Multi-mycotoxin Contamination in Maize from Niger State, Nigeria: Occurrence, Dietary Exposure and Health Risk Assessment

Multi-mycotoxin Contamination in Maize from Niger State, Nigeria: Occurrence, Dietary Exposure and Health Risk Assessment | 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 Multi-mycotoxin Contamination in Maize from Niger State, Nigeria: Occurrence, Dietary Exposure and Health Risk Assessment Fatimah Omolola Badmos, Hadiza Lami Muhammad, Victor Tope Oyetunji, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7479286/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 6 You are reading this latest preprint version Abstract This study investigated fungal contamination and the occurrence of multiple mycotoxins in maize consumed in Niger State, Nigeria, and evaluated the associated health risks. A total of 240 maize samples (white and yellow varieties) were collected from markets and storage facilities across four distinct microclimatic zones. Fungal isolation was carried out using dilution plating, while mycotoxins were quantified using a combination of HPLC (aflatoxins, deoxynivalenol, nivalenol, cyclopiazonic acid) and ELISA (fumonisins, ochratoxin A, zearalenone) methods. The predominant fungi were Aspergillus species, particularly A. flavus and A. parasiticus , followed by Fusarium and Penicillium . Aflatoxins were present in all maize samples, with concentrations ranging from 0.003 to 682.9 µg/kg. Fumonisins and OTA were also widespread, detected in over 70% of samples, with fumonisin levels frequently exceeding EU maximum limits. Other mycotoxins (CPA, DON, NIV, ZEN) occurred at lower frequencies. Co-contamination was common, with up to five toxins detected simultaneously. Dietary exposure modeling based on local maize consumption indicated that fumonisins posed the highest intake risk, exceeding tolerable daily intake (TDI) values, while MOE calculations for aflatoxins and OTA were consistently < 10,000, suggesting significant health concern. The estimated hepatocellular carcinoma (HCC) burden associated with maize consumption was substantial, particularly among populations with high hepatitis B virus prevalence. The widespread maize consumption and frequent mycotoxin contamination pose a major food safety threat with public and trade implications. These findings emphasize the need for improved control measures, including better storage practices and regular surveillance, to mitigate risks associated with maize in Nigeria. maize mycotoxins aflatoxins fumonisins ochratoxin A dietary exposure Figures Figure 1 Figure 2 Figure 3 Introduction Maize ( Zea mays L .) is one of the most significant staple crops in sub-Saharan Africa, providing a major portion of caloric intake for both rural and urban populations (Mudadirwa et al. 2023 ). In Nigeria, it ranks among the leading cereal crops, with Niger State serving as the nation’s largest producer (Wossen et al. 2023 ; Abu et al. 2024 ). Despite its central role in food security and trade, maize is highly vulnerable to fungal infestation during cultivation, storage, and marketing. Under favorable environmental conditions, this contamination frequently leads to the synthesis of mycotoxins, toxic fungal metabolites that compromise food safety and public health (Alam et al. 2022 ). Several groups of mycotoxins are of concern in cereals, with aflatoxins, fumonisins, trichothecenes (such as deoxynivalenol (DON) and nivalenol (NIV)), zearalenone (ZEN), ochratoxin A (OTA), and cyclopiazonic acid (CPA) being the most prominent. Aflatoxin B 1 (AFB 1 ) is recognized as a class 1 human carcinogen by the International Agency for Research on Cancer (IARC 2002) and is linked to hepatocellular carcinoma, child growth impairment, reproductive complications, and acute aflatoxicosis episodes reported in parts of Africa (Francis et al., 2024 ). Fumonisins, especially FB 1 , are associated with human esophageal and liver cancers, as well as developmental disorders such as neural tube defects (Anumudu et al. 2024 ). Trichothecenes inhibits protein synthesis resulting to vomiting, immunosuppression, and haemorrhaging (Zhang et al. 2025 ). ZEN exhibits estrogenic activity, leading to reproductive disorders in livestock and potential cervical cancer risks in humans. OTA, a nephrotoxin, is implicated in kidney disease and liver impairment. CPA, often co-occurring with aflatoxins, contributes to gastrointestinal and systemic toxicity in various animals (Ochieng et al. 2021 ). The widespread occurrence of these toxins in cereals has economic, health, and trade implications. Previous surveys have demonstrated contamination of maize with multiple mycotoxins in Nigeria (Nji et al. 2022 ; Oyebamiji, et al. 2023 ; Chidimma et al. 2024 ). Nevertheless, important gaps remain. For instance, limited information exists on CPA and NIV in maize from Niger State, and data on co-contamination patterns across its different agro-ecological zones are scarce. While dietary risk assessments have been performed for sorghum and other cereals, there is relatively little information on maize, despite its higher consumption rates. Comprehensive surveillance of maize contamination is thus necessary to inform mitigation strategies and food safety policies. The present study addresses this need by investigating fungal diversity and quantifying major mycotoxins in maize from four microclimatic zones of Niger State. Furthermore, dietary exposure and health risks associated with maize consumption are characterized, with emphasis on cumulative risks of multiple mycotoxin intake. The findings are envisioned to contribute to evidence-based interventions for reducing mycotoxin related health and economic burdens in Nigeria. Materials and Methods Chemicals and Reagents The following analytical grade chemicals: deoxynivalenol solution − 200µg/ml (Sigma-Aldrich), dichloromethane (CH 2 Cl 2 ), (Merck KGaA Germany), cyclopiazonic acid − 1mg/ml (Sigma-Aldrich), aflatoxin total − 500 µg/l (AFG 2 : AFG 1 : AFB 2 : AFB 1 ) (Sigma-Aldrich),acetonitrile (CH 3 CN) (LiChrosolv®, Merck KGaA Germany), zearalenone ELISA kit (AgraQuant®), fumonisin ELISA kit (AgraQuant®), ochratoxin A ELISA kit (AgraQuant®), sodium hydrogen carbonate (NaHCO 3 ), distilled water H 2 O, nitrogen gas (N 2 ), N- hexane (C 6 H 14 ), methanol (CH 2 OH), (LiChrosolv®, Merck KGaA Germany), iso-octane and disodium sulphate salt (Na 2 SO 4 ) with percentage purity in the range 99.5–99.9% were obtained and used without further purification. Study Area and Sampling Niger State, located in central Nigeria, is the country’s largest state with a generally hot climate (average annual temperature ~ 29.5°C) and humid (mean relative humidity ~ 73%) (Animashaun et al. 2023 ). These conditions, particularly between May and October, favor fungal growth and mycotoxin production. The state is subdivided into four microclimatic zones according to rainfall: (i) > 1400 mm/year (wettest), (ii) 1200–1400 mm/year (wet), (iii) 1000–1200 mm/year (dry), and (iv) < 1000 mm/year (driest). During July 2019, 240 maize grain samples (white and yellow varieties) were collected from local markets and storage facilities across the four zones. Each sample (1 kg) was purchased or received as donation following EU Commission sampling guidelines (Regulation Amendiing 2010). Samples were labelled, sealed in polythene bags, and transported to the laboratory. To create representative test portions, the grains were combined into 32 composite samples: 16 from storage (8 white, 8 yellow) and 16 from markets (8 white, 8 yellow). For each zone, eight composite samples were prepared. The maize was milled (Greiffenberger Antriebstechnik, Germany) to fine flour (0.5–1 mm sieve size) and stored at -20°C until analysis. Isolation and Identification of Fungi Fungal occurrence was evaluated using a modified dilution plate method (Kaufman et al. 1963 ; Badmos et al. 2024 ). One gram of milled maize was suspended in 9 mL sterile Ringer’s solution and vortexed. Serial dilutions (10-fold) were prepared, and 1 mL aliquots from the 10⁻⁶ dilution were inoculated in triplicate on potato dextrose agar (PDA). Plates were incubated under two conditions: 28 ± 2°C for 3–5 days for Aspergillus enumeration, and 25°C for 7 days for Fusarium and Penicillium . Colony morphology and sporulation were observed microscopically, and counts were expressed as colony-forming units per gram (CFU/g). Extraction and HPLC Analysis of Selected Mycotoxins Multi-mycotoxin extraction followed an adaptation of Patterson and Roberts ( 1979 ). Twelve and a half grams of maize flour were mixed with 50 mL phosphate-buffered saline (PBS) and shaken at 200 rpm for 30 minutes. After centrifugation (1693 × g, 15 min, Thermo Fisher Fiberlite F9-4x1000y rotor, r = 16.8 cm), the supernatant was filtered through glass microfiber filters. A methanol/PBS mixture (80:20 and 70:30 v/v, 35 mL) was then added, shaken, and centrifuged again (3810 × g, 15 min, 4°C). The secondary extract (20 mL diluted with 90 mL PBS) was loaded onto a Myco6in1™ immunoaffinity column. After sequential washes with PBS and deionized water, toxins were eluted in 3 mL methanol, evaporated at 50°C under nitrogen, and reconstituted in acetonitrile/dichloromethane (70:30, v/v). Quantification of aflatoxins (AFs), deoxynivalenol (DON), nivalenol (NIV), and cyclopiazonic acid (CPA) was carried out using an HPLC system (LC98II, Searchtech) equipped with a C-18 column and UV detection. Operating conditions includes; column temperature: 40°C (AFs), 30°C (DON, NIV), 37°C (CPA); mobile phases: water/methanol/acetonitrile (60:20:20) for AFs; water/acetonitrile (90:10) for DON/NIV; ZnSO₄·7H₂O/methanol (70:30, v/v) for CPA; flow rates: 1 mL/min (AFs), 0.8 mL/min (DON/NIV), 0.6 mL/min (CPA); injection volume: 20 µL; detection wavelengths: 365 nm (AFs), 220 nm (DON/NIV), 248 nm (CPA). Method Validation Analytical performance was evaluated following Monbaliu et al. ( 2010 ), European Commission guidelines (EC 2006), and Abia et al. ( 2013 ). Linearity was checked via five-point calibration curves of each toxin standard (r² >0.90) (supplementary Table s1 ). Recovery tests were conducted by spiking minimally contaminated maize samples (5 g each, n = 5) with known toxin concentrations. Samples were equilibrated overnight, re-extracted, and analyzed. Recovery was expressed as the ratio of detected to spiked concentration. Sensitivity was assessed through limits of detection (LOD) and quantification (LOQ) derived from signal-to-noise ratios. ELISA Determination of Fumonisins, Ochratoxin A, and Zearalenone. Commercial ELISA kits (Romer Labs, Singapore) were used for fumonisins (FUMs, COKAQ3000), ochratoxin A (OTA, COKAQ2000), and zearalenone (ZEN, COKAQ5000). Ten grams of maize flour were extracted with 20 mL methanol-water (70:30, v/v) under constant agitation for 2 hours. Filtrates were passed through a 5 µm microfilter and stored at 4°C. ELISA was performed by adding 100 µL of standard or sample extract into antibody-coated wells, followed by 200 µL HRP-conjugated antibody solution. Plates were incubated for 15 min, washed five times with distilled water, and treated with substrate solution. After 5 min in the dark at 25°C, the reaction was stopped with 0.18 M H₂SO₄. Absorbance was read at 450/630 nm using an ELISA microplate reader. Validation included recovery assessment (spikes at 20 µg/kg OTA, 250 µg/kg FUM, 300 µg/kg ZEN), repeatability, and reproducibility across time (Table s2). Questionnaire Survey for Exposure Assessment A well-structured questionnaire was administered to 120 adult respondents (20–60 years), balanced by sex and age groups. Information collected included maize consumption frequency, dietary habits, and body weight (measured using portable scales). This dataset was used to estimate maize intake at the population level. Estimation of Dietary Exposure and Risk The estimated daily intake (EDI) for each toxin was calculated using the formula presented in Eq. 1 $$\:{EDI}_{m}=\frac{{C}_{m\:}\times\:\:k}{{b}_{w}}$$ 1 where EDI m is the estimated daily intake (ng/kg bw/day) for each mycotoxin; C m is the average mycotoxin concentration (ng/kg); k is daily maize consumption; bw is mean body weight. Risk assessment was performed by comparing EDI with tolerable daily intake (TDI) values to obtain %TDI. For aflatoxins and OTA, the margin of exposure (MOE) was calculated using benchmark dose lower limits (400 ng/kg bw/day for AFs; 17.86 ng/kg bw/day for OTA). Public health concern was indicated when MOE < 10,000 (EFSA 2020; Nugraha et al. 2025 ). To evaluate hepatocellular carcinoma (HCC) risk, cancer potency factors from JECFA were applied (0.3 cases/100,000 per ng/kg bw/day for HBsAg + populations; 0.01 cases/100,000 for HBsAg−). Estimates incorporated Nigerian HBV prevalence rates of 13.6% and 8.1% with a population of approximately 190 million to derive annual HCC burden attributable to maize consumption (Adeyinka et al. 2019 ; Badmos et al. 2024 ). Results Validation of HPLC and ELISA Techniques for Mycotoxin Detection in Maize Details of the validation results for the HPLC and ELISA analyses are summarized in Supplementary Tables S1 and S2. Both methods showed acceptable analytical performance when applied to maize matrices. In spiked samples, recovery values for the tested mycotoxins were within the acceptable ranges (60–120%) according to Codex Alimentarius and 70–125% with RSDr < 15% according to the AOAC (AOAC 1995; Codex 2008). Calibration of the HPLC assays yielded highly linear standard curves, with coefficients of determination (r²) between 0.9904 and 0.9952 across the different mycotoxins. For the ELISA assays, precision was established by repeatability ranging from 0.98–3.5% and reproducibility between 2.4% and 12% (Table S2). These findings confirm that the ELISA method performs reliably within the acceptable limits established in Commission Regulation (EC) No. 401/2006 for mycotoxin monitoring in official control programs (EC 2006). Overall, the validation results confirm that both HPLC and ELISA are suitable for quantifying mycotoxins in maize samples and meet internationally accepted performance requirements. Fungal Contamination in Maize Samples The fungal load in the analyzed maize samples varied according to grain type and sampling location. Yellow maize harbored significantly higher colony-forming units (33.31 ± 9.86 x 10^6 CFU/g CFU/g) than white maize (24.19 ± 7.43 x 10^6 CFU/g), while no significant difference was observed between market and storage samples (Fig. 1 ). Across the four climatic zones, fungal counts were greatest in the wetter regions, particularly Zone 2 (median: 70 x 10^6 CFU/g), whereas drier zones consistently showed lower values. These results confirm that high rainfall and humidity create favorable conditions for fungal proliferation. A total of 205 fungal isolates were obtained, dominated by Aspergillus species, followed by Fusarium , Penicillium , Mucor , and Rhizopus (Table 1 ). The most frequently isolated species were A. flavus and A. parasiticus , along with F. verticillioides and A. niger . Market maize (113) contained a broader diversity of fungi compared to stored maize (92), while yellow maize (117) again showed higher contamination levels than white (88). No significant variation in fungal incidence was observed across the agro-ecological zones. Table 1 Distribution of Fungal Species in Maize Samples across Locations, Varieties, and Zones in Niger State. Fungi Species Sample Places Type of maize Sample Location/Zones Total Market Store White Yellow Wettest Wet Dry Driest ( n = 16 ) ( n = 16 ) (n = 16) ( n = 16 ) ( n = 8 ) ( n = 8 ) ( n = 8 ) ( n = 8 ) ( n = 32 ) A.flavus 16 (100) 9 (56.25) 12 (75.00) 14 (87.50) 6 (75.00) 5 (62.50) 7 (87.50) 8 (100) 26 (81.25) A.niger 7 (43.75) 10 (62.50) 9 (56.25) 8 (50.00) 4 (50.00) 3 (37.50) 6 (75.00) 4 (50.00) 17 (53.13) A.parasiticus 11 (68.75) 12 (75.00) 9 (56.25) 14 (87.50) 5 (62.50) 7 (87.50) 6 (75.00) 5 (62.50) 23 (71.88) A.fumigatus 6 (37.50) 4 (25.00) 3 (18.75) 7 (43.75) 2 (25.00) 1 (12.50) 4 (50.00) 3 (37.50) 10 (31.25) A.ochraceus 5 (31.25) 2 (12.50) 3 (18.75) 4 (25.00) 3 (37.50) 1 (12.50) 1 (12.50) 2 (25.00) 7 (21.88) A.terreus 4 (25.00) 1 (6.25) 2 (12.50) 3 (18.75) 2 (25.00) 0 (0.00) 1 (12.50) 2 (25.00) 5 (15.63) A.glaucus 3 (18.75) 3 (18.75) 4 (50.00) 2 (12.50) 1 (12.50) 3 (37.50) 1 (12.50) 1 (12.50) 6 (18.75) F. verticilloides 13 (81.25) 8 (50.00) 9 (56.25) 12 (75.00) 4 (50.00) 6 (75.00) 5 (62.50) 6 (75.00) 21 (65.63) F. graminearum 5 (31.25) 6 (37.50) 7 (43.75) 4 (25.00) 5 (62.50) 0 (0.00) 2 (25.00) 4 (50.00) 11 (34.38) F.equiseti 6 (37.50) 2 (12.50) 1 (6.25) 7 (43.75) 3 (37.50) 3 (37.50) 2 (25.00) 0 (0.00) 8 (25.00) F. sporotrichiodes 7 (43.75) 4 (25.00) 3 (18.75) 8 (50.00) 2 (25.00) 2 (25.00) 4 (50.00) 3 (37.50) 11 (34.38) F. subglutinans 3 (18.75) 4 (25.00) 2 (12.50) 5 (31.25) 3 (37.50) 1 (12.50) 1 (12.50) 2 (25.00) 7 (21.88) P.expansum 6 (37.50) 8 (50.00) 6 (37.50) 8 (50.00) 4 (50.00) 2 (25.00) 3 (37.50) 5 (75.00) 14 (43.75) P.verrucosum 6 (37.50) 4 (25.00) 4 (25.00) 6 (37.50) 3 (37.50) 2 (25.00) 1 (12.50) 4 (50.00) 10 (31.25) P.cyclopium 5 (31.25) 3 (18.75) 4 (25.00) 4 (25.00) 1 (12.50) 3 (37.50) 2 (25.00) 2 (25.00) 8 (25.00) P.griseofulvum 2 (12.50) 0 (0.00) 1 (6.25) 1 (6.25) 1 (12.50) 0 (0.00) 0 (0.00) 1 (12.50) 2 (6.25) Mucor spp. 3 (18.75) 7 (43.75) 6 (37.50) 4 (25.00) 2 (25.00) 4 (50.00) 3 (37.50) 1 (12.50) 10 (31.25) Rhizopus spp. 4 (25.00) 5 (31.25) 3 (18.75) 6 (37.50) 2 (25.00) 1 (12.50) 3 (37.50) 3 (37.50) 9 (28.13) Mycotoxin Contamination All maize samples tested positive for aflatoxins. Fumonisins and OTA were also highly prevalent, with occurrences of 90.63% and 78.13%, respectively. Other mycotoxins (DON, CPA, NIV, and ZEN) occurred less frequently (Supplementary Table s3). Fumonisins (1692.88 ± 1034.24 µg/kg), showed the highest contamination level among Fusarium toxins, followed by ZEN and DON, while Aspergillus toxins (AFs, CPA, AFB 1 , AFG 1 ) were also notable, with OTA and NIV occurring at low levels (Table S2). In terms of regulatory limits, 75.00% and 71.88% of positive samples exceeded the EU maximum limit of 2 µg/kg for AFB 1 and 4 µg/kg for total aflatoxins, respectively. Mean fumonisin levels (364.15–4889.31 µg/kg) frequently exceeded the European Union (EU) maximum permissible level of 1000 µg/kg for unprocessed maize. OTA was detected in 78% of samples, and more than half of these surpassed the EU regulatory limit of 5 µg/kg. In contrast, DON and ZEN concentrations were within permissible limits (Table S3). There was no significant variation in levels of AFB 1 , AFB 2 , FUMs, DON, and NIV across agroecological zones (Figs. 2 and 3 ). Regarding maize types, FUMs, NIV, and AFG 1 levels were significantly higher (p < 0.05) in yellow maize than white maize, while OTA concentrations were significantly higher (p < 0.05) in market samples than in stored samples. Conversely, CPA levels were higher in stored samples (Fig. S1 ). Co-contamination was common. Up to five toxins were detected in a single sample, with the most frequent combinations being AFs + CPA + DON + FUM + OTA, followed by AFs + FUM + OTA + NIV (Table S5). Dietary Exposure and Risk Estimates Average maize consumption among respondents was estimated at 0.33196 kg/person/day, while the average body weight of the sampled population was 61.58 kg, with average weight of 63.03 kg and 64.29 kg for men and women, respectively. Based on measured concentrations, fumonisins and OTA accounted for the highest estimated daily intake (EDI) values, both exceeding their tolerable daily intake (TDI) thresholds. By contrast, DON, NIV, and ZEN exposures were below TDI values (Table 3 ). The calculated margins of exposure (MOE) for aflatoxins and OTA were consistently below 10,000, indicating serious health risks (Table 2 ). Table 2 Comparative Risk Assessment of Aflatoxin-Attributable HCC Burden in HBsAg + and HBsAg – Maize Consumers in Niger State, Nigeria. AFB 1 AFB 2 AFG 1 AFG 2 AFs OTA Average concentration (x 10 3 ng/kg) 27.24 3.81 25.66 1.03 57.74 5.94 Estimated daily Intake (EDI) (ng/kg. bw/day) Male 143.46 20.07 135.14 5.42 304.10 31.28 Total Population 146.84 20.54 138.33 5.55 311.26 32.02 Female 140.65 19.67 132.49 5.32 298.14 30.67 Margin of Exposure (MOE) Male 2.79 19.93 2.96 73.80 1.32 0.57 Total Population 2.72 19.47 2.89 72.07 1.29 0.56 Female 2.84 20.34 3.02 75.19 1.34 0.58 Estimated Annual HCC (per 100,000) HBsAg + Male 43.04 6.02 40.54 1.63 91.23 9.38 Total Population 44.05 6.16 41.50 1.67 93.38 9.61 Female 42.20 5.90 39.75 1.60 89.44 9.20 HBsAg- Male 1.43 0.20 1.35 0.05 3.04 0.31 Total Population 1.47 0.21 1.38 0.06 3.11 0.32 Female 1.41 0.20 1.32 0.05 2.98 0.31 Annual HCC cases (HBsAg Prevalence = 13.6%) (x10 3 ) HBsAg + Male 11.12 1.56 10.48 0.42 23.57 2.42 Total Population 11.38 1.59 10.72 0.43 24.13 2.48 Female 10.90 1.52 10.27 0.41 23.11 2.38 HBsAg- Male 2.36 0.33 2.22 0.09 5.00 0.51 Total Population 2.41 0.34 2.27 0.10 5.10 0.53 Female 2.31 0.33 2.17 0.09 4.89 0.50 HCC Risk/year (13.6%) (x10 5 ) Male 4.82 0.09 4.28 0.007 21.65 0.23 Total Population 5.05 0.10 4.48 0.007 22.69 0.24 Female 4.63 0.09 4.11 0.007 20.82 0.22 Annual HCC cases (HBsAg Prevalence = 8.1%) (x10 3 ) HBsAg + Male 6.62 0.93 6.24 0.25 14.04 1.44 Total Population 6.78 0.95 6.39 0.26 14.37 1.48 Female 6.49 0.91 6.12 0.25 13.76 1.42 HBsAg- Male 2.50 0.35 2.36 0.09 5.31 0.55 Total Population 2.57 0.37 2.41 0.10 5.43 0.56 Female 2.46 0.35 2.30 0.09 5.20 0.54 HCC Risk/year (8.1%) (x10 5 ) Male 2.89 0.06 2.56 0.004 12.97 0.14 Total Population 3.02 0.06 2.68 0.004 13.59 0.14 Female 2.78 0.05 2.46 0.004 12.47 0.13 Keys: EDI = Estimated daily intake; TDI = Tolerable daily intake; %TDI = Percentage tolerable daily intake; NA = Not applicable; TP = Total population Table 3 Assessment of Dietary Exposure to DON, FUMs, NIV, ZEN, CPA, and OTA from Maize in Niger State, Nigeria. Mycotoxins Average concentration (x 10 3 ng/kg) Estimated daily Intake (EDI) (ng/kg. bw/day) TDI (x 10 3 ng/kg) Risk Characterization (% TDI) Male Total Population Female Male Total Population DON 24.86 130.93 134.01 128.36 1 13.09 13.40 12.84 FUM 1692.88 8915.89 9125.83 8741.15 2 445.79 456.29 437.06 NIV 1.57 8.27 8.46 8.11 0.7 1.18 1.21 1.16 ZEN 26.32 138.62 141.88 135.90 0.5 27.72 28.38 27.18 CPA 28.8 151.68 155.25 148.71 NA Na Na Na OTA 5.94 31.28 32.02 30.67 0.014 223.43 228.71 219.07 Predicted Hepatocellular Carcinoma Burden At a hepatitis B virus prevalence of 13.6%, maize consumption in Nigeria was estimated to contribute approximately 505,000 cases of hepatocellular carcinoma (HCC) annually due to AFB1, alongside 24,000 cases associated with OTA. At a lower HBV prevalence of 8.1%, predicted cases were reduced but remained substantial (Table 2 ). Discussion The high fungal loads and toxin concentrations observed confirm that maize in Niger State is heavily contaminated, particularly in wetter agro-ecological zones. This is consistent with earlier Nigerian surveys (Chidimma et al. 2024 ), and South African findings, where climatic conditions strongly influenced aflatoxin contamination across different agro-climatic regions (Nji et al. 2024 ). Aspergillus species, especially A. flavus and A. parasiticus , were dominant, consistent with their known role in aflatoxin production across Africa (Akinola et al. 2021 ). The higher fungal burden in yellow maize suggests varietal susceptibility, which may relate to grain composition and storage characteristics. Same pattern was also noted in Kenyan maize where yellow varieties supported greater Aspergillus colonization (Mutiga et al. 2015 ).The isolation of Fusarium , Penicillium , Mucor and Rhizopus is consistent with maize pre- and post-harvest ecology. Fusarium spp. (notably F. verticillioides/F. proliferatum ) are field fungi that infect kernels through silks and wounds and are favoured by drought/heat stress and insect injury; these infections often persist into storage and are the major producer of fumonisins in maize. This has been shown across environments where planting date, drought and insect pressure increase ear rot and FB1 levels (Blacutt et al. 2018 ; Omotayo et al. 2023). In contrast, Penicillium spp. are storage molds adapted to lower water activity than many aspergilli and proliferate on damaged, insufficiently dried grain. While OTA in tropical cereals is more often linked to Aspergillus sections Circumdati/Nigri (for instance; A. niger, A. westerdijkiae ), Penicillium verrucosum (main producer of OTA) predominates in cooler storage conditions. Consequently in Niger State, Penicillium likely reflects sub-optimal storage rather than being the main OTA source (Oluwakayode et al. 2024 ; Ben Miri et al. 2024 ).The presence of rapid-growing spoilage molds such as Mucor and Rhizopus typically shows high kernel moisture and mechanical damage during handling/shelling; although they are not major producers of the regulated mycotoxins targeted here, they accelerate spoilage and can promote micro-niches that facilitate colonization by toxigenic species (Deligeorgakis et al. 2023 ). Hence, rapid post-harvest drying, upholding safe storage moisture (≤ 13–13.5% for maize) and preventing moisture stratification in bins are crucial to suppress these storage molds (Baidhe et al. 2024 ; Lehmane et al. 2022 ; FAO 2025). The toxin profiles highlight maize as a major dietary source of fumonisins in Nigeria, with levels frequently exceeding international safety thresholds and posing a significant public health concern. The findings align with the report of Adelodun et al. ( 2023 ) who identified fumonisins as a major hazard in maize-consuming populations of sub-Saharan Africa. Aflatoxins were also detected at concerning levels, with several samples surpassing EU and Codex limits, consistent with the prevalence of A. flavus and A. parasiticus and in agreement with earlier Nigerian surveys (Afolabi et al. 2020; Chilaka et al. 2023). Multi-mycotoxin contamination was widespread, with up to five toxins detected in a single sample. This mirrors recent multi-year surveillance in South African commercial maize, which found frequent co-occurrence of aflatoxins, fumonisins, and emerging toxins such as citrinin (Nji and Mwanza 2024 ). Similar contamination patterns have been reported in Ethiopia, where post-harvest maize was found to contain DON, ZEN, and multiple other toxins (Mohammed et al. 2023 ). Such evidence reinforces the complexity of maize safety management across African food systems. Dietary exposure assessments confirmed that fumonisins and OTA present the greatest risks, as their %TDI values exceeded recommended limits. Similar exposure levels have been reported in Tanzanian populations, where maize-based diets were linked with fumonisin intakes above safe thresholds (Kayanda et al. 2023). Recent risk assessments in Tanzania smallholder farming systems also revealed alarming co-exposures to aflatoxins and fumonisins, reinforcing regional concerns about dietary safety (Stafstrom et al. 2025 ). In spite the in availability of a defined aflatoxin TDI, the consistently low MOEs observed in this study highlight an unacceptable health risk. This aligns with earlier reports linking aflatoxin exposure and hepatitis B virus infection to elevated hepatocellular carcinoma incidence in African populations (Alao et al. 2023 ). The estimated hepatocellular carcinoma burden associated with maize consumption in Nigeria emphasizes the scale of risk posed by aflatoxin exposure. While other cereals also contribute to mycotoxin intake, maize represents a disproportionate risk owing to its widespread consumption and high fumonisin burden, which has also been documented in recent African studies (Nji et al. 2024 ; Mohammed et al. 2023 ). Despite the fact that OTA’s contribution to cancer burden is lesser, its nephrotoxic effects add to the overall health risks. These findings strengthen the urgent need for preventive measures. Improved drying, hermetic storage, and biocontrol strategies could significantly reduce fungal growth and toxin production. Furthermore, targeted regulatory surveillance for maize would have greater public health impact than for less-consumed cereals. Declarations Ethics approval All experimental procedures in this study were carried out in accordance with the relevant legal and ethical requirements of the Federal Republic of Nigeria and South Africa, where the work was undertaken. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during this study can be obtained from the corresponding author upon reasonable request. Competing of Interests The authors declare that they have no financial or non-financial conflicts of interest. Funding This research was partly supported by the Tertiary Education Trust Fund (TETFUND), Nigeria (TETFUND/FUTMINNA/2019/B7/16), which funded part of the mycotoxin analysis. Additional support for the fungal investigations was provided by the Centre for Resilient Agri-Food Systems (CRAFS), University of Malawi, through the World Bank–funded Africa Centres of Excellence II Additional Financing (ACE II AF) project. Author Contributions: F.O.B.: Data collection, curation, laboratory investigation, methodology, validation, and manuscript writing (original draft, review, and editing). H.L.M., V.T.O., A.D., A.A., S.S., H.K.M., D.O.A: Conceptualization, methodology design, resource provision, supervision, validation, and critical manuscript review. M.M., L.M., M.Mw., H.A.M.: Formal analysis, data visualization, validation, and contribution to manuscript review and editing. All authors have read and approved the final version of the manuscript. Acknowledgements The authors gratefully acknowledge the technical support provided by the Department of Animal Health, North-West University, Mafikeng, South Africa. References Abia WA, Warth B, Sulyok M, Krska R, Tchana AN, Njobeh PB, Moundipa PF (2013) Determination of multi-mycotoxin occurrence in cereals, nuts and their products in cameroon by liquid chromatography tandem mass spectrometry (LC-MS/MS). 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Food Chem Toxicol 195:115119. https://doi.org/10.1016/j.fct.2024.115119 Ochieng PE, Scippo ML, Kemboi DC, Croubels S, Okoth S, Kang’ethe EK, Doupovec B, Gathumbi JK, Lindahl JF, Antonissen G (2021) Mycotoxins in poultry feed and feed ingredients from Sub-Saharan Africa and their impact on the production of broiler and layer chickens: A review. Toxins 13(9):633. https://doi.org/10.3390/toxins13090633 Oluwakayode A, Sulyok M, Krska R, Medina A (2024) The effect of the interactions of water activity, and temperature on OTA, OTB, and OTα produced by Penicillium verrucosum in a mini silo of natural and inoculated wheat using CO2 production as fungal activity sentinel. Food Chem 460:140590. https://doi.org/10.1016/j.foodchem.2024.140590 Omotayo OP, Babalola OO (2023) Fusarium verticillioides of maize plant: Potentials of propitious phytomicrobiome as biocontrol agents. Front Fungal Biol 4:1095765. https://doi.org/10.3389/ffunb.2023.1095765 Oyebamiji YO, Shamsudin NAA, Adigun BA, Usman OK (2023) Prevalence of mycotoxins in Nigerian’s staple food. AgroTech-Food Sci Technol Environ 2(1):38–47. https://doi.org/10.53797/agrotech.v2i1.5.2023 Patterson DS, Roberts BA (1979) Mycotoxins in animal feedstuffs: sensitive thin layer chromatographic detection of aflatoxin, ochratoxin A, sterigmatocystin, zearalenone, and T-2 toxin. J AOAC Int 62(6):1265–1267. https://doi.org/10.1093/jaoac/62.6.1265 Regulation Amending (2010) Setting maximum levels for certain contaminants in foodstuffs as regards aflatoxins. Officials J Eur Union 165:8–12 Stafstrom W, Ngure F, Mshanga J, Wells H, Nelson RJ, Mischler J (2025) Modeling maize aflatoxins and fumonisins in a Tanzanian smallholder system: Accounting for diverse risk factors improves mycotoxin models. PLoS ONE 20(1):e0316457. https://doi.org/10.1371/journal.pone.0316457 Wossen T, Menkir A, Alene A, Abdoulaye T, Ajala S, Badu-Apraku B, Gedil M, Mengesha W, Meseka S (2023) Drivers of transformation of the maize sector in Nigeria. Global Food Secur 38:100713. https://doi.org/10.1016/j.gfs.2023.100713 Zhang J, Li H, Zhang E, Lu Y, Liu B, Yan K, Yang X, Lv H (2025) Trichothecenes toxicity in humans and animals: Unraveling the mechanisms and harnessing phytochemicals for prevention. Comp Biochem Physiol C Toxicol Pharmacol 296:110226. https://doi.org/10.1016/j.cbpc.2025.110226 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialforMaizeManuscript.docx Cite Share Download PDF Status: Under Revision Version 1 posted Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 28 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7479286","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512136601,"identity":"d3824178-1122-4198-bf26-66dabb6f576d","order_by":0,"name":"Fatimah Omolola 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colonisation (CFU/g) in maize samples (a) across the four micro-climatic zones (b) categorised by sample location and (c) maize type. *** indicates significant differences (\u003cem\u003ep\u003c/em\u003e = 0.001); ns = not significant. Dots represent outliers, error bars show data spread, and the box limit correspond to the first and third quartiles.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7479286/v1/cc0fd2a57479aa0e40dac414.png"},{"id":91465089,"identity":"e767bdd3-6366-4287-9eff-dbcd16d3bb90","added_by":"auto","created_at":"2025-09-16 18:28:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48179,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of aflatoxin levels in maize grain samples across the four microclimatic zones of Niger State (see text). Zones labeled with different letters differ significantly (p = 0.05). Dots indicate outliers, error bars show data spread, and box limits represent the first and third quartiles.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7479286/v1/81cdd3348c4873f3892f166d.png"},{"id":91465092,"identity":"ac457baa-9e73-4ddb-9249-9d7b420d11ba","added_by":"auto","created_at":"2025-09-16 18:28:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44095,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of fumonisins, trichothecenes (DON, NIV, ZEN), OTA, and CPA levels in maize grain samples across the four microclimatic zones of Niger State (see text). Zones with different letters differ significantly (p = 0.05). Dots represent outliers, error bars indicate data spread, and box limits correspond to the first and third quartiles.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7479286/v1/20caa51decf6e6697e1a6730.png"},{"id":91466854,"identity":"0f9f2b09-702f-43da-be74-c94b9ce3dbc8","added_by":"auto","created_at":"2025-09-16 18:52:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1411281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7479286/v1/eaca5c4b-3531-4038-a6d2-8b2b7e2e67b5.pdf"},{"id":91465094,"identity":"99c68397-37ae-4b79-8289-6e5f92d1fe7e","added_by":"auto","created_at":"2025-09-16 18:28:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":112571,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialforMaizeManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7479286/v1/376d793584b6b81639fd8428.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-mycotoxin Contamination in Maize from Niger State, Nigeria: Occurrence, Dietary Exposure and Health Risk Assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays L\u003c/em\u003e.) is one of the most significant staple crops in sub-Saharan Africa, providing a major portion of caloric intake for both rural and urban populations (Mudadirwa et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Nigeria, it ranks among the leading cereal crops, with Niger State serving as the nation\u0026rsquo;s largest producer (Wossen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Abu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite its central role in food security and trade, maize is highly vulnerable to fungal infestation during cultivation, storage, and marketing. Under favorable environmental conditions, this contamination frequently leads to the synthesis of mycotoxins, toxic fungal metabolites that compromise food safety and public health (Alam et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral groups of mycotoxins are of concern in cereals, with aflatoxins, fumonisins, trichothecenes (such as deoxynivalenol (DON) and nivalenol (NIV)), zearalenone (ZEN), ochratoxin A (OTA), and cyclopiazonic acid (CPA) being the most prominent. Aflatoxin B\u003csub\u003e1\u003c/sub\u003e (AFB\u003csub\u003e1\u003c/sub\u003e) is recognized as a class 1 human carcinogen by the International Agency for Research on Cancer (IARC 2002) and is linked to hepatocellular carcinoma, child growth impairment, reproductive complications, and acute aflatoxicosis episodes reported in parts of Africa (Francis et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Fumonisins, especially FB\u003csub\u003e1\u003c/sub\u003e, are associated with human esophageal and liver cancers, as well as developmental disorders such as neural tube defects (Anumudu et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Trichothecenes inhibits protein synthesis resulting to vomiting, immunosuppression, and haemorrhaging (Zhang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). ZEN exhibits estrogenic activity, leading to reproductive disorders in livestock and potential cervical cancer risks in humans. OTA, a nephrotoxin, is implicated in kidney disease and liver impairment. CPA, often co-occurring with aflatoxins, contributes to gastrointestinal and systemic toxicity in various animals (Ochieng et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe widespread occurrence of these toxins in cereals has economic, health, and trade implications. Previous surveys have demonstrated contamination of maize with multiple mycotoxins in Nigeria (Nji et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Oyebamiji, et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chidimma et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, important gaps remain. For instance, limited information exists on CPA and NIV in maize from Niger State, and data on co-contamination patterns across its different agro-ecological zones are scarce. While dietary risk assessments have been performed for sorghum and other cereals, there is relatively little information on maize, despite its higher consumption rates.\u003c/p\u003e\u003cp\u003eComprehensive surveillance of maize contamination is thus necessary to inform mitigation strategies and food safety policies. The present study addresses this need by investigating fungal diversity and quantifying major mycotoxins in maize from four microclimatic zones of Niger State. Furthermore, dietary exposure and health risks associated with maize consumption are characterized, with emphasis on cumulative risks of multiple mycotoxin intake. The findings are envisioned to contribute to evidence-based interventions for reducing mycotoxin related health and economic burdens in Nigeria.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eChemicals and Reagents\u003c/p\u003e\u003cp\u003eThe following analytical grade chemicals: deoxynivalenol solution \u0026minus;\u0026thinsp;200\u0026micro;g/ml (Sigma-Aldrich), dichloromethane (CH\u003csub\u003e2\u003c/sub\u003eCl\u003csub\u003e2\u003c/sub\u003e), (Merck KGaA Germany), cyclopiazonic acid \u0026minus;\u0026thinsp;1mg/ml (Sigma-Aldrich), aflatoxin total \u0026minus;\u0026thinsp;500 \u0026micro;g/l (AFG\u003csub\u003e2\u003c/sub\u003e: AFG\u003csub\u003e1\u003c/sub\u003e: AFB\u003csub\u003e2\u003c/sub\u003e: AFB\u003csub\u003e1\u003c/sub\u003e) (Sigma-Aldrich),acetonitrile (CH\u003csub\u003e3\u003c/sub\u003eCN) (LiChrosolv\u0026reg;, Merck KGaA Germany), zearalenone ELISA kit (AgraQuant\u0026reg;), fumonisin ELISA kit (AgraQuant\u0026reg;), ochratoxin A ELISA kit (AgraQuant\u0026reg;), sodium hydrogen carbonate (NaHCO\u003csub\u003e3\u003c/sub\u003e), distilled water H\u003csub\u003e2\u003c/sub\u003eO, nitrogen gas (N\u003csub\u003e2\u003c/sub\u003e), N- hexane (C\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e14\u003c/sub\u003e), methanol (CH\u003csub\u003e2\u003c/sub\u003eOH), (LiChrosolv\u0026reg;, Merck KGaA Germany), iso-octane and disodium sulphate salt (Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) with percentage purity in the range 99.5\u0026ndash;99.9% were obtained and used without further purification.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area and Sampling\u003c/h2\u003e\u003cp\u003eNiger State, located in central Nigeria, is the country\u0026rsquo;s largest state with a generally hot climate (average annual temperature\u0026thinsp;~\u0026thinsp;29.5\u0026deg;C) and humid (mean relative humidity\u0026thinsp;~\u0026thinsp;73%) (Animashaun et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These conditions, particularly between May and October, favor fungal growth and mycotoxin production. The state is subdivided into four microclimatic zones according to rainfall: (i)\u0026thinsp;\u0026gt;\u0026thinsp;1400 mm/year (wettest), (ii) 1200\u0026ndash;1400 mm/year (wet), (iii) 1000\u0026ndash;1200 mm/year (dry), and (iv)\u0026thinsp;\u0026lt;\u0026thinsp;1000 mm/year (driest). During July 2019, 240 maize grain samples (white and yellow varieties) were collected from local markets and storage facilities across the four zones. Each sample (1 kg) was purchased or received as donation following EU Commission sampling guidelines (Regulation Amendiing 2010). Samples were labelled, sealed in polythene bags, and transported to the laboratory. To create representative test portions, the grains were combined into 32 composite samples: 16 from storage (8 white, 8 yellow) and 16 from markets (8 white, 8 yellow). For each zone, eight composite samples were prepared. The maize was milled (Greiffenberger Antriebstechnik, Germany) to fine flour (0.5\u0026ndash;1 mm sieve size) and stored at -20\u0026deg;C until analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIsolation and Identification of Fungi\u003c/h3\u003e\n\u003cp\u003eFungal occurrence was evaluated using a modified dilution plate method (Kaufman et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Badmos et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One gram of milled maize was suspended in 9 mL sterile Ringer\u0026rsquo;s solution and vortexed. Serial dilutions (10-fold) were prepared, and 1 mL aliquots from the 10⁻⁶ dilution were inoculated in triplicate on potato dextrose agar (PDA). Plates were incubated under two conditions: 28\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C for 3\u0026ndash;5 days for \u003cem\u003eAspergillus\u003c/em\u003e enumeration, and 25\u0026deg;C for 7 days for \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e. Colony morphology and sporulation were observed microscopically, and counts were expressed as colony-forming units per gram (CFU/g).\u003c/p\u003e\n\u003ch3\u003eExtraction and HPLC Analysis of Selected Mycotoxins\u003c/h3\u003e\n\u003cp\u003eMulti-mycotoxin extraction followed an adaptation of Patterson and Roberts (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Twelve and a half grams of maize flour were mixed with 50 mL phosphate-buffered saline (PBS) and shaken at 200 rpm for 30 minutes. After centrifugation (1693 \u0026times; g, 15 min, Thermo Fisher Fiberlite F9-4x1000y rotor, r\u0026thinsp;=\u0026thinsp;16.8 cm), the supernatant was filtered through glass microfiber filters. A methanol/PBS mixture (80:20 and 70:30 v/v, 35 mL) was then added, shaken, and centrifuged again (3810 \u0026times; g, 15 min, 4\u0026deg;C). The secondary extract (20 mL diluted with 90 mL PBS) was loaded onto a Myco6in1\u0026trade; immunoaffinity column. After sequential washes with PBS and deionized water, toxins were eluted in 3 mL methanol, evaporated at 50\u0026deg;C under nitrogen, and reconstituted in acetonitrile/dichloromethane (70:30, v/v).\u003c/p\u003e\u003cp\u003eQuantification of aflatoxins (AFs), deoxynivalenol (DON), nivalenol (NIV), and cyclopiazonic acid (CPA) was carried out using an HPLC system (LC98II, Searchtech) equipped with a C-18 column and UV detection. Operating conditions includes; column temperature: 40\u0026deg;C (AFs), 30\u0026deg;C (DON, NIV), 37\u0026deg;C (CPA); mobile phases: water/methanol/acetonitrile (60:20:20) for AFs; water/acetonitrile (90:10) for DON/NIV; ZnSO₄\u0026middot;7H₂O/methanol (70:30, v/v) for CPA; flow rates: 1 mL/min (AFs), 0.8 mL/min (DON/NIV), 0.6 mL/min (CPA); injection volume: 20 \u0026micro;L; detection wavelengths: 365 nm (AFs), 220 nm (DON/NIV), 248 nm (CPA).\u003c/p\u003e\n\u003ch3\u003eMethod Validation\u003c/h3\u003e\n\u003cp\u003eAnalytical performance was evaluated following Monbaliu et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), European Commission guidelines (EC 2006), and Abia et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Linearity was checked via five-point calibration curves of each toxin standard (r\u0026sup2; \u0026gt;0.90) (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e). Recovery tests were conducted by spiking minimally contaminated maize samples (5 g each, n\u0026thinsp;=\u0026thinsp;5) with known toxin concentrations. Samples were equilibrated overnight, re-extracted, and analyzed. Recovery was expressed as the ratio of detected to spiked concentration. Sensitivity was assessed through limits of detection (LOD) and quantification (LOQ) derived from signal-to-noise ratios.\u003c/p\u003e\u003cp\u003e\u003cb\u003eELISA Determination of Fumonisins, Ochratoxin A, and Zearalenone.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCommercial ELISA kits (Romer Labs, Singapore) were used for fumonisins (FUMs, COKAQ3000), ochratoxin A (OTA, COKAQ2000), and zearalenone (ZEN, COKAQ5000). Ten grams of maize flour were extracted with 20 mL methanol-water (70:30, v/v) under constant agitation for 2 hours. Filtrates were passed through a 5 \u0026micro;m microfilter and stored at 4\u0026deg;C. ELISA was performed by adding 100 \u0026micro;L of standard or sample extract into antibody-coated wells, followed by 200 \u0026micro;L HRP-conjugated antibody solution. Plates were incubated for 15 min, washed five times with distilled water, and treated with substrate solution. After 5 min in the dark at 25\u0026deg;C, the reaction was stopped with 0.18 M H₂SO₄. Absorbance was read at 450/630 nm using an ELISA microplate reader. Validation included recovery assessment (spikes at 20 \u0026micro;g/kg OTA, 250 \u0026micro;g/kg FUM, 300 \u0026micro;g/kg ZEN), repeatability, and reproducibility across time (Table s2).\u003c/p\u003e\n\u003ch3\u003eQuestionnaire Survey for Exposure Assessment\u003c/h3\u003e\n\u003cp\u003eA well-structured questionnaire was administered to 120 adult respondents (20\u0026ndash;60 years), balanced by sex and age groups. Information collected included maize consumption frequency, dietary habits, and body weight (measured using portable scales). This dataset was used to estimate maize intake at the population level.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEstimation of Dietary Exposure and Risk\u003c/h2\u003e\u003cp\u003eThe estimated daily intake (EDI) for each toxin was calculated using the formula presented in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{EDI}_{m}=\\frac{{C}_{m\\:}\\times\\:\\:k}{{b}_{w}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eEDI\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e is the estimated daily intake (ng/kg bw/day) for each mycotoxin; \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e is the average mycotoxin concentration (ng/kg); \u003cem\u003ek\u003c/em\u003e is daily maize consumption; \u003cem\u003ebw\u003c/em\u003e is mean body weight.\u003c/p\u003e\u003cp\u003eRisk assessment was performed by comparing EDI with tolerable daily intake (TDI) values to obtain %TDI. For aflatoxins and OTA, the margin of exposure (MOE) was calculated using benchmark dose lower limits (400 ng/kg bw/day for AFs; 17.86 ng/kg bw/day for OTA). Public health concern was indicated when MOE\u0026thinsp;\u0026lt;\u0026thinsp;10,000 (EFSA 2020; Nugraha et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To evaluate hepatocellular carcinoma (HCC) risk, cancer potency factors from JECFA were applied (0.3 cases/100,000 per ng/kg bw/day for HBsAg\u0026thinsp;+\u0026thinsp;populations; 0.01 cases/100,000 for HBsAg\u0026minus;). Estimates incorporated Nigerian HBV prevalence rates of 13.6% and 8.1% with a population of approximately 190\u0026nbsp;million to derive annual HCC burden attributable to maize consumption (Adeyinka et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Badmos et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eValidation of HPLC and ELISA Techniques for Mycotoxin Detection in Maize\u003c/h2\u003e\u003cp\u003eDetails of the validation results for the HPLC and ELISA analyses are summarized in Supplementary Tables S1 and S2. Both methods showed acceptable analytical performance when applied to maize matrices. In spiked samples, recovery values for the tested mycotoxins were within the acceptable ranges (60\u0026ndash;120%) according to Codex Alimentarius and 70\u0026ndash;125% with RSDr\u0026thinsp;\u0026lt;\u0026thinsp;15% according to the AOAC (AOAC 1995; Codex 2008). Calibration of the HPLC assays yielded highly linear standard curves, with coefficients of determination (r\u0026sup2;) between 0.9904 and 0.9952 across the different mycotoxins. For the ELISA assays, precision was established by repeatability ranging from 0.98\u0026ndash;3.5% and reproducibility between 2.4% and 12% (Table S2). These findings confirm that the ELISA method performs reliably within the acceptable limits established in Commission Regulation (EC) No. 401/2006 for mycotoxin monitoring in official control programs (EC 2006). Overall, the validation results confirm that both HPLC and ELISA are suitable for quantifying mycotoxins in maize samples and meet internationally accepted performance requirements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFungal Contamination in Maize Samples\u003c/h2\u003e\u003cp\u003eThe fungal load in the analyzed maize samples varied according to grain type and sampling location. Yellow maize harbored significantly higher colony-forming units (33.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.86 x 10^6 CFU/g CFU/g) than white maize (24.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43 x 10^6 CFU/g), while no significant difference was observed between market and storage samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Across the four climatic zones, fungal counts were greatest in the wetter regions, particularly Zone 2 (median: 70 x 10^6 CFU/g), whereas drier zones consistently showed lower values. These results confirm that high rainfall and humidity create favorable conditions for fungal proliferation. A total of 205 fungal isolates were obtained, dominated by \u003cem\u003eAspergillus\u003c/em\u003e species, followed by \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003eMucor\u003c/em\u003e, and \u003cem\u003eRhizopus\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most frequently isolated species were \u003cem\u003eA. flavus\u003c/em\u003e and \u003cem\u003eA. parasiticus\u003c/em\u003e, along with \u003cem\u003eF. verticillioides\u003c/em\u003e and \u003cem\u003eA. niger\u003c/em\u003e. Market maize (113) contained a broader diversity of fungi compared to stored maize (92), while yellow maize (117) again showed higher contamination levels than white (88). No significant variation in fungal incidence was observed across the agro-ecological zones.\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\u003eDistribution of Fungal Species in Maize Samples across Locations, Varieties, and Zones in Niger State.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFungi Species\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample Places\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eType of maize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eSample Location/Zones\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarket\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWettest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWet\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDriest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;16\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;16\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;16\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;8\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;8\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;8\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;8\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(\u003cem\u003en\u0026thinsp;=\u0026thinsp;32\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA.flavus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (56.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (87.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7 (87.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e26 (81.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA.niger\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (43.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (56.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e17 (53.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA.parasiticus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (68.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (56.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (87.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7 (87.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e23 (71.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA.fumigatus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7 (43.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e10 (31.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA.ochraceus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (31.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e7 (21.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA.terreus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5 (15.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA.glaucus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6 (18.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF. verticilloides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (81.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (56.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e21 (65.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF. graminearum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (31.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (43.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5 (62.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e11 (34.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF.equiseti\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7 (43.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8 (25.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF. sporotrichiodes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (43.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e11 (34.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF. subglutinans\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5 (31.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e7 (21.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP.expansum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e14 (43.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP.verrucosum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e10 (31.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP.cyclopium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (31.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8 (25.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP.griseofulvum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1 (6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2 (6.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMucor spp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (43.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e10 (31.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRhizopus spp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (31.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (18.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2 (25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3 (37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e9 (28.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMycotoxin Contamination\u003c/h2\u003e\u003cp\u003eAll maize samples tested positive for aflatoxins. Fumonisins and OTA were also highly prevalent, with occurrences of 90.63% and 78.13%, respectively. Other mycotoxins (DON, CPA, NIV, and ZEN) occurred less frequently (Supplementary Table s3). Fumonisins (1692.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1034.24 \u0026micro;g/kg), showed the highest contamination level among \u003cem\u003eFusarium\u003c/em\u003e toxins, followed by ZEN and DON, while Aspergillus toxins (AFs, CPA, AFB\u003csub\u003e1\u003c/sub\u003e, AFG\u003csub\u003e1\u003c/sub\u003e) were also notable, with OTA and NIV occurring at low levels (Table S2).\u003c/p\u003e\u003cp\u003eIn terms of regulatory limits, 75.00% and 71.88% of positive samples exceeded the EU maximum limit of 2 \u0026micro;g/kg for AFB\u003csub\u003e1\u003c/sub\u003e and 4 \u0026micro;g/kg for total aflatoxins, respectively. Mean fumonisin levels (364.15\u0026ndash;4889.31 \u0026micro;g/kg) frequently exceeded the European Union (EU) maximum permissible level of 1000 \u0026micro;g/kg for unprocessed maize. OTA was detected in 78% of samples, and more than half of these surpassed the EU regulatory limit of 5 \u0026micro;g/kg. In contrast, DON and ZEN concentrations were within permissible limits (Table S3). There was no significant variation in levels of AFB\u003csub\u003e1\u003c/sub\u003e, AFB\u003csub\u003e2\u003c/sub\u003e, FUMs, DON, and NIV across agroecological zones (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Regarding maize types, FUMs, NIV, and AFG\u003csub\u003e1\u003c/sub\u003e levels were significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in yellow maize than white maize, while OTA concentrations were significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in market samples than in stored samples. Conversely, CPA levels were higher in stored samples (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Co-contamination was common. Up to five toxins were detected in a single sample, with the most frequent combinations being AFs\u0026thinsp;+\u0026thinsp;CPA\u0026thinsp;+\u0026thinsp;DON\u0026thinsp;+\u0026thinsp;FUM\u0026thinsp;+\u0026thinsp;OTA, followed by AFs\u0026thinsp;+\u0026thinsp;FUM\u0026thinsp;+\u0026thinsp;OTA\u0026thinsp;+\u0026thinsp;NIV (Table S5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDietary Exposure and Risk Estimates\u003c/h2\u003e\u003cp\u003eAverage maize consumption among respondents was estimated at 0.33196 kg/person/day, while the average body weight of the sampled population was 61.58 kg, with average weight of 63.03 kg and 64.29 kg for men and women, respectively. Based on measured concentrations, fumonisins and OTA accounted for the highest estimated daily intake (EDI) values, both exceeding their tolerable daily intake (TDI) thresholds. By contrast, DON, NIV, and ZEN exposures were below TDI values (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The calculated margins of exposure (MOE) for aflatoxins and OTA were consistently below 10,000, indicating serious health risks (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eComparative Risk Assessment of Aflatoxin-Attributable HCC Burden in HBsAg\u0026thinsp;+\u0026thinsp;and HBsAg \u003csup\u003e\u0026ndash;\u003c/sup\u003e Maize Consumers in Niger State, Nigeria.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAFB\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAFB\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAFG\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAFG\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAFs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOTA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAverage concentration (x 10\u003csup\u003e3\u003c/sup\u003e ng/kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e57.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u003cp\u003eEstimated daily Intake (EDI) (ng/kg. bw/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e143.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e304.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e31.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e138.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e311.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e132.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e298.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e30.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u003cp\u003eMargin of Exposure (MOE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e75.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eEstimated Annual HCC (per 100,000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHBsAg\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e91.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHBsAg-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAnnual HCC cases\u003c/p\u003e\u003cp\u003e(HBsAg Prevalence\u0026thinsp;=\u0026thinsp;13.6%) (x10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHBsAg\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHBsAg-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u003cp\u003eHCC Risk/year (13.6%) (x10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\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\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAnnual HCC cases\u003c/p\u003e\u003cp\u003e(HBsAg Prevalence\u0026thinsp;=\u0026thinsp;8.1%) (x10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHBsAg\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHBsAg-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u003cp\u003eHCC Risk/year (8.1%) (x10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eKeys: EDI\u0026thinsp;=\u0026thinsp;Estimated daily intake; TDI\u0026thinsp;=\u0026thinsp;Tolerable daily intake; %TDI\u0026thinsp;=\u0026thinsp;Percentage tolerable daily intake; NA\u0026thinsp;=\u0026thinsp;Not applicable; TP\u0026thinsp;=\u0026thinsp;Total population\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssessment of Dietary Exposure to DON, FUMs, NIV, ZEN, CPA, and OTA from Maize in Niger State, Nigeria.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMycotoxins\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAverage concentration\u003c/p\u003e\u003cp\u003e(x 10\u003csup\u003e3\u003c/sup\u003e ng/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimated daily Intake (EDI)\u003c/p\u003e\u003cp\u003e(ng/kg. bw/day)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTDI\u003c/p\u003e\u003cp\u003e(x 10\u003csup\u003e3\u003c/sup\u003e ng/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRisk Characterization (% TDI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDON\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e128.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFUM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1692.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8915.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9125.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8741.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e445.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e456.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e437.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZEN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e141.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e135.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCPA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e148.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNa\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOTA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e223.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e228.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e219.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePredicted Hepatocellular Carcinoma Burden\u003c/h2\u003e\u003cp\u003eAt a hepatitis B virus prevalence of 13.6%, maize consumption in Nigeria was estimated to contribute approximately 505,000 cases of hepatocellular carcinoma (HCC) annually due to AFB1, alongside 24,000 cases associated with OTA. At a lower HBV prevalence of 8.1%, predicted cases were reduced but remained substantial (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe high fungal loads and toxin concentrations observed confirm that maize in Niger State is heavily contaminated, particularly in wetter agro-ecological zones. This is consistent with earlier Nigerian surveys (Chidimma et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and South African findings, where climatic conditions strongly influenced aflatoxin contamination across different agro-climatic regions (Nji et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eAspergillus\u003c/em\u003e species, especially \u003cem\u003eA. flavus\u003c/em\u003e and \u003cem\u003eA. parasiticus\u003c/em\u003e, were dominant, consistent with their known role in aflatoxin production across Africa (Akinola et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The higher fungal burden in yellow maize suggests varietal susceptibility, which may relate to grain composition and storage characteristics. Same pattern was also noted in Kenyan maize where yellow varieties supported greater \u003cem\u003eAspergillus\u003c/em\u003e colonization (Mutiga et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).The isolation of \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003eMucor\u003c/em\u003e and \u003cem\u003eRhizopus\u003c/em\u003e is consistent with maize pre- and post-harvest ecology. \u003cem\u003eFusarium\u003c/em\u003e spp. (notably \u003cem\u003eF. verticillioides/F. proliferatum\u003c/em\u003e) are field fungi that infect kernels through silks and wounds and are favoured by drought/heat stress and insect injury; these infections often persist into storage and are the major producer of fumonisins in maize. This has been shown across environments where planting date, drought and insect pressure increase ear rot and FB1 levels (Blacutt et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Omotayo et al. 2023). In contrast, \u003cem\u003ePenicillium\u003c/em\u003e spp. are storage molds adapted to lower water activity than many aspergilli and proliferate on damaged, insufficiently dried grain. While OTA in tropical cereals is more often linked to \u003cem\u003eAspergillus\u003c/em\u003e sections Circumdati/Nigri (for instance; \u003cem\u003eA. niger, A. westerdijkiae\u003c/em\u003e), \u003cem\u003ePenicillium verrucosum\u003c/em\u003e (main producer of OTA) predominates in cooler storage conditions. Consequently in Niger State, \u003cem\u003ePenicillium\u003c/em\u003e likely reflects sub-optimal storage rather than being the main OTA source (Oluwakayode et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ben Miri et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).The presence of rapid-growing spoilage molds such as \u003cem\u003eMucor\u003c/em\u003e and \u003cem\u003eRhizopus\u003c/em\u003e typically shows high kernel moisture and mechanical damage during handling/shelling; although they are not major producers of the regulated mycotoxins targeted here, they accelerate spoilage and can promote micro-niches that facilitate colonization by toxigenic species (Deligeorgakis et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hence, rapid post-harvest drying, upholding safe storage moisture (\u0026le;\u0026thinsp;13\u0026ndash;13.5% for maize) and preventing moisture stratification in bins are crucial to suppress these storage molds (Baidhe et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lehmane et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; FAO 2025).\u003c/p\u003e\u003cp\u003eThe toxin profiles highlight maize as a major dietary source of fumonisins in Nigeria, with levels frequently exceeding international safety thresholds and posing a significant public health concern. The findings align with the report of Adelodun et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who identified fumonisins as a major hazard in maize-consuming populations of sub-Saharan Africa. Aflatoxins were also detected at concerning levels, with several samples surpassing EU and Codex limits, consistent with the prevalence of \u003cem\u003eA. flavus\u003c/em\u003e and \u003cem\u003eA. parasiticus\u003c/em\u003e and in agreement with earlier Nigerian surveys (Afolabi et al. 2020; Chilaka et al. 2023). Multi-mycotoxin contamination was widespread, with up to five toxins detected in a single sample. This mirrors recent multi-year surveillance in South African commercial maize, which found frequent co-occurrence of aflatoxins, fumonisins, and emerging toxins such as citrinin (Nji and Mwanza \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar contamination patterns have been reported in Ethiopia, where post-harvest maize was found to contain DON, ZEN, and multiple other toxins (Mohammed et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such evidence reinforces the complexity of maize safety management across African food systems.\u003c/p\u003e\u003cp\u003eDietary exposure assessments confirmed that fumonisins and OTA present the greatest risks, as their %TDI values exceeded recommended limits. Similar exposure levels have been reported in Tanzanian populations, where maize-based diets were linked with fumonisin intakes above safe thresholds (Kayanda et al. 2023). Recent risk assessments in Tanzania smallholder farming systems also revealed alarming co-exposures to aflatoxins and fumonisins, reinforcing regional concerns about dietary safety (Stafstrom et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In spite the in availability of a defined aflatoxin TDI, the consistently low MOEs observed in this study highlight an unacceptable health risk. This aligns with earlier reports linking aflatoxin exposure and hepatitis B virus infection to elevated hepatocellular carcinoma incidence in African populations (Alao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The estimated hepatocellular carcinoma burden associated with maize consumption in Nigeria emphasizes the scale of risk posed by aflatoxin exposure. While other cereals also contribute to mycotoxin intake, maize represents a disproportionate risk owing to its widespread consumption and high fumonisin burden, which has also been documented in recent African studies (Nji et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mohammed et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the fact that OTA\u0026rsquo;s contribution to cancer burden is lesser, its nephrotoxic effects add to the overall health risks. These findings strengthen the urgent need for preventive measures. Improved drying, hermetic storage, and biocontrol strategies could significantly reduce fungal growth and toxin production. Furthermore, targeted regulatory surveillance for maize would have greater public health impact than for less-consumed cereals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures in this study were carried out in accordance with the relevant legal and ethical requirements of the Federal Republic of Nigeria and South Africa, where the work was undertaken.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during this study can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or non-financial conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partly supported by the Tertiary Education Trust Fund (TETFUND), Nigeria (TETFUND/FUTMINNA/2019/B7/16), which funded part of the mycotoxin analysis. Additional support for the fungal investigations was provided by the Centre for Resilient Agri-Food Systems (CRAFS), University of Malawi, through the World Bank–funded Africa Centres of Excellence II Additional Financing (ACE II AF) project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.O.B.: Data collection, curation, laboratory investigation, methodology, validation, and manuscript writing (original draft, review, and editing). H.L.M., V.T.O., A.D., A.A., S.S., H.K.M., D.O.A: Conceptualization, methodology design, resource provision, supervision, validation, and critical manuscript review. M.M., L.M., M.Mw., H.A.M.: Formal analysis, data visualization, validation, and contribution to manuscript review and editing.\u003cbr\u003e\u0026nbsp;All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the technical support provided by the Department of Animal Health, North-West University, Mafikeng, South Africa.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbia WA, Warth B, Sulyok M, Krska R, Tchana AN, Njobeh PB, Moundipa PF (2013) Determination of multi-mycotoxin occurrence in cereals, nuts and their products in cameroon by liquid chromatography tandem mass spectrometry (LC-MS/MS). 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Comp Biochem Physiol C Toxicol Pharmacol 296:110226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cbpc.2025.110226\u003c/span\u003e\u003cspan address=\"10.1016/j.cbpc.2025.110226\" 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":false,"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":"mycotoxin-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"myre","sideBox":"Learn more about [Mycotoxin Research](http://link.springer.com/journal/12549)","snPcode":"12550","submissionUrl":"https://submission.nature.com/new-submission/12550/3","title":"Mycotoxin Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"maize, mycotoxins, aflatoxins, fumonisins, ochratoxin A, dietary exposure","lastPublishedDoi":"10.21203/rs.3.rs-7479286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7479286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated fungal contamination and the occurrence of multiple mycotoxins in maize consumed in Niger State, Nigeria, and evaluated the associated health risks. A total of 240 maize samples (white and yellow varieties) were collected from markets and storage facilities across four distinct microclimatic zones. Fungal isolation was carried out using dilution plating, while mycotoxins were quantified using a combination of HPLC (aflatoxins, deoxynivalenol, nivalenol, cyclopiazonic acid) and ELISA (fumonisins, ochratoxin A, zearalenone) methods. The predominant fungi were \u003cem\u003eAspergillus\u003c/em\u003e species, particularly \u003cem\u003eA. flavus\u003c/em\u003e and \u003cem\u003eA. parasiticus\u003c/em\u003e, followed by \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e. Aflatoxins were present in all maize samples, with concentrations ranging from 0.003 to 682.9 \u0026micro;g/kg. Fumonisins and OTA were also widespread, detected in over 70% of samples, with fumonisin levels frequently exceeding EU maximum limits. Other mycotoxins (CPA, DON, NIV, ZEN) occurred at lower frequencies. Co-contamination was common, with up to five toxins detected simultaneously. Dietary exposure modeling based on local maize consumption indicated that fumonisins posed the highest intake risk, exceeding tolerable daily intake (TDI) values, while MOE calculations for aflatoxins and OTA were consistently\u0026thinsp;\u0026lt;\u0026thinsp;10,000, suggesting significant health concern. The estimated hepatocellular carcinoma (HCC) burden associated with maize consumption was substantial, particularly among populations with high hepatitis B virus prevalence. The widespread maize consumption and frequent mycotoxin contamination pose a major food safety threat with public and trade implications. These findings emphasize the need for improved control measures, including better storage practices and regular surveillance, to mitigate risks associated with maize in Nigeria.\u003c/p\u003e","manuscriptTitle":"Multi-mycotoxin Contamination in Maize from Niger State, Nigeria: Occurrence, Dietary Exposure and Health Risk Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 18:28:47","doi":"10.21203/rs.3.rs-7479286/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"2011578419469425544930330744993714049","date":"2025-09-16T15:46:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110973583230279885201648475627482949204","date":"2025-09-16T08:14:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-08T21:10:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T12:04:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T12:02:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mycotoxin Research","date":"2025-08-28T10:23:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mycotoxin-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"myre","sideBox":"Learn more about [Mycotoxin Research](http://link.springer.com/journal/12549)","snPcode":"12550","submissionUrl":"https://submission.nature.com/new-submission/12550/3","title":"Mycotoxin Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"810447de-a1a1-44f7-9e90-c96549f73b1c","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T11:10:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 18:28:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7479286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7479286","identity":"rs-7479286","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}