platform.\u003c/h2\u003e\u003cp\u003eElectrochemical Impedance Spectroscopy (EIS) served as the primary technique to probe interfacial electrochemical changes on the sensor during \u003cem\u003eAflatoxin B1\u003c/em\u003e (AFB1) detection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Measurements were recorded using a 10 mV AC excitation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], allowing sensitive monitoring of alterations in the electrical double layer produced when antigens bind to antibodies immobilized on the electrode. Repeated impedance recordings were combined to build Calibrated Dose Response (CDR) curves that map impedance changes to known antigen concentrations. Performance evaluation used overnight-soaked corn samples spiked with AFB1 across the 0.01\u0026ndash;40.96 ng/mL range.\u003c/p\u003e\u003cp\u003eThe Nyquist analysis provides an in-depth evaluation of dose-dependent impedance spectra for Aflatoxin B1 (AFB1), as shown in Supplementary Fig. S3. These plots illustrate the relationship between the imaginary component (Zimg) and the real component (Zreal) across all the AFB1 concentrations. A clear shift toward the imaginary axis was observed with increasing toxin levels, indicating enhanced antigen\u0026ndash;antibody binding on the electrode surface. This behavior reflects modulation of the electrical double-layer charge, which was systematically examined across a wide frequency range to determine the optimal conditions for sensor operation. Dose-dependent impedance data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) show a clear, nearly linear increase in percentage impedance change as AFB1 concentration rises, consistent with progressive modulation of the double-layer charge. The CDR exhibited strong linearity (R\u0026sup2; = 0.956), supporting the sensor\u0026rsquo;s quantitative capability. Frequency-sweep analysis identified 200 Hz as the most favorable frequency for these measurements, providing the best signal-to-noise performance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Specificity controls were performed using unmodified (bare) electrodes and electrodes treated only with blocking solution. As presented in Supplementary Figures S4A and S4B, both controls produced less than 25% maximum impedance change, with the bare electrodes\u0026rsquo; response saturating after the second dose\u0026mdash;evidence of negligible specific binding and effective suppression of nonspecific adsorption by the blocking layer. These controls confirm that antibody functionalization is essential for selective AFB1 capture. To interpret the electrochemical processes, impedance spectra were fitted with a Randles equivalent circuit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), including solution resistance (R\u003csub\u003es\u003c/sub\u003e), charge-transfer resistance (R\u003csub\u003ect\u003c/sub\u003e), and a constant phase element (CPE) to account for non-ideal capacitive behavior arising from surface heterogeneity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Extracted CPE values increased with higher AFB1 concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), reflecting sensitivity to antigen\u0026ndash;antibody interactions at the interface. Collectively, these analyses demonstrate the platform\u0026rsquo;s precision, reproducibility, and robustness for quantitative AFB1 sensing in realistic food matrices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Spike-and-Recovery Validation of the Sensor\u003c/h2\u003e\u003cp\u003eSpike-and-recovery testing was conducted to validate the sensor\u0026rsquo;s accuracy and precision. Defined amounts of AFB1 were spiked into overnight-soaked corn samples and measured concentrations were determined from the Calibrated Dose Response (CDR). Comparing the introduced (expected) concentrations with the sensor-read values quantifies any systematic bias or measurement error and provides a rigorous assessment of method reliability. Three AFB1 levels were randomly introduced into the matrix\u0026mdash;0.045 ng/mL (low), 2.88 ng/mL (mid), and 22.96 ng/mL (high)\u0026mdash;with N\u0026thinsp;=\u0026thinsp;8 replicates per level. The limit of detection (LoD) was estimated using a signal-to-noise criterion: the specific signal threshold (SST) was set as the baseline mean plus three times its standard deviation. By this approach the sensor achieved an LoD of 0.005 ng/mL and a limit of quantification (LOQ) of 0.01 ng/mL, demonstrating its capability to resolve trace AFB1 concentrations. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD displays the percentage-recovery results for the overnight-soaked corn matrix, with recoveries consistently exceeding 80% across the tested range [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], further corroborating the assay\u0026rsquo;s robustness and analytical accuracy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Fingerprinting of \u003cem\u003eAflatoxin B1\u003c/em\u003e: Cross-reactivity and Specificity Evaluation\u003c/h2\u003e\u003cp\u003eTo establish the molecular fingerprinting capability of the developed electrochemical sensor towards \u003cem\u003eAflatoxin B1\u003c/em\u003e, a systematic cross-reactivity study was performed using a representative toxin cocktail containing \u003cem\u003eAflatoxin B1\u003c/em\u003e (AFB1) and \u003cem\u003eAflatoxin M1\u003c/em\u003e (AFM1). The schematic illustration of this experimental strategy is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, where the sensor was selectively modified with AFB1-specific antibodies via a DTSSP linker, and exposed to varying concentrations of AFB1-AFM1 mixtures (low: 0.045 ng/mL, mid: 2.88 ng/mL, and high: 22.96 ng/mL for each toxin). The Nyquist plots generated from electrochemical impedance spectroscopy (EIS) for the cocktail exposures are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. A clear progressive shift in the impedance was observed with increasing concentrations, signifying an enhanced capacitive response. This shift is indicative of an increased surface dielectric polarization attributed to the antigen\u0026ndash;antibody interactions at the electrode-electrolyte interface. The increase in capacitive behavior reflects the formation of a well-packed biomolecular layer on the sensor surface, which disrupts ion transport and modulates the interfacial charge dynamics. AFM1 produced only a minor impedance change, with a slight shift at 400 Hz suggesting weak nonspecific binding. However, this response was significantly lower than that of AFB1, confirming the sensor\u0026rsquo;s frequency-dependent selectivity and its ability to discriminate structurally similar toxins.\u003c/p\u003e\u003cp\u003eTo validate the molecular recognition fidelity in real samples, recovery studies were conducted using a corn flour matrix spiked with AFB1 and AFM1 at the same concentrations (0.045, 2.88, and 22.96 ng/mL). The results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC demonstrate that the recovery rates for AFB1 consistently exceeded 80% across all concentration levels, confirming the robustness of the antibody\u0026ndash;antigen affinity under complex matrix interference. This high recovery further emphasizes the sensor\u0026rsquo;s practical applicability for AFB1 detection in food-grade matrices. To interrogate potential cross-reactivity with AFM1, frequency-resolved impedance signatures were analyzed. At a fixed frequency of 400 Hz, where impedance behavior is sensitive to dielectric modulation, the AFM1 recovery appeared elevated relative to other frequencies; however, quantitative analysis revealed that the % recovery of AFM1 remained below 60% (see Supplementary Fig. S5). This suggests that although AFM1 may induce partial binding or nonspecific surface interactions at this frequency, the extent of signal overlap remains significantly lower compared to AFB1, thereby confirming partial discrimination capability. A more decisive specificity assessment was performed at 200 Hz, a frequency regime dominated by resistive contributions arising from strong molecular binding events. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD shows that the % recovery of AFB1 was consistently above 85%, whereas the corresponding recovery for AFM1 fell below 20%. This result strongly supports that the sensor exhibits minimal cross-reactivity to AFM1 under optimal detection conditions. The pronounced differential in recovery further validates that the AFB1 antibody-functionalized sensor leverages conformational and epitope-specific interactions that are absent in AFM1 due to structural divergence, particularly in the terminal furan ring and lactone moiety. Similarly, \u003cem\u003eZearalenone\u003c/em\u003e (ZEA) exhibited recovery values below 20%, indicating low binding affinity and potential matrix interference during detection. The structural differences between ZEA and AFB1, including variations in the lactone ring and overall steric conformation, likely limit its interaction with the AFB1-specific antibodies. Collectively, these data underscore the fingerprinting capacity of the sensor for selective AFB1 detection while highlighting the minimal cross-reactivity toward AFM1 and ZEA. The concerted analysis across different frequencies and toxin combinations not only confirms the functional specificity of the immobilized antibodies but also highlights the importance of frequency-domain analysis in deciphering cross-reactivity mechanisms in multi-analyte systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Validation of Sensor Performance: Reproducibility, Repeatability, and Device Correlation\u003c/h2\u003e\u003cp\u003eTo evaluate the practical applicability of the developed electrochemical biosensor for food safety monitoring, we conducted a comprehensive validation of its analytical performance in detecting trace levels of \u003cem\u003eAflatoxin B1\u003c/em\u003e (AFB1) in real-world matrices. Specifically, overnight-soaked corn samples\u0026mdash;selected for their relevance in mycotoxin contamination studies\u0026mdash;were spiked with known concentrations of AFB1, and tested for analytical precision and reproducibility under both intra-assay and inter-assay conditions. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the sensor exhibited high precision across the concentration range of 0.045 ng/mL, 2.88 ng/mL, and 22.96 ng/mL of AFB1, with coefficient of variation (%CV) values consistently below the critical threshold of 20%. This level of precision meets the performance benchmarks stipulated by the Clinical and Laboratory Standards Institute (CLSI) guidelines, underscoring the robustness of the sensing platform for trace-level quantification in complex food matrices [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Notably, the low %CV at the femtomolar-to-nanomolar range further demonstrates the stability of the electrode functionalization and the reliability of the impedance-based transduction mechanism, even in the presence of sample matrix components that often introduce variability in biosensing assays. To explore the feasibility of deploying this sensor in resource-limited or field settings, we developed and tested a portable version of the detection platform. The aim was to determine whether the sensor's electrochemical signature could be faithfully reproduced using a miniaturized, battery-operated potentiostat, simulating a real-time, on-site detection environment. The portable device was subjected to the same analytical protocol as the benchtop laboratory instrument, using identical spiked corn samples for direct performance comparison.\u003c/p\u003e\u003cp\u003eTo statistically validate the agreement between the two systems, a Pearson correlation analysis was performed on the impedance response data obtained from both platforms. The resulting correlation coefficient (Pearson r\u0026thinsp;=\u0026thinsp;0.965), shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, reflects a strong linear relationship between the laboratory-grade potentiostat and the portable unit. This high degree of correlation confirms that the portable sensor retains analytical fidelity comparable to its benchtop counterpart, with no significant loss of sensitivity or specificity during miniaturization. Together, these findings establish the platform\u0026rsquo;s capability for reproducible, accurate detection of AFB1 in a high-moisture corn matrix. Moreover, the successful translation of the sensing mechanism to a compact format underscores its potential as a deployable tool for point-of-need toxin screening. This opens avenues for rapid food safety diagnostics in agricultural supply chains, grain storage facilities, and rural inspection sites where conventional laboratory infrastructure may be inaccessible.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Performance Evaluation of the AFB1 Classifier Model\u003c/h2\u003e\u003cp\u003eThe classifier model developed in this study demonstrated high accuracy in distinguishing between safe and unsafe levels of \u003cem\u003eAflatoxin B1\u003c/em\u003e in 48 overnight soaked corn samples. A threshold-based classification approach was implemented, where samples were categorized as \"Safe\" if their scores exceeded 85 and \"Unsafe\" otherwise. The model's performance was evaluated using a confusion matrix, which revealed an accuracy of 90.6% for \u003cem\u003eAflatoxin B1\u003c/em\u003e, confirming its ability to correctly classify most samples (Supplementary Fig. S6). To further assess the model's effectiveness, the Receiver Operating Characteristic (ROC) curve was analyzed, highlighting the trade-off between sensitivity (true positive rate) and specificity. The Area Under the Curve (AUC) value for \u003cem\u003eAflatoxin B1\u003c/em\u003e was 0.83, indicating a strong ability to differentiate between safe and unsafe concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The high AUC score reinforces the model's robustness, confirming its suitability for real-time toxin detection. This validation underscores the classifier's potential for on-site food safety monitoring, providing a reliable tool for detecting \u003cem\u003eAflatoxin B1\u003c/em\u003e contamination and mitigating public health risks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn a Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, the x-axis represents the mean impedance value for each concentration of \u003cem\u003eAflatoxin B1\u003c/em\u003e in the overnight-soaked corn sample, comparing measurements between the lab benchtop potentiostat and the portable device. The y-axis denotes the difference in impedance values between the two methods for each concentration, highlighting the variation in readings across different \u003cem\u003eAflatoxin B1\u003c/em\u003e levels. The system demonstrates a mean bias of 4.06, suggesting no significant variation in measurement cycles between the two devices, particularly in the upper concentration range. Additionally, all test samples fall within \u0026plusmn;\u0026thinsp;1.96 times the standard deviation, forming the upper and lower 95% confidence interval, which confirms strong agreement between both methods. The balanced distribution of data points above and below the mean bias line further indicates the absence of systematic over- or underestimation, ensuring consistent and unbiased detection of \u003cem\u003eAflatoxin B1\u003c/em\u003e levels across different concentrations.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eWe developed a portable, electrochemical impedance-based sensor for the rapid detection of \u003cem\u003eAflatoxin B1\u003c/em\u003e in overnight-soaked corn samples, achieving a detection limit of 0.005 ng/mL using just 5 \u0026micro;L of sample with results in under 5 minutes. The sensor integrates specific antibodies for high selectivity and consistent performance across a wide concentration range in both lab and field settings. By optimizing the sensing response at 200 Hz, the platform leverages frequency-resolved interfacial charge behavior to discriminate \u003cem\u003eAflatoxin B1\u003c/em\u003e from structurally similar toxins like \u003cem\u003eAflatoxin M1\u003c/em\u003e, even in complex food matrices. This frequency-specific signal tuning, validated through cross-reactivity studies, enables highly selective detection without preprocessing or labeling. With its sensitivity, speed, and ease of use, the platform shows strong potential for on-site food safety monitoring.In future studies, efforts will be directed toward improving long-term sensor stability, exploring real-time measurements in complex food matrices, and validating performance across a broader range of environmental conditions to further enhance field applicability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, K.K.M.; methodology, K.K.M.; software, K.K.M.; validation, K.K.M. and V.N.D.; formal analysis, K.K.M.; investigation, V.N.D., S.M. and S.P.; resources, K.K.M. and S.P.; data curation, K.K.M. and K.M.T.; writing\u0026mdash;original draft preparation, K.K.M., K.M.T.; writing\u0026mdash;review and editing, K.K.M.; visualization, K.K.M. and S.P.; supervision, S.P.; project administration, K.K.M. and S.P.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by funding from EnLiSense LLC.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Drs. Shalini Prasad and Sriram Muthukumar have a significant interest in Enlisense LLC, a company that may have a commercial interest in the results of this research and technology. The potential individual conflict of interest has been reviewed and managed by The University of Texas at Dallas, and played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report, or in the decision to submit the report for publication. Portable device and technology platform is a proprietary of EnLiSense LLC.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eV. Kumar, M. S. Basu, and T. P. Rajendran, \u0026ldquo;Mycotoxin research and mycoflora in some commercially important agricultural commodities,\u0026rdquo; \u003cem\u003eCrop Protection\u003c/em\u003e, vol. 27, no. 6, pp. 891\u0026ndash;905, 2008, doi: 10.1016/j.cropro.2007.12.011.\u003c/li\u003e\n\u003cli\u003eS. Marchese, A. Polo, A. Ariano, S. Velotto, S. Costantini, and L. 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Prasad, \u0026ldquo;Advancing food Safety: Two-plex electrochemical biosensor for mycotoxin detection in food matrices,\u0026rdquo; \u003cem\u003eBiosens Bioelectron X\u003c/em\u003e, vol. 25, Sep. 2025, doi: 10.1016/j.biosx.2025.100626.\u003c/li\u003e\n\u003cli\u003eB. Jagannath, S. Muthukumar, and S. Prasad, \u0026ldquo;Electrical double layer modulation of hybrid room temperature ionic liquid/aqueous buffer interface for enhanced sweat based biosensing,\u0026rdquo; \u003cem\u003eAnal Chim Acta\u003c/em\u003e, vol. 1016, pp. 29\u0026ndash;39, 2018, doi: 10.1016/j.aca.2018.02.013.\u003c/li\u003e\n\u003cli\u003e\u0026ldquo;CLSI. Evaluation of Precision of Quantitative Measurement Procedures; Approved Guideline. CLSI document EP05-A3; Clinical and Laboratory Standards Institute Wayne (PA),\u0026rdquo; no. October, 2014.\u003c/li\u003e\n\u003cli\u003eK. Kumar Mishra, V. Narayanan Dhamu, S. Muthukumar, and S. Prasad, \u0026ldquo;Quick and Sensitive Two‐Plex Electrochemical Platform for Pathogen Detection in Water,\u0026rdquo; \u003cem\u003eNano Select\u003c/em\u003e, Apr. 2025, doi: 10.1002/nano.70017.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Electrochemical Impedance Spectroscopy, toxins, Aflatoxin, immunosensor","lastPublishedDoi":"10.21203/rs.3.rs-7583817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7583817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eAflatoxin B1\u003c/em\u003e (AFB1) is one of the most hazardous foodborne toxins, posing a major risk to food safety and human health worldwide. Traditional detection techniques are often limited by lengthy procedures, high costs, and insufficient sensitivity for on-site applications. To overcome these challenges, we developed a portable, non-faradaic electrochemical impedance spectroscopy (EIS) platform designed for rapid and highly sensitive detection of AFB1 in overnight-soaked corn samples. The working electrode was modified with a semiconducting-gold composite layer, followed by DTSSP crosslinker chemistry and antibody immobilization, which provided enhanced surface activity and stable biofunctionalization. This configuration enabled fast detection within 5 minutes, achieving an impressive detection limit of 0.005 ng/mL and a wide dynamic range of 0.01\u0026ndash;40.96 ng/mL. The sensor demonstrated excellent reproducibility, with intra- and inter-study %CV consistently below 20%. Validation against laboratory benchtop systems showed a strong correlation (Pearson r\u0026thinsp;=\u0026thinsp;0.977). Diagnostic evaluation further confirmed its robustness, yielding 90.6% accuracy and an AUC of 0.83. These results highlight the combined benefits of nanocomposite surface engineering and non-faradaic EIS detection in achieving highly sensitive performance. Compact, user-friendly, and reliable, this sensing platform represents a promising solution for on-site toxin detection in food supply chains, thereby contributing to improved food safety monitoring and reduced public health risks associated with AFB1 exposure.\u003c/p\u003e","manuscriptTitle":"Portable Electrochemical Sensing Platform for Aflatoxin B1 Detection in Food Matrices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 12:48:34","doi":"10.21203/rs.3.rs-7583817/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-10T13:53:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T12:25:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181736879133468631156556445918673018543","date":"2025-09-27T14:17:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-17T00:54:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T23:31:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T23:30:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Analytical Methods","date":"2025-09-10T14:09:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"799ae554-9695-4e7c-896f-5eb894377c84","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-07T21:23:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 12:48:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7583817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7583817","identity":"rs-7583817","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}