Parallel validation of a green-solvent extraction method and quantitative estimation of multi-mycotoxins in staple cereals using LC-MS/MS


In this study, 15 different mycotoxins were estimated in three staple cereals from selected agro-ecological regions in Nigeria using a ‘novel’ green extraction method, pressurized hot water extraction (PHWE) in comparison to a conventional solvent extraction method. Discrimination of the results of PHWE and solvent extraction using principal component analysis (PCA) and orthogonal projection to latent structures discriminate analysis (OPLS-DA) did not yield any differential clustering patterns. All maize samples (n = 16), 32% (n = 38) of sorghum and 35% (n = 37) of millet samples were positive for at least one of the 15 tested mycotoxins. Contamination levels for the cereals were higher in the warm humid rain forest region and gradually decreased towards the hot and arid region in the north of the country. The results demonstrate the applicability of PHWE as a possible alternative extraction method to conventional methods of extraction, which are solvent based.


Mycotoxins are well-known food and feed contaminants that are produced by ubiquitous toxigenic fungal species belonging mainly to the Aspergillus, Penicillium, Fusarium, Claviceps and Alternaria genera1,2,3. It has been estimated that approximately 4.5 billion people in the world, of which a majority from sub-Saharan Africa are chronically exposed to uncontrolled amounts of these toxins via exposure to contaminated foods4. Although about 300 to 400 different mycotoxins have been clearly identified in nature, only a few have received significant research attention due to their economic and health significance5, some of which include fumonisins (FBs), aflatoxins (AFs), zearalenone (ZEN) and its analogues, ochratoxins (OTs), T-2 toxin (T-2), and some emerging ones such as alternariol monomethyl ether (AME) and sterigmatocystin (STEG).

Relative to their incessant prevalence in the sub-Saharan African food supply chain, mycotoxins have been implicated in a number of adverse socio-economic effects, ranging from human and animal health, impact on food security, impact on livelihood, damage to the African agricultural export market brand, and impact on Africa’s self-sustainability and increased dependence on foreign aid5,6,7. In the global food market, annual losses associated with mycotoxins have been estimated at approximately one billion metric tons of agricultural produce and food commodities8,9. Nigeria, a sub-Saharan African nation, and the most populous country on the continent, is one of the countries that have been severely plagued by the mycotoxin menace in Africa3,10,11.

Amidst several factors, favorable environmental conditions for colonization of crops by toxigenic fungal species, coupled with susceptibility of endemic staple crops such as maize, sorghum and millet to mycotoxin proliferation have been identified as a critical precursor to the pervasive impact of the toxins in Nigeria12,13,14,15. A majority of the Nigerian populace (78 to >85%) rely on these cereal grains for dietary supply of energy, household incomes, as well as food and feed ingredients for their livestock12,16,17. The farmers produce these crops under varying agronomic practices and different climatic conditions across the country. Nigeria has a humid tropical climate in its southern region which is close to the equator, and a hot arid climate gradually unfolds towards the northern part of the country, resulting in definable floral and agroecological patterns across the country.

Since these cereal crops are mostly produced by peasant farmers and marketed in local markets within the country and to other neighboring countries via unofficial sales channels18,19, surveillance of mycotoxin contamination levels seldom occurs20. In this regard, despite the compelling evidence of risk exposure to mycotoxins on the Nigerian populace and their effects thereof 3,21, there is limited data on mycotoxins in Nigeria. It is thus imperative to routinely monitor the prevalence and levels of these toxins in food/feed in order to adopt appropriate control measures and policies. While it is important to adopt sensitive testing methods for mycotoxins, it is equally expedient to use methods that are fast, effective, more sustainable and environmentally friendly. The present study describes the screening of samples of three staple cereals (maize, sorghum and millet) obtained from selected regions within Nigeria for possible contamination with mycotoxins using a novel green and effective method, pressurized hot water extraction (PHWE) in contrast to a conventional solvent-based extraction followed by HPLC and tandem MS analysis.

Materials and methods


Reference materials, i.e. mycotoxin standards used for the PHWE experiments were purchased from the National Metrological Institute of South Africa (NMISA), and included fumonisin B1 (FB1), fumonisin B2 (FB2), fumonisin B3 (FB3), aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1), aflatoxin G2 (AFG2), alternariol monomethyl ether (AME), sterigmatocystin (STEG), T-2 toxin (T-2), zearalenone (ZEN), α-zearalenol (α-ZEL), β-zearalenol (β-ZEL), ochratoxin A (OTA) and ochratoxin B (OTB). For the solvent extraction, reference standards used included zearalanone (ZAN) and all those mentioned above which were purchased from Biopure (RomerLabs, Tulln, Austria), with the exception of deepoxy-deoxynivalenol (DOM) and AME which were purchased from Sigma-Aldrich (Bornem, Belgium) and FB3 supplied by Promec unit (Tygerberg, South Africa).

C18 solid phase extraction (SPE) columns, Biotage Isolute SAX cartridges and MultiSep226 AflaZon+ multifunctional columns were purchased from Alltech (Lokeren, Belgium), Anatech Instruments (Pty) Ltd (South Africa) and Romer Labs (Gernsheim, Germany), respectively. Ten (10) mL NORM-JECT plastic syringe (with Luer lock) and PVDF syringe filters (0.22 µm, with Luer lock) were purchased from Restek (Restek Corporation, Pennsylvania USA). VWR International (VWR International, Leuven, Belgium) supplied the Whatman glass microfiber filters (47 mm diameter, grade GF/A), while Millipore (Bredford, MA, USA) supplied the Ultrafree-MC PVDF centrifugal filters (0.22 µm).

The solvents used for the PHWE experiments included MS-grade acetonitrile, formic acid, dichloromethane (DCM), methanol, iso-octane, ethanol and sulphuric acid which were also purchased from Sigma (South Africa). A Milli-Q Gradient A10 dispensing system (Millipore, Billerica, MA, USA) was used to produce the deionized (ultrapure) water. Sodium sulphate, diatomaceous earth, anhydrous sodium sulphate, potassium chloride, sodium bicarbonate, and dialysis tubing were supplied by Sigma (South Africa). High performance liquid chromatography (HPLC) grade acetonitrile and LC-MS grade methanol, acetonitrile and glacial acetic acid which was used for the solvent extraction were purchased from (Biosolve BV, Valkenswaard, The Netherlands), while Merck (Merck KGaA, Darmstadt, Germany) supplied the ammonium acetate. Nitrogen gas (Air Liquide, Aalter, Belgium) and N-hexane was purchased from BDH Hipersolv Chromanorm (VWR International, Leuven, Belgium). A Milli-QSP Reagent water system (Millipore Corp., Brussels, Belgium) was used to produce the ultrapure water used for this method of extraction (i.e. solvent extraction).


Sampling and sampling preparations

Sampling. Sampling was done as described by Atehnkeng et al.22 Sixteen maize (Zea mays) samples, 38 sorghum (Sorghum bicolor) samples and 37 millet (Pennisetum glaucum) samples were obtained from farmers/traders from five different agroecological zones in Nigeria10,23 as described in Table 1. Choice of grains was based on consumption patterns of the West African diet, which is characterized by increased consumption of the selected cereals.

Table 1 Selected sampling locations for maize, sorghum and millet in Nigeria.

The Humid Rain Forest zone (HRF) falls within latitudes 6°4′ & 7°5′N and longitudes 3°5′ & 8°8′E, with maximum temperatures ranging from 26 to 28 °C, and average rainfall between 1,300 and 2,000 mm from two raining seasons in a year. Lying between latitudes 6°8′ & 9°30′N and longitudes 2°40′ & 12°15′E is the Derived Savannah zone (DRS), with maximum temperatures in a year averaging 25 to 35 °C and a bimodal rainfall averaging between 1,300 mm and 1,500 mm annually. The Southern Guinea Savannah (SGS) is characterized by a mean bimodal rainfall distribution between 1,000 to 1,300 mm per annum, maximum temperatures averaging 26 to 38 °C, and lies within latitudes 8°4′ & 11°3′N and longitudes 2°41′ & 13°33′E, while the Northern Guinea Savannah (NGS) is characterized by a single raining season per annum averaging between 900 to 1000 mm, and maximum temperatures ranging between 28 and 40 °C, and lies within latitudes 9°10′ & 11°59′N and longitudes 3°19′ & 13°37′E. The Sahel Savannah (SHS) lies within latitudes 12°2′ & 13°8′N and longitudes 3°9′ & 13°9′E, and is characterized by a Saharan climate, with maximum temperatures averaging between 30 to 40 °C and a single raining season per annum with rainfall distribution averaging between 650 and 1,000 mm22,23.

Pressurized hot water extraction (PHWE). The cereal samples (maize, sorghum and millet) were milled to sieve size of <0.6 to 1 mm using a mechanical blender. Extraction was performed using a laboratory-scale PHWE equipment24 operated at previously optimized extraction conditions of 55/45 (water/ethanol v/v) solvent composition and 162 °C temperature25. For the extraction, 4 g of grounded cereal sample was thoroughly mixed with 3 g of diatomaceous earth and transferred into the extraction cell (70 × 30 mm and approximately 20 mL) which was contained in a (gas chromatography) GC oven (Carlo Erba Instruments, Italy) maintained at a temperature of 162 °C using a digital temperature controllable unit (±1 °C). The extraction solvent [55/45 (water/ethanol v/v)] was pumped at a constant flow rate of 5 mL/min through the extraction cell via a stainless-steel tubbing (1.58 mm in outer dimension and 0.18 mm inner dimension), and the pressure maintained at 1000 ± 200 psi by means of a back-pressure valve (Swagelok, Johannesburg, South Africa). The extract was made to pass through a cooling coil and collected into a 50 mL centrifuge tube up to the 50 mL mark. Two (2) mL of the extracts were filtered through a 0.22 µm PVDF syringe filter into a 2 mL HPLC vial for subsequent analysis on HPLC-MS/MS.

Solvent extraction. Solvent extraction was performed using the method of Majeed et al.26. Briefly, 5 g of samples were spiked with internal standards, DOM (1 µg/kg) and ZAN (1 µg/kg), prior to extraction. For the extraction, 20 mL of extraction solvent, acetonitrile/water/acetic acid (79/20/1, v/v/v), added to the spiked samples, agitated for 1 h on an overhead shaker (AG 6 A, Exacta, Mery sour Oise, France) and centrifuged for 15 min at 3,300 g using an IEC Central (type MP4) centrifuge (VWR International, Leuven Belgium). The supernatant was passed through a pre-conditioned (10 mL of the extraction solvent) octadecyl (C18) solid phase extraction (SPE) column (Grace octadecyl C18, Lokeren, Belgium) under gravity. A second extraction was performed on similar samples by adding 5 mL of extraction solvent, agitating, centrifuging and passing through the SPE column as described above. The total volume of the eluate was adjusted to 25 mL and defatted with 10 mL n-hexane. The defatted extract was split into two equal parts for further clean-up using different approaches. The first portion (10 mL defatted extract) was diluted with 20 mL acetonitrile/acetic acid (99/1, v/v) and was subjected to clean-up by a Multisep226, Afla-ZON + multifunctional column, under gravity, followed by further column washing using 5 mL of acetonitrile/acetic acid (99/1, v/v). The second portion (10 mL defatted extract) was filtered using a Whatman glass microfilter (VWR International, Zaventem, Belgium). All of the first extract was combined with 2 mL of the second extract, evaporated to dryness under a stream of nitrogen gas at 40 °C. The residue was reconstituted in 150 µL of mobile phase, methanol/water/acetic acid (57.20/41.80/1, v/v/v) and 5 mM ammonium acetate, and filtered through a 0.22 µm Ultrafree-MC centrifugal filter (Bedford, MA, USA) at 14,000 g for 5 min.

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)

Chromatographic separation and HPLC-MS/MS for PHWE. Chromatographic separation, detection and quantitation of mycotoxin levels was achieved using Shimadzu HPLC-MS/MS 8030 equipment (Shimadzu Corporation, Tokyo, Japan). The system consisted of a chromatograph (LC-30AD Nexera) linked to an autosampler (SIL-30 AC Nexera). Two µL of sample were injected and pumped through a Raptor ARC-18 column (2.7 µm, 2.1 mm × 100 mm) (Restek Corporation, Pennsylvania USA) maintained at 40 °C in a column oven (CTO-20 AC Prominence). A binary pump (LC-20AD) connected to the system was used to pump the mobile phases A (aqueous phase) and B (organic phase) through the column at a constant flow rate of 0.2 mL/min. Mobile phase A consisted of 0.1% formic acid (FA) in deionized water, and mobile phase B consisted of 0.1% FA in methanol/acetonitrile (50/50 v/v). The elution gradient program started with pumping 10% B for 0.1 min, which was steadily increased to 95% B within 8.4 min, held constant at 95% B for 3 min, initial gradient condition of 10% B re-established within 1 min, and then the column was allowed to re-equilibrate at this condition for 4.5 min prior to the next injection, making a total run time of 17 min.

Separated analytes were delivered to a Shimadzu triple-quadrupole MS model 8030 (Shimadzu Corporation, Kyoto, Japan) instrument equipped with an electron spray ionization operated in positive mode (ESI+). A time-scheduled ultrafast multiple reaction monitoring (MRM) MS method was used for the identification and quantification of the analytes of interest. To improve specificity and confidence in analytical detection, two MRM-transitions were monitored per analyte. The desolvation line (DL) temperature was 250 °C, heat block temperature was 400 °C, drying gas flow rate was 15 L/min, and interface nebulizing gas flow rate was 3 L/min. The Shimadzu LabSolutions software was used for subsequent data visualization and analysis. The optimized chromatographic and MS method parameters of the 15 mycotoxins under investigation are presented in Table 2.

Table 2 MRM transitions, optimized MS conditions and retention times of mycotoxins.

Chromatographic separation and LC-MS/MS for solvent extraction. A Waters Acquity UPLC system (Waters, Milford, MA, USA) linked to a Waters Micromass Quattro Micro triple-quadrupole mass spectrometer (Waters, Milford, MA, USA) was used for chromatographic separation, detection and quantification of the extracts obtained from the solvent extraction. The above described LC-MS/MS system was equipped with a Waters Symmetry C18 analytical column (5 µm, 2.1 × 150 mm) and a Waters Sentry guard column (3.5 µm 2.1 × 10 mm) purchased from the same vendor. The column oven was kept at room temperature (25 °C), and 20 µL of sample was injected into the column. The aqueous portion of the mobile phase (i.e., mobile phase A) consisted of water/methanol/acetic acid (94/5/1, v/v/v) and 5 mM ammonium acetate, while the organic portion of the mobile phase (i.e., mobile phase B) consisted of methanol/water/acetic acid (97/2/1, v/v/v) and 5 mM, ammonium acetate. The gradient elution program and MS parameters are as described by Monbaliu et al.27.

Validation of the modified PHWE method for multi-mycotoxin extraction

The effects of the cereal-matrix components on the analytical signals of the different mycotoxins in the MS were determined using the signal suppression/enhancement method described by Arroyo-Manzanares et al.28 and Sulyok et al.29. Calibration curves were plotted for standards prepared in analyte-free sample extracts, as well as for standards prepared in neat organic solvents (100% methanol). Matrix-effect (ME) was determined as the percentage ratio of the difference between the slope of matrix-matched calibration curve and neat standards calibration curve divided by the slope of neat standards calibration curve (Eq. 1).

$$ME=\frac{Slop{e}_{m}-Slop{e}_{n}}{Slop{e}_{n}}\times 100$$

where ME is the matrix effect, Slopem is the slope of calibration curve of standards prepared in sample extracts and Slopen is the slope of standards prepared in neat solvent.

The limits of detection (LOD) and limits of quantification (LOQ) (Eq. 2) of the mycotoxins were determined using the signal-to-noise ratio of the matrix-matched standards as described by Kim et al.30. Linearity was determined by least-square regression of a 6-point matrix-matched calibration curve within the ranges of 9 to 5,000 µg/kg depending on the mycotoxin. Recovery efficiency of the method was determined by spiking analyte-free samples with known concentrations of mycotoxins, extracting the analytes the same day using PHWE as described above. The percentage ratio of post-extraction concentration (recovered concentration) to that of pre-extraction concentration (initial concentration) was taken as the recovery value (Eq. 3)28.

$$Limi{t}_{DQ}=F\times \left[\frac{C}{\left(\frac{S}{N}\right)}\right]$$
$$Recovery( \% )=\frac{{E}_{r}}{{E}_{i}}\times 100$$

where LimitDQ is the LOD or LOQ depending on the value of the multiplication factor F, which is 3.33 for LOD and 10 for LOQ. C is the concentration, while S is the signal at concentration C, and N is the noise level at similar concentration. Er is the recovered concentration after spiking, and Ei is the spiked concentration.

Multivariate discriminant analysis

The pre-processed data set was subjected to multivariate discriminant analysis in order to scrutinize for discriminatory patterns between the two extraction methods. Using the SIMCA-P+ 14.0 chemometrics software (Umetrics, MKS Instruments Inc., Sweden), the data was mean-centered, Pareto-scaled31 and subjected to PCA and OPLSD-DA analysis in order to extract maximum information from the data set. The adopted models, PCA and OPLS-DA, are advanced dimensionality reduction tools which highlights contrasts or similarities between data groups via construction of few interpretable linearly uncorrelated variables called latent variable or principal components from the dataset32. PCA does not supervise the construction of latent variables from the dataset while the OPLS-DA supervises the construction of latent variables from the dataset31,32.

For the OPLS-DA model, the data variables were classified into two major groups depending on the adopted method of analysis, either PHWE or solvent extraction. This was critical because, OPLS-DA is a supervised model, as such, information regarding variable class member is a prerequisite for location of the principal components. Usually, OPLS-DA is best applicable when there are only two classification groups in the data set, such as a control group and a dependent group. As such, classification of the data set into two groups permitted the extraction of a between-class variation referred to as the Y-predictive block, and a within-class variation referred to as the Y-orthogonal block or uncorrelated variation33. By doing so, OPLS-DA maximizes the discrimination of the two groups of variables and provides an improved model interpretability without modifying its predictive power34.

For the evaluation of model performances, the quantitative goodness-of-fit parameters i.e., R2X(cum) and Q2X(cum) values, and the goodness-of-prediction parameters i.e., the Q2(cum) values, were calculated. The R2X(cum) and Q2X(cum) values for the PCA model were used to measure the degree to which the latent structures (i.e., principal components) describe the variations and patterns in the data set32. Whereas for the OPLS-DA model, the R2X(cum) was used to estimate the cumulative fraction of the variation of the X variables explained by the model, R2Y(cum) was used to measure the cumulative ratio of the variation of the Y variables explained by the model, and the Q2(cum) estimated the cumulative predictive capacity of the full model31,35. For internal validation of the OPLS-DA models to assess the statistical significance of the model, the goodness-of-fit and goodness-of-prediction of the OPLS-DA model was compared with those of 100 random Y-permutated models, which generates a distribution of Q2 values that are suitable for testing the null hypothesis for a model’s Q232,36.

Results and discussion

Pressurized hot water extraction, a ‘novel’ green extraction technique was adopted for the analysis of multi-mycotoxin in 91 samples of maize, sorghum and millet intended for human consumption obtained from different agroecological zones in Nigeria as described in Section 2.2.1. In order to authenticate the PHWE method, a method validation was performed, as well as, a comparison with a solvent-based extraction method.

Method validation and comparative evaluation of PHWE and solvent extraction

The results of PHWE method validation in comparison with solvent extraction is presented in Appendix A and Table 3. In order to compensate for matrix effects, matrix-matched calibrations were adopted for quantification of the mycotoxin concentrations in the samples for the two methods.

Table 3 Method validation parameters for PHWE and solvent extraction# of multi-mycotoxin in maize, sorghum and millet matrices.

Validation of PHWE and solvent extraction

The performances of both methods showed good consistency with EC, AOAC, and ICH guidelines28,37,38,39,40. The linearity correlation (R2) of PHWE ranged from 0.98 to 1.00 for the 15 mycotoxins in all 3 sample matrices within the linear ranges of 60 to 2,000 µg/kg for FB1, 16 to 500 µg/kg for FB2, 10 to 300 µg/kg for AFB1 and 30 to 1,000 µg/kg for the other analytes (Appendix A). The benchmark for the acceptance of linearity of R2 equal or higher than 0.95 by the International Conference on Harmonization (ICH) was fulfilled for all understudied analytes39, though there existed a significant difference (p ≤ 0.05) in the linearities of the two methods. The sensitivity of the methods was determined by assessing the LOD’s and LOQ’s of the method for each of the mycotoxins. For PHWE, the LOD’s and LOQ’s ranged respectively from 0.06 to 41 µg/kg and 0.32 to 123 µg/kg for maize, 0.07 to 98 µg/kg and 0.21 to 295 µg/kg for sorghum, and 0 to 26 µg/kg and 0.01 to 79 µg/kg for millet (Table 3). These values were sufficiently low for detection and quantitation of small amounts of the analytes under investigation in cereal grains12,30, as such, trace amounts of the analytes in the sample extracts can be quantitatively reported with a high degree of confidence. There was no statistically significant difference (p ≤ 0.05) between the LOD’s and LOQ’s of PHWE and the solvent extraction method.

The recovery rates of the 15 mycotoxins varied from 74 to 126% in maize, 73 to 115% in sorghum and 67 to 133% in millet for PHWE, which is not far from the AOAC recommendations of between 60% to 125% for foodstuff contaminated with 10 μg/kg of mycotoxins, and the EC recommendations of 60 and 130%28,40. The recovery rates for the solvent extraction method varied from 99 to 100% for maize, 100 to 101% in sorghum, and 100 to 102% for millet. Though the recoveries of the solvent extraction method were more consistent and closer to 100%, when compared to those of PHWE using the Independent Sample’s T-test at a 95% probability, there existed no statistically significant differences in the mean recoveries of the two methods. The RSDr values for all the analytes in maize, sorghum and millet matrices ranged from 2 to 19% for PHWE and satisfied the guideline criterion of <25% by CODEX and the EC28,38,40. In comparison with the RSD values for the solvent extraction, those of maize ranged from 0 to 5% and 5 to 20% for sorghum. These values were significantly different (p ≤ 0.05) from the corresponding RSD values for PHWE while those of millet (3 to 15%) were not significantly different (p > 0.05).

In general, there was less variability in the validation parameters (i.e., recovery, linearity and RSD) of the solvent extraction method as compared to those of PHWE. This could be due to the fact that the solvent extraction method involved multiple clean-up steps (defatting and two SPE purification procedures using different SPE cartridges). This was in addition to the use of two internal standards to correct for loss of analytes during the sample preparation or injection steps in the analytical process. On the other hand, PHWE was designed with the aim of reduction of cost and the amount of harmful organic solvents used during extraction and increased speed of the analytical process. Forfeiture of a clean-up step facilitated the achievement of these objectives. This however contributed to the higher variations observed in the PHWE analytical results, which was not unexpected. Such variations are negligible provided they were within the acceptable limits stipulated by regulatory bodies28,38,40,41,42,43. As would be discussed in the succeeding sections of this chapter, the overall variations between the two analytical procedures have been shown to be statistically insignificant.

Comparative evaluation of PHWE and solvent extraction

Principal component analysis (PCA) and orthogonal projection to latent structures discriminate analysis (OPLS-DA) approaches were adopted to scrutinize the entire data set (i.e., combined data from method validation and samples analysis) for inherent global discriminatory patterns such as multiple pairwise correlations and/or co-variances between the data obtained by the two extraction methods, which may not be readily observed by using conventional statistical analysis.

Discriminatory analysis of PHWE and solvent extraction methods using PCA and OPLS-DA. The results of the PCA and OPLS-DA analysis of the data and their corresponding model-fit quality parameters are shown in Table 4 and Fig. 1. PCA was used for an initial screening of the overall structure of the data set for discriminatory patterns and detection of outliers. The results revealed a single latent variable, which indicated there were no differential patterns between the variables pertaining to PWHE and corresponding variables pertaining to the solvent extraction. Notwithstanding, the PCA model accounted for 97% of the variations in the data set [i.e., R2X(cum) = 0.97], with a predictive ability of 77% [i.e., Q2X(cum) = 0.77]. This observation was in agreement with the results of the Independent samples t-test performed on each of the variables from the two extraction methods (Appendix A), where it was observed that the majority of the variables were not significantly different (p > 0.05).

Table 4 Summary of PCA-X model and OPLS-DA models with the corresponding model validation parameters.
Figure 1

OPLS-DA scores plot and variable importance plot (VIP) plot for discriminate analysis of PHWE and solvent extraction.

Since PCA does not take into account the classification label associated with the data set, discrimination of the data groups is not maximized. This problem is solved by the OPLS-DA model which supervises the construction of the latent variables, hence, yielding a more class-specific discrimination of the data. Results of the OPLS-DA analysis showed the construction of 2 explanatory and 1 predictive principal component with a 99% total explained variation in X [i.e., R2X(cum) = 0.99]. However, the model could not account for the cumulative variations in Y [R2Y(cum) = −0.11], and the total amount of predicted variability in the full model was low [i.e., Q2(cum) = 0.05]. As such, despite the OPLS-DA models making reference to the pre-defined sample class membership in order to maximize separation of the data, there was no clear separation of the data groups. A pattern which can be more clearly visualized on the OPLS-DA scores plot (Fig. 1). On this score plot, the green circular dots represent variables of the PHWE class, while the red triangular dots represent variables of the solvent extraction (i.e., SOLV-EXT) class. Variables from the two data groups can be seen clustered together towards the middle of the plot and while some arbitrarily distributed across the graph. This is an indication of intrinsic similarities and randomness in the patterns within and between the groups.

This observation is in strong agreement to the results of PCA analysis as well as the Independent Sample’s T-test. Overall, PHWE compared well with the solvent-based extraction method, and other validated methods for multi-mycotoxin extraction reported in literature28,29,30,44. Hence, considering its good validation performance, it was deemed adequate for investigating the natural occurrence of mycotoxins in food commodities.

Mycotoxin contamination of staple cereals

The above validated PHWE method together with the solvent extraction method was used for the screening of 15 different mycotoxin in samples of maize (n = 16), sorghum (n = 38) and millet (n = 37) obtained from six agroecological zones from Nigeria (Table 5). Only samples contaminated with mycotoxins at levels above the respective LODs were considered positive. The minor differences between the results from the two extraction methods, are possibly due to the differences in the LOD’s and LOQ’s, as well as the recovery rates of the two methods. For the sake of discussing the results, in the remaining sections of this paper, reference is made only to the results of the PHWE.

Table 5 Mycotoxin contamination of maize, sorghum and millet from Nigeria using PHWE and solvent extraction#.

Mycotoxin levels and incidence rate in maize, sorghum and millet from Nigeria

The incidence rates and levels of mycotoxin contamination in maize, sorghum and millet samples are presented in Table 5. All of the maize samples (n = 16), 32% (n = 38) of sorghum and 35% (n = 37) of millet samples were positive for at least one of the 15 tested mycotoxins. Fumonisins, in particular FB1 had the highest prevalence in terms of rate of occurrence and levels of contamination in all three cereals. All the maize samples (n = 16) were positive for FBs with contamination levels ranging from 17 to 7,947 µg/kg for FB1 and 30 to 14,603 µg/kg for ΣFB1&FB2. Out of the 16 analyzed maize samples, 7 samples (i.e., 44%) contained ΣFB1&FB2 at levels above the maximum levels of 4,000 µg/kg in unprocessed maize stipulated by the European Commission (EC) and CODEX28,45,46. Previous studies have equally reported high incidence rates and levels of FBs contamination in maize from Nigeria20,47. Bankole and Mabekoje48 found that FB1 was the predominant mycotoxin, occurring in 79% of samples of maize obtained from a similar region from Nigeria as we sampled (Southern Nigeria).

Such high incidence rates and even higher levels of FBs contamination have likewise been reported in other West African countries. Ngoko et al.49 found FB1 in 16 out of 18 maize samples from Cameroon at levels within the range 300 to 26,000 µg/kg. Fumonisins occurred in sorghum samples at relatively lower levels, with a mean value of 17 µg/kg and maximum value of 248 µg/kg for ΣFB1&FB2, while, no FBs contamination was recorded in millet samples. Chilaka et al.12 reported a mean value of 83 µg/kg and a maximum value of 180 µg/kg for ΣFB1&FB2 in sorghum. The International Agency for Research on Cancer (IARC) has classified FB1 as a group 2B carcinogen (possibly carcinogenic to humans)50. Consumption of foods contaminated with FBs have been directly linked with upper gastro-intestinal tract cancer51. Moreover, FBs are also nephrotoxic, hepatotoxic, immunosuppressive, atherogenic and embryotoxic in experimental animal systems52.

AFs contamination was also relatively high in the cereal samples. In maize, levels for AFB1 ranged from 6.5 to 315 µg/kg, with a mean of 54 µg/kg, while ΣAFs ranged from 16 to 323 µg/kg. Sixty-nine percent (n = 16) of the maize samples were contaminated above the maximum level of 2 and 4 µg/kg for AFB1 and ΣAFs, respectively, stipulated by the European Commission (EC)28,53. These levels are similar to those reported by Bandyopadhyay et al.18, who reported ΣAFs contamination ranging from 1.1 to 480 µg/kg and a mean of 36 µg/kg in freshly harvested maize in Nigeria. In sorghum, 8% of samples were above the EC limits for AFB1 (2 µg/kg) and ΣAFs (4 µg/kg), respectively, whereas, 5% of millet samples exceeded similar limits for AFB1 and ΣAFs. Aflatoxin B2, AFG1 and AFG2 were not detected in any of the millet samples, however, the observed levels for AFB1 (5.5 to 50 µg/kg) were in agreement to those reported by Apeh et al.13 (1.1 to 15 µg/kg) in millet grain from Nigeria. Observed levels for ΣAFs in sorghum (15 to 116 µg/kg) were less than those reported by Makun et al.54 (<LOQ to 1,164 μg/kg) in stored sorghum samples. This could be due to variations in fungi colonization of crops over different years which could be stimulated by annual variations in temperature and rainfall, in addition to other climatic conditions55,56. Generally, AFs were more prevalent in maize, followed by sorghum and then millet. A similar trend was observed by Bandyopadhyay et al.18, in their study on the relative severity of AFs contamination of cereal crops in West Africa. In fact, they observed that Nigerians consume 138 kg cereals annually, and if the main cereal is sorghum instead of maize, associated AF problems will be diminished 4-fold, whereas, if it is millet, then the AF-related risks will be reduced at least 8-fold18. Diversification of diets, instead of diets that are heavily dependent on maize could greatly reduce exposure to AFs and their consequent health-related problems. Aflatoxins are highly carcinogenic and are equally recognized as being immunosuppressive. Among the AFs group, AFB1 is considered the most toxic, and has been identified as the most potent naturally occurring carcinogen known to man50,57.

Ochratoxin A was present in 31% (n = 16) of the maize samples at levels ranging from 6.5 to 54 µg/kg. All positive maize samples for OTA were above the maximum level of 5 µg/kg in unprocessed cereals stipulated by the EC53. In sorghum, the levels of OTs varied from 7.5 to 15 µg/kg, while those for millet ranged from 0.75 to 20 µg/kg. Sangare-Tigori et al.58 reported higher OTA levels (17 to 204 μg/kg) in millet from the West African country of Côte d’Ivoire sampled between 1998 to 2002, which may be due to yearly variations in mycotoxin contamination patterns across the continent55,56,59. Exposure to OTA has been linked with nephropathy60, urinary tract tumors61 and oxidative DNA damage leading to mutagenesis and eventually cancer62. Based on its carcinogenicity in animal studies, OTA has also been classified as a group 2B possible human carcinogen by the International Agency for Research on Cancer (IARC)63.

The incidence rate and contamination range of ZEN in maize and sorghum and millet was 19% (7.4 to 33 µg/kg) and 11% (4.8 to 22 µg/kg), respectively. These levels are negligible when compared with the EC maximum limit of 350 μg/kg for unprocessed maize and 100 µg/kg for unprocessed cereals other than maize, respectively45. On the other hand, 5 out of 37 millet samples were positive for ZEN within the range 80 to 94 µg/kg, none of which exceeded the EC limits of 100 µg/kg in unprocessed cereals other than maize45. The ZEN analogues, α-ZEL and β-ZEL, were also detected in at least one of maize and sorghum samples each, while none of the analogues was detected in millet samples Chilaka et al. (2016) and Adetunji et al. (2014). reported maize and millet contamination with α-ZEL and β-ZEL12,20.

Cereal contamination by ZENs could be a major health concern as this toxin is known to be chemically stable both during various food processing operations such as cooking, heating, fermentation, milling etc., and has been quantified in a number of processed cereal-based products from Africa64,65,66. Zearalenone has been implicated in the manifestations of gynecomastia with testicular atrophy in rural males in Southern Africa67. Among human populations, children are the most vulnerable to ZEN exposure and the toxin has been implicated in several incidents of precocious pubertal changes68 and other fertility problems69. The potency of ZEN’s estrogenic activity is reportedly greater than that of many naturally occurring non-steroidal estrogens70. Exposure to high concentrations of ZEN in cattle feed has been linked with enlargement of the mammary gland, infertility, reduced milk production, vaginal secretions and vaginitis particularly in young dairy heifers71. Whereas in swine, effects of ZEN include enlargement of the uterus, vaginal prolapse, swelling of the vulva, infertility, reduced litre size and embryonic death72.

The Alternaria toxin, AME, and the trichothecene toxin, T-2, were not detected in any of the maize samples. Bankole et al.73 also reported the absence of T-2 contamination in maize from Nigeria. In sorghum (n = 38) and millet (n = 37) samples, T-2 occurred in 3% each, whereas, the average AME contamination was 0.99 and 21 µg/kg for sorghum and millet, respectively. Sterigmatocystin occurred in 13% of both maize (n = 16) and sorghum (n = 38) samples within the ranges of 6.5 to 7.3 µg/kg and 1.9 to 330 µg/kg, respectively, whereas 9 out of 37 millet samples were positive for STEG at concentrations ranging from 5 to 208 µg/kg. Elsewhere, STEG was reported as a contaminant of Nigerian maize20. While the toxicity of T-2 has been established in literature74,75, the toxic effects of STEG and AME to humans have remained largely limited. Nonetheless, it is known that STEG is a precursor for the biosynthesis of AFB1 and both have similar structural configurations, as such, STEG is considered as a potent mutagen, carcinogen, and teratogen76,77. The IARC classifies STEG as a group 2B carcinogen77,78.

A number of studies have demonstrated the stability of AME during extreme food processing conditions such as during wet baking of bread66,79, as such, there is a risk of secondary exposure to AME through processed cereal-based foods. Adekoya et al.65 reported AME contamination of gruels derived directly from maize and sorghum in Nigeria. Some studies have demonstrated the possible carcinogenicity and mutagenicity of AME80,81,82. For example, NIH/3T3 cells mutated by AME caused subcutaneous tumors in mice83. It has also been shown to induce DNA strand breaks in cell cultures84. In general, mycotoxin contamination was higher in maize, followed by sorghum and then millet (Table 5). A similar trend was observed by Makun et al.85 and Gwary et al.86. It has been shown that tannin-rich varieties of sorghum and millet are less susceptible to fungal colonization13,87, indicating that phytochemicals present in these cereals could exert antimycological properties as such resulting in less mycotoxin contamination.

Mycotoxin distribution patterns in maize, sorghum and millet from different agroecological zones of Nigeria

Geoclimatic conditions under which crops are cultivated are critical for fungi proliferation and attendant mycotoxin production13,88,89. Table 6 presents the distribution patterns of mycotoxins in maize, sorghum and millet across 5 agroecological zones in Nigeria. Maize samples generally had higher incidence rate and mycotoxin contamination levels. This could be because all the maize samples were obtained from the HRF/DRS agroecological zones. Also, as stated earlier in Section 3.2.1, maize is generally more susceptible to contamination by mycotoxigenic fungal species and consequent mycotoxin production as compared to sorghum and millet12,90. Likewise, in millet samples contamination levels increased from Southern to Northern Nigeria, with samples from Northern having the lowest levels of contamination and incidence rates. A similar trend was observed by Chilaka et al.12, who reported contamination/incidence rate of multi-mycotoxin contamination in DRS > NGS > SHS.

Table 6 Distribution of mycotoxins in maize, sorghum and millet across different agroecological zones in Nigeria as analyzed using PHWE followed by HPLC-MS/MS.

The observed high incidence rate and contamination levels of mycotoxins in maize, sorghum and millet samples from the HRF/DRS agroecological zones could be partly due to the climatic conditions in these regions that favor the proliferation of mycotoxigenic fungi species and subsequent mycotoxin production22. As already described in Section 2.2.1, the HRS is characterized by abundant rainfall (1,500 to 2,000 mm/yr), high humidity (78 to 100%) and average temperatures between 25 to 28 °C91,92, while the DRS vegetation represents a transition between the humid rainforest and guinea savannah zones. Annual rainfall in this zone ranges from 1,200 mm to 1,700 mm, while average humidity and temperature are 66 to 78% and 26 to 27 °C, respectively93. Adejumo and Adejoro93, noted that the important agroecological zones in terms of mycotoxin research in Nigeria are the HRF, DRS and SGS/NGS zones.

Interestingly, this HRF zone spans across many of the West African countries, as such, similar patterns of mycotoxin contamination have been recorded in some of these countries49,94. Hell et al.94, Udoh et al.95 and Gong et al.96 observed significant AFs contamination of maize in lowland areas in Cameroon, Nigeria, Benin and Togo, respectively. A similar trend was reported for FBs (10 to 16,040 μg/kg) in maize from Burkina Faso97 and FB1 (300 to 26,000 μg/kg) in maize from Cameroon49. On the other hand, the lower levels of mycotoxin contamination and incidence rates observed in the Middle-Belt and Northern parts of Nigeria i.e., the SGS/NGS and SHS, respectively, could be due to dryer and more arid climatic conditions in these regions. Particularly the SHS has a much lower annual rain fall and humidity as compared to DRS and HRF. Temperatures can reach as high as 40 °C22,98. Such conditions may not favor the proliferation of mycotoxigenic fungi species.

Simultaneous occurrence of multiple mycotoxins in maize, sorghum and millet from Nigeria as analyzed using PHWE followed by HPLC-MS/MS

Based on the results of this study, exposure of humans and animals to multiple mycotoxins is highly likely in the selected regions in Nigeria, as we observed co-occurrence of different groups of mycotoxins in many of the analyzed samples. This observation is in line with previous literature reports12,99,100. Mycotoxin co-contamination of crops is a complex phenomenon, and a number of interrelating factors, such as fungi species, crop genotype, and climatic conditions may be responsible for the co-occurrence of mycotoxins in foods. It is known that a single fungal species may be able to produce more than one mycotoxin101,102, while one mycotoxin can be produced by different fungi species103,104.

Table 7 and Appendix B describe all the possible co-occurrence patterns of the five groups of regulated mycotoxins detected in the samples, which are AFs, FBs, OTs, T-2 and ZENs. This was achieved using the Venn diagram web application (Bioinformatics and Evolutionary Genomics, Ghent University, Belgium)105. The 5 groups of mycotoxins subjected to this analysis yielded 9 different unique intersections as summarized in Table 7. The highest co-occurrence of mycotoxins that appeared in the maize samples was the co-contamination of AFs and FBs (i.e. AFs + FBs) which occurred in 6 (n = 16) of the maize samples, while 3 of the samples simultaneously contained AFs, FBs and OTs. A similar pattern was observed in 5,000 samples submitted to the GEMS/Food contaminants database between 2011 and 2016, where reiterations of FBs + AFs combinations occurred in approximately 6% of the maize samples, 1% of sorghum106. A combination of AFs + FBs + OTs + ZENs also occurred in one (n = 16) of the maize samples, whereas, a combination of AFs + OTs + ZENs occurred in one (n = 38) of the sorghum samples. The co-occurrence of OTs + ZENs was observed in a single millet sample.

Table 7 Co-occurrence of different groups of regulated mycotoxins in maize, sorghum and millet samples from Nigeria.

Such co-contamination patterns have previously been reported in cereals from Nigeria12,20,48,107. Bankole and Mabekoje48 reported that 15 samples (n = 103) of pre-harvest maize from Southern Nigeria were contaminated with both FBs and AFs simultaneously. A prevalence rate of 10% and mean contamination level of 111 µg/kg have been reported for OTA, concurrently with OTB 7% (7.5 µg/kg), STEG 37% (3 µg/kg), ZEN 17% (174 µg/kg), α-ZEL 1% (17 µg/kg), and β-ZEL 1% (13 µg/kg) in stored maize from five different agroecological zones in Nigeria20. A review of over a hundred papers between 1987 to 2016, revealed 127 mycotoxin combinations, of which AFs + FBs, AFs + OTA, DON + ZEN, and FBs + ZEN were amongst the most frequently co-occurring combinations in cereal crops108. In Tanzania, co-exposure to FBs + AFs has been confirmed by means of plasma or urinary biomarkers of AF1 and FB1106. Co-occurrence can be caused by substrate colonization with a single fungus that produces more than one mycotoxin, or due to colonization by different fungi species that produce different mycotoxins. It has been reported that ZEN usually co-occurs with one or more of the trichothecenes (THs), because of the ability of its producing fungi to synthesize more than one mycotoxin109.

Since the individual toxins that make these combinations are all amongst the most potent mycotoxins, their co-existence must not be neglected108. The combined effects of different mycotoxins have been extensively reviewed in literature106,108,110, and could manifest as additive, synergistic or antagonistic106. For example, exposure of F344 rats to FB1 + AFB1 combinations increased liver preneoplastic changes suggestive of a synergistic interaction111, whereas, health concerns in humans include possible childhood stunting112. All cytotoxic effects of the binary combinations of OTA, FB1 and AFB1 in low concentrations at their EU regulatory limits to MDBK cell lines were additive, and in the order OTA + FB1 > AFB1 + FB1 > AFB1 + OTA113, while FB1 + α-ZEL combination significantly diminished interferon γ mRNA expression as compared to α-ZEL alone114.

Significance of mycotoxin contamination of Nigerian staple cereals

We reiterate that mycotoxin contamination of maize, sorghum and millet crops in Nigeria represents a major food safety concern because these crops are staples. Atanda et al.14 asserted that AF contamination in maize and peanuts in Nigeria contributed to at least 7,761 cases of liver cancer, resulting in a total burden of 100,965 Disability-Adjusted Life Years (DALYs)14. Derived food products from these cereals such as masa, tuwo, ogi, ogi-baba, kunu, burukutu and others are also at risk of mycotoxin contamination12,65,93,115,116,117,118. Some of these processed food products (e.g. ogi and ogi-baba) are used as weaning foods for children. Sadly, a number of studies have reported mycotoxin contamination in these weaning foods65,119,120. In fact, a study120, reported AFB1 contamination levels up to 4,806 µg/kg in home-made weaning food made from maize and soybeans120. Moreover, a posthumous autopsy of infants who suffered from kwashiorkor showed a significant level of AFs in their brains, because of consumption of contaminated maize-derived gruels15.

Aside the health effects on humans and animals, mycotoxin prevalence in Nigeria and Africa as a whole has other significant socio-economic impacts, ranging from food security, decreased market value of crops, regulatory rejections of goods mainly at ports of exit, damage to the African agricultural export brand amongst others5. For example, in 2010, the monetized burden of AFs contamination in Nigeria was estimated to be between 112 million U$D and 942 million U$D, which accounts for roughly 0.5% of the nation’s Gross Domestic Product (GDP)14. These enormous impacts of mycotoxin could significantly jeopardize prospects of attaining the UN’s sustainable development goal number 2, of achieving food security, improved nutrition, and a healthy agroeconomic growth by 20305.

Based on the results obtained in this study and considering the economic importance of these cereals in Nigeria, it is thus imperative to prioritize the adoption of functional mitigation strategies that are both cost-effective, crop-specific and locally adapted to the climatic conditions and agronomic practices of the region, in order to adequately combat the prevalence of these toxins in Nigeria. Further to this, more research should be done to determine the toxicological effects of different mycotoxin combinations in order to better understand their associated public health risks. If this is done, then future establishment of regulatory limits or as the case may be, reassessment of already existing limits could take into account the effects of co-existing mycotoxins. The successful adoption of PHWE in this study further represents a step closer to sustainability in green-solvent extraction in the field of mycotoxicology.


  1. 1.

    Njobeh, B. P., Dutton, F. M. & Makun, H. A. Mycotoxins and human health: Significance, prevention and control. In Smart Biomolecules in Medicine (eds. A. K., Mishra, A., Tiwari & S. B., Mishra) 132–177 (VBRI Press, India, 2010).

  2. 2.

    Chilaka, C. A. et al. Fungal and mycotoxin contamination of South African commercial maize. J. Food, Agric. Environ. 10, 296–303 (2012).

    CAS  Google Scholar 

  3. 3.

    Atanda, O. et al. Fungal and mycotoxin contamination of Nigerian foods and feeds. in Mycotoxin and food safety in developing countries (ed. H. A., Makun) 1–38 (InTech, 2013).

  4. 4.

    Turner, P. C. et al. Aflatoxin exposure in utero causes growth faltering in Gambian infants. Int. J. Epidemiol. 36, 1119–1125 (2007).

    PubMed  Google Scholar 

  5. 5.

    Gbashi, S. et al. The Socio-economic impact of mycotoxin contamination in Africa. in Fungi and mycotoxins - their occurrence, impact on health and the economy as well as pre- and postharvest management strategies (ed. Njobeh, P. B.) 1–20 (InTech, 2018).

  6. 6.

    Wagacha, J. M. & Muthomi, J. W. Mycotoxin problem in Africa: current status, implications to food safety and health and possible management strategies. Int. J. Food Microbiol. 124, 1–12 (2008).

    CAS  PubMed  Google Scholar 

  7. 7.

    Darwish, W. S., Ikenaka, Y., Nakayama, S. M. & Ishizuka, M. An overview on mycotoxin contamination of foods in Africa. J. Vet. Med. Sci. 76, 789–797 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Wilson, N. et al. Modification of the mycotoxin deoxynivalenol using microorganisms isolated from environmental samples. Toxins (Basel). 9, 141 (2017).

    PubMed Central  Google Scholar 

  9. 9.

    Schmale, D. G. & Munkvold, G. P. Mycotoxins in crops: A threat to human and domestic animal health. Plant Heal. Instr. 3, 340–353 (2009).

    Google Scholar 

  10. 10.

    Ezekiel, C. N. et al. Mycotoxin exposure in rural residents in northern Nigeria: A pilot study using multi-urinary biomarkers. Environ. Int. 66, 138–145 (2014).

    CAS  PubMed  Google Scholar 

  11. 11.

    Makun, H. A., Dutton, M. F., Njobeh, P. B., Gbodi, T. A. & Ogbadu, G. H. Aflatoxin contamination in foods and feeds: A special focus on Africa. In Trends in vital food and control engineering (ed. Ayman, A. E.) 187–234, (InTech, 2012).

  12. 12.

    Chilaka, C. A., De Boevre, M., Atanda, O. O. & De Saeger, S. Occurrence of Fusarium mycotoxins in cereal crops and processed products (Ogi) from Nigeria. Toxins (Basel). 8, 342 (2016).

    PubMed Central  Google Scholar 

  13. 13.

    Apeh, D. O. et al. Mycotoxicological concerns with sorghum, millet and sesame in Northern Nigeria. J. Anal. Bioanal. Tech. 7, 336 (2016).

    Google Scholar 

  14. 14.

    Atanda, O., Ndenn, J. & Diedhiou, P. The economic impact of aflatoxins in West Africa: The case of Nigeria, Gambia and Senegal. Partnership for Aflatoxin Control in Africa (PACA) (2015). Available at, impact of aflatoxin in West Africa.pdf. (Accessed: 5th February 2018).

  15. 15.

    Oyelami, O. A., Maxwell, S. M., Adelusola, K. A., Aladekoma, T. A. & Oyelese, A. O. Aflatoxins in the lungs of children with kwashiorkor and children with miscellaneous diseases in Nigeria. J. Toxicol. Environ. Health 51, 623–628 (1997).

    CAS  PubMed  Google Scholar 

  16. 16.

    Awotide, D. O. Supply response of maize, millet and sorghum in Nigeria: an error correction model approach. Niger. J. Agric. Econ. 3, 19–27 (2012).

    Google Scholar 

  17. 17.

    Nicely, R., David, M. & Nzeka, U. Nigerian grain and feed annual report. Global agricultural information network (GAIN) report number NI1204. (2012).

  18. 18.

    Bandyopadhyay, R., Kumar, M. & Leslie, J. F. Relative severity of aflatoxin contamination of cereal crops in West Africa. Food Addit. Contam. 24, 1109–1114 (2007).

    CAS  PubMed  Google Scholar 

  19. 19.

    Akhidenor, J. & Nzeka, U. Nigeria: grain and feed annual report 2018. (2018).

  20. 20.

    Adetunji, M. et al. Fungal and bacterial metabolites of stored maize (Zea mays, L.) from five agro-ecological zones of Nigeria. Mycotoxin Res. 30, 89–102 (2014).

    CAS  PubMed  Google Scholar 

  21. 21.

    Liu, Y. & Wu, F. Global burden of aflatoxin-induced hepatocellular carcinoma: a risk assessment. Environ. Health Perspect. 118, 818 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Atehnkeng, J. et al. Distribution and toxigenicity of Aspergillus species isolated from maize kernels from three agro-ecological zones in Nigeria. Int. J. Food Microbiol. 122, 74–84 (2008).

    PubMed  Google Scholar 

  23. 23.

    Adetuniji, M. C. et al. Distribution of mycotoxins and risk assessment of maize consumers in five agro-ecological zones of Nigeria. Eur. Food Res. Technol. 239, 287–296 (2014).

    CAS  Google Scholar 

  24. 24.

    Gbashi, S., Njobeh, P., Steenkamp, P. & Madala, N. Pressurized hot water extraction and chemometric fingerprinting of flavonoids from Bidens pilosa by UPLC-tandem mass spectrometry. CYTA - J. Food 15, 171–180 (2017).

    CAS  Google Scholar 

  25. 25.

    Gbashi, S., Madala, N. E., De Saeger, S., De Boevre, M. & Njobeh, P. B. Development, chemometric-assisted optimization and in-house validation of a pressurized hot-water-extraction methodology for multi-mycotoxins in maize. (2019).

  26. 26.

    Majeed, S. et al. Multiple mycotoxins in rice: occurrence and health risk assessment in children and adults of Punjab, Pakistan. Toxins (Basel). 10, 77 (2018).

    PubMed Central  Google Scholar 

  27. 27.

    Monbaliu, S. et al. Development of a multi-mycotoxin liquid chromatography/tandem mass spectrometry method for sweet pepper analysis. Rapid Commun. Mass Spectrom. 23, 3–11 (2009).

    ADS  CAS  PubMed  Google Scholar 

  28. 28.

    Arroyo-Manzanares, N. et al. In-house validation of a rapid and efficient procedure for simultaneous determination of ergot alkaloids and other mycotoxins in wheat and maize. Anal. Bioanal. Chem. 410, 5567–5581 (2018).

    CAS  PubMed  Google Scholar 

  29. 29.

    Sulyok, M., Berthiller, F., Krska, R. & Schuhmacher, R. Development and validation of a liquid chromatography/tandem mass spectrometric method for the determination of 39 mycotoxins in wheat and maize. Rapid Commun. Mass Spectrom. 20, 2649–2659 (2006).

    ADS  CAS  PubMed  Google Scholar 

  30. 30.

    Kim, D. H. et al. Simultaneous determination of multi-mycotoxins in cereal grains collected from South Korea by LC/MS/MS. Toxins (Basel). 9, 106 (2017).

    PubMed Central  Google Scholar 

  31. 31.

    Aliferis, K. A., Tarantilis, P. A., Harizanis, P. C. & Alissandrakis, E. Botanical discrimination and classification of honey samples applying gas chromatography/mass spectrometry fingerprinting of headspace volatile compounds. Food Chem. 121, 856–862 (2010).

    CAS  Google Scholar 

  32. 32.

    Worley, B. & Powers, R. Multivariate analysis in metabolomics. Curr. Metabolomics 1, 92–107 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Song, H. H., Kim, D. Y., Woo, S., Lee, H. K. & Oh, S. R. An approach for simultaneous determination for geographical origins of Korean Panax ginseng by UPLC-QTOF/MS coupled with OPLS-DA models. J. Ginseng Res. 37, 341 (2013).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Trygg, J. & Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemom. 16, 119–128 (2002).

    CAS  Google Scholar 

  35. 35.

    Matthews, S. B. et al. Metabolite profiling of a diverse collection of wheat lines using ultraperformance liquid chromatography coupled with time-of-flight mass spectrometry. Plos One 7, e44179 (2012).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Zhang, L. et al. Plasma metabolomic profiling of patients with diabetes-associated cognitive decline. Plos One 10, e0126952 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    EC. The Commission of the European Communities. Commission Decision 2002/657/EC of 12 August 2002. Implementing Council Directive 96/23/EC, Concerning the Performance of Analytical Methods and the Interpretation of Results. Brussels. Off. J. Eur. Communities (2002).

  38. 38.

    AOAC. A method for the determination of multiple mycotoxins in feeds and raw grains intended for feeds. in AOAC Agricultural Materials Task Force. Sub-Committee on Feed Additives and Contaminants 2–3 (Association of Official Agricultural Chemists (AOAC), 2009).

  39. 39.

    Shabir, G. A. Step-by-step analytical methods validation and protocol in the quality system compliance industry. J. Valid. Technol. 10, 314–325 (2005).

    Google Scholar 

  40. 40.

    Commission Regulation, E. Commission Regulation (EC) No. 401/2006 of laying down the methods of sampling and analysis for the official control of the levels of mycotoxins in foodstuffs. Off. J. Eur. Communities L70 (2006).

  41. 41.

    CODEX. General standard for contaminants and toxins in food and feed (CODEX STAN 193-1995). CODEX Aliment. Int. Food Stand. CODEX STAN, 25 (2015).

  42. 42.

    FDA. Guidelines for the validation of chemical methods for the FDA FVM program. in US Food and Drug Administration. Office of Foods and Veterinary Medicine (eds. P. A., Orlandi et al.) (2015).

  43. 43.

    Anklam, E., Stroka, J. & Boenke, A. Acceptance of analytical methods for implementation of EU legislation with a focus on mycotoxins. Food Control 13, 173–183 (2002).

    CAS  Google Scholar 

  44. 44.

    Lattanzio, V. M. T., Ciasca, B., Powers, S. & Visconti, A. Improved method for the simultaneous determination of aflatoxins, ochratoxin A and Fusarium toxins in cereals and derived products by liquid chromatography–tandem mass spectrometry after multi-toxin immunoaffinity clean up. J. Chromatogr. A 1354, 139–143 (2014).

    CAS  PubMed  Google Scholar 

  45. 45.

    EC. Commission Regulation (EC) No 1126/2007 of 28 September 2007 amending Regulation (EC) No 1881/2006 setting maximum levels for certain contaminants in foodstuffs as regards Fusarium toxins in maize and maize products. Off. J. Eur. Union L255, vol. L255, pp. 14–17 (2007).

  46. 46.

    WHO. Evaluation of certain contaminants in food (Eighty-third report of the Joint FAO/WHO Expert Committee on Food Additives). World Heal. Organ. Tech. Rep. Ser. WHO Tech. Rep. Ser. No. 1002 67–68 (2017).

  47. 47.

    Afolabi, C. G., Bandyopadhyay, R., Leslie, J. F. & Ekpo, E. J. Effect of sorting on incidence and occurrence of fumonisins and fusarium verticillioides on maize from Nigeria. J. Food Prot. 69, 2019–2023 (2006).

    PubMed  Google Scholar 

  48. 48.

    Bankole, S. A. & Mabekoje, O. O. Occurrence of aflatoxins and fumonisins in preharvest maize from south-western Nigeria. Food Addit. Contam. 21, 251–255 (2004).

    CAS  PubMed  Google Scholar 

  49. 49.

    Ngoko, Z. et al. Fungal infection and mycotoxin contamination of maize in the humid forest and the western highlands of Cameroon. Phytoparasitica 29, 352–360 (2001).

    CAS  Google Scholar 

  50. 50.

    IARC. IARC monographs on the evaluation of carcinogenic risks to humans. Some traditional herbal medicines, some mycotoxins, naphthalene and styrene. Volume 82. (2002).

  51. 51.

    Soriano, J. M. & Dragacci, S. Occurrence of fumonisins in foods. Food Res. Int. 37, 985–1000 (2004).

    CAS  Google Scholar 

  52. 52.

    Nair, M. G. Fumonisins and human health. Ann. Trop. Paediatr. 18, S47–S52 (1998).

    PubMed  Google Scholar 

  53. 53.

    Commission Regulation, E. Commission Regulation (EC) No 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs. Off. J. Eur. Communities 2006R1881, 5–24 (2006).

  54. 54.

    Makun, H. A., Timothy, A. G., Olufunmilayo, H. A., Ezekiel, A. S. & Godwin, H. O. Fungi and some mycotoxins found in mouldy Sorghum in Niger State, Nigeria. World J. Agric. Sci. 5, 5–17 (2009).

    Google Scholar 

  55. 55.

    Guerre, P. Worldwide mycotoxins exposure in pig and poultry feed formulations. Toxins (Basel). 8, 350 (2016).

    PubMed Central  Google Scholar 

  56. 56.

    Streit, E., Naehrer, K., Rodrigues, I. & Schatzmayr, G. Mycotoxin occurrence in feed and feed raw materials worldwide: long-term analysis with special focus on Europe and Asia. J. Sci. Food Agric. 93, 2892–2899 (2013).

    CAS  PubMed  Google Scholar 

  57. 57.

    D’Mello, J. Mycotoxins in cereal grains, nuts and other plant products: Food safety: Contaminants and toxins. (CAB International, 2003).

  58. 58.

    Sangare-Tigori, B. et al. Preliminary survey of ochratoxin A in millet, maize, rice and peanuts in Cote d’Ivoire from 1998 to 2002. Hum. Exp. Toxicol. 25, 211–216 (2006).

    CAS  PubMed  Google Scholar 

  59. 59.

    Van der Fels-Klerx, H. J., Liu, C. & Battilani, P. Modelling climate change impacts on mycotoxin contamination. World Mycotoxin J. 9, 717–726 (2016).

    Google Scholar 

  60. 60.

    Maaroufi, K. et al. Foodstuffs and human blood contamination by the mycotoxin ochratoxin A: correlation with chronic interstitial nephropathy in Tunisia. Arch. Toxicol. 69, 552–558 (1995).

    CAS  PubMed  Google Scholar 

  61. 61.

    Gazinska, P., Herman, D., Gillett, C., Pinder, S. & Mantle, P. Comparative immunohistochemical analysis of ochratoxin A tumourigenesis in rats and urinary tract carcinoma in humans; mechanistic significance of p-S6 ribosomal protein expression. Toxins (Basel). 4, 643–662 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Zepnik, H., Pähler, A., Schauer, U. & Dekant, W. Ochratoxin A-induced tumor formation: Is there a role of reactive Ochratoxin A metabolites? Toxicol. Sci. 59, 59–67 (2001).

    CAS  PubMed  Google Scholar 

  63. 63.

    IARC. Ochratoxin A. Some naturally occurring substances: food items and constituents, heterocyclic aromatic amines and mycotoxins. IARC Monogr. Eval. Carcinog. Risks to Humans 56, 489–521 (1993).

    Google Scholar 

  64. 64.

    Hueza, I. M., Raspantini, P. C. F., Raspantini, L. E. R., Latorre, A. O. & Górniak, S. L. Zearalenone, an estrogenic mycotoxin, is an immunotoxic compound. Toxins (Basel). 6, 1080–1095 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Adekoya, I. et al. Awareness and prevalence of mycotoxin contamination in selected Nigerian fermented foods. Toxins (Basel). 9, 363 (2017).

    PubMed Central  Google Scholar 

  66. 66.

    Abia, W. A. et al. Determination of multi-mycotoxin occurrence in cereals, nuts and their products in Cameroon by liquid chromatography tandem mass spectrometry (LC-MS/MS). Food Control 31, 438–453 (2013).

    CAS  Google Scholar 

  67. 67.

    Shephard, G. S. Impact of mycotoxins on human health in developing countries. Food Addit. Contam. 25, 146–151 (2008).

    CAS  Google Scholar 

  68. 68.

    Zain, M. E. Impact of mycotoxins on humans and animals. J. Saudi Chem. Soc. 15, 129–144 (2011).

    CAS  Google Scholar 

  69. 69.

    Sherif, S. O., Salama, E. E. & Abdel-Wahhab, M. A. Mycotoxins and child health: The need for health risk assessment. Int. J. Hyg. Environ. Health 212, 347–368 (2009).

    CAS  PubMed  Google Scholar 

  70. 70.

    Bennett, J. & Klich, M. Mycotoxins. Clin. Microbiol. Rev. 16, 497–516 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Zinedine, A., Soriano, J. M., Molto, J. C. & Manes, J. Review on the toxicity, occurrence, metabolism, detoxification, regulations and intake of zearalenone: An oestrogenic mycotoxin. Food Chem. Toxicol. 45, 1–18 (2007).

    CAS  PubMed  Google Scholar 

  72. 72.

    Agag, B. I. Mycotoxins in foods and feeds 3-zearalenone. Assiut Univ. Bull. Environ. Res. 7, 169–176 (2004).

    Google Scholar 

  73. 73.

    Bankole, S. A., Schollenberger, M. & Drochner, W. Survey of ergosterol, zearalenone and trichothecene contamination in maize from Nigeria. J. Food Compos. Anal. 23, 837–842 (2010).

    CAS  Google Scholar 

  74. 74.

    Li, Y. et al. T-2 toxin, a trichothecene mycotoxin: review of toxicity, metabolism, and analytical methods. J. Agric. Food Chem. 59, 3441–3453 (2011).

    CAS  PubMed  Google Scholar 

  75. 75.

    Adhikari, M. et al. T-2 mycotoxin: toxicological effects and decontamination strategies. Oncotarget 8, 33933–33952 (2017).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Davis, N. D. Sterigmatocystin and other mycotoxins produced by Aspergillus species. J. Food Prot. 44, 711–714 (1981).

    CAS  PubMed  Google Scholar 

  77. 77.

    Chu, F. S. Mycotoxins | Toxicology. in Encyclopedia of food sciences and nutrition (eds. B., Caballero, L. C., Trugo & P. M., Finglas) 4096–4108 (Academic Press, (Elsevier Science Ltd), 2003).

  78. 78.

    IARC. Sterigmatocystin. IARC monographs on the evaluation of the carcinogenic risk of chemicals to humans. World Heal. Organ. Int. Agency Res. Cancer 10, 245 (1976).

    Google Scholar 

  79. 79.

    Siegel, D., Feist, M., Proske, M., Koch, M. & Nehls, I. Degradation of the Alternaria mycotoxins alternariol, alternariol monomethyl ether, and altenuene upon bread baking. J. Agric. Food Chem. 58, 9622–9630 (2010).

    CAS  PubMed  Google Scholar 

  80. 80.

    Brugger, E. M. et al. Mutagenicity of the mycotoxin alternariol in cultured mammalian cells. Toxicol. Lett. 164, 221–230 (2006).

    CAS  PubMed  Google Scholar 

  81. 81.

    Ostry, V. Alternaria mycotoxins: an overview of chemical characterization, producers, toxicity, analysis and occurrence in foodstuffs. World Mycotoxin J. 1, 175–188 (2008).

    CAS  Google Scholar 

  82. 82.

    Liu, G. T. et al. Etiological role of Alternaria alternata in human esophageal cancer. Chin. Med. J. (Engl). 105, 394–400 (1992).

    CAS  PubMed  Google Scholar 

  83. 83.

    Liu, G. T. et al. Relationships between Alternaria alternata and oesophageal cancer. In Relevance to human cancer of N-nitroso compounds, tobacco smoke and mycotoxins (eds. O’Neill, I. K., Chen, J. & Bartsch, H.) 258–262 (International Agency for Research on Cancer, 1991).

  84. 84.

    Podlech, J. & Marko, D. Alternariol acts as a topoisomerase poison, preferentially affecting the II isoform. Mol. Nutr. Food Res. 53, 441–451 (2009).

    PubMed  Google Scholar 

  85. 85.

    Makun, H. A. et al. Natural occurrence of ochratoxin A in some marketed Nigerian foods. Food Control 31, 566–571 (2013).

    CAS  Google Scholar 

  86. 86.

    Gwary, O. M., Hati, S. S., Dimari, G. A. & Ameh, J. A. Assessment of mycotoxins (total aflatoxins and ochratoxin-A) contamination of staple cereals. Int. J. Chem. Biochem. Sci. 2, 1–6 (2012).

    Google Scholar 

  87. 87.

    Dykes, L. & Rooney, L. W. Sorghum and millet phenols and antioxidants. J. Cereal Sci. 44, 236–251 (2006).

    CAS  Google Scholar 

  88. 88.

    Medina, A., Rodriguez, A. & Magan, N. Effect of climate change on Aspergillus flavus and aflatoxin B1 production. Front. Microbiol. 5, 348 (2014).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Leema, G., Chou, D. S., Jesudasan, C. A. N., Geraldine, P. & Thomas, P. A. Expression of genes of the aflatoxin biosynthetic pathway in Aspergillus flavus isolates from keratitis. Mol. Vis. 17, 2889 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Sirma, A. J. et al. Aflatoxin B1 occurrence in millet, sorghum and maize from four agro-ecological zones in Kenya. African J. Food, Agric. Nutr. Dev. 16, 10991–11003 (2016).

    CAS  Google Scholar 

  91. 91.

    Amajama, J., Fina, F. & Mopta, S. E. Variations of tropospheric radio wave refractivities across Southern Nigeria – the rainforest to mangrove. Int. J. Sci. Eng. Technol. Res. 5, 1914–1918 (2016).

    Google Scholar 

  92. 92.

    Afolabi, C. G., Ephraim, J. & Bandyopadhyay, R. Maize contamination by zearalenone and T-2 toxin and human exposure in Nigeria. Mycotoxins 63, 143–149 (2013).

    CAS  Google Scholar 

  93. 93.

    Adejumo, T. O. & Adejoro, D. O. Incidence of aflatoxins, fumonisins, trichothecenes and ochratoxins in Nigerian foods and possible intervention strategies. Food Sci. Qual. Manag. 31, 127–146 (2014).

    Google Scholar 

  94. 94.

    Hell, K., Cardwell, K. F., Setamou, M. & Poehling, H. M. The influence of storage practices on aflatoxin contamination in maize in four agroecological zones of Benin, West Africa. J. Stored Prod. Res. 36, 365–382 (2000).

    CAS  PubMed  Google Scholar 

  95. 95.

    Udoh, J. M., Cardwell, K. F. & Ikotun, T. Storage structures and aflatoxin content of maize in five agroecological zones of Nigeria. J. Stored Prod. Res. 36, 187–201 (2000).

    Google Scholar 

  96. 96.

    Gong, Y. Y. et al. Determinants of aflatoxin exposure in young children from Benin and Togo, West Africa: the critical role of weaning. Int. J. Epidemiol. 32, 556–562 (2003).

    CAS  PubMed  Google Scholar 

  97. 97.

    Nikiema, P. N., Worrillow, L., Traore, A. S., Wild, C. P. & Turner, P. C. Fumonisin contamination of maize in Burkina Faso, West Africa. Food Addit. Contam. 21, 865–870 (2004).

    CAS  PubMed  Google Scholar 

  98. 98.

    Benson, N., Nwokike, C., Williams, A., Adedapo, A. & Fred-Ahmadu, O. Changes in diurnal temperature and precipitation extremes in North Central Nigeria. in The 2nd International Electronic Conference on Atmospheric Sciences (ECAS 2017), 16–31 July 2017; Sciforum Electronic Conference Series, Volume 2, 2017 1–12 (Sciforum, 2017).

  99. 99.

    Adekoya, I. et al. Mycobiota and co-occurrence of mycotoxins in South African maize-based opaque beer. Int. J. Food Microbiol. 270, 22–30 (2018).

    CAS  PubMed  Google Scholar 

  100. 100.

    Somorin, Y., Akinyemi, A., Bertuzzi, T. & Pietri, A. Co-occurrence of aflatoxins, ochratoxin A and citrinin in “egusi” melon (Colocynthis citrullus L.) seeds consumed in Ireland and the United Kingdom. Food Addit. Contam. 9, 230–235 (2016).

    CAS  Google Scholar 

  101. 101.

    Bayman, P. & Baker, J. L. Ochratoxins: A global perspective. Mycopathologia 162, 215–223 (2006).

    CAS  PubMed  Google Scholar 

  102. 102.

    McCormick, S. P., Stanley, A. M., Stover, N. A. & Alexander, N. J. Trichothecenes: from simple to complex mycotoxins. Toxins (Basel). 3, 802–814 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Ismaiel, A. & Papenbrock, J. Mycotoxins: producing fungi and mechanisms of phytotoxicity. Agriculture 5, 492–537 (2015).

    CAS  Google Scholar 

  104. 104.

    Alasiri, H. S. et al. Detection of Aspergillus and Penicillium species producing aflatoxin in rice grains imported into Saudi Arabia. Wulfenia 20, 313–323 (2013).

    Google Scholar 

  105. 105.

    BEG Group. Venn diagrams online bioinformatics software. Bioinformatics and Evolutionary Genomics, Ghent University, Belgium (2018). Available at, (Accessed: 9th October 2018).

  106. 106.

    JECFA. Co-exposure of fumonisins with aflatoxins. (2016).

  107. 107.

    Makun, H. A., Dutton, M. F., Njobeh, P. B., Mwanza, M. & Kabiru, A. Y. Natural multi-occurrence of mycotoxins in rice from Niger State, Nigeria. Mycotoxin Res. 27, 97–104 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Smith, M. C., Madec, S., Coton, E. & Hymery, N. Natural co-occurrence of mycotoxins in foods and feeds and their in vitro combined toxicological effects. Toxins (Basel). 8, 94 (2016).

    PubMed  PubMed Central  Google Scholar 

  109. 109.

    Grenier, B. & Oswald, I. Mycotoxin co-contamination of food and feed: Meta-analysis of publications describing toxicological interactions. World Mycotoxin J. 4, 285–313 (2011).

    CAS  Google Scholar 

  110. 110.

    De Ruyck, K., De Boevre, M., Huybrechts, I. & De Saeger, S. Dietary mycotoxins, co-exposure, and carcinogenesis in humans: short review. Mutat. Res. Mutat. Res. 766, 32–41 (2015).

    Google Scholar 

  111. 111.

    Qian, G. et al. Sequential dietary exposure to aflatoxin B1 and fumonisin B1 in F344 rats increases liver preneoplastic changes indicative of a synergistic interaction. Food Chem. Toxicol. 95, 188–195 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Chen, C. et al. Exposure to aflatoxin and fumonisin in children at risk for growth impairment in rural Tanzania. Environ. Int. 115, 29–37 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Clarke, R., Connolly, L., Frizzell, C. & Elliott, C. T. Cytotoxic assessment of the regulated, co-existing mycotoxins aflatoxin B1, fumonisin B1 and ochratoxin, in single, binary and tertiary mixtures. Toxicon 90, 70–81 (2014).

    CAS  PubMed  Google Scholar 

  114. 114.

    Luongo, D. et al. Interactive effects of fumonisin B1 and α-zearalenol on proliferation and cytokine expression in Jurkat T cells. Toxicol. in-Vitron Vitr. 20, 1403–1410 (2006).

    CAS  Google Scholar 

  115. 115.

    Njobeh, P. B. & Olotu, I. The socio-economic impact of mycotoxin contamination in Africa. in Proceedings of the 1st MYCOKEY International Conference. Global Mycotoxin Reduction in the Food and Feed Chain. September 11-14 2017 (Toxins, 2017).

  116. 116.

    Chilaka, C. A., De Boevre, M., Atanda, O. O. & De Saeger, S. Fate of Fusarium mycotoxins during processing of Nigerian traditional infant foods (ogi and soybean powder). Food Res. Int. 116, 408–418 (2018).

    PubMed  Google Scholar 

  117. 117.

    Ezekiel, C. N. et al. Fate of mycotoxins in two popular traditional cereal-based beverages (kunu-zaki and pito) from rural Nigeria. LWT-Food Sci. Technol. 60, 137–141 (2015).

    CAS  Google Scholar 

  118. 118.

    Kpodo, K., Sørensen, A. K. & Jakobsen, M. The occurrence of mycotoxins in fermented maize products. Food Chem. 56, 147–153 (1996).

    CAS  Google Scholar 

  119. 119.

    Adetunji, M. C., Atanda, O. O. & Ezekiel, C. N. Risk assessment of mycotoxins in stored maize grains consumed by infants and young children in Nigeria. Children 4, 58 (2017).

    PubMed Central  Google Scholar 

  120. 120.

    Oluwafemi, F. & Ibeh, I. N. Microbial contamination of seven major weaning foods in Nigeria. J. Heal. Popul. Nutr. 29, 415 (2011).

    CAS  Google Scholar 

  121. 121.

    Commission Recommendation, E. Commission Recommendation of 27 March 2013 on the presence of T-2 and HT-2 toxin in cereals and cereal products. Off. J. Eur. Communities L 91/12, 12–15 (2013).

Download references


This work was financially supported via the Global Excellence and Stature (GES) Fellowship of the University of Johannesburg granted to the main author (S. Gbashi) as well as support from the South African National Research Foundation (NRF). Dr Riaan Meyer and Mr Darryl Harris from Shimadzu South Africa are acknowledged for their technical assistance. MYTOX-SOUTH (the Ghent University International Thematic Network) is duly acknowledged for its technical support and provision of a traineeship scholarship to the main author.

Author information




All authors contributed significantly to the work presented herein. S.G. and N.E.M. conceived and designed the study. S.G., V.K. and M.B. performed the experiments and analyzed the data. S.S. and M.B. developed and validated the chromatographic and MS analytical methods. S.G. and V.K. performed the literature review and wrote the main manuscript. P.B., N.E.M., S.S., and M.B. supervised the study and provided critical review of the manuscript. All authors discussed the results and commented on the manuscript at all stages.

Corresponding authors

Correspondence to Sefater Gbashi or Patrick Berka Njobeh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gbashi, S., Njobeh, P.B., Madala, N.E. et al. Parallel validation of a green-solvent extraction method and quantitative estimation of multi-mycotoxins in staple cereals using LC-MS/MS. Sci Rep 10, 10334 (2020).

Download citation


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.