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

Safe detoxification on acid-washed activated carbon combined with chitosan for aflatoxins from contaminated peanut oil

Aflatoxins are one of the most toxic mycotoxins and can cause serious harm to humans and animals. Adsorption is a practical decontamination technique favored by the industry because of its advantages of low cost, speed and simplicity, and environmental friendliness. In this work, the adsorption features of activated carbon and chitosan were fabricated in a composite through chemical co-precipitation to improve its properties for adsorption. Furthermore, the capacity of the synthesized chitosan and acid-washed activated carbon composite (CS-AAC) to attenuate the aflatoxins in contaminated peanut oil and the adsorption capacity at different initial aflatoxins content, contact duration, and temperature were evaluated. The results showed a higher adsorption capacity (removal efficiency to 93.45% of AFB1, 94.05% of AFB2, 89.16% of AFG1, 83.26% of AFG2). The Freundlich isothermal and D-R model and the pseudo-second-order rate expression both implied a good correlation with the test data and explained the adsorption mechanism well. The adsorption mechanism was found to be accomplished primarily via ion exchange and chelation. According to thermodynamic results (△G < 0, △H > 0, △S > 0), the adsorption process was endothermic and spontaneous. Compared to acid-washed activated carbon, CS-AAC enhanced the retention of VE and sterols (especially VE by 23%), and the safety of CS-AAC adsorbent was explored by cellular experiments. In conclusion, CS-AAC is a promising adsorbent material for the removal of aflatoxins from edible oils.

Insights from modelling sixteen years of climatic and fumonisin patterns in maize in South Africa

Mycotoxin contamination of agricultural commodities is a global public health problem that has remained elusive to various mitigation approaches, particularly in developing countries. Climate change and its impact exacerbates South Africa's vulnerability to mycotoxin contamination, and significantly threatens its's food systems, public health, and agro-economic development. Herein we analyse sixteen years (2005/2006-2020/2021) of annual national meteorological data on South Africa which reveals both systematic and erratic variability in critical climatic factors known to influence mycotoxin contamination in crops. Within the same study period, data on fumonisin (FB) monitoring show clear climate-dependent trends. The strongest positive warming trend is observed between 2018/2019 and 2019/2020 (0.51 °C/year), and a strong positive correlation is likewise established between FB contamination and temperature (r ranging from 0.6 to 0.9). Four machine learning models, viz support vector machines, eXtreme gradient boosting, random forest, and orthogonal partial least squares, are generalized on the historical data with suitable performance (RMSE as low as 0.00). All the adopted models are able to predict future FB contamination patterns with reasonable precision (R2 ranging from 0.34 to 1.00). The most important model feature for predicting average FB contamination (YA) is the historical pattern of average FB contamination in maize within the region (ΣFBs_avg). The two most significant features in modelling maximum FB contamination (YM) are minimum temperature from the CMIP6 data (Pro_tempMIN) and observed precipitation from the CRU data (O_prep). Our study provides strong evidence of the impact of climate change on FB in South Africa and reiterates the significance of machine learning modelling in predicting mycotoxin contamination in light of changing climatic conditions, which could facilitate early warnings and the adoption of relevant mitigation measures that could help in mycotoxin risk management and control.

Ultrasensitive detection of zearalenone based on electrochemiluminescent immunoassay with Zr-MOF nanoplates and Au@MoS2 nanoflowers

In this work, an effective competitive-type electrochemiluminescence (ECL) immunosensor was constructed for zearalenone determination by using Zr-MOF nanoplates as the ECL luminophore and Au@MoS2 nanoflowers as the substrate material. Zr-MOF have an ultra-thin sheet-like structure that accelerates the transfer of electrons, ions and co-reactant intermediates, which exhibited strong and stable anodic luminescence. The three-dimensional Au@MoS2 nanoflowers would form a thin film modification layer on the glassy carbon electrode (GCE). And its good electrical conductivity and higher specific surface area utilization further improving the sensitivity of the ECL immunosensor. Under the optimized conditions, the proposed immunosensor exhibited satisfactory stability, sensitivity and accuracy, and its ECL signal was proportional to the logarithm of ZEN concentration (0.0001-100 ng/mL) and the limit of detection (LOD) was 0.034 pg/mL. In addition, the results of recovery experiment acquired for wheat flour and pig urine samples further proved the feasibility of the immunosensor for the detection of real samples, indicating its potential for ultrasensitive detection of ZEN.

Aflatoxins: Occurrence, biosynthesis, mechanism of action and effects, conventional/emerging detection techniques

Aflatoxins (AFs) are toxic secondary metabolites prevalent in various food and agricultural products, posing significant challenges to global food safety. The detection and quantification of AFs through high-precision analytical techniques are crucial in mitigating AF contamination levels and associated health risks. Variousmethods,including conventional and emerging techniques, have been developed for detecting and quantifyingAFsinfood samples. This review provides an in-depth analysis of the global occurrence of AF in food commodities, covering their biosynthesis, mode of action, and effects on humans and animals. Additionally, the review discusses different conventional strategies, including chromatographic and immunochemical approaches, for AF quantification and identification in food samples. Furthermore, emerging AF detection strategies, such as solid-state gas sensors and electronic nose technologies, along with their applications, limitations, and future perspectives, were reviewed. Sample purification, along with their respective advantages and limitations, are also discussed herein.

Assessment of dietary exposure and levels of mycotoxins in sorghum from Niger State of Nigeria

This study reports levels of mycotoxins in sorghum from Niger State, Nigeria, and provides a comprehensive assessment of their potential health risks by combining mycotoxin levels and dietary exposure assessment. A total of 240 samples of red and white sorghum were collected from both stores and markets across four microclimatic zones. Fungal species were identified using a dilution plate method. Aflatoxins (AFs), deoxynivalenol, nivalenol, and ochratoxin (OTA) were quantified using HPLC, whereas cyclopiazonic acid, fumonisins (FUMs) and zearalenone were quantified using ELISA. A. flavus and A. fumigatus were dominant fungal species followed by F. verticilloides, A. oryzae and P. verrucosum. Aflatoxins (mean: 29.97 µg/kg) were detected in all samples, whereas OTA (mean: 37.5 µg/kg) and FUMs (mean: 3269.8 µg/kg) were detected in 72% and 50% of the samples, respectively. Mycotoxins frequently co-occurred in binary mixtures of AFs + OTA and AFs + FUMs. Dietary exposure estimates were highest for FUMs at 230% of TDI and margin of exposures (MOEs) for both AFs and OTA (<10,000) indicating a potential risk associated with combined exposure to AFs and OTA. The Risk of hepatocellular carcinoma cases (HCC/year) attributable to AFs and OTA exposure from sorghum was estimated to be 5.99 × 105 and 0.24 × 105 cases for HBsAg + individuals based on 13.6% HBV incidence. Similarly, the HCC/year for AFs and OTA were assessed to be 3.59 × 105 and 0.14 × 105 at an 8.1% prevalence rate. Therefore, the results of this study demonstrate the high prevalence and dietary exposure to mycotoxins through sorghum consumption, raising public health and trade concerns.

Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd-grape beverage production

Artificial neural networks (ANNs) have in recent times found increasing application in predictive modelling of various food processing operations including fermentation, as they have the ability to learn nonlinear complex relationships in high dimensional datasets, which might otherwise be outside the scope of conventional regression models. Nonetheless, a major limiting factor of ANNs is that they require quite a large amount of training data for better performance. Obtaining such an amount of data from biological processes is usually difficult for many reasons. To resolve this problem, methods are proposed to inflate existing data by artificially synthesizing additional valid data samples. In this paper, we present a generative adversarial network (GAN) able to synthesize an infinite amount of realistic multi-dimensional regression data from limited experimental data (n = 20). Rigorous testing showed that the synthesized data (n = 200) significantly conserved the variances and distribution patterns of the real data. Further, the synthetic data was used to generalize a deep neural network. The model trained on the artificial data showed a lower loss (2.029 ± 0.124) and converged to a solution faster than its counterpart trained on real data (2.1614 ± 0.117).

Pressurized Liquid Extraction: A Powerful Tool to Implement Extraction and Purification of Food Contaminants

Pressurized liquid extraction (PLE) is considered an advanced extraction technique developed in the mid-1990s with the aim of saving time and reducing solvent with respect to traditional extraction processes. It is commonly used with solid and semi-solid samples and employs solvent extraction at elevated temperatures and pressures, always below the respective critical points, to maintain the solvent in a liquid state throughout the extraction procedure. The use of these particular pressure and temperature conditions changes the physicochemical properties of the extraction solvent, allowing easier and deeper penetration into the matrix to be extracted. Furthermore, the possibility to combine the extraction and clean-up steps by including a layer of an adsorbent retaining interfering compounds directly in the PLE extraction cells makes this technique extremely versatile and selective. After providing a background on the PLE technique and parameters to be optimized, the present review focuses on recent applications (published in the past 10 years) in the field of food contaminants. In particular, applications related to the extraction of environmental and processing contaminants, pesticides, residues of veterinary drugs, mycotoxins, parabens, ethyl carbamate, and fatty acid esters of 3-monochloro-1,2-propanediol and 2-monochloro-1,3-propanediol from different food matrices were considered.

Ultra-sensitive determination of Ochratoxin A in coffee and tea samples using a novel semi-automated in-syringe based coagulant-assisted fast mycotoxin extraction (FaMEx) technique coupled with UHPLC-MS/MS

In this study, we demonstrated a novel semi-automated in-syringe-based coagulant-assisted liquid-liquid microextraction (IS-CGA-LLME) as fast mycotoxin extraction (FaMEx) technique coupled with ultra-high-performance liquid chromatography connected with a tandem-mass spectrometer (UHPLC-MS/MS) for the quantification of mycotoxin (Ochratoxin A, OT-A) in coffee and tea samples. IS-CGA-LLME is a three-step extraction process that includes extraction of OT-A from sample matrix using low-volume solvent extraction, then the extractant was cleaned-up using a coagulation process, and finally, the decolorized/matrix removed sample solution was processed for LLME for target analyte's pre-concentration. The final extractant was analyzed using UHPLC-MS/MS for OT-A quantification. Under the optimized experimental conditions, highly sensitive detection and quantification limits were obtained at 0.001 and 0.003 ng g^-1 for OT-A with excellent extraction recovery (93-111%) and precision <10%. These results proved that the developed method is a simple, highly sensitive, semi-automated, low-matrix effect and efficient procedure for the determination of mycotoxins in food samples.

A review of toxigenic fungi and mycotoxins in feeds and food commodities in West Africa

Fungal contamination is a threat to food safety in West Africa with implications for food and feed due to their climate, which is characterised by high temperatures and high relative humidity, which are environmental favourable for fast fungal growth and mycotoxin production. This report gives perspective on studies on toxigenic fungi (Aspergillus, Fusarium and Penicillium) and their toxins, mainly aflatoxins, fumonisins and ochratoxins commonly found in some West African countries, including Benin, Burkina Faso, Gambia, Ghana, Ivory Coast, Mali, Nigeria, Senegal, Sierra Leone, and Togo. Only four of these countries have mycotoxins regulations in place for feeds and food products (Ghana, Ivory Coast, Nigeria, and Senegal). Food commodities that are widely consumed and were thoroughly investigated in this region include cereals, peanuts, cassava chips (flakes), cassava flour, chilies, peanuts, locust beans, melon, and yam products. In conclusion, authorities and scientists needed to consider research and approaches to monitor mycotoxins in foods and feeds produced and consumed in West Africa.

Study on Closely Related Citrus CMMs based on Chemometrics and Prediction of Components-Targets-Diseases Network by Ingenuity Pathway Analysis

As the representatives of closely related Chinese medicinal materials (CMMs) originated from Rutaceae family, Aurantii fructus immaturus (AFI), Aurantii fructus (AF), Citri reticulatae pericarpium viride (CRPV), and Citri reticulatae pericarpium (CRP) have better functions in regulating qi and promoting gastrointestinal motility. However, differences in the quality of closely related Citrus CMMs have not yet been revealed until now. Herein, this study focused on the systematic differentiation and in-depth understanding of closely related Citrus CMMs by a strategy integrating chemometrics and network pharmacology. Determined by ultra performance liquid chromatography, the content of nine flavonoids showed obvious fluctuations in the decoction pieces from different species (Citrus aurantium Linnaeus and Citrus reticulate Blanco) with decreasing levels in the samples of ripe fruits. Decoction pieces from the different species and ripening stages were well distinguished by orthogonal projection to latent structure-discriminate analysis (OPLS-DA) and cluster analysis. As a result, four active components including narirutin, naringenin, hesperidin, and 3,5,6,7,8,3′,4′-heptemthoxyflavone were filtered out by variable importance for the projection (VIP) value (VIP > 1.0), which were regarded as chemotaxonomic markers. Furthermore, a components-targets-diseases network was constructed via ingenuity pathway analysis (IPA), and the correlations were predicted between four chemotaxonomic markers, 223 targets, and three diseases including colitis, breast cancer, and colorectal cancer. The obtained results will be of great significance for identifying closely related Citrus CMMs and conduce to improving the resource utilization of CMMs.

Developments in mycotoxin analysis: an update for 2020-2021

This review summarises developments published in the period from mid-2020 to mid-2021 on the analysis of a number of diverse matrices for mycotoxins. Notable developments in all aspects of mycotoxin analysis, from sampling and quality assurance/quality control of analytical results, to the various detection and quantitation technologies ranging from single mycotoxin biosensors to comprehensive instrumental methods are presented and discussed. The summary and discussion of this past year’s developments in detection and quantitation technology covers chromatography with targeted or non-targeted high resolution mass spectrometry, tandem mass spectrometry, detection other than mass spectrometry, biosensors, as well as assays using alternatives to antibodies. This critical review aims to briefly present the most important recent developments and trends in mycotoxin determination, as well as to address limitations of the presented methodologies.

Organic Cation Receptor for Colorimetric Lateral Flow Device: Detection of Zearalenone in Food Samples

As per the WHO reports, it has been estimated that almost 25% of food crops contain mycotoxins as the major contaminant. In this work, we developed a paper-based colorimetric lateral flow device (CLFD) impregnated with an organic cation receptor (OCR) for sensitive and selective detection of zearalenone (ZEN). Various techniques such as ultraviolet (UV)-visible absorption, cyclic voltammetry, and fluorescence spectroscopy were used for the detection of mycotoxins, and it was observed that OCR shows sensitivity and selectivity toward zearalenone (ZEN) only, irrespective of any other analytes. Furthermore, the colorimetric test revealed that the developed OCR shows a change in color with the addition of ZEN from greenish-gray to blue that is visible to the naked eye. The quantification of ZEN was also achieved using RGB analysis and compared with UV-visible spectroscopy data. Further, for the on-site detection of ZEN, a paper-based CLFD was also developed and used to evaluate the spiked corn sample containing ZEN, and it provided significant results with a limit of detection (LOD) of 0.31 nM (3σ method), good linearity (R² = 0.9702), good reproducibility (SD = ±6%, triplicate), and good recovery of ZEN of 95-102% with a variation coefficient (VC) varying from 1.56 to 4.62%. Therefore, the device has the potential to check the mycotoxin toxicity in food products and is helpful in remote and developing areas.