Changing climate, shifting mycotoxins: A comprehensive review of climate change impact on mycotoxin contamination.

Climate change (CC) is a complex phenomenon that has the potential to significantly alter marine, terrestrial, and freshwater ecosystems worldwide. Global warming of 2°C is expected to be exceeded during the 21st century, and the frequency of extreme weather events, including floods, storms, droughts, extreme temperatures, and wildfires, has intensified globally over recent decades, differently affecting areas of the world. How CC may impact multiple food safety hazards is increasingly evident, with mycotoxin contamination in particular gaining in prominence. Research focusing on CC effects on mycotoxin contamination in edible crops has developed considerably throughout the years. Therefore, we conducted a comprehensive literature search to collect available studies in the scientific literature published between 2000 and 2023. The selected papers highlighted how warmer temperatures are enabling the migration, introduction, and mounting abundance of thermophilic and thermotolerant fungal species, including those producing mycotoxins. Certain mycotoxigenic fungal species, such as Aspergillus flavus and Fusarium graminearum, are expected to readily acclimatize to new conditions and could become more aggressive pathogens. Furthermore, abiotic stress factors resulting from CC are expected to weaken the resistance of host crops, rendering them more vulnerable to fungal disease outbreaks. Changed interactions of mycotoxigenic fungi are likewise expected, with the effect of influencing the prevalence and co-occurrence of mycotoxins in the future. Looking ahead, future research should focus on improving predictive modeling, expanding research into different pathosystems, and facilitating the application of effective strategies to mitigate the impact of CC.

From Grapes to Wine: Impact of the Vinification Process on Ochratoxin A Contamination.

Ochratoxin A (OTA) is one of the major mycotoxins, classified as "potentially carcinogenic to humans" (Group 2B) by the International Agency for Research on Cancer (IARC), and wine is one of its main sources of intake in human consumption. The main producer of this toxin is Aspergillus carbonarius, a fungus that contaminates grapes early in the growing season. The vinification process, as a whole, reduces the toxin content in wine compared to the grapes; however, not all vinification steps contribute equally to this reduction. During the maceration phase in red wines, toxin concentrations generally tend to increase. Based on previous studies, this review provides an overview of how each step of the vinification process influences the final OTA contamination in wine. Moreover, certain physical, chemical, and microbiological post-harvest strategies are useful in reducing OTA levels in wine. Among these, the use of fining agents, such as gelatin, egg albumin, and bentonite, must be considered. Therefore, this review describes the fate of OTA during the winemaking process, including quantitative data when available, and highlights actions able to reduce the final OTA level in wine.

DEFHAZ: A Mechanistic Weather-Driven Predictive Model for Diaporthe eres Infection and Defective Hazelnut Outbreaks.

The browning of the internal tissues of hazelnut kernels, which are visible when the nuts are cut in half, as well as the discolouration and brown spots on the kernel surface, are important defects that are mainly attributed to Diaporthe eres. The knowledge regarding the Diaporthe eres infection cycle and its interaction with hazelnut crops is incomplete. Nevertheless, we developed a mechanistic model called DEFHAZ. We considered georeferenced data on the occurrence of hazelnut defects from 2013 to 2020 from orchards in the Caucasus region and Turkey, supported by meteorological data, to run and validate the model. The predictive model inputs are the hourly meteorological data (air temperature, relative humidity, and rainfall), and the model output is the cumulative index (Dh-I), which we computed daily during the growing season till ripening/harvest time. We established the probability function, with a threshold of 1% of defective hazelnuts, to define the defect occurrence risk. We compared the predictions at early and full ripening with the observed data at the corresponding crop growth stages. In addition, we compared the predictions at early ripening with the defects observed at full ripening. Overall, the correct predictions were >80%, with <16% false negatives, which confirmed the model accuracy in predicting hazelnut defects, even in advance of the harvest. The DEFHAZ model could become a valuable support for hazelnut stakeholders.

Electronic Nose for the Rapid Detection of Deoxynivalenol in Wheat Using Classification and Regression Trees.

Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014−2015 and 2017−2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose "AIR PEN 3" (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach "Classification and regression trees" (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 µg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method.

Machine Learning for Predicting Mycotoxin Occurrence in Maize.

Meteorological conditions are the main driving variables for mycotoxin-producing fungi and the resulting contamination in maize grain, but the cropping system used can mitigate this weather impact considerably. Several researchers have investigated cropping operations' role in mycotoxin contamination, but these findings were inconclusive, precluding their use in predictive modeling. In this study a machine learning (ML) approach was considered, which included weather-based mechanistic model predictions for AFLA-maize and FER-maize [predicting aflatoxin B1 (AFB1) and fumonisins (FBs), respectively], and cropping system factors as the input variables. The occurrence of AFB1 and FBs in maize fields was recorded, and their corresponding cropping system data collected, over the years 2005-2018 in northern Italy. Two deep neural network (DNN) models were trained to predict, at harvest, which maize fields were contaminated beyond the legal limit with AFB1 and FBs. Both models reached an accuracy >75% demonstrating the ML approach added value with respect to classical statistical approaches (i.e., simple or multiple linear regression models). The improved predictive performance compared with that obtained for AFLA-maize and FER-maize was clearly demonstrated. This coupled to the large data set used, comprising a 13-year time series, and the good results for the statistical scores applied, together confirmed the robustness of the models developed here.

Ecology of Diaporthe eres, the causal agent of hazelnut defects.

Diaporthe eres has been recently reported as the causal agent of hazelnut defects, with characteristic brown spots on the kernels surface and internal fruit discoloration. Knowledge regarding the ecology of this fungus is poor but, is critical to support a rationale and effective hazelnut crop protection strategy. Therefore, a study was performed to describe and model the effect of different abiotic factors such as temperature (T, 5-35°C, step 5°C) and water activity (aw 0.83-0.99, step 0.03) regimes on D. eres mycelial growth, pycnidial conidiomata development and asexual spore production during a 60-day incubation period. Alpha conidia germination was tested in the same T range and at different relative humidities (RH = 94, 97 and 100%) over 48 h incubation period. Fungal growth was observed from the first visual observation; regarding pycnidia and cirrhi, their development started after 8 and 19 days of incubation, respectively and increased over time. The optimum T for growth was 20-25°C and for pycnidia and cirrhi development was 30°C; aw ≥ 0.98 was optimal for the tested steps of the fungal cycle. The best condition for conidial germination of D. eres was at 25°C with RH = 100%. Quantitative data obtained were fitted using non- linear regression functions (Bete, logistic and polynomial), which provided a very good fit of the biological process (R2 = 0.793-0.987). These functions could be the basis for the development of a predictive model for the infection of D. eres of hazelnuts.

AFLA-PISTACHIO: Development of a Mechanistic Model to Predict the Aflatoxin Contamination of Pistachio Nuts.

In recent years, very many incidences of contamination with aflatoxin B1 (AFB1) in pistachio nuts have been reported as a major global problem for the crop. In Europe, legislation is in force and 12 µg/kg of AFB1 is the maximum limit set for pistachios to be subjected to physical treatment before human consumption. The goal of the current study was to develop a mechanistic, weather-driven model to predict Aspergillus flavus growth and the AFB1 contamination of pistachios on a daily basis from nut setting until harvest. The planned steps were to: (i) build a phenology model to predict the pistachio growth stages, (ii) develop a prototype model named AFLA-pistachio (model transfer from AFLA-maize), (iii) collect the meteorological and AFB1 contamination data from pistachio orchards, (iv) run the model and elaborate a probability function to estimate the likelihood of overcoming the legal limit, and (v) manage a preliminary validation. The internal validation of AFLA-pistachio indicated that 75% of the predictions were correct. In the external validation with an independent three-year dataset, 95.6% of the samples were correctly predicted. According to the results, AFLA-pistachio seems to be a reliable tool to follow the dynamic of AFB1 contamination risk throughout the pistachio growing season.

Modelling Fungal Growth, Mycotoxin Production and Release in Grana Cheese.

No information is available in the literature about the influence of temperature (T) on Penicillium and Aspergillus spp. growth and mycotoxin production on cheese rinds. The aim of this work was to: (i) study fungal ecology on cheese in terms of T requirements, focusing on the partitioning of mycotoxins between the rind and mycelium; and (ii) validate predictive models previously developed by in vitro trials. Grana cheese rind blocks were inoculated with A. versicolor, P. crustosum, P. nordicum, P. roqueforti, and P. verrucosum, incubated at different T regimes (10-30 °C, step 5 °C) and after 14 days the production of mycotoxins (ochratoxin A (OTA); sterigmatocystin (STC); roquefortine C (ROQ-C), mycophenolic acid (MPA), Pr toxin (PR-Tox), citrinin (CIT), cyclopiazonic acid (CPA)) was quantified. All the fungi grew optimally around 15-25 °C and produced the expected mycotoxins (except MPA, Pr-Tox, and CIT). The majority of the mycotoxins produced remained in the mycelium (~90%) in three out of five fungal species (P. crustosum, P. nordicum, and P. roqueforti); the opposite occurred for A. versicolor and P. verrucosum with 71% and 58% of STC and OTA detected in cheese rind, respectively. Available predictive models fitted fungal growth on the cheese rind well, but validation was not possible for mycotoxins because they were produced in a very narrow T range.

Aspergillus flavus and Fusarium verticillioides Interaction: Modeling the Impact on Mycotoxin Production.

The influence of climate change on agricultural systems has been generally accepted as having a considerable impact on food security and safety. It is believed that the occurrence of mycotoxins will be greatly affected by future climate scenarios and this has been confirmed by recent data. Temperature (T) and CO2 increases, variation in rain intensity and distribution, as well as extreme weather events, affect the dominant fungal species in different ways, depending on their ecological needs. Therefore, the aim of this work was to study Aspergillus flavus (Af) and Fusarium verticillioides (Fv) co-occurrence in vitro in order to collect quantitative data on the effect of fungal interaction on growth and mycotoxin production and develop functions for their description. Experimental trials were organized with the cited fungi grown alone or together. They were incubated at different T regimes (10-40°C, step 5°C) for 21 days. Fungal growth was measured weekly, while AFs and FBs were quantified at the end of the incubation period. Temperature and incubation time significantly affected fungal growth both for Af and Fv (p ≤ 0.01), and a significant interaction between T and the presence of one versus both fungi influenced the amount of AFs and FBs produced. Each fungus was affected by the presence of the other fungus; in particular, Af and Fv showed a decrease in colony diameter of 10 and 44%, respectively, when they were grown together, compared to alone. The same influence was not found for mycotoxin production. In fact, the dynamics of toxin production in different temperature regimes followed a comparable trend with fungi grown alone or together, but a significant impact of inoculum × temperature interaction was highlighted. Fungal growth and toxin production in different T regimes were well described, both for AFs and FBs, by a Bete function. These results are the first attempt to model mycotoxigenic fungal co-occurrence under several T regimes; this is essential in order to improve effective prediction of growth and mycotoxin production by such fungi.

Modelling the sporulation of some fungi associated with cheese, at different temperature and water activity regimes.

The objectives of this study were to determine, in-vitro, the influence of temperature (T; 10-30 °C, step 5°), water activity (aw, 0.83-0.99; step 0.04) and time on sporulation (SPO) of some cheese-related fungi belonging to Penicillium spp. and A. versicolor. Overall, sporulation started rapidly (8 h in optimal conditions); it was significantly influenced by T and aw and the fungi studied were clearly distinguished based on their thermo-hydro adaptation. Boundary conditions for sporulation were defined for all the fungi considered and the sporulation rate was successfully modelled, especially based on T and time regimes. Penicillium crustosum, P. nordicum and P. verrucosum showed optimum for SPO at T between 20 and 25 °C and their sporulation continued up to aw = 0.87 (aw = 0.83 for P. nordicum). They resulted the fungi best adapted to the environmental conditions of ripening grana cheese storehouses; therefore, it is expected they dominate on the grana cheese surface. Studies on cheese are necessary to validate these results obtained on artificial media and without fungi co-occurrence.

Modeling Growth and Toxin Production of Toxigenic Fungi Signaled in Cheese under Different Temperature and Water Activity Regimes.

The aim of this study was to investigate in vitro and model the effect of temperature (T) and water activity (aw) conditions on growth and toxin production by some toxigenic fungi signaled in cheese. Aspergillus versicolor, Penicillium camemberti, P. citrinum, P. crustosum, P. nalgiovense, P. nordicum, P. roqueforti, P. verrucosum were considered they were grown under different T (0-40 °C) and aw (0.78-0.99) regimes. The highest relative growth occurred around 25 °C; all the fungi were very susceptible to aw and 0.99 was optimal for almost all species (except for A. versicolor, awopt = 0.96). The highest toxin production occurred between 15 and 25 °C and 0.96-0.99 aw. Therefore, during grana cheese ripening, managed between 15 and 22 °C, ochratoxin A (OTA), penitrem A (PA), roquefortine-C (ROQ-C) and mycophenolic acid (MPA) are apparently at the highest production risk. Bete and logistic function described fungal growth under different T and aw regimes well, respectively. Bete function described also STC, PA, ROQ-C and OTA production as well as function of T. These models would be very useful as starting point to develop a mechanistic model to predict fungal growth and toxin production during cheese ripening and to help advising the most proper setting of environmental factors to minimize the contamination risk.

Hydro- and thermotimes for conidial germination kinetics of the ochratoxigenic species Aspergillus carbonarius in vitro, on grape skin and grape flesh.

The objective was to compare the ability of spores of Aspergillus carbonarius to germinate in vitro, in situ on grape skin and grape flesh in relation to temperature (15-40 °C) and different relative humidities (100-85% RH). Spores were inoculated as a spore suspension (10(6) spores ml(-1)) onto the surface of white organic grapes and directly onto cut grape flesh. For comparison, spores were spread plate onto a synthetic grape juice medium (SGM) modified to the equivalent water activity (a(w)) range of 0.995-0.85. This showed that conidia germinated more rapidly on grape flesh (6 h) followed by that on the SGM medium (9 h) and then grape skin (24 h) under optimal condition of 30-35 °C and 100 % RH. At marginal conditions, such as 15 °C and 85-90% RH, germination was very slow. The time to 5% germination was significantly shorter on grape flesh than in vitro on grape medium and slowest on grape skin. This suggests that damaged grapes provide the main method of infection and contamination of grapes and grape products with ochratoxin A (OTA). The combined effect of temperature and RH on conidial germination of A. carbonarius on SGM and grape skin was described by combining Beta and polynomial equations. The equations developed in this work provided a good fit of the biological processes; they could be integrated in a predictive model for infection and OTA prediction in ripening grapes.

Co-occurrence of type A and B trichothecenes and zearalenone in wheat grown in northern Italy over the years 2009-2011.

The occurrence of the most widespread type A and B trichothecenes and of zearalenone was surveyed in soft and durum wheat produced in northern Italy. A total of 293 wheat fields, grown in the years 2009-2011, were surveyed; for each field, weather and cropping system data were collected. The results indicated a high deoxynivalenol incidence, with durum always more contaminated than soft wheat; in 2010, the percentage of durum wheat samples exceeding the European Commission legal limit was 39.6%. As regards type A trichothecenes, widespread contamination was observed in 2010. In soft wheat, an incidence of 70% and 85% was found for T-2 and HT-2 toxins, respectively; all the durum wheat samples were contaminated. The trichothecene contamination was affected by weather conditions; copious rainfall and high relative humidity (RH) during flowering occurred in 2010, when the highest contamination of both type A and B trichothecenes was found.

Comparison of temperature and moisture requirements for sporulation of Aspergillus flavus sclerotia on natural and artificial substrates.

A key step in the infection cycle by Aspergillus flavus in maize is sporulation of sclerotia present in soil or in crop debris. However, little information is available on this critical and important phase. This study included experiments on artificial (Czapek Dox Agar (CZ)) and natural (maize stalks) substrates under different conditions of temperature (T; from 5 to 45 °C) and water activity (a(w); from 0.50 to 0.99) levels to quantify sporulation from sclerotia. The mean numbers of spores were higher on defined nutritional medium in vitro on CZ agar than on maize stalks (4.5×10(6) spores/sclerotium versus 4.2×10(4) spores/sclerotium) with production initiated after 6 and 24h, respectively. Surprisingly, the optimal temperature was found at 30-35 °C for CZ agar (9.23×10(6) spores/sclerotium) and to be 20-25 °C for maize stalks (6.26×10(4) spores/sclerotium). Water stress imposition only reduced sporulation at ≤0.90 a(w.) With more available water no significant differences were found between 0.90 and 0.99 a(w). This type of data is critical in the development of a mechanistic model to predict the infection cycle of A. flavus in maize in relation to meteorological conditions.

Biocontrol of Penicillium nordicum growth and ochratoxin A production by native yeasts of dry cured ham.

Twelve yeast strains isolated from the surface of Italian typical dry-cured hams, belonging to D. hansenii, D. maramus, C. famata, C. zeylanoides and H. burtonii species, and previously selected for their ability to grow in dry-cured ham-like substrates, were screened for antagonistic activity against a toxigenic strain of P. nordicum and inhibition of ochratoxin A (OTA) biosynthesis. On average, yeast inhibitory activity was lowered by increasing fungal inoculum and enhanced by NaCl presence. In the assay conditions, H. burtonii and C. zeylanoides were the most effective, both in inhibiting P. nordicum growth and OTA production. D. hansenii was the species with the lowest inhibitory activity, especially in the absence of salt. OTA production dropped from the range < LOD - 5000 ppb in P. nordicum control plates to the range < LOD - 200 ppb in yeast-added plates. OTA production increased in the presence of NaCl in P. nordicum control plates, while salt enhanced inhibition against OTA production in yeast-added plates.