Exploring the impact of lactic acid bacteria on the biocontrol of toxigenic Fusarium spp. and their main mycotoxins.

The occurrence of fungi and mycotoxins in foods is a serious global problem. Most of the regulated mycotoxins in food are produced by Fusarium spp. This work aimed to assess the antifungal activity of selected lactic acid bacteria (LAB) strains against the main toxigenic Fusarium spp. isolated from cereals. Various machine learning (ML) algorithms such as neural networks (NN), random forest (RF), extreme gradient boosted trees (XGBoost), and multiple linear regression (MLR), were applied to develop models able to predict the percentage of fungal growth inhibition caused by the LAB strains tested. In addition, the ability of the assayed LAB strains to reduce/inhibit the production of the main mycotoxins associated with these fungi was studied by UPLC-MS/MS. All assays were performed at 20, 25, and 30 °C in dual culture (LAB plus fungus) on MRS agar-cereal-based media. All factors and their interactions very significantly influenced the percentage of growth inhibition compared to controls. The efficacy of LAB strains was higher at 20 °C followed by 30 °C and 25 °C. Overall, the order of susceptibility of the fungi to LAB was F. oxysporum > F. poae = F. culmorum ≥ F. sporotrichioides > F. langsethiae > F. graminearum > F. subglutinans > F. verticillioides. In general, the most effective LAB was Leuconostoc mesenteroides ssp. mesenteroides (T3Y6b), and the least effective were Latilactobacillus sakei ssp. carnosus (T3MM1 and T3Y2). XGBoost and RF were the algorithms that produced the most accurate predicting models of fungal growth inhibition. Mycotoxin levels were usually lower when fungal growth decreased. In the cultures of F. langsethiae treated with LAB, T-2 and HT-2 toxins were not detected except in the treatments with Pediococcus pentosaceus (M9MM5b, S11sMM1, and S1M4). These three strains of P. pentosaceus, L. mesenteroides ssp. mesenteroides (T3Y6b) and L. mesenteroides ssp. dextranicum (T2MM3) inhibited fumonisin production in cultures of F. proliferatum and F. verticillioides. In F. culmorum cultures, zearalenone production was inhibited by all LAB strains, except L. sakei ssp. carnosus (T3MM1) and Companilactobacillus farciminis (T3Y6c), whereas deoxynivalenol and 3-acetyldeoxynivalenol were only detected in cultures of L. sakei ssp. carnosus (T3MM1). The results show that an appropriate selection and use of LAB strains can be one of the most impacting tools in the control of toxigenic Fusarium spp. and their mycotoxins in food and therefore one of the most promising strategies in terms of efficiency, positive impact on the environment, food safety, food security, and international economy.

Lactic Acid Bacteria as Potential Agents for Biocontrol of Aflatoxigenic and Ochratoxigenic Fungi.

Aflatoxins (AF) and ochratoxin A (OTA) are fungal metabolites that have carcinogenic, teratogenic, embryotoxic, genotoxic, neurotoxic, and immunosuppressive effects in humans and animals. The increased consumption of plant-based foods and environmental conditions associated with climate change have intensified the risk of mycotoxin intoxication. This study aimed to investigate the abilities of eleven selected LAB strains to reduce/inhibit the growth of Aspergillus flavus, Aspergillus parasiticus, Aspergillus carbonarius, Aspergillus niger, Aspergillus welwitschiae, Aspergillus steynii, Aspergillus westerdijkiae, and Penicillium verrucosum and AF and OTA production under different temperature regiments. Data were treated by ANOVA, and machine learning (ML) models able to predict the growth inhibition percentage were built, and their performance was compared. All factors LAB strain, fungal species, and temperature significantly affected fungal growth and mycotoxin production. The fungal growth inhibition range was 0-100%. Overall, the most sensitive fungi to LAB treatments were P. verrucosum and A. steynii, while the least sensitive were A. niger and A. welwitschiae. The LAB strains with the highest antifungal activity were Pediococcus pentosaceus (strains S11sMM and M9MM5b). The reduction range for AF was 19.0% (aflatoxin B1)-60.8% (aflatoxin B2) and for OTA, 7.3-100%, depending on the bacterial and fungal strains and temperatures. The LAB strains with the highest anti-AF activity were the three strains of P. pentosaceus and Leuconostoc mesenteroides ssp. dextranicum (T2MM3), and those with the highest anti-OTA activity were Leuconostoc paracasei ssp. paracasei (3T3R1) and L. mesenteroides ssp. dextranicum (T2MM3). The best ML methods in predicting fungal growth inhibition were multilayer perceptron neural networks, followed by random forest. Due to anti-fungal and anti-mycotoxin capacity, the LABs strains used in this study could be good candidates as biocontrol agents against aflatoxigenic and ochratoxigenic fungi and AFL and OTA accumulation.

Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys.

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst.

Comparative Analysis of Machine Learning Methods to Predict Growth of F. sporotrichioides and Production of T-2 and HT-2 Toxins in Treatments with Ethylene-Vinyl Alcohol Films Containing Pure Components of Essential Oils.

The efficacy of ethylene-vinyl alcohol copolymer films (EVOH) incorporating the essential oil components cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG), or linalool (LIN) to control growth rate (GR) and production of T-2 and HT-2 toxins by Fusarium sporotrichioides cultured on oat grains under different temperature (28, 20, and 15 °C) and water activity (aw) (0.99 and 0.96) regimes was assayed. GR in controls/treatments usually increased with increasing temperature, regardless of aw, but no significant differences concerning aw were found. Toxin production decreased with increasing temperature. The effectiveness of films to control fungal GR and toxin production was as follows: EVOH-CIT > EVOH-CINHO > EVOH-IEG > EVOH-LIN. With few exceptions, effective doses of EVOH-CIT, EVOH-CINHO, and EVOH-IEG films to reduce/inhibit GR by 50%, 90%, and 100% (ED50, ED90, and ED100) ranged from 515 to 3330 µg/culture in Petri dish (25 g oat grains) depending on film type, aw, and temperature. ED90 and ED100 of EVOH-LIN were >3330 µg/fungal culture. The potential of several machine learning (ML) methods to predict F. sporotrichioides GR and T-2 and HT-2 toxin production under the assayed conditions was comparatively analyzed. XGBoost and random forest attained the best performance, support vector machine and neural network ranked third or fourth depending on the output, while multiple linear regression proved to be the worst.

Potential Health Risk Associated with Mycotoxins in Oat Grains Consumed in Spain.

Spain is a relevant producer of oats (Avena sativa), but to date there has been no study on the occurrence/co-occurrence of mycotoxins in oats marketed in Spain. The present study is addressed to overcome this lack of knowledge. One hundred oat kernel samples were acquired across different Spanish geographic regions during the years 2015-2019 and analyzed for mycotoxin content using an ultra-high performance liquid chromatography electrospray ionization tandem mass spectrometry (UPLC-ESI-MS/MS) method and matrix-matched calibration. The focus was on the regulated mycotoxins although other relevant mycotoxins were considered. The percentage of incidence (levels ≥ limit of detection), mean and range (ng/g) of mycotoxins were as follows: zearalenone (66%, mean 39.1, range 28.1-153), HT-2 toxin (47%, mean 37.1, range 4.98-439), deoxynivalenol, (34%, mean 81.4, range 19.1-736), fumonisin B1 (29%, mean 157.5, range 63.2-217.4), and T-2 toxin, (24%, mean 49.9, range 12.3-321). Fumonisin B2, 3-acetyldeoxynivalenol, aflatoxins B1, B2, and G2, and ochratoxin A were also detected at low levels, but aflatoxin G1 was not. The maximum limits established by the European Commission for unprocessed oats were not exceeded, except for zearalenone (in one sample), and the sum of aflatoxins (in two samples). Mycotoxin co-occurrence at quantifiable levels in the same sample (two to five combinations) was found in 31% of samples. The most common mixtures were those of HT-2 + T-2 toxins alone or together with deoxynivalenol and/or zearalenone.

Potential use of machine learning methods in assessment of Fusarium culmorum and Fusariumproliferatum growth and mycotoxin production in treatments with antifungal agents.

Fusarium-controlling fungicides are necessary to limit crop loss. Little is known about the effect of antifungal formulations at sub-lethal doses, and their interaction with abiotic factors, on Fusarium culmorum and F. proliferatum development and on zearalenone and fumonisin biosynthesis, respectively. In the present study different treatments based on sulfur, trifloxystrobin and demethylation inhibitor fungicides (cyproconazole, tebuconazole and prothioconazole) under different environmental conditions, in Maize Extract Medium, are assayed in vitro. Several machine learning methods (neural networks, random forest and extreme gradient boosted trees) have been applied for the first time for modeling growth of F. culmorum and F. proliferatum and zearalenone and fumonisin production, respectively. The most effective treatment was prothioconazole, 250 g/L + tebuconazole, 150 g/L. Effective doses of this formulation for reduction or total growth inhibition ranged as follows ED50 0.49-1.70, ED90 2.57-6.02 and ED100 4.0-8.0 µg/mL, depending on the species, water activity and temperature. Overall, the growth rate and mycotoxin levels in cultures decreased when doses increased. Some treatments in combination with certain aw and temperature values significantly induced toxin production. The extreme gradient boosted tree was the model able to predict growth rate and mycotoxin production with minimum error and maximum R2 value.

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.

Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumonisins (FUM) in different points of the food chain. Application of antifungal chemicals to control these fungi and mycotoxins increases the risk of toxic residues in foods and feeds, and induces fungal resistances. In this study, a new and multidisciplinary approach based on the use of bioactive ethylene-vinyl alcohol copolymer (EVOH) films containing pure components of essential oils (EOCs) and machine learning (ML) methods is evaluated. Bioactive EVOH-EOC films were made incorporating cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG) or linalool (LIN). Several ML methods (neural networks, random forests and extreme gradient boosted trees) and multiple linear regression (MLR) were applied and compared for modeling fungal growth and toxin production under different water activity (aw) (0.96 and 0.99) and temperature (20 and 28 °C) regimes. The effective doses to reduce fungal growth rate (GR) by 50, 90 and 100% (ED50, ED90, and ED100) of EOCs in EVOH films were in the ranges 200 to >3330, 450 to >3330, and 660 to >3330 µg/fungal culture (25 g of partly milled maize kernels in Petri dish), respectively, depending on the EOC, aw and temperature. The type of EVOH-EOC film and EOC doses significantly affected GR in both species and ZEA and FUM production. Temperature also affected GR and aw only affected GR and FUM production of F. proliferatum. EVOH-CIT was the most effective film against both species and ZEA and FUM production. Usually, when the EOC levels increased, GR and mycotoxin levels in the medium decreased although some treatments in combination with certain aw and temperature values induced ZEA production. Random forest models predicted the GR of F. culmorum and F. proliferatum and ZEA and FUM production better than neural networks or extreme gradient boosted trees. The MLR mode provided the worst performance. This is the first approach on the ML potential in the study of the impact that bioactive EVOH films containing EOCs and environmental conditions have on F. culmorum and F. proliferatum growth and on ZEA and FUM production. The results suggest that these innovative packaging systems in combination with ML methods can be promising tools in the prediction and control of the risks associated with these toxigenic fungi and mycotoxins in food.

Antifungal effect of engineered silver nanoparticles on phytopathogenic and toxigenic Fusarium spp. and their impact on mycotoxin accumulation.

Cereal grains are essential ingredient in food, feed and industrial processing. One of the major causes of cereal spoilage and mycotoxin contamination is the presence of toxigenic Fusarium spp. Nanoparticles have immense applications in agriculture, nutrition, medicine or health but their possible impact on the management of toxigenic fungi and mycotoxins have been very little explored. In this report, the potential of silver nanoparticles (AgNPs) (size 14-100 nm) against the major toxigenic Fusarium spp. affecting crops and their effect on mycotoxin accumulation is evaluated for the first time. The studied Fusarium spp. (and associated mycotoxins) were F. graminearum and F. culmorum (deoxynivalenol, 3-acetyldeoxynivalenol and zearalenone), F. sporotrichioides and F. langsethiae (T-2 and HT-2 toxins), F. poae (nivalenol), F. verticillioides and F. proliferatum (fumonisins B1 and B2) and F. oxysporum (mycotoxins no detected). The factors fungal species, AgNP dose (range 2-45 µg/mL), exposure time (range 2-30 h) and their interactions significantly influence spore viability, lag period and growth rate (GR) in subsequent cultures in maize-based medium (MBM) of all the studied species. The effective lethal doses (ED50, ED90 and ED100) to control spore viability and GR were in the range 1->45 µg/mL depending on the remaining factors. At high exposure times (20-30 h), the three effective doses ranged 1-30 µg/mL for all the studied species. At the end of the incubation period (10 days) mycotoxin levels in MBM cultures inoculated with fungal spores from treatments were strongly related with the size reached by the colony at that time. None of the treatments produced stimulation in conidia germination, GR or mycotoxin biosynthesis with respect to controls. Thus, the antifungal effect of the assayed AgNPs against the tested Fusarium spp. suggests that AgNPs could be a new antifungal ingredient in bioactive polymers (paints, films or coating) likely to be implemented in the agro-food sector for controlling these important toxigenic Fusarium spp. and their main associated mycotoxins.

Risk management of ochratoxigenic fungi and ochratoxin A in maize grains by bioactive EVOH films containing individual components of some essential oils.

Aspergillus steynii and Aspergillus tubingensis are possibly the main ochratoxin A (OTA) producing species in Aspergillus section Circumdati and section Nigri, respectively. OTA is a potent nephrotoxic, teratogenic, embryotoxic, genotoxic, neurotoxic, carcinogenic and immunosuppressive compound being cereals the first source of OTA in the diet. In this study bioactive ethylene-vinyl alcohol copolymer (EVOH) films containing cinnamaldehyde (CINHO), linalool (LIN), isoeugenol (IEG) or citral (CIT) which are major components of some plant essential oils (EOs) were produced and tested against A. steynii and A. tubingensis growth and OTA production in partly milled maize grains. Due to the favourable safety profile, these bioactive compounds are considered in the category "GRAS". The study was carried out under different water activity (0.96 and 0.99 aw), and temperature (24 and 32 °C) conditions. ANOVA showed that class of film, fungal species, aw and temperature and their interactions significantly affected growth rates (GR), ED50 and ED90 and the doses for total fungal growth inhibition and OTA production. The most effective EVOH films against both species were those containing CINHO. ED50, ED90 and doses for total growth and OTA inhibition were 165-405, 297-614, 333-666 µg of EVOH-CINHO/plate (25 g of maize grains), respectively, depending on environmental conditions. The least efficient were EVOH-LIN films. ED50, ED90 and doses for total growth and OTA inhibition were 2800->3330, >3330 and >3330 µg of EVOH-LIN/plate (25 g of maize grains), respectively. The effectiveness of the bioactive films increased with increasing doses. Overall, A. tubingensis was less sensitive to treatments than A. steynii. Depending on the species, aw and temperature affected GR and OTA production in a different way. In A. steynii cultures, optimal growth occurred at 0.96 aw and 32 °C while optimal OTA production happened at 0.99 aw and 32 °C. In A. tubingensis cultures optimal growth happened at 0.99 aw and 32 °C, although the best conditions for OTA production were 0.99 aw and 24 °C. Thus, these species can be very competitive in warm climates and storage conditions. The EVOH-CINHO films followed by EVOH-IEG and EVOH-CIT films, designed in this study and applied in vapour phase, can be potent antifungal agents against A. steynii and A. tubingensis and strong inhibitors of OTA biosynthesis in maize grains at very low doses. This is the first study on the impact that interacting environmental conditions and bioactive films containing individual components of EOs have on the growth of these ochratoxigenic fungi and on OTA production in maize grains.

Selected plant essential oils and their main active components, a promising approach to inhibit aflatoxigenic fungi and aflatoxin production in food.

Recent research has showed that Aspergillus flavus and Aspergillus parasiticus are aflatoxigenic species that can become very competitive in the framework of climate change. Aflatoxins show carcinogenic, mutagenic, immunotoxic and teratogenic effects on human and animals. Effective and sustainable measures to inhibit these species and aflatoxins in food are required. Origanum vulgare and Cinnamomum zeylanicum essential oils (EOs) and their major active constituents, carvacrol and cinnamaldehyde, respectively, were assayed for inhibiting these species and aflatoxin production in maize extract medium under different environmental conditions. Doses of 10-1000 mg l-1 were assayed and the effective doses for 50 (ED50) and 90% (ED90) growth inhibition were determined. The ED50 of cinnamaldehyde, carvacrol, oregano EO, and cinnamon EO against A. flavus were in the ranges 49-52.6, 98-145, 152-505, 295-560 mg l-1 and against A. parasiticus in the ranges 46-55.5, 101-175, 260-425 and 490-675 mg l-1, respectively, depending on environmental conditions. In A. flavus treatments ED90 were in the ranges 89.7-90.5, 770-860 and 820->1000 mg l-1 for cinnamaldehyde, carvacrol and cinnamon EO, and in A. parasiticus treatments in the ranges 89-91, 855->1000 and 900->1000 mg l-1, respectively. ED90 values for oregano EO against both species were >1000 mg l-1. Growth rates of both species were higher at 37 than at 25°C and at 0.99 than at 0.96 aw. Aflatoxin production was higher at 25 than at 37°C. Stimulation of aflatoxin production was observed at low doses except for cinnamaldehyde treatments. The effectiveness of EOs and their main constituents to inhibit fungal growth and aflatoxin production in contact assays was lower than in vapour phase assays using bioactive EVOH-EO films previously reported.