Ochratoxin a Detection in Coffee: Matrix Inteferences and Implications for Food Safety Monitoring

Ochratoxin a Detection in Coffee: Matrix Inteferences and Implications for Food Safety Monitoring | 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 Ochratoxin a Detection in Coffee: Matrix Inteferences and Implications for Food Safety Monitoring Isabela Fracalossi Mancini, Gabriel Fernandes Souza Santos, Isabella Oliveira Britto, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9137063/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The accurate monitoring of Ochratoxin A (OTA) in coffee, a globally traded commodity, is paramount for public health. However, the chemical complexity of the coffee matrix presents a significant challenge to analytical accuracy. This study systematically investigated the impact of six key coffee components, caffeine, caffeine, caffeic acid, chlorogenic acid, cafestol, acrylamide, and melanoidins, on OTA detection using two established methods: immunoaffinity cleanup coupled with HPLC-FLD and a commercial Lateral Flow Immunoassay (LFIA). The results reveal a pronounced method-dependency in matrix interference. In HPLC-FLD, caffeine and caffeic acid caused significant, concentration-dependent reductions in OTA recovery (to ~65-70%). Conversely, LFIA performance was most compromised by chlorogenic acid and melanoidins, which decreased recoveries to 48.5% and 35.7%, respectively. Molecular docking simulations indicated that stable non-covalent interactions (hydrogen bonding and π–π stacking) between OTA and specific interferents can sequester the toxin and hinder antibody recognition. Analysis of commercial Brazilian coffees confirmed these interferences, with notable discrepancies in OTA levels between methods. These findings demonstrate that matrix effects are not uniform but are dictated by the analytical platform's underlying principle. Consequently, this work underscores the necessity of matrix-specific validation for both conventional and rapid methods to ensure reliable OTA monitoring and robust food safety protection. Mycotoxin detection Cleanup Chromatography LFIA Immunoaffinity Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Brazilian agribusiness occupies a strategic position in the global economy, with coffee production emerging as a critical sector due to its substantial contribution to international trade and export revenues. Within this context, Brazil stands as the foremost global producer and exporter of coffee, particularly the Conilon variety, which increasing international demand underscores its economic and social relevance (FAO 2024). However, its safety is frequently threatened by contamination with mycotoxins, especially ochratoxin A (OTA), a secondary metabolite synthesized predominantly by Aspergillus and Penicillium species under favorable environmental conditions such as elevated moisture during post-harvest handling and storage (Bazin et al. 2013 ; European Commission, 2023 ; Fabian 2024 ; Więckowska et al. 2024 ; Massahi et al. 2024 ). OTA is recognized for its potent nephrotoxic, immunosuppressive, teratogenic, and carcinogenic properties, which have profound implications for public health worldwide (Wang et al. 2023 ; Ding et al. 2023 ; Fabian 2024 ; Massahi et al. 2024 ). Due to these health concerns, strict maximum limits for OTA have been established by international regulatory agencies, including the European Regulation 2023/915 and 2022/1370, to control its occurrence in food products such as cereals, wine, and coffee. Exceeding such thresholds often results in significant economic losses and compromises the quality of Brazilian coffee in global markets, emphasizing the exigency for robust, reliable, and efficient detection and quality control methodologies (Ding et al. 2023 ). Traditionally, the determination of OTA relies on high-performance liquid chromatography (HPLC) coupled with different detectors. Although liquid chromatography coupled to tandem mass spectrometry (HPLC–MS/MS) offers superior sensitivity and selectivity, its high operational cost, infrastructure demands, and requirement for specialized personnel restrict its routine application. On the other hand, HPLC with fluorescence detection (HPLC–FLD) has been established as a more accessible and cost-effective alternative and is widely regarded as the gold standard for OTA quantification (Fujii et al. 2007 ; Tittlemier et al. 2025 ; Leeman et al. 2025 ). Given that OTA typically occurs at trace levels, the use of immunoaffinity columns (IACs) is strongly recommended prior to chromatographic analysis (Castegnaro et al. 2006 ; Uchigashima et al. 2012 ; Li et al. 2022 ). This pre-treatment step offers several advantages, primarily by preconcentrating the analyte and reducing the presence of matrix interferences that hinder quantification (Maphaisa et al. 2025 ). The selectivity of the IAC arises from antibodies immobilized within the column that specifically recognize and bind OTA, thereby enhancing analytical sensitivity and specificity (Atumo 2020 ). Despite the clear benefits of IACs in mycotoxin analysis, several studies have reported low recovery rates in dark or highly pigmented matrices such as wine and coffee. In the case of coffee samples, compounds including caffeine, other mycotoxins, and melanoidins have been identified as major interferents that can compromise antibody recognition and reduce OTA recovery (Entwisle et al. 2001 ; Fujii et al. 2007 ; Bazin et al. 2013 ; Vieira et al. 2015 ). Complementary to chromatographic methods, antibody-based immunoassays have gained prominence as powerful screening tools, offering high sensitivity and specificity through antibody recognition (Meulenberg 2012 ; Fadlalla et al. 2020 ; Liew and Sabran 2022 ; Karachaliou et al. 2025 ). Among these, the lateral flow immunoassay (LFIA) has become particularly popular due to its operational simplicity, rapid response, and potential for on-site analysis (Bazin et al. 2010 ; Fadlalla et al. 2020 ). In competitive LFIA formats, typical for small molecules such as OTA, the analyte competes with an immobilized conjugate for antibody-labeled nanoparticles, generating a signal inversely proportional to OTA concentration (Chen et al. 2021 ; Pedreira-Rincón et al. 2025 ). Recent research has emphasized that matrix-derived chromogens and particulate matter markedly increase background signals and reduce assay sensitivity in LFIA applications to coffee samples. Strategies to circumvent these effects typically involve optimized sample dilution and buffer composition to stabilize antibody binding and diminish nonspecific interactions, which are critical for reliable semi-quantitative assessments (Contreras Alvarez et al. 2024 ). Whereas, balancing dilution to reduce background without compromising detection limits remains an ongoing methodological challenge. However, analytical methods that rely on immunorecognition, whether for sample clean-up such as immunoaffinity columns or for direct antibody recognition in biosensors, can have their efficiency severely compromised by certain compounds present in complex food matrices. The determination of OTA in coffee is analytically challenging because of the matrix complexity (Guo et al. 2023 ; Banahene et al. 2024 ). Coffee contains diverse compounds, such caffeine, chlorogenic acids, caffeic acid, cafestol, acrylamide, and high-molecular-weight melanoidins formed during roasting, that can interfere with extraction, clean-up, and detection (Nieber 2017 ; Aytar and Aydın 2025 ; Alcantara et al. 2025 ; Schaffel et al. 2025 ). Such interferences may result in reduced recoveries or false-negative results, compromising accurate quantification. Given this challenge, several studies have sought to improve OTA quantification in coffee through the optimization of pre-treatment and cleanup strategies aimed at removing matrix components that hinder immunochemical recognition. Scott and Trucksess ( 1997 ) demonstrated that sample interferences may originate not only from the analyte matrix itself but also from extraction solvents or even from the sorbents used in cleanup columns, emphasizing the need for careful optimization of analytical workflows. Although the adoption of IACs has significantly enhanced the selectivity and sensitivity of mycotoxin analyses, their performance remains dependent on the composition of the sample matrix (Liu et al. 2018 ). To address these limitations, studies have explored additional purification steps prior to IAC application (Duarte et al. 2013 ; Monaci and Palmisano 2004 ; Nakajima 2003 ; Valenta 1998 ), including aminopropyl (NH₂) and phenylsilane SPE cartridges for roasted coffee extracts, which improved peak definition, reduced matrix interferences, and achieved OTA recoveries of 72–85% (Entwisle et al. 2001 ; Sibanda et al. 2002 ). Other SPE sorbents such as silica and C18 phases have also been reported as complementary cleanup approaches (Scott and Trucksess, 1997 ; Vieira et al. 2015 ). For LFIA sensors, highly colored samples often cause high background and poor detectability (Anfossi et al. 2012 ; Contreras Alvarez et al. 2024 ). Despite significant advancements in analytical methodologies for ochratoxin A quantification, the accurate and reliable determination of OTA in coffee matrices remains an exigent analytical challenge. Therefore, the aim of this study was to investigate the impact of representative coffee components on OTA detection using two analytical strategies: immunoaffinity column cleanup followed by HPLC-FLD, and commercial LFIA. Six characteristic compounds of coffee chemistry (caffeine, caffeic acid, chlorogenic acid, cafestol, acrylamide, and melanoidins) were individually tested in OTA-contaminated coffee samples. Additionally, molecular docking simulations were performed to provide mechanistic insights into potential interactions between OTA and matrix constituents. The findings provide a basis for method validation and cleanup selection in routine OTA monitoring of coffee and highlight the need for matrix-specific validation of both chromatographic and immunoassay-based methods. Materials and methods Chemicals and reagents Analytical grade ochratoxin A (O1877), 5-hydroxymethylfurfural (HMF, W501808), caffeic acid (C0625), chlorogenic acid (C3878), and acrylamide (08267) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Caffeine anhydrous (813) and glacial acetic acid P.A. (141) were obtained from Vetec (Duque de Caxias, RJ, Brazil), whereas cafestol (82294) was sourced from PhytoLab (Vestenbergsgreuth, Germany). Chromatographic solvents of HPLC grade (methanol 9093, acetonitrile 34851) were supplied by J.T. Baker (Phillipsburg, NJ, USA) and Honeywell (Charlotte, NC, USA), respectively. A Zorbax ODS column (250 × 4.6 mm, 5 µm; Agilent, Santa Clara, CA, USA) was employed for chromatographic separations. Specific immunoaffinity columns (OchraTest WB, G1033) and SPE cartridges (aminopropyl NH2, 500 mg/6 mL; phenyl PH, 500 mg/3 mL) were sourced from Vicam (Watertown, MA, USA) and Applied Separations (Allentown, PA, USA), respectively. The LFIA kit was the Ochratoxin A Qualitative Rapid Lateral Flow Test Kit kindly supplied by NanoSmart®. Ultrapure water (18.2 MΩ·cm) was generated using a Merck Synergy system. Coffee sample Four types of Brazilian coffee were selected for analysis: Arabica specialty coffee (UFLA), traditional extra-strong coffee (Meridiano®), instant coffee (Café Iguaçu®), and decaffeinated coffee (3 Corações®). All samples were commercially available products purchased from local markets in Espírito Santo, Brazil. The samples were stored under controlled temperature and humidity conditions in airtight containers to prevent contamination or degradation prior to analysis. Each type of coffee was analyzed in triplicate to ensure the accuracy and reproducibility of the results. Preparation of OTA and interferent standards Working solutions of OTA for calibration and sample fortification were prepared from a 100 µg L − 1 stock solution by serial dilution with ultrapure water to obtain six calibration levels ranging from 0.125 to 5.0 µg L − 1 . Standard solutions of each potential interferent (caffeine, caffeic acid, chlorogenic acid, acrylamide, cafestol, 5-hydroxymethylfurfural (HMF), and melanoidins) were prepared in ultrapure water. The low and high concentration levels for each compound were selected according to values reported in the literature for coffee matrices (Table 1 ). Their low and high concentration levels were selected based on values reported in the literature for typical coffee matrices (Table 1 ). Melanoidins were synthesized according to the procedure described by Bhamare and Kakulte ( 2022 ) with minor adaptations for this study. Equimolar aqueous solutions of glucose (180.16 g L − 1 ) and glycine (37.54 g L − 1 ) were heated at 90 ± 1°C for 6 h to promote Maillard polymerization. The resulting dark solid was filtered, finely ground, and stored in a desiccator under dry conditions until further use. Table 1 Concentration levels of selected coffee matrix interferents. Compound Low level (mg L − 1 ) High level (mg L − 1 ) References Caffeic acid 800 1300 and 5000 (Higdon and Frei 2006 ; Iwahashi 2015 ; Kalthoff et al. 2020 ) Chlorogenic acid 800 3000 (Fujioka and Shibamoto 2008 ; Vitaglione et al. 2012 ) Acrylamide 0.04 0.2 (Bagdonaite et al. 2008 ; Schouten et al. 2020 ; Strocchi et al. 2022 ) Caffeine 200 2000 and 4000 (Fujioka and Shibamoto 2008 ; Vignoli et al. 2011 ; Olechno et al. 2021 ) Cafestol 150 300 (Kurzrock and Speer 2001 ; Souza et al. 2010 ; Scholz et al. 2014 ) HMF 40 300 (Park et al. 2021 ; Xie et al. 2023 ) Melanoidins 4000 15000 and 30000 (Bekedam et al. 2006 ; Vignoli et al. 2011 ; Antonietti et al. 2022 ) Pool Low High Table 1 (here) Preparation of interference assay samples For each experimental condition, OTA was combined with either a single interferent or a mixture containing all interferents (pool). The final volume was adjusted to 10.0 mL in polypropylene tubes using ultrapure water for analyses involving IAC/HPLC-FLD or phosphate-buffered saline (PBS) for LFIA assays. The final OTA concentration in each extract was 1.25 µg L − 1 . In the individual assays, the concentration of each interferent was set at either the low or high level reported in the literature. In the pooled assays, all interferents were added simultaneously at their respective low or high levels. The tubes were vortex-mixed for 30 s and processed immediately. All experimental conditions were prepared in triplicate to ensure reproducibility. Analytical procedures IAC cleanup and HPLC-FLD (coffee and test solutions) For OTA determination by HPLC-FLD, both coffee extracts and aqueous test solutions were subjected to immunoaffinity cleanup followed by chromatographic analysis. The procedure used was recommended by the fabricant of IAC column (Vicam®). Briefly, ground coffee (12.5 g) was extracted with a total volume of 250 mL consisting of methanol and 3% (w/v) sodium bicarbonate solution mixed in a 1:1 (v/v) ratio. The suspension was stirred vigorously for 5 min, vacuum-filtered, and passed through glass microfiber filters (1.5 µm) to remove fine particulates. An aliquot of 4 mL of the clarified extract was diluted to 100 mL with phosphate-buffered saline (PBS), and 10 mL of this diluted solution were loaded onto an OTA-specific immunoaffinity column (OcraTest WB Vicam® ) at a flow rate of approximately one to two drops per second. The column was washed with 10 mL of water, and OTA was eluted with 4 mL of methanol at a flow rate below one drop per second. The eluate was evaporated at 45°C under a gentle nitrogen stream to near dryness, and the residue was reconstituted in 400 µL of water, methanol, and acetic acid (50:48:2, v/v/v). The solution was vortex-mixed and transferred to amber autosampler vials for HPLC analysis. Chromatographic separations were performed using a Shimadzu liquid chromatography system comprising a CBM-20A control unit, LC-20AT pumps, an RF-20AXL fluorescence detector, and a CTO-20A column oven. Separation was achieved on a Zorbax ODS analytical column (250 × 4.6 mm, 5 µm particle size) under isocratic elution using a mobile phase composed of acetonitrile, water, and acetic acid in a volumetric ratio of 50:48:2 (v/v). The flow rate was set at 1.0 mL min⁻¹, with the column temperature rigorously maintained at 40°C. Sample injection volume was standardized at 20 µL. Fluorescence detection was conducted at excitation and emission wavelengths of 333 nm and 477 nm, respectively. External calibration curves were constructed over six concentration points, ranging from 0.125 to 5.0 µg L⁻¹, utilizing standard solutions prepared in the mobile phase to ensure matrix consistency. Both calibration standards and unknown samples underwent identical chromatographic conditions. The limit of quantification (LOQ) was established based on a minimum signal-to-noise ratio of 10:1, whereas the limit of detection (LOD) corresponded to a signal-to-noise ratio of 3:1. Lateral Flow Immunoassay (LFIA) OTA qualitative lateral flow test strips were kindly provided by NanoSmart®. The analysis of coffee samples followed the manufacturer's protocol with adaptations for quantitative image analysis. Briefly, 5 g of ground coffee were extracted with 20 mL of a 5% (w/v) sodium bicarbonate (NaHCO 3 ) solution. The suspension was vigorously shaken for 1 min and subsequently filtered to obtain a clear extract. A 200 µL aliquot of the filtrate was mixed with 200 µL of the kit's running buffer in the provided microwell. After gentle homogenization, 200 µL of this diluted sample were transferred to a new microwell. Following a 3-minute incubation period at ambient temperature (25 ± 2°C), the test strip was inserted into the well and allowed to develop for 5 minutes. The strips were photographed immediately after the development time, with all readings completed within a 1-minute window to ensure consistency. For quantitative analysis, the developed test strips were photographed using the rear camera of an iPhone XR (12 MP, f/1.8 aperture) mounted on a fixed tripod to maintain a consistent distance and angle. All images were captured under uniform white LED illumination without flash, as illustrated in Online Resource 2, to minimize shadows and color temperature variations. The images were analyzed using ImageJ software (version 1.54g, National Institutes of Health, USA). Each image was split into its red, green, and blue (RGB) color channels. The green channel was selected for analysis due to its superior contrast for the gold nanoparticle-based test lines. A rectangular region of interest (ROI) was defined along the length of the strip, encompassing both the test (T) and control (C) lines. The pixel intensity profile across this ROI was generated using the "Plot Profile" function, producing a grayscale intensity distribution graph (Online Resource 3). The minimum intensity values corresponding to the T and C lines were identified, and the Test-to-Control (T/C) ratio was calculated for the quantitative assessment of OTA. Solid-phase extraction (SPE) pre-treatment SPE pretreatment was performed following the procedures described by Entwisle et al. ( 2001 ) and Sibanda et al. ( 2002 ), with minor modifications. For selected assays, coffee extracts underwent additional cleanup using aminopropyl (NH₂) or phenyl SPE cartridges prior to IAC purification. Cartridges were conditioned sequentially with 5 mL methanol followed by 5 mL ultrapure water. Sample extracts were then loaded at a controlled flow rate of ~ 1–2 drops s⁻¹ to maximize analyte retention and matrix removal. SPE eluates were collected and processed by IAC as previously described. Molecular Docking Studies Molecular docking simulations were performed to investigate the interactions between OTA and the selected coffee components. The molecular structures of acrylamide, cafestol, and HMF were obtained from the PubChem database, whereas those of OTA, caffeic acid, and chlorogenic acid were generated using Chemicalize to represent their neutral-pH dissociation states. All molecular geometries were optimized in ORCA 5.0.4 using the BP86 functional and the def2-SVP basis set. Receptor and ligand input files were prepared in AutoDockTools 1.5.7, and docking calculations were carried out with AutoDock Vina® using a cubic grid of 90 × 90 × 90 points with a spacing of 0.375 Å, centered at coordinates (7.574, 1.017, 1.384). Multiple independent runs were performed to confirm reproducibility of the results. The best-scoring binding poses were examined and annotated in UCSF Chimera (version 1.16) and BIOVIA Discovery Studio (version 21.1) to identify hydrogen-bond interactions and π–π stacking contacts. Coffee sample preparation and analysis with SPE cleanup SPE was performed prior to IAC cleanup, the cartridges were preconditioned with 5 mL of methanol followed by 5 mL of water, and the extracts were loaded at a controlled flow rate of approximately one drop per second. For the phenyl (PH, 500 mg/3 mL) cartridge workflow, 10 mL of the percolate were collected, and the cartridge was subsequently washed with 2 mL of methanol:3% (w/v) sodium bicarbonate (50:50, v/v) and 1 mL of methanol. The collected eluate and washing fractions were combined, diluted to 100 mL with PBS, and processed through the IAC as previously described. Quantification of OTA was carried out by HPLC-FLD using the same chromatographic conditions established for the interference assays. Method validation Method validation was carried out to assess analytical performance in terms of recovery, repeatability, linearity, and detection capability. Recovery and precision were determined through spiking experiments at representative concentration levels, while linearity was evaluated using calibration curves constructed from six concentration points. The limits of detection (LOD) and quantification (LOQ) were defined at signal-to-noise ratios of 3:1 and 10:1, respectively. All experiments were performed in triplicate, and mean recoveries were reported as percentage ± standard deviation. Method accuracy was determined through recovery experiments by fortifying blank and naturally contaminated coffee samples at a representative level of 25 µg kg⁻¹. The recovery percentage was calculated accounting for the natural OTA content, when detected, to ensure accurate assessment of the method's performance. Validation results were interpreted according to the general criteria established by ANVISA Resolution No. 899 IN 88/2021, International guidelines (European Commission, 2023 ), and the Codex Alimentarius recommendations for mycotoxin determination in food matrices. Statistical Analysis and Analytical Parameters All assays were carried out in triplicate, and results were expressed as mean ± standard deviation. Method performance was evaluated in terms of recovery, repeatability, linearity, limit of detection (LOD), and limit of quantification (LOQ). Calibration curves were constructed within the analytical range of each method, whereas LOD and LOQ were determined using signal to noise ratios of 3:1 and 10:1, respectively. For LFIA, quantitative analysis was based on the test to control (T/C) ratio obtained by digital image processing. Graphical representations were generated using OriginPro 2018 Pro (OriginLab Corp., Northampton, MA, USA) and GraphPad Prism 11.0.0 (GraphPad Software, San Diego, CA, USA). Results and discussion Analytical performance (HPLC-FLD and LFIA) For HPLC-FLD analysis, the calibration curve constructed from six concentration levels (0.125–5.0 µg L⁻¹) demonstrated adequate linearity, consistent with the acceptance criteria of ANVISA Resolution No. 899 as shown in Fig. 1 a Fig. 1 b illustrates the representative chromatogram of the calibration range. The method achieved a limit of quantification (LOQ) of 0.528 µg L⁻¹, suitable for confirmatory OTA quantification in coffee at concentrations relevant to regulatory limits. These results are in agreement with recent studies reporting comparable LOQs for immunoaffinity cleanup coupled with fluorescence detection in complex matrices (Hwang et al. 2023 ; Barboráková et al. 2025 ). For the LFIA, a semi-quantitative calibration was established in triplicate using seven OTA concentrations spanning 5–30 µg L⁻¹, in accordance with the manufacturer’s specified working range for the strips (Fig. 1 c), while Fig. 1 d represents the corresponding test line responses used to construct the calibration curve. The experimentally determined LOQ of 1.96 µg kg⁻¹ was lower than the nominal visual cut-off of 5 µg kg⁻¹ declared by the manufacturer, indicating that digital image–based signal acquisition improves the effective analytical sensitivity of the assay. This enhanced performance supports the use of LFIA as a reliable screening tool for OTA in coffee at concentrations aligned with international regulatory limits, particularly when integrated into a tiered testing strategy alongside confirmatory HPLC-FLD. Overall, these analytical benchmarks highlight the distinct yet complementary strengths of the two methods. HPLC-FLD provides the precision and sensitivity necessary for confirmatory determinations, while LFIA offers a rapid, low-cost, and field-deployable option for preliminary screening. Although LFIA exhibits inherently higher variability due to its immunochemical nature and matrix sensitivity, its operational simplicity and speed make it an invaluable first-line tool for large-scale monitoring programs. Together, the combined use of HPLC-FLD and LFIA provides a robust analytical framework that balances analytical rigor with practical feasibility in OTA surveillance across diverse coffee matrices. Impact of individual matrix components Recovery results for OTA in the presence of individual coffee constituents and pooled mixtures are summarized in Fig. 2 and described in Online Resource 1. In ultrapure water (matrix-free control), OTA recovery reached 98.54% by HPLC-FLD, confirming the excellent performance of IAC when no interfering compounds are present. However, the introduction of matrix components led to varying degrees of signal suppression. Caffeine, for instance, reduced OTA recovery from 84.08% and 86.0% at 200 and 2000 mg L − 1 , respectively, to 65.17% at 4000 mg L − 1 , evidencing a concentration-dependent inhibitory effect. Studies consistently demonstrate that decaffeinated coffee exhibits elevated OTA levels compared to caffeinated counterparts, attributable to the absence of this natural antifungal activity. Concentrations of caffeine as low as 1.0–2.0% completely inhibit fungal proliferation and OTA production, thus positioning caffeine as a vital endogenous bio-protector within the coffee matrix that mitigates mycotoxin risks during processing (Nehad et al. 2005 ). The significant, concentration-dependent suppression of OTA recovery by caffeine shown in Fig. 2 indicates that even within the normal range of coffee compositions, this alkaloid can impact the immunoaffinity cleanup. This is a major concern for validating methods across diverse coffee types, from light roasts (lower caffeine) to robusta blends (higher caffeine), and underscores that caffeine removal during sample prep is not just beneficial but often necessary for accurate quantification. Similarly, melanoidins maintained high recoveries at moderate levels (97.41% at 4.0 g L − 1 ) but caused a gradual decrease to 93.31% and 88.22% at 15 and 30 g L − 1 , respectively. The severe suppression of LFIA signal by melanoidins highlights a critical limitation for on-site testing of dark-roasted coffees, potentially leading to false-negative results. These results reinforce earlier reports by Scott et al. (1997) and Vieira et al. ( 2015 ), which identified caffeine and melanoidins as major contributors to matrix interference in OTA detection. Such compounds may compete with OTA for antibody binding sites or modify the microenvironment of the antigen–antibody interaction, diminishing binding affinity and column efficiency. Furthermore, melanoidins, high molecular weight nitrogenous and brown compounds formed during coffee roasting from polysaccharides, proteins, and chlorogenic acids, exhibit structural complexity that enables them to interact competitively with OTA during immunoaffinity purification (Moreira et al. 2012 ). The observed concentration-dependent decrease suggests that even within the dynamic range of typical coffee compositions, these molecules can significantly impact the recovery performance of immunoaffinity-based methods, underscoring the need for careful optimization of sample preparation to mitigate matrix effects. Caffeic acid exhibited negligible interference at 800 mg L − 1 (85.26%) and 1300 mg L − 1 (93.57%), but recovery declined to 69.39% at 5000 mg L − 1 , indicating concentration-dependent effects. Acrylamide, tested at 40 µg L − 1 and 500 µg L − 1 , showed recoveries of 92.22% and 98.38%, respectively, suggesting minimal interference. Chlorogenic acid presented recoveries of 98.53% at 800 mg L − 1 and 110.18% at 3000 mg L − 1 , while HMF did not affect recovery at 40 mg L − 1 (98.53%) but reduced it to 85.78% at 300 mg L − 1 . Cafestol demonstrated consistently high recovery (101.09%) across both tested concentrations. When all compounds were combined in the pooled mixture, OTA recovery by HPLC-FLD was 93.86% at the high level and 92.08% at the low level. This result suggests that the effects of the individual interferents were not simply additive under the chromatographic conditions employed. A possible explanation is that, in the pooled system, the simultaneous presence of multiple co extracted compounds altered the overall matrix effect, thereby attenuating the influence observed for some compounds when evaluated individually (Matuszewski et al. 2003 ; Cortese et al. 2020 ). In addition, the reduced relative contribution of each interferent within the mixture may have lessened compound specific effects on OTA determination, resulting in recoveries closer to the expected values. In the study by Prelle et al. ( 2013 ), a comparison of clean-up methods for ochratoxin A in wine, beer, roasted coffee, and chili was carried out, and it was concluded that Immunoaffinity columns (IAC) remain the predominant clean-up method for ochratoxin A (OTA) detection in coffee by the time, due to their specific antibody-antigen interactions, which effectively reduce matrix interferences. However, their efficiency can be compromised by matrix effects such as open-ring OTA formation and isomerization during roasting, which hinder antibody recognition. Compared to other methods, IAC offers consistent recovery rates around 75–84%, with acceptable precision, but other methodologies such as Molecularly Imprinted Polymers (MIP) have shown superior performance in purifying coffee matrices and reducing interferences, achieving higher recovery and lower detection limits. This makes IAC reliable but somewhat limited by matrix-induced challenges in coffee OTA analysis (Prelle et al. 2013 ; Li et al. 2022 ). It is therefore necessary and relevant to explore alternative sample preparation methodologies for the purification of this analyte. In contrast, LFIA revealed a generally inverse relationship between recovery and interferent concentration. The most pronounced reductions were observed with chlorogenic acid at 3000 mg L − 1 (48.45%) and melanoidins at 30 g L − 1 (35.74%), suggesting that OTA recognition on the strip can be compromised by mechanisms such as direct competition, antigen masking, or chemical modifications that hinder antibody binding. In addition, the strong coloration of melanoidin-rich extracts may contribute to signal distortion or partial masking of the test and control lines, leading to an apparent underestimation or overestimation of OTA concentration depending on the optical contrast of the strip (Anfossi et al. 2012 ). The pooled mixture particularly impacted LFIA performance, with recovery dropping to 21.66% at the high level, indicating a cumulative detrimental effect of multiple matrix constituents on antibody recognition. Furthermore, the chromogenic properties of melanoidins intensify background coloration, impairing signal clarity in LFIA platforms, a phenomenon documented in recent optical immunoassay evaluations (Thenuwara et al. 2025 ). This effect complicates interpretation and quantification by increasing signal noise, highlighting the necessity for matrix-specific calibration and potential pretreatment to mitigate chromatic interference. These effects were consistently more pronounced in LFIA than in HPLC-FLD, likely due to the direct antibody–antigen interaction mechanism in the strip-based assay, which renders it more vulnerable to matrix interference. The observed patterns corroborate the chromatographic results and reinforce the concept that coffee matrix compounds can significantly compromise OTA detection in both traditional immunoanalytical systems (IAC–HPLC-FLD) and portable screening platforms (LFIA). Interestingly, the interference profiles were method-dependent: compounds that moderately affected HPLC-FLD had minimal impact on LFIA, whereas those strongly interacting with antibodies, notably chlorogenic acid and melanoidins, markedly impaired LFIA performance. These findings underscore that matrix effects are not uniform across analytical methods but are intrinsically shaped by the underlying detection principle, highlighting the importance of method-specific strategies to mitigate interference and improve the reliability of OTA analysis in complex matrices like coffee. Docking simulations as mechanistic support To elucidate the molecular basis underlying the matrix effects observed in OTA determination, molecular docking simulations were conducted to explore the interactions between OTA and key coffee constituents. We focused on the hypothesis of analyte masking, whereby interferents bind to OTA in solution, potentially altering its conformation or shielding its epitope, thereby reducing its effective concentration and accessibility for antibody recognition. In the literature, it is possible to find computational and spectroscopic studies that have shown that OTA strongly binds to proteins, particularly human serum albumin (HSA), through hydrogen bonding and π–π stacking interactions (Il’ichev et al. 2002 ; Xu et al. 2022 ; Vakili et al. 2023 ; Algethami et al. 2024 ). In coffee, however, the presence of diverse organic compounds can compromise the performance of IACs, frequently leading to underestimation and variable recovery (Castegnaro et al. 2006 ; Tozlovanu and Pfohl-Leszkowicz 2010 ). Despite these analytical challenges, molecular-level insights into how specific coffee constituents interact with OTA are not described in the literature. We hypothesized that small organic molecules abundant in coffee may engage in non-covalent interactions with OTA, primarily hydrogen bonding and π–π stacking, thereby masking the toxin or altering its conformation in solution, ultimately reducing antibody recognition. To evaluate this, the binding affinities of OTA–compound pairs were estimated from the mean free energy of binding (ΔG, kcal mol − 1 ) derived from 100 docked conformations, where less negative values denote weaker binding (Fig. 3 a). Representative docking poses highlighting hydrogen bonds and π–π contacts are shown in Fig. 3 b. Caffeine and 5-hydroxymethylfurfural (5-HMF) exhibited moderate affinity for OTA, stabilized by both hydrogen bonds and π–π interactions, in agreement with reported binding patterns of these compounds to serum albumins (Wang et al. 2013 ; Zhou et al. 2020 ; Vakili et al. 2023 ). The formation of such stable OTA-caffeine complexes in the sample extract could rationally explain the decreased recoveries observed experimentally in both IAC and LFIA, as a fraction of the OTA would be unavailable for binding. Similarly, caffeic acid and chlorogenic acid formed π–π stacking interactions with OTA, accompanied by hydrogen bonding through their hydroxyl groups. This interaction mode closely resembles that described for OTA–HSA complexes, where hydrogen bonds and aromatic stacking contribute to complex stabilization (Tang et al. 2016 ; Xiang et al. 2016 ; Vakili et al. 2023 ). The strong experimental interference of chlorogenic acid in the LFIA format, in particular, may be due to its ability to form a particularly stable complex that efficiently masks the OTA epitope recognized by the more conformationally sensitive antibodies used in the lateral flow strip. In contrast, acrylamide displayed the weakest binding energy and no detectable specific interactions with OTA, consistent with its negligible impact on experimental recoveries. Cafestol, while forming hydrogen bonds with OTA in silico, did not significantly alter OTA detection, indicating that this particular interaction may be too weak or infrequent to compete effectively with the robust antibody-antigen binding under the studied conditions. Cafestol, a coffee diterpene, also demonstrated in silico binding affinity towards OTA, primarily through hydrogen bonding. This is consistent with the known behavior of diterpenes to interact with serum proteins (Berti et al. 2020 ), However, this predicted interaction did not translate into a significant experimental reduction in OTA recovery in our study. This discrepancy suggests that while the interaction is thermodynamically possible, its kinetics or prevalence under the actual analytical conditions may be insufficient to compete effectively with the robust antibody-antigen binding. Overall, the docking simulations corroborate experimental findings by demonstrating that specific coffee matrix components, particularly caffeine, 5-HMF, caffeic acid, and chlorogenic acid, can establish stable non-covalent interactions with OTA. These interactions likely reduce antibody accessibility to the toxin, thereby diminishing the sensitivity of immunoassay-based detection methods. The molecular insights obtained here reinforce that the chemical complexity of coffee matrices plays a pivotal role in analytical performance and underscore the importance of considering such interactions when developing or validating OTA determination strategies in complex food systems. Evaluation in real coffee samples To assess the applicability of the analytical strategies in complex food matrices, OTA recovery was evaluated in commercial Brazilian coffee samples from four categories: traditional extra-strong, soluble, Arabica specialty, and decaffeinated, showed in Table 2 . Each sample was analyzed with and without fortification at 25 µg kg − 1 of OTA for HPLC-FLD and LFIA. The influence of an additional solid-phase extraction (SPE) pretreatment prior to immunoaffinity cleanup was also investigated. The diversity in coffee types reflects distinct chemical and structural matrix profiles, influenced by roasting level, bean processing, and blend composition. These factors drive differential concentrations of polyphenols, melanoidins, and alkaloids such as caffeine, which modulate matrix complexity and impact OTA recovery. Moreover, roasting-induced changes affect matrix porosity and polarity, modulating analyte accessibility and solvent interactions during extraction. Such physicochemical heterogeneity significantly influences OTA recovery and detection limits. The darker roasts typical of traditional extra-strong and soluble coffees foster increased Maillard reaction products and condensed polyphenolic content, enhancing matrix interferences compared to lighter roasted specialty coffees and decaffeinated variants, thus influencing immunoaffinity binding dynamics and assay sensitivity (Gottstein et al. 2021 ; Cwiková et al. 2022 ). Table 2 OTA recovery in different coffee samples analyzed by HPLC-FLD and LFIA, with or without SPE pretreatment. The concentration added in fortified solution was 25 µg L − 1 Coffee type HPLC-FLD Concentration (µg kg − 1 ) HPLC-FLD RSD (%) Recovery HPLC-FLD (%) LFIA Concentration (µg kg − 1 ) LFIA RSD (%) Recovery LFIA (%) Traditional extra-strong (unfortified) 6.05 1.45 – 9.26 7.66 – Traditional extra-strong (fortified) 25.35 1.15 77.2% 22.62 3.49 53.4% Traditional extra-strong NH₂ 21.50 6.77 61.8% 18.86 2.4 38.4% Decaffeinated (unfortified) < LOQ – – < LOQ – – Decaffeinated (fortified) 23.68 6 94.7% 19.36 4.28 77.4% Decaffeinated NH₂ 23.11 4.02 92.4% 13.31 4.75 53.2% Specialty (unfortified) < LOQ – – < LOQ – – Specialty (fortified) 20.63 5.12 82.5% 20.23 1.54 80.9% Specialty NH₂ 20.56 3.86 82.2% 17 3.12 68.0% Soluble (unfortified) 4.00 3.42 – 10.61 5.48 – Soluble (fortified) 15.42 8.76 45.7% 20.22 5.22 38.4% Soluble NH₂ 19.90 4.87 63.6% 17.1 0.88 26.0% Pool low (fortified) 23.02 4.76 92.1% 14.45 5.01 57.8% Pool low NH₂ 21.40 3.9 85.6% 5.82 5.03 23.3% Pool high (fortified) 23.47 0.47 93.9% 5.42 5.26 21.7% Pool high NH₂ 19.51 1.85 78.0% 4.54 4.26 18.2% For non-fortified samples, OTA concentrations were below the quantification limit in most cases. However, measurable natural contamination was detected in traditional extra-strong and soluble coffees. In traditional extra-strong coffee, OTA levels reached 6.05 µg kg − 1 by HPLC-FLD and 9.26 µg kg − 1 by LFIA, both exceeding the Regulation (EU) 2023/915 limit of 3.0 µg kg − 1 for roasted coffee. For soluble coffee, concentrations of 4.00 µg kg − 1 (HPLC-FLD) and 10.61 µg kg − 1 (LFIA) were obtained; notably, only the LFIA result slightly exceeded the 10 µg kg − 1 limit established for soluble coffee. This discrepancy likely arises from matrix-induced signal enhancement in LFIA, since soluble coffee contains high concentrations of pigments, melanoidins, and polyphenolic compounds that can intensify the apparent coloration of the test lines. These findings highlight the importance of considering matrix effects when interpreting OTA results, particularly in strongly colored or chemically complex samples where immunoassay readings may overestimate or understimate contamination levels (Prakasham et al. 2023 ). The results showed in Table 2 demonstrated that OTA could be efficiently recovered from all coffee extracts, although recovery rates varied substantially depending on coffee type and pretreatment strategy. SPE cartridges containing aminopropyl (NH 2 ) or phenyl (PH) sorbents were evaluated in combination with IAC cleanup. PH cartridges, tested exclusively for traditional extra-strong coffee, yielded low OTA recoveries and were excluded from further experiments due to their limited performance and labor-intensive preparation. NH 2 cartridges consistently produced lower recoveries compared with IAC alone, indicating limited efficiency in removing coffee matrix constituents that impair antibody–antigen interactions. Table 2 (here) When comparing coffee categories, specialty and decaffeinated coffees exhibited the highest OTA recoveries, whereas traditional extra-strong and soluble coffees showed significantly lower values. These differences can be attributed to the higher abundance of interfering compounds such as melanoidins, caffeine, and chlorogenic acids in darker and more concentrated matrices like extra-strong and soluble coffee. These constituents may bind non-specifically to antibodies or occupy recognition sites, thereby reducing OTA binding efficiency in both IAC (HPLC-FLD) and LFIA assays. Representative stages of the cleanup process using the vacuum manifold and the visual outcomes of LFIA strips for fortified and non-fortified samples are shown in Fig. 4 a and Fig. 4 b. Overall, these findings reinforce that coffee matrix composition exerts a decisive influence on the analytical performance of antibody-based OTA detection methods. While immunoaffinity columns remain effective for OTA isolation, their efficiency decreases in darker and more complex matrices due to the accumulation of coextractives that compete with OTA for antibody binding. Although SPE pretreatment can theoretically reduce such interferences, its benefits are strongly dependent on the selectivity and compatibility of the chosen sorbent. Matrix viscosity and local pH shifts induced by melanoidins and roasted phenolics can further destabilize antigen–antibody complexes, reducing immunoaffinity efficiency. Optimizing elution conditions, including buffer composition and ionic strength, can mitigate such adverse effects, enhancing reproducibility and sensitivity (Kokina et al. 2016 ; Delaunay et al. 2020 ). Therefore, tailored cleanup strategies should be applied for different coffee types to ensure reliable quantification and consistent recovery across complex food systems. Conclusion This study demonstrates that the chemical composition of the coffee matrix decisively affects the accuracy of Ochratoxin A (OTA) determination in a method-dependent manner. Caffeine and caffeic acid were identified as the main interferents affecting HPLC-FLD performance, whereas chlorogenic acid and melanoidins had a greater impact on lateral flow immunoassay (LFIA). Molecular docking provided mechanistic support for these findings, indicating that interactions between OTA and coffee matrix components may reduce toxin accessibility to antibody binding sites. These results show that a single analytical strategy may not be equally suitable for all coffee matrices and reinforce the need for matrix-specific method validation. HPLC-FLD remains a robust confirmatory approach, although optimization may be required to minimize caffeine-related interference. LFIA is valuable for rapid screening, but its higher susceptibility to matrix effects highlights the need for chromatographic confirmation in complex samples. Overall, these findings provide a relevant basis for improving OTA monitoring in coffee and support the development of more interference-resilient analytical strategies for complex food matrices. Declarations Author Contribution Isabela Fracalossi Mancini : Investigation, Methodology, Data curation, Validation, Writing - original draft. Gabriel Fernandes Souza dos Santos : Conceptualization, Methodology, Data curation, Visualization, Writing - original draft, Writing - review & editing. Ana Luiza Resende Pires : Formal analysis, Writing - review & editing. Isabella Oliveira Britto : Formal analysis, Writing - review & editing. Giovanna Pinto Pires : Software, Formal analysis, Writing - review & editing. Sérvio Tulio Alves Cassini : Formal analysis, Writing - review & editing. Marco César Cunegundes Guimarães : Formal analysis, Writing - review & editing. Jairo Pinto de Oliveira : Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Formal analysis, Writing - review & editing. Funding This publication was supported by the National Council for Scientific and Technological Development (CNPq, Grant BRAZIL/MCTI/FNDCT No. 22/2022) and the Espírito Santo Research and Innovation Support Foundation (FAPES, Grant No. 20/2022). The authors also thank the Coordination for the Improvement of Higher Education Personnel (CAPES, Finance Code 001) for their support. Acknowledgments We acknowledge National Council for Scientific and Technological Development (CNPq, Grant BRAZIL/MCTI/FNDCT No. 22/2022) and the Espírito Santo Research and Innovation Support Foundation (FAPES, Grant No. 20/2022) and the Coordination for the Improvement of Higher Education Personnel (CAPES, Finance Code 001) for their support. 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Spectrochim Acta Part Mol Biomol Spectrosc 115:57–63. https://doi.org/10.1016/j.saa.2013.05.036 Więckowska M, Cichon N, Szelenberger R et al (2024) Ochratoxin A and Its Role in Cancer Development: A Comprehensive Review. Cancers (Basel) 16:3473. https://doi.org/10.3390/cancers16203473 Xiang Y, Duan L, Ma Q et al (2016) Fluorescence spectroscopy and molecular simulation on the interaction of caffeic acid with human serum albumin. Luminescence 31:1496–1502. https://doi.org/10.1002/bio.3135 Xie C, Wang C, Zhao M, Zhou W (2023) Detection of the 5-hydroxymethylfurfural content in roasted coffee using machine learning based on near-infrared spectroscopy. Food Chem 422:136199. https://doi.org/10.1016/j.foodchem.2023.136199 Xu G, Zhao J, Yu H et al (2022) Structural Insights into the Mechanism of High-Affinity Binding of Ochratoxin A by a DNA Aptamer. J Am Chem Soc 144:7731–7740. https://doi.org/10.1021/jacs.2c00478 Zhou Z, Hu X, Hong X et al (2020) Interaction characterization of 5 – hydroxymethyl – 2 – furaldehyde with human serum albumin: Binding characteristics, conformational change and mechanism. J Mol Liq 297:111835. https://doi.org/10.1016/j.molliq.2019.111835 Additional Declarations No competing interests reported. Supplementary Files OnlineResource1.pdf OnlineResource2.pdf OnlineResource3.pdf GA.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9137063","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613298138,"identity":"84a2bb72-137a-4b72-88d5-79b7098ae40f","order_by":0,"name":"Isabela Fracalossi Mancini","email":"","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":false,"prefix":"","firstName":"Isabela","middleName":"Fracalossi","lastName":"Mancini","suffix":""},{"id":613298139,"identity":"ba3cfa93-9c97-49dd-9453-f195331ee900","order_by":1,"name":"Gabriel Fernandes Souza Santos","email":"","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"Fernandes Souza","lastName":"Santos","suffix":""},{"id":613298141,"identity":"50c14a4a-3f6e-49a4-8bbf-450775cc98a2","order_by":2,"name":"Isabella Oliveira Britto","email":"","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":false,"prefix":"","firstName":"Isabella","middleName":"Oliveira","lastName":"Britto","suffix":""},{"id":613298143,"identity":"f414d088-0dc1-4bea-98e8-2b3a8740ded2","order_by":3,"name":"Ana Luiza Resende Pires","email":"","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Luiza Resende","lastName":"Pires","suffix":""},{"id":613298144,"identity":"ad65084e-f813-4e14-885c-b7d3d09c4fb9","order_by":4,"name":"Giovanna Pinto Pires","email":"","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":false,"prefix":"","firstName":"Giovanna","middleName":"Pinto","lastName":"Pires","suffix":""},{"id":613298146,"identity":"c205e762-7641-421d-aac6-2cb37e765220","order_by":5,"name":"Sérvio Tulio Alves Cassini","email":"","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":false,"prefix":"","firstName":"Sérvio","middleName":"Tulio Alves","lastName":"Cassini","suffix":""},{"id":613298148,"identity":"4ae8c0a0-bc18-46a6-9ec1-583cde7bcff2","order_by":6,"name":"Marco César Cunegundes Guimarães","email":"","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"César Cunegundes","lastName":"Guimarães","suffix":""},{"id":613298150,"identity":"51933436-3de0-46a9-9dba-22550841b3ec","order_by":7,"name":"Jairo Pinto Oliveira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie2PsYrCQBBAJyxkm8E6hZpf2MM23LfcIsTGQjsLi5VAbBZt9/7lwCwL2oh+gI0hoG1sLOWSwzQHt7nSYl8xw8zuY2YAHI4XxBOeqHPXr0IGEAGtavIfBZ9KDJi1KA34zKZdIUuTliVE2AmG2szmRy7p8kymX5bFJE8+FcToB/GH3m9PXOKeEXWx3cITgmDQr35q4Z/4JhgDqbf7U1nnjXIstXgcuAyvZ7uimilUgl6kGZcBsBYlTzzFqltoyvRiNRxIHDOjLMrbelRAOYv6YUKKm7i/9yTd5cXEpog6sl9diwAQ2h4dDofD8cM3UxRP0OMq07cAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Espírito Santo","correspondingAuthor":true,"prefix":"","firstName":"Jairo","middleName":"Pinto","lastName":"Oliveira","suffix":""}],"badges":[],"createdAt":"2026-03-16 11:12:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9137063/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9137063/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105753915,"identity":"bfacb729-d91e-435a-bf66-38d04274f3f2","added_by":"auto","created_at":"2026-03-30 16:13:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":270029,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for ochratoxin A (OTA) obtained by (a) HPLC-FLD (n=3) and (b) the chromatograms associated. (c) Calibration curve for OTA obtained by Lateral Flow Immunoassay (LFIA) The LFIA curve was generated by plotting the Test/Control (T/C) line intensity ratio against OTA concentration in a blank coffee matrix. (d) Images of the LFIA strips used to obtain the calibration curve\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/8c875f7951e63e104c163f00.png"},{"id":105753902,"identity":"64ffeddc-fead-4f28-8680-039f2f439e9e","added_by":"auto","created_at":"2026-03-30 16:13:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134986,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of individual coffee matrix compounds and pooled mixtures on OTA recovery determined by HPLC-FLD and LFIA. Error bars represent standard deviation (n=3)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/327f12bc691aab518ff9ccdd.png"},{"id":105753932,"identity":"e99dec33-1942-4ab7-bc20-eda4da912f4f","added_by":"auto","created_at":"2026-03-30 16:13:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141785,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results for OTA with coffee matrix compounds: (a) binding free energies from 100 conformations; (b) representative binding poses highlighting hydrogen bonds and π–π interactions\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/2d659fc2f4013f7d6269d8a5.png"},{"id":105754228,"identity":"ef398751-0239-4676-a2fa-d4b027356265","added_by":"auto","created_at":"2026-03-30 16:15:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178789,"visible":true,"origin":"","legend":"\u003cp\u003ea) Immunoaffinity column cleanup using a vacuum manifold; b) example of LFIA strips applied to real coffee extracts\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/e5cca3106d9c8c011299484b.png"},{"id":107425636,"identity":"6f75796c-8f66-4682-8a8e-794765a957d0","added_by":"auto","created_at":"2026-04-21 11:12:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1357971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/d828c1bf-ef0d-429b-ade9-02f82efb7afd.pdf"},{"id":105754666,"identity":"6189e7ee-f2e4-4b51-8847-458e226a8a79","added_by":"auto","created_at":"2026-03-30 16:19:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":155050,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/40efc299017241b04e47d4a1.pdf"},{"id":105753874,"identity":"b2fc94e5-d3de-4617-813a-ec0d9253c0a8","added_by":"auto","created_at":"2026-03-30 16:13:00","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":190448,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/a3133fdf14f21fa88ab983d1.pdf"},{"id":105753938,"identity":"ad6f0426-2629-43ac-a35c-67b2766e8a14","added_by":"auto","created_at":"2026-03-30 16:13:28","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":140042,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineResource3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/c85c4346a29bfbaffd94a046.pdf"},{"id":105754253,"identity":"fe99cf21-f426-4b9a-8a4f-840713c73119","added_by":"auto","created_at":"2026-03-30 16:15:57","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":274592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-9137063/v1/7886ae40cec22e1a75e8b5c9.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOchratoxin a Detection in Coffee: Matrix Inteferences and Implications for Food Safety Monitoring\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBrazilian agribusiness occupies a strategic position in the global economy, with coffee production emerging as a critical sector due to its substantial contribution to international trade and export revenues. Within this context, Brazil stands as the foremost global producer and exporter of coffee, particularly the Conilon variety, which increasing international demand underscores its economic and social relevance (FAO 2024). However, its safety is frequently threatened by contamination with mycotoxins, especially ochratoxin A (OTA), a secondary metabolite synthesized predominantly by \u003cem\u003eAspergillus\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e species under favorable environmental conditions such as elevated moisture during post-harvest handling and storage (Bazin et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; European Commission, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fabian \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Więckowska et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Massahi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). OTA is recognized for its potent nephrotoxic, immunosuppressive, teratogenic, and carcinogenic properties, which have profound implications for public health worldwide (Wang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fabian \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Massahi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to these health concerns, strict maximum limits for OTA have been established by international regulatory agencies, including the European Regulation 2023/915 and 2022/1370, to control its occurrence in food products such as cereals, wine, and coffee. Exceeding such thresholds often results in significant economic losses and compromises the quality of Brazilian coffee in global markets, emphasizing the exigency for robust, reliable, and efficient detection and quality control methodologies (Ding et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, the determination of OTA relies on high-performance liquid chromatography (HPLC) coupled with different detectors. Although liquid chromatography coupled to tandem mass spectrometry (HPLC\u0026ndash;MS/MS) offers superior sensitivity and selectivity, its high operational cost, infrastructure demands, and requirement for specialized personnel restrict its routine application. On the other hand, HPLC with fluorescence detection (HPLC\u0026ndash;FLD) has been established as a more accessible and cost-effective alternative and is widely regarded as the gold standard for OTA quantification (Fujii et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tittlemier et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Leeman et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Given that OTA typically occurs at trace levels, the use of immunoaffinity columns (IACs) is strongly recommended prior to chromatographic analysis (Castegnaro et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Uchigashima et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This pre-treatment step offers several advantages, primarily by preconcentrating the analyte and reducing the presence of matrix interferences that hinder quantification (Maphaisa et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The selectivity of the IAC arises from antibodies immobilized within the column that specifically recognize and bind OTA, thereby enhancing analytical sensitivity and specificity (Atumo \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite the clear benefits of IACs in mycotoxin analysis, several studies have reported low recovery rates in dark or highly pigmented matrices such as wine and coffee. In the case of coffee samples, compounds including caffeine, other mycotoxins, and melanoidins have been identified as major interferents that can compromise antibody recognition and reduce OTA recovery (Entwisle et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Fujii et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bazin et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vieira et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComplementary to chromatographic methods, antibody-based immunoassays have gained prominence as powerful screening tools, offering high sensitivity and specificity through antibody recognition (Meulenberg \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fadlalla et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liew and Sabran \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karachaliou et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among these, the lateral flow immunoassay (LFIA) has become particularly popular due to its operational simplicity, rapid response, and potential for on-site analysis (Bazin et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fadlalla et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In competitive LFIA formats, typical for small molecules such as OTA, the analyte competes with an immobilized conjugate for antibody-labeled nanoparticles, generating a signal inversely proportional to OTA concentration (Chen et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pedreira-Rinc\u0026oacute;n et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent research has emphasized that matrix-derived chromogens and particulate matter markedly increase background signals and reduce assay sensitivity in LFIA applications to coffee samples. Strategies to circumvent these effects typically involve optimized sample dilution and buffer composition to stabilize antibody binding and diminish nonspecific interactions, which are critical for reliable semi-quantitative assessments (Contreras Alvarez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Whereas, balancing dilution to reduce background without compromising detection limits remains an ongoing methodological challenge.\u003c/p\u003e \u003cp\u003eHowever, analytical methods that rely on immunorecognition, whether for sample clean-up such as immunoaffinity columns or for direct antibody recognition in biosensors, can have their efficiency severely compromised by certain compounds present in complex food matrices. The determination of OTA in coffee is analytically challenging because of the matrix complexity (Guo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Banahene et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Coffee contains diverse compounds, such caffeine, chlorogenic acids, caffeic acid, cafestol, acrylamide, and high-molecular-weight melanoidins formed during roasting, that can interfere with extraction, clean-up, and detection (Nieber \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Aytar and Aydın \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Alcantara et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Schaffel et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such interferences may result in reduced recoveries or false-negative results, compromising accurate quantification. Given this challenge, several studies have sought to improve OTA quantification in coffee through the optimization of pre-treatment and cleanup strategies aimed at removing matrix components that hinder immunochemical recognition. Scott and Trucksess (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) demonstrated that sample interferences may originate not only from the analyte matrix itself but also from extraction solvents or even from the sorbents used in cleanup columns, emphasizing the need for careful optimization of analytical workflows. Although the adoption of IACs has significantly enhanced the selectivity and sensitivity of mycotoxin analyses, their performance remains dependent on the composition of the sample matrix (Liu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these limitations, studies have explored additional purification steps prior to IAC application (Duarte et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Monaci and Palmisano \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Nakajima \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Valenta \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), including aminopropyl (NH₂) and phenylsilane SPE cartridges for roasted coffee extracts, which improved peak definition, reduced matrix interferences, and achieved OTA recoveries of 72\u0026ndash;85% (Entwisle et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sibanda et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Other SPE sorbents such as silica and C18 phases have also been reported as complementary cleanup approaches (Scott and Trucksess, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Vieira et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For LFIA sensors, highly colored samples often cause high background and poor detectability (Anfossi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Contreras Alvarez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite significant advancements in analytical methodologies for ochratoxin A quantification, the accurate and reliable determination of OTA in coffee matrices remains an exigent analytical challenge. Therefore, the aim of this study was to investigate the impact of representative coffee components on OTA detection using two analytical strategies: immunoaffinity column cleanup followed by HPLC-FLD, and commercial LFIA. Six characteristic compounds of coffee chemistry (caffeine, caffeic acid, chlorogenic acid, cafestol, acrylamide, and melanoidins) were individually tested in OTA-contaminated coffee samples. Additionally, molecular docking simulations were performed to provide mechanistic insights into potential interactions between OTA and matrix constituents. The findings provide a basis for method validation and cleanup selection in routine OTA monitoring of coffee and highlight the need for matrix-specific validation of both chromatographic and immunoassay-based methods.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eChemicals and reagents\u003c/h2\u003e \u003cp\u003eAnalytical grade ochratoxin A (O1877), 5-hydroxymethylfurfural (HMF, W501808), caffeic acid (C0625), chlorogenic acid (C3878), and acrylamide (08267) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Caffeine anhydrous (813) and glacial acetic acid P.A. (141) were obtained from Vetec (Duque de Caxias, RJ, Brazil), whereas cafestol (82294) was sourced from PhytoLab (Vestenbergsgreuth, Germany). Chromatographic solvents of HPLC grade (methanol 9093, acetonitrile 34851) were supplied by J.T. Baker (Phillipsburg, NJ, USA) and Honeywell (Charlotte, NC, USA), respectively. A Zorbax ODS column (250 \u0026times; 4.6 mm, 5 \u0026micro;m; Agilent, Santa Clara, CA, USA) was employed for chromatographic separations. Specific immunoaffinity columns (OchraTest WB, G1033) and SPE cartridges (aminopropyl NH2, 500 mg/6 mL; phenyl PH, 500 mg/3 mL) were sourced from Vicam (Watertown, MA, USA) and Applied Separations (Allentown, PA, USA), respectively. The LFIA kit was the Ochratoxin A Qualitative Rapid Lateral Flow Test Kit kindly supplied by NanoSmart\u0026reg;. Ultrapure water (18.2 MΩ\u0026middot;cm) was generated using a Merck Synergy system.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCoffee sample\u003c/h3\u003e\n\u003cp\u003eFour types of Brazilian coffee were selected for analysis: Arabica specialty coffee (UFLA), traditional extra-strong coffee (Meridiano\u0026reg;), instant coffee (Caf\u0026eacute; Igua\u0026ccedil;u\u0026reg;), and decaffeinated coffee (3 Cora\u0026ccedil;\u0026otilde;es\u0026reg;). All samples were commercially available products purchased from local markets in Esp\u0026iacute;rito Santo, Brazil. The samples were stored under controlled temperature and humidity conditions in airtight containers to prevent contamination or degradation prior to analysis. Each type of coffee was analyzed in triplicate to ensure the accuracy and reproducibility of the results.\u003c/p\u003e\n\u003ch3\u003ePreparation of OTA and interferent standards\u003c/h3\u003e\n\u003cp\u003eWorking solutions of OTA for calibration and sample fortification were prepared from a 100 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e stock solution by serial dilution with ultrapure water to obtain six calibration levels ranging from 0.125 to 5.0 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Standard solutions of each potential interferent (caffeine, caffeic acid, chlorogenic acid, acrylamide, cafestol, 5-hydroxymethylfurfural (HMF), and melanoidins) were prepared in ultrapure water. The low and high concentration levels for each compound were selected according to values reported in the literature for coffee matrices (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their low and high concentration levels were selected based on values reported in the literature for typical coffee matrices (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Melanoidins were synthesized according to the procedure described by Bhamare and Kakulte (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with minor adaptations for this study. Equimolar aqueous solutions of glucose (180.16 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and glycine (37.54 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were heated at 90\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C for 6 h to promote Maillard polymerization. The resulting dark solid was filtered, finely ground, and stored in a desiccator under dry conditions until further use.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcentration levels of selected coffee matrix interferents.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow level (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh level (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1300 and 5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Higdon and Frei \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Iwahashi \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kalthoff et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorogenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Fujioka and Shibamoto \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vitaglione et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcrylamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Bagdonaite et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schouten et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Strocchi et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000 and 4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Fujioka and Shibamoto \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vignoli et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Olechno et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCafestol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Kurzrock and Speer \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Souza et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Scholz et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Park et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoidins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15000 and 30000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Bekedam et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vignoli et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Antonietti et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003e(here)\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003ePreparation of interference assay samples\u003c/h3\u003e\n\u003cp\u003eFor each experimental condition, OTA was combined with either a single interferent or a mixture containing all interferents (pool). The final volume was adjusted to 10.0 mL in polypropylene tubes using ultrapure water for analyses involving IAC/HPLC-FLD or phosphate-buffered saline (PBS) for LFIA assays. The final OTA concentration in each extract was 1.25 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In the individual assays, the concentration of each interferent was set at either the low or high level reported in the literature. In the pooled assays, all interferents were added simultaneously at their respective low or high levels. The tubes were vortex-mixed for 30 s and processed immediately. All experimental conditions were prepared in triplicate to ensure reproducibility.\u003c/p\u003e\n\u003ch3\u003eAnalytical procedures\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIAC cleanup and HPLC-FLD (coffee and test solutions)\u003c/h2\u003e \u003cp\u003eFor OTA determination by HPLC-FLD, both coffee extracts and aqueous test solutions were subjected to immunoaffinity cleanup followed by chromatographic analysis. The procedure used was recommended by the fabricant of IAC column (Vicam\u0026reg;). Briefly, ground coffee (12.5 g) was extracted with a total volume of 250 mL consisting of methanol and 3% (w/v) sodium bicarbonate solution mixed in a 1:1 (v/v) ratio. The suspension was stirred vigorously for 5 min, vacuum-filtered, and passed through glass microfiber filters (1.5 \u0026micro;m) to remove fine particulates. An aliquot of 4 mL of the clarified extract was diluted to 100 mL with phosphate-buffered saline (PBS), and 10 mL of this diluted solution were loaded onto an OTA-specific immunoaffinity column (OcraTest WB Vicam\u0026reg;\u003csup\u003e)\u003c/sup\u003e at a flow rate of approximately one to two drops per second. The column was washed with 10 mL of water, and OTA was eluted with 4 mL of methanol at a flow rate below one drop per second. The eluate was evaporated at 45\u0026deg;C under a gentle nitrogen stream to near dryness, and the residue was reconstituted in 400 \u0026micro;L of water, methanol, and acetic acid (50:48:2, v/v/v). The solution was vortex-mixed and transferred to amber autosampler vials for HPLC analysis.\u003c/p\u003e \u003cp\u003eChromatographic separations were performed using a Shimadzu liquid chromatography system comprising a CBM-20A control unit, LC-20AT pumps, an RF-20AXL fluorescence detector, and a CTO-20A column oven. Separation was achieved on a Zorbax ODS analytical column (250 \u0026times; 4.6 mm, 5 \u0026micro;m particle size) under isocratic elution using a mobile phase composed of acetonitrile, water, and acetic acid in a volumetric ratio of 50:48:2 (v/v). The flow rate was set at 1.0 mL min⁻\u0026sup1;, with the column temperature rigorously maintained at 40\u0026deg;C. Sample injection volume was standardized at 20 \u0026micro;L. Fluorescence detection was conducted at excitation and emission wavelengths of 333 nm and 477 nm, respectively. External calibration curves were constructed over six concentration points, ranging from 0.125 to 5.0 \u0026micro;g L⁻\u0026sup1;, utilizing standard solutions prepared in the mobile phase to ensure matrix consistency. Both calibration standards and unknown samples underwent identical chromatographic conditions. The limit of quantification (LOQ) was established based on a minimum signal-to-noise ratio of 10:1, whereas the limit of detection (LOD) corresponded to a signal-to-noise ratio of 3:1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLateral Flow Immunoassay (LFIA)\u003c/h3\u003e\n\u003cp\u003eOTA qualitative lateral flow test strips were kindly provided by NanoSmart\u0026reg;. The analysis of coffee samples followed the manufacturer's protocol with adaptations for quantitative image analysis. Briefly, 5 g of ground coffee were extracted with 20 mL of a 5% (w/v) sodium bicarbonate (NaHCO\u003csub\u003e3\u003c/sub\u003e) solution. The suspension was vigorously shaken for 1 min and subsequently filtered to obtain a clear extract. A 200 \u0026micro;L aliquot of the filtrate was mixed with 200 \u0026micro;L of the kit's running buffer in the provided microwell. After gentle homogenization, 200 \u0026micro;L of this diluted sample were transferred to a new microwell. Following a 3-minute incubation period at ambient temperature (25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C), the test strip was inserted into the well and allowed to develop for 5 minutes. The strips were photographed immediately after the development time, with all readings completed within a 1-minute window to ensure consistency. For quantitative analysis, the developed test strips were photographed using the rear camera of an iPhone XR (12 MP, f/1.8 aperture) mounted on a fixed tripod to maintain a consistent distance and angle. All images were captured under uniform white LED illumination without flash, as illustrated in Online Resource 2, to minimize shadows and color temperature variations. The images were analyzed using ImageJ software (version 1.54g, National Institutes of Health, USA). Each image was split into its red, green, and blue (RGB) color channels. The green channel was selected for analysis due to its superior contrast for the gold nanoparticle-based test lines. A rectangular region of interest (ROI) was defined along the length of the strip, encompassing both the test (T) and control (C) lines. The pixel intensity profile across this ROI was generated using the \"Plot Profile\" function, producing a grayscale intensity distribution graph (Online Resource 3). The minimum intensity values corresponding to the T and C lines were identified, and the Test-to-Control (T/C) ratio was calculated for the quantitative assessment of OTA.\u003c/p\u003e\n\u003ch3\u003eSolid-phase extraction (SPE) pre-treatment\u003c/h3\u003e\n\u003cp\u003eSPE pretreatment was performed following the procedures described by Entwisle et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Sibanda et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), with minor modifications. For selected assays, coffee extracts underwent additional cleanup using aminopropyl (NH₂) or phenyl SPE cartridges prior to IAC purification. Cartridges were conditioned sequentially with 5 mL methanol followed by 5 mL ultrapure water. Sample extracts were then loaded at a controlled flow rate of ~\u0026thinsp;1\u0026ndash;2 drops s⁻\u0026sup1; to maximize analyte retention and matrix removal. SPE eluates were collected and processed by IAC as previously described.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking Studies\u003c/h2\u003e \u003cp\u003eMolecular docking simulations were performed to investigate the interactions between OTA and the selected coffee components. The molecular structures of acrylamide, cafestol, and HMF were obtained from the PubChem database, whereas those of OTA, caffeic acid, and chlorogenic acid were generated using Chemicalize to represent their neutral-pH dissociation states. All molecular geometries were optimized in ORCA 5.0.4 using the BP86 functional and the def2-SVP basis set. Receptor and ligand input files were prepared in AutoDockTools 1.5.7, and docking calculations were carried out with AutoDock Vina\u0026reg; using a cubic grid of 90 \u0026times; 90 \u0026times; 90 points with a spacing of 0.375 \u0026Aring;, centered at coordinates (7.574, 1.017, 1.384). Multiple independent runs were performed to confirm reproducibility of the results. The best-scoring binding poses were examined and annotated in UCSF Chimera (version 1.16) and BIOVIA Discovery Studio (version 21.1) to identify hydrogen-bond interactions and π\u0026ndash;π stacking contacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCoffee sample preparation and analysis with SPE cleanup\u003c/h2\u003e \u003cp\u003eSPE was performed prior to IAC cleanup, the cartridges were preconditioned with 5 mL of methanol followed by 5 mL of water, and the extracts were loaded at a controlled flow rate of approximately one drop per second. For the phenyl (PH, 500 mg/3 mL) cartridge workflow, 10 mL of the percolate were collected, and the cartridge was subsequently washed with 2 mL of methanol:3% (w/v) sodium bicarbonate (50:50, v/v) and 1 mL of methanol. The collected eluate and washing fractions were combined, diluted to 100 mL with PBS, and processed through the IAC as previously described. Quantification of OTA was carried out by HPLC-FLD using the same chromatographic conditions established for the interference assays.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMethod validation\u003c/h2\u003e \u003cp\u003eMethod validation was carried out to assess analytical performance in terms of recovery, repeatability, linearity, and detection capability. Recovery and precision were determined through spiking experiments at representative concentration levels, while linearity was evaluated using calibration curves constructed from six concentration points. The limits of detection (LOD) and quantification (LOQ) were defined at signal-to-noise ratios of 3:1 and 10:1, respectively. All experiments were performed in triplicate, and mean recoveries were reported as percentage\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Method accuracy was determined through recovery experiments by fortifying blank and naturally contaminated coffee samples at a representative level of 25 \u0026micro;g kg⁻\u0026sup1;. The recovery percentage was calculated accounting for the natural OTA content, when detected, to ensure accurate assessment of the method's performance. Validation results were interpreted according to the general criteria established by ANVISA Resolution No. 899 IN 88/2021, International guidelines (European Commission, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the Codex Alimentarius recommendations for mycotoxin determination in food matrices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis and Analytical Parameters\u003c/h2\u003e \u003cp\u003eAll assays were carried out in triplicate, and results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Method performance was evaluated in terms of recovery, repeatability, linearity, limit of detection (LOD), and limit of quantification (LOQ). Calibration curves were constructed within the analytical range of each method, whereas LOD and LOQ were determined using signal to noise ratios of 3:1 and 10:1, respectively. For LFIA, quantitative analysis was based on the test to control (T/C) ratio obtained by digital image processing. Graphical representations were generated using OriginPro 2018 Pro (OriginLab Corp., Northampton, MA, USA) and GraphPad Prism 11.0.0 (GraphPad Software, San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical performance (HPLC-FLD and LFIA)\u003c/h2\u003e \u003cp\u003eFor HPLC-FLD analysis, the calibration curve constructed from six concentration levels (0.125\u0026ndash;5.0 \u0026micro;g L⁻\u0026sup1;) demonstrated adequate linearity, consistent with the acceptance criteria of ANVISA Resolution No. 899 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003ea Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eb illustrates the representative chromatogram of the calibration range. The method achieved a limit of quantification (LOQ) of 0.528 \u0026micro;g L⁻\u0026sup1;, suitable for confirmatory OTA quantification in coffee at concentrations relevant to regulatory limits. These results are in agreement with recent studies reporting comparable LOQs for immunoaffinity cleanup coupled with fluorescence detection in complex matrices (Hwang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barbor\u0026aacute;kov\u0026aacute; et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the LFIA, a semi-quantitative calibration was established in triplicate using seven OTA concentrations spanning 5\u0026ndash;30 \u0026micro;g L⁻\u0026sup1;, in accordance with the manufacturer\u0026rsquo;s specified working range for the strips (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), while Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003ed represents the corresponding test line responses used to construct the calibration curve. The experimentally determined LOQ of 1.96 \u0026micro;g kg⁻\u0026sup1; was lower than the nominal visual cut-off of 5 \u0026micro;g kg⁻\u0026sup1; declared by the manufacturer, indicating that digital image\u0026ndash;based signal acquisition improves the effective analytical sensitivity of the assay. This enhanced performance supports the use of LFIA as a reliable screening tool for OTA in coffee at concentrations aligned with international regulatory limits, particularly when integrated into a tiered testing strategy alongside confirmatory HPLC-FLD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, these analytical benchmarks highlight the distinct yet complementary strengths of the two methods. HPLC-FLD provides the precision and sensitivity necessary for confirmatory determinations, while LFIA offers a rapid, low-cost, and field-deployable option for preliminary screening. Although LFIA exhibits inherently higher variability due to its immunochemical nature and matrix sensitivity, its operational simplicity and speed make it an invaluable first-line tool for large-scale monitoring programs. Together, the combined use of HPLC-FLD and LFIA provides a robust analytical framework that balances analytical rigor with practical feasibility in OTA surveillance across diverse coffee matrices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImpact of individual matrix components\u003c/h2\u003e \u003cp\u003eRecovery results for OTA in the presence of individual coffee constituents and pooled mixtures are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e and described in Online Resource 1. In ultrapure water (matrix-free control), OTA recovery reached 98.54% by HPLC-FLD, confirming the excellent performance of IAC when no interfering compounds are present. However, the introduction of matrix components led to varying degrees of signal suppression. Caffeine, for instance, reduced OTA recovery from 84.08% and 86.0% at 200 and 2000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, to 65.17% at 4000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, evidencing a concentration-dependent inhibitory effect. Studies consistently demonstrate that decaffeinated coffee exhibits elevated OTA levels compared to caffeinated counterparts, attributable to the absence of this natural antifungal activity. Concentrations of caffeine as low as 1.0\u0026ndash;2.0% completely inhibit fungal proliferation and OTA production, thus positioning caffeine as a vital endogenous bio-protector within the coffee matrix that mitigates mycotoxin risks during processing (Nehad et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The significant, concentration-dependent suppression of OTA recovery by caffeine shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates that even within the normal range of coffee compositions, this alkaloid can impact the immunoaffinity cleanup. This is a major concern for validating methods across diverse coffee types, from light roasts (lower caffeine) to robusta blends (higher caffeine), and underscores that caffeine removal during sample prep is not just beneficial but often necessary for accurate quantification.\u003c/p\u003e \u003cp\u003eSimilarly, melanoidins maintained high recoveries at moderate levels (97.41% at 4.0 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) but caused a gradual decrease to 93.31% and 88.22% at 15 and 30 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. The severe suppression of LFIA signal by melanoidins highlights a critical limitation for on-site testing of dark-roasted coffees, potentially leading to false-negative results.\u003c/p\u003e \u003cp\u003eThese results reinforce earlier reports by Scott et al. (1997) and Vieira et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which identified caffeine and melanoidins as major contributors to matrix interference in OTA detection. Such compounds may compete with OTA for antibody binding sites or modify the microenvironment of the antigen\u0026ndash;antibody interaction, diminishing binding affinity and column efficiency. Furthermore, melanoidins, high molecular weight nitrogenous and brown compounds formed during coffee roasting from polysaccharides, proteins, and chlorogenic acids, exhibit structural complexity that enables them to interact competitively with OTA during immunoaffinity purification (Moreira et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The observed concentration-dependent decrease suggests that even within the dynamic range of typical coffee compositions, these molecules can significantly impact the recovery performance of immunoaffinity-based methods, underscoring the need for careful optimization of sample preparation to mitigate matrix effects.\u003c/p\u003e \u003cp\u003eCaffeic acid exhibited negligible interference at 800 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (85.26%) and 1300 mg L\u0026thinsp;\u0026minus;\u0026thinsp;1 (93.57%), but recovery declined to 69.39% at 5000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, indicating concentration-dependent effects. Acrylamide, tested at 40 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 500 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, showed recoveries of 92.22% and 98.38%, respectively, suggesting minimal interference. Chlorogenic acid presented recoveries of 98.53% at 800 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 110.18% at 3000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while HMF did not affect recovery at 40 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (98.53%) but reduced it to 85.78% at 300 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Cafestol demonstrated consistently high recovery (101.09%) across both tested concentrations. When all compounds were combined in the pooled mixture, OTA recovery by HPLC-FLD was 93.86% at the high level and 92.08% at the low level. This result suggests that the effects of the individual interferents were not simply additive under the chromatographic conditions employed. A possible explanation is that, in the pooled system, the simultaneous presence of multiple co extracted compounds altered the overall matrix effect, thereby attenuating the influence observed for some compounds when evaluated individually (Matuszewski et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Cortese et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, the reduced relative contribution of each interferent within the mixture may have lessened compound specific effects on OTA determination, resulting in recoveries closer to the expected values. In the study by Prelle et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), a comparison of clean-up methods for ochratoxin A in wine, beer, roasted coffee, and chili was carried out, and it was concluded that Immunoaffinity columns (IAC) remain the predominant clean-up method for ochratoxin A (OTA) detection in coffee by the time, due to their specific antibody-antigen interactions, which effectively reduce matrix interferences. However, their efficiency can be compromised by matrix effects such as open-ring OTA formation and isomerization during roasting, which hinder antibody recognition. Compared to other methods, IAC offers consistent recovery rates around 75\u0026ndash;84%, with acceptable precision, but other methodologies such as Molecularly Imprinted Polymers (MIP) have shown superior performance in purifying coffee matrices and reducing interferences, achieving higher recovery and lower detection limits. This makes IAC reliable but somewhat limited by matrix-induced challenges in coffee OTA analysis (Prelle et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is therefore necessary and relevant to explore alternative sample preparation methodologies for the purification of this analyte.\u003c/p\u003e \u003cp\u003eIn contrast, LFIA revealed a generally inverse relationship between recovery and interferent concentration. The most pronounced reductions were observed with chlorogenic acid at 3000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (48.45%) and melanoidins at 30 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (35.74%), suggesting that OTA recognition on the strip can be compromised by mechanisms such as direct competition, antigen masking, or chemical modifications that hinder antibody binding. In addition, the strong coloration of melanoidin-rich extracts may contribute to signal distortion or partial masking of the test and control lines, leading to an apparent underestimation or overestimation of OTA concentration depending on the optical contrast of the strip (Anfossi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The pooled mixture particularly impacted LFIA performance, with recovery dropping to 21.66% at the high level, indicating a cumulative detrimental effect of multiple matrix constituents on antibody recognition. Furthermore, the chromogenic properties of melanoidins intensify background coloration, impairing signal clarity in LFIA platforms, a phenomenon documented in recent optical immunoassay evaluations (Thenuwara et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This effect complicates interpretation and quantification by increasing signal noise, highlighting the necessity for matrix-specific calibration and potential pretreatment to mitigate chromatic interference.\u003c/p\u003e \u003cp\u003eThese effects were consistently more pronounced in LFIA than in HPLC-FLD, likely due to the direct antibody\u0026ndash;antigen interaction mechanism in the strip-based assay, which renders it more vulnerable to matrix interference. The observed patterns corroborate the chromatographic results and reinforce the concept that coffee matrix compounds can significantly compromise OTA detection in both traditional immunoanalytical systems (IAC\u0026ndash;HPLC-FLD) and portable screening platforms (LFIA). Interestingly, the interference profiles were method-dependent: compounds that moderately affected HPLC-FLD had minimal impact on LFIA, whereas those strongly interacting with antibodies, notably chlorogenic acid and melanoidins, markedly impaired LFIA performance. These findings underscore that matrix effects are not uniform across analytical methods but are intrinsically shaped by the underlying detection principle, highlighting the importance of method-specific strategies to mitigate interference and improve the reliability of OTA analysis in complex matrices like coffee.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDocking simulations as mechanistic support\u003c/h2\u003e \u003cp\u003eTo elucidate the molecular basis underlying the matrix effects observed in OTA determination, molecular docking simulations were conducted to explore the interactions between OTA and key coffee constituents. We focused on the hypothesis of analyte masking, whereby interferents bind to OTA in solution, potentially altering its conformation or shielding its epitope, thereby reducing its effective concentration and accessibility for antibody recognition.\u003c/p\u003e \u003cp\u003eIn the literature, it is possible to find computational and spectroscopic studies that have shown that OTA strongly binds to proteins, particularly human serum albumin (HSA), through hydrogen bonding and π\u0026ndash;π stacking interactions (Il\u0026rsquo;ichev et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vakili et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Algethami et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In coffee, however, the presence of diverse organic compounds can compromise the performance of IACs, frequently leading to underestimation and variable recovery (Castegnaro et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tozlovanu and Pfohl-Leszkowicz \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Despite these analytical challenges, molecular-level insights into how specific coffee constituents interact with OTA are not described in the literature.\u003c/p\u003e \u003cp\u003eWe hypothesized that small organic molecules abundant in coffee may engage in non-covalent interactions with OTA, primarily hydrogen bonding and π\u0026ndash;π stacking, thereby masking the toxin or altering its conformation in solution, ultimately reducing antibody recognition. To evaluate this, the binding affinities of OTA\u0026ndash;compound pairs were estimated from the mean free energy of binding (ΔG, kcal mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) derived from 100 docked conformations, where less negative values denote weaker binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Representative docking poses highlighting hydrogen bonds and π\u0026ndash;π contacts are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCaffeine and 5-hydroxymethylfurfural (5-HMF) exhibited moderate affinity for OTA, stabilized by both hydrogen bonds and π\u0026ndash;π interactions, in agreement with reported binding patterns of these compounds to serum albumins (Wang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vakili et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The formation of such stable OTA-caffeine complexes in the sample extract could rationally explain the decreased recoveries observed experimentally in both IAC and LFIA, as a fraction of the OTA would be unavailable for binding.\u003c/p\u003e \u003cp\u003eSimilarly, caffeic acid and chlorogenic acid formed π\u0026ndash;π stacking interactions with OTA, accompanied by hydrogen bonding through their hydroxyl groups. This interaction mode closely resembles that described for OTA\u0026ndash;HSA complexes, where hydrogen bonds and aromatic stacking contribute to complex stabilization (Tang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xiang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vakili et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The strong experimental interference of chlorogenic acid in the LFIA format, in particular, may be due to its ability to form a particularly stable complex that efficiently masks the OTA epitope recognized by the more conformationally sensitive antibodies used in the lateral flow strip.\u003c/p\u003e \u003cp\u003eIn contrast, acrylamide displayed the weakest binding energy and no detectable specific interactions with OTA, consistent with its negligible impact on experimental recoveries. Cafestol, while forming hydrogen bonds with OTA in silico, did not significantly alter OTA detection, indicating that this particular interaction may be too weak or infrequent to compete effectively with the robust antibody-antigen binding under the studied conditions.\u003c/p\u003e \u003cp\u003eCafestol, a coffee diterpene, also demonstrated in silico binding affinity towards OTA, primarily through hydrogen bonding. This is consistent with the known behavior of diterpenes to interact with serum proteins (Berti et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), However, this predicted interaction did not translate into a significant experimental reduction in OTA recovery in our study. This discrepancy suggests that while the interaction is thermodynamically possible, its kinetics or prevalence under the actual analytical conditions may be insufficient to compete effectively with the robust antibody-antigen binding.\u003c/p\u003e \u003cp\u003eOverall, the docking simulations corroborate experimental findings by demonstrating that specific coffee matrix components, particularly caffeine, 5-HMF, caffeic acid, and chlorogenic acid, can establish stable non-covalent interactions with OTA. These interactions likely reduce antibody accessibility to the toxin, thereby diminishing the sensitivity of immunoassay-based detection methods. The molecular insights obtained here reinforce that the chemical complexity of coffee matrices plays a pivotal role in analytical performance and underscore the importance of considering such interactions when developing or validating OTA determination strategies in complex food systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation in real coffee samples\u003c/h2\u003e \u003cp\u003eTo assess the applicability of the analytical strategies in complex food matrices, OTA recovery was evaluated in commercial Brazilian coffee samples from four categories: traditional extra-strong, soluble, Arabica specialty, and decaffeinated, showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Each sample was analyzed with and without fortification at 25 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of OTA for HPLC-FLD and LFIA. The influence of an additional solid-phase extraction (SPE) pretreatment prior to immunoaffinity cleanup was also investigated. The diversity in coffee types reflects distinct chemical and structural matrix profiles, influenced by roasting level, bean processing, and blend composition. These factors drive differential concentrations of polyphenols, melanoidins, and alkaloids such as caffeine, which modulate matrix complexity and impact OTA recovery. Moreover, roasting-induced changes affect matrix porosity and polarity, modulating analyte accessibility and solvent interactions during extraction. Such physicochemical heterogeneity significantly influences OTA recovery and detection limits. The darker roasts typical of traditional extra-strong and soluble coffees foster increased Maillard reaction products and condensed polyphenolic content, enhancing matrix interferences compared to lighter roasted specialty coffees and decaffeinated variants, thus influencing immunoaffinity binding dynamics and assay sensitivity (Gottstein et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cwikov\u0026aacute; et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOTA recovery in different coffee samples analyzed by HPLC-FLD and LFIA, with or without SPE pretreatment. The concentration added in fortified solution was 25 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHPLC-FLD Concentration\u003c/p\u003e \u003cp\u003e(\u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHPLC-FLD RSD\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecovery HPLC-FLD\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLFIA Concentration\u003c/p\u003e \u003cp\u003e(\u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLFIA RSD\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRecovery LFIA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional extra-strong\u003c/p\u003e \u003cp\u003e(unfortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional extra-strong\u003c/p\u003e \u003cp\u003e(fortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional extra-strong NH₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecaffeinated\u003c/p\u003e \u003cp\u003e(unfortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; LOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; LOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecaffeinated\u003c/p\u003e \u003cp\u003e(fortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecaffeinated NH₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialty\u003c/p\u003e \u003cp\u003e(unfortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; LOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; LOQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialty\u003c/p\u003e \u003cp\u003e(fortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialty NH₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble\u003c/p\u003e \u003cp\u003e(unfortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble\u003c/p\u003e \u003cp\u003e(fortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble NH₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePool low\u003c/p\u003e \u003cp\u003e(fortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePool low NH₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePool high (fortified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePool high NH₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor non-fortified samples, OTA concentrations were below the quantification limit in most cases. However, measurable natural contamination was detected in traditional extra-strong and soluble coffees. In traditional extra-strong coffee, OTA levels reached 6.05 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e by HPLC-FLD and 9.26 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e by LFIA, both exceeding the Regulation (EU) 2023/915 limit of 3.0 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for roasted coffee. For soluble coffee, concentrations of 4.00 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (HPLC-FLD) and 10.61 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (LFIA) were obtained; notably, only the LFIA result slightly exceeded the 10 \u0026micro;g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e limit established for soluble coffee. This discrepancy likely arises from matrix-induced signal enhancement in LFIA, since soluble coffee contains high concentrations of pigments, melanoidins, and polyphenolic compounds that can intensify the apparent coloration of the test lines. These findings highlight the importance of considering matrix effects when interpreting OTA results, particularly in strongly colored or chemically complex samples where immunoassay readings may overestimate or understimate contamination levels (Prakasham et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated that OTA could be efficiently recovered from all coffee extracts, although recovery rates varied substantially depending on coffee type and pretreatment strategy. SPE cartridges containing aminopropyl (NH\u003csub\u003e2\u003c/sub\u003e) or phenyl (PH) sorbents were evaluated in combination with IAC cleanup. PH cartridges, tested exclusively for traditional extra-strong coffee, yielded low OTA recoveries and were excluded from further experiments due to their limited performance and labor-intensive preparation. NH\u003csub\u003e2\u003c/sub\u003e cartridges consistently produced lower recoveries compared with IAC alone, indicating limited efficiency in removing coffee matrix constituents that impair antibody\u0026ndash;antigen interactions.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003e(here)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhen comparing coffee categories, specialty and decaffeinated coffees exhibited the highest OTA recoveries, whereas traditional extra-strong and soluble coffees showed significantly lower values. These differences can be attributed to the higher abundance of interfering compounds such as melanoidins, caffeine, and chlorogenic acids in darker and more concentrated matrices like extra-strong and soluble coffee. These constituents may bind non-specifically to antibodies or occupy recognition sites, thereby reducing OTA binding efficiency in both IAC (HPLC-FLD) and LFIA assays. Representative stages of the cleanup process using the vacuum manifold and the visual outcomes of LFIA strips for fortified and non-fortified samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, these findings reinforce that coffee matrix composition exerts a decisive influence on the analytical performance of antibody-based OTA detection methods. While immunoaffinity columns remain effective for OTA isolation, their efficiency decreases in darker and more complex matrices due to the accumulation of coextractives that compete with OTA for antibody binding. Although SPE pretreatment can theoretically reduce such interferences, its benefits are strongly dependent on the selectivity and compatibility of the chosen sorbent. Matrix viscosity and local pH shifts induced by melanoidins and roasted phenolics can further destabilize antigen\u0026ndash;antibody complexes, reducing immunoaffinity efficiency. Optimizing elution conditions, including buffer composition and ionic strength, can mitigate such adverse effects, enhancing reproducibility and sensitivity (Kokina et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Delaunay et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, tailored cleanup strategies should be applied for different coffee types to ensure reliable quantification and consistent recovery across complex food systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that the chemical composition of the coffee matrix decisively affects the accuracy of Ochratoxin A (OTA) determination in a method-dependent manner. Caffeine and caffeic acid were identified as the main interferents affecting HPLC-FLD performance, whereas chlorogenic acid and melanoidins had a greater impact on lateral flow immunoassay (LFIA). Molecular docking provided mechanistic support for these findings, indicating that interactions between OTA and coffee matrix components may reduce toxin accessibility to antibody binding sites. These results show that a single analytical strategy may not be equally suitable for all coffee matrices and reinforce the need for matrix-specific method validation. HPLC-FLD remains a robust confirmatory approach, although optimization may be required to minimize caffeine-related interference. LFIA is valuable for rapid screening, but its higher susceptibility to matrix effects highlights the need for chromatographic confirmation in complex samples. Overall, these findings provide a relevant basis for improving OTA monitoring in coffee and support the development of more interference-resilient analytical strategies for complex food matrices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contribution\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsabela Fracalossi Mancini\u003c/strong\u003e: Investigation, Methodology, Data curation, Validation, Writing - original draft. \u003cstrong\u003eGabriel Fernandes Souza dos Santos\u003c/strong\u003e: Conceptualization, Methodology, Data curation, Visualization, Writing - original draft, Writing - review \u0026amp; editing. \u003cstrong\u003eAna Luiza Resende Pires\u003c/strong\u003e: Formal analysis, Writing - review \u0026amp; editing. \u003cstrong\u003eIsabella Oliveira Britto\u003c/strong\u003e: Formal analysis, Writing - review \u0026amp; editing. \u003cstrong\u003eGiovanna Pinto Pires\u003c/strong\u003e: Software, Formal analysis, Writing - review \u0026amp; editing. \u003cstrong\u003eS\u0026eacute;rvio Tulio Alves Cassini\u003c/strong\u003e: Formal analysis, Writing - review \u0026amp; editing. \u003cstrong\u003eMarco C\u0026eacute;sar Cunegundes Guimar\u0026atilde;es\u003c/strong\u003e: Formal analysis, Writing - review \u0026amp; editing. \u003cstrong\u003eJairo Pinto de Oliveira\u003c/strong\u003e: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Formal analysis, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication was supported by the National Council for Scientific and Technological Development (CNPq, Grant BRAZIL/MCTI/FNDCT No. 22/2022) and the Esp\u0026iacute;rito Santo Research and Innovation Support Foundation (FAPES, Grant No. 20/2022). The authors also thank the Coordination for the Improvement of Higher Education Personnel (CAPES, Finance Code 001) for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge National Council for Scientific and Technological Development (CNPq, Grant BRAZIL/MCTI/FNDCT No. 22/2022) and the Esp\u0026iacute;rito Santo Research and Innovation Support Foundation (FAPES, Grant No. 20/2022) and the Coordination for the Improvement of Higher Education Personnel (CAPES, Finance Code 001) for their support. We also thanks the Multiuser Laboratory of Biomolecular Analysis (LABIOM), Health Science Center, Federal University of Esp\u0026iacute;rito Santo, Vit\u0026oacute;ria-ES, Brazil and Center for Research, Innovation and Development of Esp\u0026iacute;rito Santo, Cariacica-ES, Brazil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of Competing Interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlcantara GMRN, Martins LC, Gomes WPC et al (2025) Effect of roasting on chemical composition of coffee. 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J Mol Liq 297:111835. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molliq.2019.111835\u003c/span\u003e\u003cspan address=\"10.1016/j.molliq.2019.111835\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mycotoxin detection, Cleanup, Chromatography, LFIA, Immunoaffinity, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-9137063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9137063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe accurate monitoring of Ochratoxin A (OTA) in coffee, a globally traded commodity, is paramount for public health. However, the chemical complexity of the coffee matrix presents a significant challenge to analytical accuracy. This study systematically investigated the impact of six key coffee components, caffeine, caffeine, caffeic acid, chlorogenic acid, cafestol, acrylamide, and melanoidins, on OTA detection using two established methods: immunoaffinity cleanup coupled with HPLC-FLD and a commercial Lateral Flow Immunoassay (LFIA). The results reveal a pronounced method-dependency in matrix interference. In HPLC-FLD, caffeine and caffeic acid caused significant, concentration-dependent reductions in OTA recovery (to ~65-70%). Conversely, LFIA performance was most compromised by chlorogenic acid and melanoidins, which decreased recoveries to 48.5% and 35.7%, respectively. Molecular docking simulations indicated that stable non-covalent interactions (hydrogen bonding and π–π stacking) between OTA and specific interferents can sequester the toxin and hinder antibody recognition. Analysis of commercial Brazilian coffees confirmed these interferences, with notable discrepancies in OTA levels between methods. These findings demonstrate that matrix effects are not uniform but are dictated by the analytical platform's underlying principle. Consequently, this work underscores the necessity of matrix-specific validation for both conventional and rapid methods to ensure reliable OTA monitoring and robust food safety protection.\u003c/p\u003e","manuscriptTitle":"Ochratoxin a Detection in Coffee: Matrix Inteferences and Implications for Food Safety Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 16:02:44","doi":"10.21203/rs.3.rs-9137063/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e67e30c-460b-4daa-9f6f-a9c5ef53c897","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T02:25:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 16:02:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9137063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9137063","identity":"rs-9137063","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}