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. 2019 May 22;11(5):290. doi: 10.3390/toxins11050290

Prevalent Mycotoxins in Animal Feed: Occurrence and Analytical Methods

Carolina Santos Pereira 1,*, Sara C Cunha 1,*, José O Fernandes 1,*
PMCID: PMC6563184  PMID: 31121952

Abstract

Today, we have been witnessing a steady tendency in the increase of global demand for maize, wheat, soybeans, and their products due to the steady growth and strengthening of the livestock industry. Thus, animal feed safety has gradually become more important, with mycotoxins representing one of the most significant hazards. Mycotoxins comprise different classes of secondary metabolites of molds. With regard to animal feed, aflatoxins, fumonisins, ochratoxins, trichothecenes, and zearalenone are the more prevalent ones. In this review, several constraints posed by these contaminants at economical and commercial levels will be discussed, along with the legislation established in the European Union to restrict mycotoxins levels in animal feed. In addition, the occurrence of legislated mycotoxins in raw materials and their by-products for the feeds of interest, as well as in the feeds, will be reviewed. Finally, an overview of the different sample pretreatment and detection techniques reported for mycotoxin analysis will be presented, the main weaknesses of current methods will be highlighted.

Keywords: mycotoxins, feed, fungi, occurrence, analytical methods, contaminants

1. Introduction

Feed is described by the European Commission as any substance or product, including additives, whether processed, semi-processed or unprocessed, intended to be used for oral feeding of animals [1]. It can be classified into the following four groups [2]:

  • Forages—silage made from grass or cereal crops;

  • Cereals and other home-grown crops—feeds with a high energy and/or protein content;

  • Compound feeds—manufactured mixtures of single feed materials, minerals, and vitamins;

  • Products and by-products of the human food and brewing industries—residues of vegetable processing, spent grains from brewing and malting and by-products of the baking, bread-making, and confectionery industries.

Livestock diets typically include a combination of feeds that are designed to meet not only the nutritional needs of animals with minimal costs, but also to provide everything they need for their health, welfare, and production [2,3]. However, cereals and cereal-based products are possibly the most commonly used ingredients in animal feed, supplying most of the nutrients for livestock [4,5,6,7]. In developed countries, up to 70% of the cereal harvest is used in the daily diet of animals, whereas, in developing countries this commodity is mainly used for human consumption [8]. In addition, plant protein sources, such as by-products from the extraction of oil from oilseed crops, are regularly present in animal feeding and complement the cereal grains which are usually poor in protein [4,6,9,10].

Cereals for the global feed industry include maize, wheat, barley, sorghum, and oats grains [7,9]. Essentially maize, as well as wheat, are considered key global agricultural commodities in regard to farm animal diets [4,11,12,13]. In fact, the majority of the maize production in the world (approximately 55%) goes into animal feed, because maize and products derived thereof are widely used feed raw materials [7,12,13,14,15,16]. Wheat in feedstuffs represents around 20% of the total wheat, with the remainder of the wheat used for human consumption. Nevertheless, in the European Union (EU) almost half of the wheat is used in feed [17,18]. Therefore, wheat grains and the respective by-products are also seen as suppliers of various significant materials in livestock feed [13,18,19].

Oilseed crops like soybeans, cottonseed, sunflower, sesame, and palm are also used as vegetable protein sources in the manufacturing of animal feed [9,10]. However, soybean products remain universally accepted as the most important and preferred feed commodities due to their high-quality protein content [4,10,13,20,21,22]. In fact, soybean meal, which is the by-product of oil extraction from soybeans, represents two-thirds of the total world output of protein feedstuffs [20].

The global demand for agricultural crops has been increasing over the years, with an expected growth of 84% between 2000 to 2050 [4,11,23,24,25]. This development is intended, in part, to meet the rapid growth and strengthening of the livestock industry, propelled by the rising demand for livestock products [2,10,25]. This is, in turn, driven essentially by increases in world population and urbanization rates, as well as changes in lifestyles and food preferences [10,11,23,25]. Consequently, animal feed safety has become even more of a concern for both producers and governments since feed consumption is, eventually, a potential route for hazards to reach the human food chain [10,25,26,27]. Thus, in accordance with the Directive 2002/32/EC, the quality and safety of products intended for animal feed must be assessed prior to their use in feed to ensure that they do not represent any danger to human health, animal health or the environment, or do not adversely affect livestock production [27,28]. Among the undesirable substances laid down in this Directive, mycotoxins have been increasingly targeted as becoming one of the most important dangers in the raw materials of feed, due to the verified increase in their formation [29,30]. In this review, several constraints posed by these contaminants at the economical and the commercial level will be discussed, along with the legislation established in the European Union to restrict mycotoxins levels in animal feed. In addition, the occurrence of legislated mycotoxins in the raw materials and their by-products of the feeds of interest, as well as in the feed, will be reviewed. Additionally, an overview of the different sample preparation and detection techniques reported for mycotoxin analysis will be discussed.

2. Mycotoxins Classes and Toxicity

Mycotoxins are a relatively large and chemically diverse group of toxic secondary metabolites of low molecular weight. They are typically produced by filamentous fungi, especially those belonging to the genus Aspergillus, Penicillium, Alternaria, and Fusarium, although Claviceps and Stachybotrys are also important mycotoxins producers. Approximately 300 to 400 mycotoxins have been identified and reported so far [5,31,32]. However, regarding their prevalence in feeds and their known effects on livestock health, only a few groups of mycotoxins are considered to be a safety and economic concern, namely, aflatoxins (AFs), fumonisins (FMs), ochratoxins (OTs), trichothecenes (TRCs), and zearalenone (ZEN) [5,27,33]. Other mycotoxins, such as patulin, citrinin, and other emerging mycotoxins are beyond the scope of this review. With these relevant classes in mind, a brief introduction about each one will be provided along with the associated toxicological effects.

2.1. Aflatoxins

Aspergillus flavus and A. parasiticus are the main species of aflatoxin-producing fungi, although A. nomius and A. pseudotamarri are known to produce them, as well. The AFs group encompasses several different toxins, however, only the following four types are most abundant: aflatoxin B1 (AFB1), B2 (AFB2), G1 (AFG1), and G2 (AFG2) [32,34,35]. The metabolic products derived from AFs are aflatoxin M1 (AFM1) and M2 (AFM2) which are also referred to as important contaminants of this class [32,36,37].

AFs represent the group of fungal toxins of greatest concern in terms of human toxicity. Their toxic effects advert from their entry in the human food chain in two ways: (i) First, directly, after human exposure by consumption of contaminated crops or finished processed food products, since aflatoxins are very stable and may resist food processing operations. (ii) Secondly, indirectly from tissues, eggs, milk, and dairy products of animals fed with aflatoxin-contaminated feeds, through excretion of the hydroxylated derivative of AFB1 and AFM1. Actually, AFB1 is the most commonly occurring aflatoxin and most potent hepatocarcinogen, classified by the International Agency for Research on Cancer (IARC) as a human carcinogen (group 1) and AFM1 as possibly carcinogenic to humans (group 2B) [33,38,39,40,41,42]. Concerning livestock health, AFs are also a major problem causing acute death to chronic disease. Clinical signs of animal intoxication include gastrointestinal dysfunction, anemia, jaundice, hemorrhage, and an overall decrease in productive parameters, such as reduction in weight gain, lower feed efficiency, decreased egg or milk production, inferior carcass quality, and increased susceptibility to environmental and microbial stressors [32,41,42,43]. Ultimately, prolonged exposure to low dietary levels of AFs can result in extensive functional and structural liver lesions, including cancer. It is important to note that nursing animals, as well, are exposed to the AFB1 toxic metabolite secreted in milk [32,41,42,43].

2.2. Fumonisins

FMs are commonly classified as Fusarium toxins since they can be produced by several species of this genus, with F. verticillioides (previously classified as F. moniliforme) and F. proliferatum as the main producing species. However, A. niger was recently found to also produce FMs [36,42,44]. Within the 16 fumonisin analogues known to date, the B-series FMs (FBs), which compromise fumonisin B1, B2, B3, and B4, are the most important ones [36,42,45].

Fumonisin B1 (FB1) is reported as the predominant and most toxic member of the FMs family and has been recognized as a possible human carcinogen (group 2B) [38,42,46]. Fumonisin B2 (FB2) is also toxicologically significant. Apparently, the carcinogenic character of FBs is not related to direct DNA damage, but rather it is associated with the disruption of sphingolipid biosynthesis due to structural similarities of these toxins with the backbone precursors of sphingolipids [36,40,41]. In animals, ingestion of feed contaminated with FBs can cause significant disease in horses, swine, and rabbits which are considerably more sensitive than cattle and poultry [32,41,47]. Leukoencephalomalacia syndrome appears mainly in horses triggering primary symptoms like lethargy, blindness, and decreased feed intake, and ultimately, convulsions and death. In pigs, FB1 is associated with pulmonary oedema whose clinical signs typically include reduced feed consumption, dyspnea, weakness, cyanosis, and death [36,40,41]. In addition, these mycotoxins have also shown hepatotoxicity [32,40].

2.3. Ochratoxins

Production of the OTs, ochratoxin A (OTA) and ochratoxin B (OTB), occurs essentially by fungi belonging to the genus Aspergillus and Penicillium, namely by the species A. ochraceus, A. carbonarius, P. verrucosum, and P. nordicum [32,36,37,48].

OTAs are linked with potent nephrotoxic effects in animals as a consequence of exposure to naturally occurring levels in feed, since the kidneys are the major target organ [32,40,41,46]. In fact, OTAs have been associated with endemic nephropathy in swine [36,46]. High dietary doses of this toxin may cause liver damage and necrosis of intestinal and lymphoid tissue [32,40]. Regarding humans toxicity, OTAs have been implicated in a fatal kidney disease typical in the Balkan countries (Balkan endemic nephropathy) and have been classified as possibly carcinogenic (group 2B) [32,38,41,46]. Additionally, there has been a public health concern with respect to the transfer of OTA to animal-derived food [42].

2.4. Trichothecenes

TRCs are produced to a great extent by Fusarium species, although not exclusively, since some Cephalosporium, Myrothecium, Stachybotrys, and Trichoderma species also produce these mycotoxins. This is a large class of fungal metabolites with more than 150 structurally related compounds, which are chemically divided into four types (A to D) [32,41,43]. TRCs from type A and B are the most important. Type A-TRCs comprises mainly HT-2 and T-2 toxins (HT-2 and T-2), while type B-TRCs are frequently represented by deoxynivalenol (DON), its derivatives 3-acetyldeoxynivalenol (3-AcDON), 15-acetyldeoxynivalenol (15-AcDON) and nivalenol (NIV) [49,50].

HT-2 and T-2, although not being very prevalent, are the most toxic members of type A-TRCs [40,41,42,43]. They were found to inhibit protein and DNA synthesis and weaken cellular immune responses, in animals [40,42]. Symptoms include decreased feed intake and weight gain, bloody diarrhea, hemorrhaging, oral lesions, low egg and milk production, abortion, and death in some cases [40,41,42,43].

DON is one of the least acutely toxic TRCs, however, as it is highly incident, it is considered very relevant in animal husbandry [32,40,42,51]. Exposure to DON more severely affects monogastric animals, especially swine, and may cause feed refusal, vomiting, and anorexia, as well as the symptoms described previously for HT-2 and T-2 [32,41,43]. Overall, ingestion of low to moderate levels of this mycotoxin by animals leads to increased susceptibility to pathogens and to a poor performance [32,41]. DON was categorized by IARC as not classifiable with respect to its carcinogenicity to humans (group 3) [38].

2.5. Zearalenone

ZEN is a Fusarium mycotoxin produced particularly by F. graminearum and also by F. culmorum, F. cerealis, F. equiseti, among others, and it has α-Zearalenol (α-ZEL) and β-Zearalenol (β-ZEL) as derivatives [36,41,52]. Once ZEN has structural similarities to the female sex hormone, estradiol, it is classified commonly as a nonsteroidal estrogen. This chemical characteristic gives it the capability of binding to estrogen receptors, causing adverse effects associated with reproductive disorders and hyperestrogenism, both in humans and breeding animals [32,36,37,42]. According to IARC, ZEN belongs to group three, which means it is not classifiable regarding its carcinogenicity to humans [38].

3. Mycotoxins Economic and Commercial Implications

Animal consumption of mycotoxin-contaminated crops may cause adverse health effects which include occult conditions (for example, growth retardation, impaired immunity, and decreased disease resistance), chronic to acute disease, and even death. Basically, these hazards affect animal performance to a great extent, representing a global concern for the livestock industry [5,32,46]. Therefore, a threat, such as mycotoxins, to the safety of the feed supply chain becomes a significant constraint to animal production systems [5,53]. These metabolites cause perturbations in the feed industry due to the decrease in the quality of commodities which may even lead to the rejection and disposal of highly contaminated crops [5,32,46]. Naturally, large costs on the economy of these industries arise from mycotoxin contamination. Apart from the aforementioned problems, economic losses may be associated with increased costs for health care, finding alternative feed sources, prevention strategies, investment in testing methods, and for regulations [5,8,32,33]. Additionally, mycotoxins presence may impact on international commodity trade, propelled by increasing globalization [32,34].

In an attempt to avoid the adverse effects and implications discussed above, several worldwide institutions and organizations have restricted the accepted levels of certain mycotoxins in animal feeds, since truly mycotoxin-free feedstuffs are impossible to guarantee. Naturally, the limits and the mycotoxins targeted by legislation vary from country to country since different scientific, economic, and political factors influence this decision-making process [26,32,33,43].

Particularly, in the EU, the legislations (regulation or recommendation) established so far cover AFs, FBs, OTA, some types of A and B TRCs, and ZEN, in different feeding matrices. Directive 2002/32/EC specifies maximum content for AFB1 in products intended for animal feed [28]. Guidance values for DON, FBs, OTA, and ZEN contamination were set in the Commission Recommendation 2006/576/EC [54].

4. Mycotoxin Occurrence

Various factors are known to influence the incidence of mycotoxins, despite their unavoidable and unpredictable nature. Their production can start in the field throughout the crop growing cycle and continue during harvesting, drying, processing, and storage steps, strongly depending on various environmental conditions. These comprehend not only climatic factors, such as temperature and moisture content which are the main aspects modulating fungal growth and mycotoxins production, but also pH, bioavailability of micronutrients, and insect damage, for example [32,33,37,46,50,55]. Others factors like geographic location, agricultural practices, harvest year, and the length and conditions of storage affect the extent of the contamination of a particular commodity [32,33,56,57]. However, the substrate susceptibility to fungal invasion plays a major role in mycotoxin production [58]. Moreover, due to the climate changes across the globe, some changes in the distribution and cycles of the molds are expected, since every mold species has its own optimum conditions of temperature and water activity for growth and formation of toxic metabolites.

In order to understand the mycotoxin prevalence and contamination levels in the main raw materials of feed that are the subject of this work, global mycotoxin occurrence data was gathered in Table A1, Table A2 and Table A3. These tables represent an overview of contamination in maize, wheat, and soybeans and their by-products, respectively, collected by several authors, in the last three years (2016–2018) through searches in PubMED and ScienceDirect. Globally, maize and wheat are by far the most studied matrices, while soybean is the least studied, which is in agreement with previous reports [59]. In all substrates, the raw ingredients themselves were more subjected to mycotoxin contamination surveys than the respective by-products, perhaps because the last ones are usually more complex matrices.

Considering Table A1, it can be pointed out that in 2016 there was an increase in the samples of maize and the derived by-products in which mycotoxins were researched. This may be because this crop is among the most susceptible to mycotoxigenic fungi infection, and also since its production is growing from year to year the need to target these impurities has also raised [12]. The fact that maize by-products are increasingly used in animal diets may also explain the larger number of assayed samples of these feedstuffs [58]. In maize, most studies focused on ZEN and type A-TRCs, followed by the occurrence of AFs and FMs. According to FAO [15,27], maize is especially linked with these two contaminants, having a relevant role in economic losses in maize production [60]. Regarding the levels found, AFB1 was the mycotoxin that exceeded the EU legislative level more often, with a maximum value of 1137.4 µg/kg in a sample of raw-cereal from Kenya [61]. ZEN, T-2, and HT-2 have also been reported to exceed the EU legislative levels in some cases, as reported in Table A1.

Inversely, the wheat samples examined decreased from 2016 to 2018 (Table A2). Concerning the mycotoxins targeted in the reports reviewed, DON was by far the most searched, probably because it is frequently associated with this grain [44]. Nevertheless, ZEN and AFs were also studied in this matrix, and the last one exceeded the EU legislative level in three studies (Table A2).

From Table A3 it is possible to observe the mycotoxins that were studied more and these were AFs, followed by ZEN, and DON. Additionally, fewer samples of soybeans and its by-products were analyzed as compared with maize or wheat, and the by-products were studied more than the raw leguminous. Generally, this substrate is not considered a relevant problem in terms of mycotoxin contamination which may be because of its low moisture content and composition (high protein/carbohydrate ratio) that inhibit fungi growth, and also the better conditions used in the storage of this commodity due to the high price of soybean [37,62]. Nevertheless, the mycotoxin contamination verified in the studies reported was remarkable. In the future, more research on this commodity is needed, especially if this trend of production growth continues, in order to better understand which mycotoxins are most commonly associated with soybean and their by-products and whether contamination levels are of concern.

Overall, it seems that the common association between certain raw materials and a specific mycotoxin contamination profile has led researchers to favor the determination of these same contaminants. However, in addition to the fact that mycotoxins formation is a complex and multifactor phenomenon, worldwide contamination and distribution patterns of fungi and their secondary metabolites are predicted to be affected significantly by climate change scenarios, as a result of the appearance of favorable environmental conditions for fungal proliferation in uncommon places [33,46,53]. Therefore, mycotoxins presence is unpredictable and multi-mycotoxins surveys end up being more realistic and preferred.

Safety complications arising in the feed manufacturing process include aspects like the practice of mixing different batches of distinct raw ingredients, which creates a new matrix with an entirely new risk profile, and the fact that the majority of mycotoxins are stable compounds that are not destroyed during the storage, milling or high-temperature feed manufacturing process [63]. For these reasons, the knowledge of the occurrence and distribution of mycotoxins in animal feeds is of extreme importance, and it provides the opportunity to determine the direct risk posed to animals. Therefore, occurrence data of these toxins in animal feed, collected by several authors from various countries, from 2016 to 2018 was gathered in Table A4. Globally, AFs, DON, and ZEN were the mycotoxins most studied, but the determination of AFs and ZEN derivatives experienced a great increase, from 2016 to 2018. In this late year, the number of samples analyzed was far less than in 2016. The incidence of the mycotoxin, AFB1, most exceeded the EU legislative level in this kind of samples (Table A4).

Once formed, most mycotoxins are very stable during harvesting and storage. This draws attention to the need for prevention and control strategies such as hazard analysis and critical control point (HACCP) approaches, good agricultural and manufacturing practices (GAP and GMP), and quality control from the field through to the final product [64,65]. However, contaminated feed may be redirected to less vulnerable animal species, or, ultimately, detoxification methods can be used which involve the addition of feed additives “that can suppress or reduce the absorption, promote the excretion of mycotoxins or modify their mode of action” [30,66,67]. These substances have to be authorized under the feed additive Regulation 1831/2003 amended by Regulation 386/2009 [68]. In this way, the hygienic and nutritional quality of feed is guaranteed, ensuring the safety and productivity of the farm animals [30,65].

It is important to mention that when constructing Table A1, Table A2, Table A3 and Table A4, only papers that quantitatively determined mycotoxins were included and the ones that mentioned explicitly the use of the raw materials for human consumption were excluded. Moreover, year-to-year variations were reduced to the maximum because this parameter is beyond the scope of this review, and whenever the results permitted, the percentage and the average of positive samples was calculated. In addition, since all the information was obtained from different methodologies of analysis with distinct sensitivity and accuracy, the quantitative comparison might be quite complex.

Co-Occurrence

Natural contamination of raw ingredients and feeds with an array of mycotoxins is a frequent scenario around the world, which can be explained by the ability of molds to simultaneously produce different kinds of mycotoxins and because commodities may be concurrently, or in rapid succession, infected with different fungal species. In addition, composite feed is made up of a mixture of several raw ingredients, making it particularly vulnerable to multiple mycotoxins contamination [5,33].

When it comes to the reports considered for this review, several described this phenomenon within the regulated mycotoxins. In maize and products derived thereof, Chen et al. [69] found co-contamination with AFB1 and ZEN. Chilaka et al. [70] reported that 60% of maize samples were infected with at least two mycotoxins and FBs co-existing with DON in 11% of samples. ZEN and DON were simultaneously found by Dagnac et al. [71], who reported a frequent co-contamination of more than one mycotoxins in the samples under analysis. Jovaišienė et al. [72] found DON and ZEN co-occurring in all samples of silage and DON, ZEN, and T-2/ HT-2 co-occurred in all fermented silage samples. Kamala et al. [73] detected that 33% of the samples were contaminated with both AFs and FB1 + FB2. Kosicki et al. [15] frequently identified this phenomenon in their study, with the combination of DON and ZEN being the most prevalent, however, the co-occurrence of DON, T-2 and HT-2; ZEN, T-2 and HT-2; DON, T-2, HT-2, and ZEN; and DON and FMs were commonly found in maize. While in maize silage, apart from these groups, the co-existence of DON and OTA; DON, OTA and ZEN; ZEN and OTA; and T-2, HT-2, and OTA were also detected. Mngqawa et al. [74] reported the occurrence of a wide variety of mycotoxins in their samples with relevance to AFs and FBs. Finally, Murugesan [75] verified that 50% of samples were contaminated with more than one these analytes. In wheat, co-occurrence between ZEN and DON was found by Calori-Domingues et al. [76], in several samples. Already in soybeans and derived by-products, Egbuta et al. [77] showed that there was simultaneous occurrence of AFs and FB1. Regarding animal feed, Hu et al. [78] concluded that combinations of two mycotoxins were more frequent than three but highlighted the presence of AFB1, OTA, and ZEN. Kongkapan et al. [79] detected that AFs co-occurred with ZEN and AFB1 with DON. Kosicki et al. [15] frequently identified this phenomenon with combinations of DON and ZEN; DON, T-2 and HT-2; ZEN, T-2 and HT-2; DON, T-2, HT-2, and ZEN; DON and FMs; DON and OTA; DON, OTA, and ZEN; ZEN and OTA; and also T-2, HT-2 and OTA. Lastly, a high incidence of co-contamination was reported by Kovalsky et al. [43]. Globally, these results are in line with the statements that combinations of two mycotoxins occur more frequently [32,33]. It was verified that DON and ZEN along with AFB1 and FBs were commonly reported as co-existing in the reviewed samples, followed by DON and FBs as well as DON, ZEN, and HT-2/T-2.

Multiple mycotoxin contaminations pose great concerns since it is completely stated that adverse effects on animal health and performance can be additive and/or synergistic, which means that the overall toxicity is not only the sum but the multiple of the individual toxicities of the mycotoxins [5,80]. This means that the study of just one of these toxins provides insufficient information about the risk associated with a respective feedstuff and that attention toward mycotoxin co-occurrence should be increased [42,81]. Additionally, the use of raw materials from different types and origin contributes to increase the likelihood of multi-mycotoxin contamination, including nonregulated compounds, usually called “emerging mycotoxins”. The presence of conjugated mycotoxins, sometimes in amounts similar or even higher than the corresponding free mycotoxins, is another issue that deserves detailed attention, although it is out of the scope of this review. However, the potential for biological effects remains, and the toxicological potential can be substantial enhanced.

Currently, legislation over the world, including in Europe, only considers mycotoxin mono-exposure data and does not address relevant mycotoxin combinations, which is considered a loophole that should be taken into account in the future.

5. Mycotoxin Determination Methods

Evaluation of mycotoxin contamination on feed materials and feed is a direct requirement of the adoption of legislation limits for these compounds, providing information to producers, manufacturers, traders, and researchers [43,63,82,83]. Moreover, analytical data are fundamental for assessment of the potential risk to livestock and for global trade of their commodities, in the diagnosis of mycotoxicosis, and in monitoring mitigation strategies [84,85]. The determination of these contaminants is quite complex since they represent structurally diverse chemical groups which frequently appear in low concentrations, in a vast range of matrices, and sometimes in various combinations [56,57,85]. Nevertheless, sufficiently reliable, accurate, sensitive and selective methods are available for the qualitative and quantitative analysis of these secondary metabolites. As previously mentioned, feed may also contain the so-called “emerging mycotoxins” and/or conjugate mycotoxins, however, the analytical methods used for these are beyond the scope of the present review. Generally, the following three steps are involved in testing for mycotoxins: sampling, sample preparation, and analytical procedure.

5.1. Sampling

Obtaining sufficiently representative samples of a batch, in other words sampling, is crucial in the entire process of mycotoxins determination. In fact, this step accounts for the greatest source of error since the analytes under discussion often appear unevenly distributed and in trace levels [82,86]. Thus, sampling plans for different commodities were established by several agencies. In the EU, Regulation No 691/2013 amending Regulation No 152/2009 describes methods of sampling in feed materials for the official control of AFs and other mycotoxins. Briefly, representative laboratory samples are prepared from the sampling points by (i) selecting one or more characteristic lots, (ii) repeatedly collecting incremental samples at numerous single positions in the lot, (iii) forming an aggregate sample by combining the incremental samples by mixing, and (iv) preparing the final samples by representative dividing [87,88].

5.2. Sample Preparation

Sample preparation steps, grinding and subsampling, accomplish the conversion of the aggregate sample into a representative subsample, from which is prepared the laboratory sample. Ideally, a subsampling mill is used, performing these two processes simultaneously. However, a conventional grinder can also be used, where the aggregate sample is crushed, and then a subsample is taken. In the Annex II of the Regulation No 401/2006, it is possible to withdraw the criteria for sample preparation, although it is for the official control of the levels of mycotoxins in foodstuffs [89].

5.3. Analytical Procedure

For the majority of the methods, the analytical procedure includes a step of sample pretreatment where mycotoxins are (i) solvent-extracted from the laboratory sample, (ii) the obtained extract is further purified to remove unwanted co-extracted matrix components, and finally (iii) an optional sample concentration step takes place, before the final separation and detection steps.

The following sections provide an overview of the different sample pretreatment techniques and detection methods that have been reported for mycotoxin analyses in maize, wheat, soybeans, their by-products, and animal feed, published in the last years. Additionally, included are enzyme-linked immunosorbent assay as well as gas and liquid chromatography methods that are applied in this field of analysis.

5.3.1. Sample Pretreatment

Sample pretreatment makes it possible to obtain an enriched extract of the compounds of interest, as clean as possible, reducing matrix effects. As there is a great diversity in these techniques, a careful choice has to be made depending on the type of matrix, the physicochemical properties of the target analyte(s), and the final detection method.

Extraction with Solvents—Classical Solid-Liquid Extraction

In solid-liquid extraction (SLE), a mixture of solvents or a solvent is intended to extract the analyte quantitatively from the solid sample, with as little additional compounds as possible [82]. As the majority of the mycotoxins are soluble in polar and slightly polar solvents and insoluble in apolar solvents, mixtures of organic solvents, such as acetone, acetonitrile (MeCN), chloroform, dichloromethane, ethyl acetate, and methanol (MeOH) are often used. Small amounts of diluted acids (i.e., formic acid, acetic acid, and citric acid) or water are usually added to improve the extraction efficiency, since an acidic environment can break interactions between the toxins and other sample constituents like proteins or sugars, and water increases penetration of the solvent into the material [57]. Following the addition of the extraction solvent, shaking is used to favor the procedure, and then centrifugation or filtration is normally carried out, before concentration and/or cleanup steps [57,82]. Since the selection of a suitable extraction solvent is a challenging process during the optimization of a method, it is common to test different extraction mixtures in order to understand which one is capable of yielding the highest recovery rates [90]. For example, Sifou et al. [90] tried MeCN/water/formic acid (89/10/1 v/v/v), MeOH/water/formic acid (89/10/1 v/v/v), water/MeCN (84/16 v/v), MeCN/water/acetic acid (79/20/1 v/v/v), MeOH (100%), and MeCN (100%) to extract OTA in poultry feed samples, concluding that MeOH (100%) provided the most efficient extraction.

Instrumental Solvent Extraction—Microwave-Assisted Extraction

Microwave-assisted extraction (MAE) is a relatively quick process that through highly localized temperature and pressure causes selective migration of target compounds from the material to the surroundings using microwave energy [57,91]. A pretreatment technique using MAE followed by solid-phase extraction (SPE) was successfully developed by Chen and Zhang [91] to determine AFs in grains and grain products with liquid chromatography (LC) coupled to a fluorescence detector (FLD). To perform MAE, 12 mL of MeCN were added to 3 g of sample. This mixture was then heated at 80 °C for 15 min and 350 psi.

Instrumental Solvent Extraction—Ultrasonic Extraction

Ultrasonic extraction (USE) uses acoustic cavitation to cause molecular movement of the solvent and sample, aggressively improving the transfer of the analytes from the matrix into the solvent with improved efficiency. This technique is carried out in an ultrasonic bath and the duration of the ultrasound application depends on the matrix [82]. Generally, USE enables the reduction of the extraction time, consumes low solvent, is economical, and offers a high level of automation as compared with traditional extraction methods [82,92]. For example, Fan et al. [93] ultra-sonicated the sample together with MeCN 50% for 40 min at 40 °C in order to quantify DON and its derivatives in feed with an ultra-high-pressure liquid chromatography (UHPLC) coupled to the MS/MS method.

Cleanup Methods—Solid-Phase Extraction

Solid-phase extraction (SPE) is a technique commonly applied to solid matrices as a purification and/or concentration step, after the extraction of mycotoxins [57]. For the analysis of FB1 in soya bean meal and feed and T-2 in corn, Abdou et al. [63] developed a high performance liquid chromatography (HPLC) coupled to FLD (HPLC-FLD) in which the cleanup was performed using a Sep-Pak C18 column eluted with 15 mL of MeOH/water (60/40 v/v). In an LC coupled to tandem MS (MS/MS) method (LC-MS/MS), this C18 reverse-phase SPE column was only used by Chilaka et al. [70] to determine FBs, DON and 15-AcDON, ZEN and its metabolites, and HT-2 in maize. Relatively to SAX columns, they were merely employed to purify FBs and further detect them with HPLC-FLD, in soya bean seeds and processed soya bean powder [77] and in maize [73,94]. Plus, for example, grade polypropylene depth hydrophilic-lipophilic balance (GPD HLB) SPE column was applied in UHPLC-MS/MS to determine DON and its derivatives in feed after the extraction with MeCN 50% [93].

Enhanced Solid-Phase Extraction—Immuno-Affinity Columns

Immuno-affinity columns (IACs) are a particular case of SPE, based on the principle of antigen-antibody interactions [82,87]. IACs allow a highly selective purification, resulting in cleaner extracts with minimal interfering matrix components and low LOQ [82,95]. Although this is an automated sample cleanup method, it is time and solvent consuming, requires a high level of expertise, and the use of expensive disposable cartridges [82]. Moreover, in the presence of low concentrations of organic solvents, the denaturation of the antibodies is verified, which means that the extract must be an aqueous solution containing little or no organic solvent. Besides, there is the possibility of nonspecific interactions occurring due to cross-reactivity with other mycotoxins [57]. Differently, in multi-mycotoxins LC-MS/MS surveys, multiple IACs that allowed the specific capture of multiple mycotoxins were just used by Hu et al. [78] in feed, and Zhang et al. [96] in corn and wheat.

Enhanced Solid-Phase Extraction—Multifunctional Columns

Multifunctional columns (MFCs) allow the performance of a one-step purification process where compounds, such as proteins, fats, pigments, etc., that may interfere in the analytical method are retained in the solid phase, allowing the analytes of interest to pass through the column, at the same time [57,82,95]. The MycoSep®/MultiSep® columns, suitable for mycotoxins, are filled by adsorbents such as charcoal, celite, ion-exchange resins, polymers, and other materials, packed into a plastic tube between two filter discs. Overall this is a simple and quick process because it does not require the washing and elution steps [57,82]. Plus, MFCs eliminate the problems of irreversible adsorption or premature elution of analytes from the sorbent material [82]. In raw feed ingredients and feed analysis for mycotoxin contamination, MycoSep® 226 and 227 and MultiSep® 211 were the MFCs most used. For example, Wu et al. [97] applied MycoSep® 226 column to clean extracts for the subsequent determination of AFB1 in corn and by-products, wheat and by-products, soybean meal, and diverse feeds with HPLC-FLD. The MycoSep® 227 column was used for TRCs analysis in wheat with a GC-MS method [98]. Finally, Kosicki et al. [15] reported the employment of the MultiSep® 211 column to purify maize and feed extracts to further quantify FBs with LC-MS/MS. Additionally, the MycoSep® 224 and MycoSep® 225 columns were used for the determination of ZEN and DON, respectively, in wheat with HPLC coupled to diode array detection (DAD) (HPLC-DAD) [76].

Enhanced Solid-Phase Extraction—Molecularly Imprinted Polymers

Molecularly imprinted polymers (MIPs) represent a purification method based on the chemical creation of simulated binding sites using a template molecule for the analytes of interest, in a cross-linked polymer matrix. The target molecule is retained as a result of the shape recognition [57,82,87].

This technique has some potential given its high selectivity and great stability to heating and pH shifts, as well as being considered a cheaper alternative for cleanup [57,82]. However, their development and optimization require considerable time, which includes finding the best template molecule for imprinting and testing the resultant material in relevant applications [99]. Additionally, MIP are applied usually for determination of one analyte. Wang et al. [100] developed a solid-state electrochemiluminescence sensor that combined with the MIP technique allowed ultrasensitive determination of OTA. This sensor was successful applied to OTA determination in real corn samples, obtaining recoveries ranging from 88.0% and 107.9%.

Combined Extraction/Clean-up/Concentration—QuEChERS

The QuEChERS method, which means quick, easy, cheap, effective, rugged, and safe, even though it was not initially developed for the analysis of mycotoxins, has been successfully applied with this objective [87,101]. It involves a micro-scale extraction using MeCN, followed by a salting-out step of the analytes into the MeCN phase and then a purification based on a quick dispersive SPE. Basically, in the extraction step, MgSO4 and NaCl are used to reduce sample water, while in the purification step simple sorbent materials like primary secondary amine (PSA), C18, and alumina are used to retain co-extracted compounds [57,87,101]. With the aim of ensuring an efficient extraction of mycotoxins, the original method may suffer some modifications, for example, changes in the salts used, in their quantity or in the amount of C18, or addition of formic acid, water or MeOH to the extraction solvent. Plus, in dried matrices, a swelling step with water is recommended to make samples more accessible to the extraction solvent [57]. Xu et al. [102] applied a modified QuEChERS procedure to extract DON and its derivatives from wheat. The extraction was performed with water, MeCN, and salts (MgSO4 and NaCl), followed by the use of n-hexane to remove fat. An Oasis® MAX SPE cartridge was used to clean up the extract before the injection in the UHPLC-DAD system. This method allowed good recoveries to be obtained, between 80.0% and 102.2%. Bryla et al. [103] prepared wheat samples for multi-mycotoxins determination with UHPLC combined to high-resolution MS (HRMS), applying a modified QuEChERS procedure. The extraction solvent consisted of a mixture of water and 10% formic acid in MeCN, to which MgSO4, NaCl, sodium citrate dihydrate, and sodium citrate dibasic sesquihydrate were added. Then, to eliminate the lipid faction, hexane was used. Finally, MgSO4, C18 silica gel, neutral alumina, and PSA were added to perform cleanup. With [104], which aimed multi-mycotoxins analysis in feed, a QuEChERS-based approach performed in one step was chosen. So, water along with MeCN containing 1% acetic acid and MgSO4, NaCl, sodium citrate, and disodium citrate sesquihydrate were used. The extract was then analyzed using a UHPLC-HRMS system.

Combined Extraction/Clean-up/Concentration—Matrix Solid-Phase Dispersion

Matrix solid-phase dispersion (MSPD) consists of mixing a small amount of sample with an abrasive solid support material that has been derivatized to produce a bound organic phase on its surface (SPE sorbent), using a mortar and a pestle. According to Ye et al. [105], this technique was extensively applied to solid and semisolid samples for the extraction of drugs, pesticides, pollutants, among others. However, in mycotoxins quantification, MSPD is an unconventional alternative for classical SPE. In the field of analysis reviewed here, Ye et al. [105] developed a new simple and efficient MSPD procedure coupled to HPLC-DAD for the determination of FB1 and FB2 in corn. Various conditions were optimized, namely the type, volume, and pH of the eluting solvent, the dispersion sorbent, and the ratio of dispersing material to the matrix. They concluded that 10 mL of MeOH with 10 mM formic acid was the eluting solvent that provided better recoveries, with a C18 sorbent in a 2:1 ratio of sample:sorbent.

Combined Extraction/Clean-up/Concentration—Dispersive Liquid-Liquid Micro-Extraction

Dispersive liquid-liquid micro-extraction (DLLME) is a novel miniaturized extraction technique in which there is a rapid injection of a mixture of extraction solvent (organic) and dispersive solvent (water-organic miscible) into an aqueous solution that contains the analytes. This leads to the formation of a cloudy solution, and consequently the very large surface area formed between the two phases, and the analytes are enriched rapidly and efficiently in the extraction solvent. After centrifugation, they can be separated in the sedimented phase [57,82]. Although DLLME is more appropriate for aqueous samples, it is possible to apply this method to solid samples after an adequate pretreatment [57]. A novel, rapid and efficient two-step micro-extraction technique, based on the combination of ionic-liquid-based DLLME (IL-DLLME) with magnetic SPE, was developed by Zhao [106], for the preconcentration and separation of AFs in animal feedstuffs before HPLC-FLD. The ionic liquid extractant, 1-octyl-3-methylimidazolium hexafluorophosphate, was used in DLLME to extract AFs in the sample extracting solution medium. Then, hydrophobic pelargonic acid modified Fe3O4 magnetic nanoparticles were used as an efficient adsorbent to retrieve the AFs-containing ionic liquid from the DLLME step. Therefore, the target of the magnetic SPE was the ionic liquid instead of the mycotoxins. The authors compared the proposed method with other HPLC-FLD in which the cleanup was done with IAC and found no significant differences between data obtained by the two methods at a 95% confidence level.

5.3.2. Detection

A broad range of techniques can be used for this purpose and are generally divided into two categories which are screening methods and chromatographic methods coupled to different detectors. Currently, EU regulations do not require specific methods for the determination of mycotoxin levels, but any method of analysis should be characterized by the criteria defined in Annex III of the Regulation No 882/2004 [107]. Additionally, and although it is for the official control of the levels of mycotoxins in foodstuffs, Regulation No 401/2006 amended by Regulation No 519/2014 lays down, in the Annex II, the specific requirements that the method shall comply with in relation to individual mycotoxins [89,108].

Screening Methods

Usually, screening assays are developed in the form of kits which are extremely relevant tools for monitoring mycotoxin in feed ingredients and feed either by analysts with time constraints for making decisions or by those where other methods may not be available due to cost or situation [57,99]. These methods for single or whole mycotoxin classes compromise both qualitative tests that show the presence or absence of the target impurity and tests that yield semi-quantitative or quantitative results [57,109]. Immunoassay-based methods, biosensors, and non-invasive techniques are among the screening methods.

Immunoassay-Based Methods

Methods based on immunoassays are settled in the recognition of specific antibodies with mycotoxins that act like antigens [57,109]. Detection is typically facilitated by the presence of a marker. This compound can be radioactive, chromogenic or fluorescent and reacts with an enzyme, generally horseradish peroxidase (HRP). Immunoassays without the marker are based on the natural fluorescence of some mycotoxins, or in measures of conductivity [57]. These tests are preferably employed for the first level screening and survey studies on mycotoxin contamination due to their simplicity, cheapness, sensitivity, and selectivity, although cross-reactivity with structural analogues can occur [57,110]. Plus, they do not require sophisticated equipment or skilled personnel [109]. However, the signal obtained from these techniques can be influenced by co-extractives and by nonspecific interactions or matrix effects [99]. Additionally, in the new scenario of mycotoxin investigation, immunoassay-based methods may have a potential limitation related to the overall selectivity for only one mycotoxin or a small group of compounds, making difficult the simultaneous determination of different compounds and the detection of unknown toxins and conjugated mycotoxins [57,110]. Nevertheless, these methods are in continuous development in various formats, aiming to provide rapid, portable and easy to operate systems [110]. Enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay (LFIA). and fluorescence polarization immunoassay (FPIA) are included in this category of screening methods [57,99].

Enzyme-Linked Immunosorbent Assay

Enzyme-linked immunosorbent assay (ELISA) methods represent a commonly used immunoassay method to rapidly monitor mycotoxins and are routinely used by agro-food laboratories [57,82,101]. For all regulated mycotoxins there are commercially available ELISA microtiter plate kits that have well-defined applicability, analytical range, and validation criteria [82,109,110]. There are several ELISA formats commonly accessible, however, in this field of analysis the predominant form is the competitive one. This is a strategy normally used when the antigen is small and has only one antibody binding site (epitope), which is the case of mycotoxins [82,111,112]. The competitive format is characterized by the fact that the signal intensity is inversely correlated with the concentration of antigen in the sample [113,114]. Within this format type, it is possible to distinguish the classical competitive ELISA and the competitive inhibition ELISA [113]. The classical competitive format consists of the immobilization of the antigen standard on the surface of the plate. Then, there is an incubation of the antibodies directed against the target mycotoxin with the sample. The antigens in the sample will compete with the immobilized ones for binding to these antibodies. After the washing step, the antibodies bounded to the analyte are rinsed away [113]. In this case, detection can be performed directly or indirectly, which mainly determines the sensitivity of an ELISA. Direct detection uses an enzyme-labelled primary antibody that reacts with the antigen, while an enzyme-labelled secondary antibody with affinity for the primary antibody is used in indirect detection [111]. In the competitive inhibition format, the competition occurs between unlabeled antigens from samples and enzyme-labelled antigens (enzyme conjugate) for binding to an antibody directed against the target mycotoxin. In this format, the plate can be coated with capture antibodies with affinity for the analyte or for a primary antibody [111,113]. Common to both types of competitive assays is the addition of an adequate substrate that is allowed to incubate so that the enzyme that conjugated with the antibody or antigen (classical or inhibition format, respectively) can act and produce changes in a given parameter [111,112,114]. A large variety of substrates are available, and the choice depends upon the required assay sensitivity and the instrumentation available for signal-detection, although a mixture of hydrogen peroxide and a chromogen are usually applied [111,112]. Indeed, the simplest detection is a visual color change which provides qualitative and semi-quantitative results [57]. The last step of all assays is the addition of a stop solution causing the reaction between the enzyme and the substrate to stop. The results are usually determined in a plate reader. The signal intensity weakens as the sample antigen concentration increases, since a larger quantity of analyte results in either fewer enzyme-labelled antibodies bound to the antigen adsorbed to the plate (classical format), or less enzyme-labelled antigens bound to the antibody on the plate [112,113,114]. Advantages of ELISA include, in addition to the specificity of antibody-antigen binding, a relatively low limit of detection (LOD), high sample throughput with low sample volume, minimal cleanup procedures, and ease of application [82,109]. However, this method is not so reliable in the case of complex matrices, since it is quite time-consuming and the kits are for single use and are not suitable for field-testing [57,82,87,109]. In addition, the possibility of false positive and false negative results requires additional confirmatory analysis [82,109]. From Table A5, where the ELISA methods are reviewed, it is possible to conclude that all analytes were quantified with competitive ELISA after SLE mainly with an aqueous solution of MeOH or with water. Additionally, absorbance was the detection method most used, followed by optical density (OD), while FLD was only used by [115] to detect OTA in corn. Regarding mycotoxins studied with ELISA, the more targeted mycotoxins were AFs and DON.

Lateral Flow Immunoassay

Lateral flow immunoassay (LFIA) or membrane-based test strips are commercially available in the form of kits providing mainly visual qualitative results that indicate the presence or absence of a specific mycotoxin below a predetermined fixed level [57,116]. More recently, semi-quantitative detection is possible using a portable photometric strip reader [99]. In LFIA, the sample flows along the strip by capillary migration and two lines are formed, the test line whose intensity is inversely correlated to the mycotoxin concentration, and the control line that allows the assay validation [57,109]. This is an inexpensive screening tool that enables rapid, one-step, and in situ analysis [57,82,109]. Nonetheless, LFIAs often show false-positive results due to matrix interferences and reproducibility and sensitivity problems [57,109]. Chen et al. [69] developed and optimized a multiplex LFIA for the simultaneous on-site determination of AFB1, ZEN, and OTA in corn. This device provided both qualitative and quantitative results. LFIA was also used by Carvalho et al. [117] to evaluate mycotoxin presence in corn silages. FM, DON, AF, OTA, ZEN, and T-2/HT-2 were quantified using Reveal Q+ kits from Neogen Corporation. Beloglazova et al. (2017) developed a flow-through membrane–based assay for the screening of four mycotoxins DON, ZEN, OTA, and AFB1 in feed matrices. This approach allowed the separation of different test zones, and therefore minimized the across-influence.

Fluorescence Polarization Immunoassay

Fluorescence polarization immunoassay (FPIA) indirectly measures the rate of rotation of a fluorophore (tracer) in solution based on the competition between the free mycotoxin on the sample and the mycotoxin labelled with the tracer towards a specific antibody. When tracers bind to the antibodies their rotation is restricted, and consequently, the fluorescence polarization value increases. Therefore, if a sample has a high concentration in the target mycotoxin it competes with the tracer for the interaction with the antibody resulting in free tracers with a faster motion, in other words, a low fluorescence polarization signal. Basically, this value is inversely proportional to the amount of free mycotoxin in the sample. The FPIA is reliable, rapid, easy to perform, and relatively suitable for automation, however, their solution-based nature makes it less easy to use in field scenarios [57,109,118]. Concerning mycotoxin analysis in raw feed ingredients and feed, Li et al. [119] developed a homologous and high-throughput multi-wavelength FPIA for the multiplexed detection of DON, T-2, and FB1 in maize flour with an LOD of 242.0 µg/kg, 17.8 µg/kg, and 331.5 µg/kg, respectively.

Biosensors and Biosensor-Based Methods

Biosensors or immuno-sensors are analytical devices composed of one antibody which is a recognition element that reacts in a sensitive and selective way towards the target mycotoxin, and a transducing element which is responsible for converting the change of the physical variable produced by the reaction into a measurable signal [57,109]. In fact, antibodies are the most widely used recognition element in sensors but there is an extensive range of other components [87,120]. Alternatives to this classic element include, among others, enzymes, peptides, aptamers, and MIPs [87]. Similarly, techniques comprised of various transducing elements are available and are commonly applied with an optical or electrochemical nature, along with the piezoelectric and magnetic systems [120]. Optical detectors can be based on surface plasmon resonance, fluorescence, optical waveguide light mode spectroscopy, and total internal reflection ellipsometry. Electrochemical detectors are based on potentiometry with a carbon working electrode, differential pulse voltammetry, conductometry, etc. [57]. These methods are very promising since they provide results in a faster way, have a low price, high-throughput, greater sensitivity, and are portable [57,87,109]. However, they rely on specialist analytical equipment and their low selectivity and reproducibility make it necessary to confirm the results [57,87]. Plus, their applicability to routine analysis needs to be further investigated. Several authors developed biosensors and biosensors-based methods for mycotoxin analysis in raw feed ingredients and feed. For example, electrochemical immunosensors were designed to determine AFB1 in maize [121,122] and for FB1 and DON determination in the same matrix [123]. Plotan et al. [124] applied an innovatively biochip array technology to multi-mycotoxin semi-quantitative screening in a large variety of feed ingredients, obtaining an overall average recovery of 104%. An optical aptasensor was developed based on the hybridization chain reaction amplification strategy and fluorescent perylene probe/DNA composites for ultrasensitive detection of OTA [125]. The application to corn samples demonstrated the feasibility and potential of the proposed enzyme-free amplification method in the practical applications of agricultural products. Wang et al. [126] developed a novel and ultrasensitive aptamer-based biosensor for the detection of AFB1 in corn. For this, fluorescent nitrogen-doped carbon dots were synthesized and assembled on aptamer-modified gold nanoparticles.

Noninvasive Methods

Some noninvasive methods have been developed to assess mycotoxin contamination allowing simple, rapid, and in situ analysis. These kinds of methods enable decisions to be made promptly and avoid possible loss of an entire lot. However, due to the high matrix dependency and lack of appropriate calibration materials, their application is still limited. The nondestructive approaches include infrared spectroscopy (IR) techniques and Raman spectroscopy [57,82].

Infrared Spectroscopy

Promising IR techniques include near-infrared (NIR) spectroscopy either in combination or not with Fourier-transform (FT-NIR). Basically, NIR spectroscopy is based on the measurement of the absorption or reflectance of a given incident NIR radiation in the sample. The exposition to radiation in this region of the spectrum causes a change in the energy of the chemical bonds involving hydrogen (for example, C-H, N-H, O-H, and S-H). However, the bands observed in the NIR spectral region are very difficult to assign to specific compounds because of the complexity of the samples and also due to spectra overlapping and interference from other functional chemical groups. This implies the application of modern chemometrics methods in the calibration development process. The detection of the NIR radiation absorbed by the sample is conducted by transmittance, reflectance, interaction, and/or transflectance measurement [57,82]. This promising technique requires minimal or no sample pretreatment and it is environmentally friendly, and therefore it does not require reagents and does not produce chemical waste [82,127]. In addition, NIR is highly accurate, needs little expert training, and has the ability to analyze both large and small quantities of feeds which avoids errors associated with inconsistent sampling [128]. Beyond the difficulties in the interpretation of spectral data posed by this technique, other drawbacks are related to the fact that NIR is only useful at high contamination levels, as well as the system is heavily dependent on the establishment of an accurate calibration procedure [57,128,129]. A nondestructive detection of DON by ultraviolet-visible near-infrared diffuse reflection spectroscopy in unprocessed, solid maize kernels was investigated by Smeesters et al. [130]. They proposed a two-stage measurement methodology enabling efficient monitoring of the local DON-contamination on a large number of maize kernels. Mignani et al. [131] presented a novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize at regulatory limits. They investigated the classification ability of a decision tree at 1750 µg/kg for DON in maize, which corresponds to the regulatory limit set by the EU for unprocessed maize in food.

Raman Spectroscopy

The principle behind Raman spectroscopy relies on the irradiation of matter with monochromatic light to further detect the loss of energy in the form of scattered light. Thus, information about the vibrational transition energy of the molecules is provided by this technique. Symmetrical vibrations of the covalent bonds in nonpolar groups (e.g., C = C) enhance the sensitivity of Raman spectroscopy [129,132,133]. This method provides a unique expression of the molecular structure, and therefore it is considered to be a molecular fingerprint providing more useful qualitative and quantitative information on chemical functional groups of mycotoxin compounds and its derivatives than the conventional spectroscopic techniques [132,133]. Despite this advantages, Raman spectroscopy has received remarkably little attention for detection of mycotoxins in grains and oilseed [133]. In 2016, Lee and Herrman [134] investigated the potential and feasibility of a surface-enhanced Raman spectroscopy (SERS) method as an alternative accelerated technique to screen ground maize contaminated with FMs. Chemometric models developed based on SERS spectra showed an acceptable predictive performance and ability for qualitative and quantitative analysis.

Chromatographic Methods

Chromatographic separation coupled to a suitable detection system is the most widely used strategy to quantitatively analyze mycotoxin contamination, unambiguously confirm positive findings, and also serve as a reference method to validate other tests. These are methods which are highly selective, accurate, and reproducible that need expensive instrumentation and chromatographic expertise. In feed analysis, LC is the most common method, although gas chromatography (GC) and thin layer chromatography (TLC) are still considered [82,109,110].

Thin Layer Chromatography

Contrary to what happens in developed countries, TLC is a method that is still commonly used in countries under development, especially if coupled to an ultraviolet (UV) or fluorescence scanner [82,99]. TLC allows qualitative and semi-quantitative determination of naturally fluorescent mycotoxins. The qualitative confirmation can be done through the retention factor value and the fluorescence color after comparison with an external standard. In semi-quantification, the sample is compared with authentic standards using the visual estimation of fluorescence of the separated spots under long wavelength UV light. Therefore, with this approach precise and reliability results depend directly on skilled and experienced people. Quantification is mainly achieved by measuring fluorescence intensity or absorbance when separated spots on the TLC plate are exposed to UV light. TLC can be applied both in one- and two-dimensional formats. This method makes possible rapid analyze of several samples in a short period of time, has a low cost per sample analyzed, and easy estimation of contamination levels [82]. However, low sensitivity and reproducibility along with the need of large quantities of solvent, intensive laboratory procedures. and difficulties in automation have led TLC to be commonly replaced by other chromatographic techniques [82,87]. Betancourt and Denise [135] applied this method to screen AFs contamination in corn hybrids. The TLC plates were exposed to UV light at a short wavelength (250 nm) and visual comparison to standards allowed the identification of positive samples. Mona et al. [136] performed AFB1 detection in cattle feed with TLC, where standard and test samples were inspected under a long-wave UV lamp (360 nm).

Gas Chromatography

In GC, volatile compounds are separated into open tubular columns coupled to a variety of general or specific detectors. GC coupled with an MS detector (GC-MS) simultaneously allows the identification and quantification of compounds, and based on these reasons is the first choice in mycotoxin analysis [57,137]. The methods of GC-MS are described mainly for TRCs and mainly in wheat, generally after extraction of the compounds with MeCN, cleanup with MFCs (Table A6) and derivatization [57,87,109]. The derivatization procedure aims to counteract the low volatility and the high polarity of many mycotoxins, and therefore allow their analysis. The silylation and acylation reactions are the most common approaches, converting mycotoxins into more volatile, less polar, and thermally more stable derivatives. In silylation, the introduction of a silyl group by a silyl reagent is valuable for the MS applications because it produces either more interesting diagnostic fragments or ions with particular characteristic ions for single ion monitoring (SIM). The derivatization method is applied majorly when detecting mycotoxins with GC-MS. Alternatively, acylation is preferable when acylated compounds are more stable than the silylated compounds [57,137]. (Table A6). The GC-MS methods allow for the reliable and sensitivity determination of multi-mycotoxins in one single run.

Liquid Chromatography

Liquid chromatographic methods are the mainstay separation method for mycotoxin analysis. Several variations of LC are available offering good sensitivity, high dynamic range, and versatility. On the other hand, these methods suffer from portability, cost, and issues related to the sample type such as the matrix effect, the choice of calibration, and the sample preparation [82,87].

HPLC is a well-established and prevalent method for the identification and quantification of mycotoxins [109]. To date, both normal- and reverse-phase columns have been used for this purpose. However, the great majority of separations are performed on reverse-phase columns because the majority of mycotoxins are soluble in polar organic solvents such as methanol, acetonitrile, water, and in their mixtures. This HPLC procedure relies mostly on C18 columns and mobile phases composed of mixtures of water with MeOH and/or MeCN in proper ratios [82,99]. HPLC has high separation power, is easy to use, and suitable for automation [82]. Traditionally, this chromatographic method is equipped with spectrometric detectors like UV (HPLC-UV) and fluorescence that depend on the analyte. From Table A7, it is possible to see that HPLC-UV was not used only once. The studies [97,138,139,140] applied this technique to quantify DON, ZEN, and OTA in raw feed ingredients and feed. On the contrary, FLD was abundantly used, after SLE mostly with MeOH and cleanup by IACs, to analyze mainly AFs, and also FBs, T-2, ZEN, and OTA in those matrices. Commonly, pre- or postcolumn derivatization procedures are used to improve mycotoxins fluorescence properties, and consequently increase sensitivity. In the precolumn approach, trifluoroacetic acid is majorly applied, converting AFs into their corresponding hemiacetals derivatives which have stronger fluorescence. However, since this is a toxic and corrosive chemical and the derivatives formed have relative instabilities, this is not the preferred method. Additionally, postcolumn derivatization offers the advantage of being automated [82]. Therefore, this strategy is applied more to detect mycotoxins (Table A7). Different methods can be used, such as bromination by an electrochemical cell (Kobra cell) which is the addition of bromide or pyridinium hydrobromide perbromide and the formation of an iodine derivative. Although these postcolumn derivatization approaches produce molecules that are more fluorescent than their precursors, the use of bromine or iodine requires extra pumps and chemical reactors for the HPLC system and it takes a long time to prepare the mobile phase. The use of postcolumn photochemical reactors is a novel derivatization methodology where the outlet of the HPLC is simply connected to ultraviolet permeable polytetrafluoroethylene tubing and wrapped over a high-intensity UV lamp. Stable and highly fluorescent derivatives are yielded from the reaction of mycotoxins with hydroxyl radicals from water, generated from the UV light irradiation. This alternative technique is simple, the response is linear, it has reproducibility and it does not require chemical reagents, additional pumps or electrochemical cells, and therefore it is more economical than the conventional postcolumn derivatization [82]. Lee et al. [141] applied photochemical derivatization to enhance AFs, OTA, and ZEN fluorescence in feed, Ok et al. [142] used it to increase this property in AFs present in corn, and Wu et al. [97] applied it to detect AFB1 in feed and raw feed ingredients (Table A7).

Recently, the use of HPLC-DAD techniques has increased but they are incapable of dealing with a large number of analytes in complicated samples [82]. This technique was used to quantify DON and ZEN in wheat [76], DON and 3-AcDON in corn and feed [143], and DON in wheat and their by-products [144,145] (Table A7).

The UHPLC/UPLC methods have been newly introduced. Columns filled with uniform particles of small size and instruments with high-pressure fluidic modules are used. This rising technique allows decreased run times and solvent consumptions, resulting in more efficient chromatographic separations with higher sensitivity and resolution [57,82,99]. UHPLC/UPLC was explored by several authors to detect mycotoxins in feed and raw ingredients for feed (Table A7 and Table A8).

LC can be coupled to MS (LC-MS) or to MS/MS, which occurs via atmospheric pressure ionization (API) techniques such as electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI). This has resulted in a very versatile analytical tool whose applications include not only single mycotoxin analysis, but, most importantly, true multi-mycotoxin determination. This is a current trend in this field since commodities can be contaminated with more than one mycotoxin, as discussed earlier.

Relatively to API methods, ESI is mostly well suited for the analysis of polar compounds. APPI is highly effective for the analysis of medium- and low-polar substances and APCI is often more sensitive than the majority of polar functional groups which are of moderate polarity [57,146]. Normally, as a consequence of API, protonated or deprotonated molecules can be produced [57]. With respect to ESI and mycotoxins, the protonated precursor ions are mainly formed, but additional information can be found in [147,148,149,150]. In Table A8, LC-MS methods are reviewed and it can be seen that the use of ESI interface is predominant in multi-mycotoxins applications. However, APCI and APPI interfaces usually have better performances in terms of chemical noise and signal suppression than ESI, despite being less used [146]. Normally, APCI is applied only to mycotoxins of the TRCs group, although its feasibility has also been examined in a few multi-mycotoxin methods [57]. Actually, Hofgaard et al. [151] employed this interface to quantify not only TRCs, but also ZEN and FBs in wheat. Nowadays, most of the instruments offer combined interfaces (ESI/APCI) which have a compromised sensitivity between both modes, however, offer the main advantage of enabling the detection of polar and nonpolar analytes in a single run [57]. In LC-MS/MS, the ionization process may have some problems and the analytical signal is unpredictable and it is affected by the matrix effects. Therefore, the use of isotope-labelled internal standards that are not naturally occurring in the samples and have identical chemical properties to the analytes will compensate for both losses during the sample pretreatment steps and for ion suppression or enhancement effects in the ion source. Despite being the best approach, these standards are only available for a limited number of mycotoxins and are very expensive [57,152].

The LC system can be combined with a single quadrupole, an ion trap (IT), a triple quadrupole (QqQ), or with a hybrid quadrupole/linear ion trap detector (QTRAP) [57,153]. The LC-MS/MS is enabled by QqQ or QTRAP [146]. As can be seen in Table A8, QqQ instruments by far surpassed by remaining analyzers, perhaps, due to improved signal to noise ratios from the additional selectivity of the second MS step [146]. In this field of analysis, IT was only used to detect multi-mycotoxins in finished feed, maize, and maize silage [43].

HRMS can be performed using time-of-flight (TOF) and Orbitrap analyzers, that have a high mass accuracy, high resolving power, and high dynamic range [57,104]. Even when these instruments are operating in full scan mode they are able to provide high sensitivity, which makes the identification of analytes easier even when they are present at very low levels. Additionally, they have rapid spectral acquisition speed that allow them to record virtually an unlimited number of compounds. Between the authors that chose these detectors (Table A8), TOF was more frequently applied than Orbitrap, despite knowing the advantage of this last detector to screen unknown compounds in full scan mode, in parallel to the quantification of known analytes [154].

Relying on the strengths of the exceptional sensitivity and separation capabilities of modern LC-MS equipment, “dilute and shoot” (DaS) methods have been developed [87]. They rely on sample dilution followed by a direct injection and they avoid a cleanup stage, which limits the potential loss of analytes. This is a rapid method that covers a wide range of polarities, and therefore allows a wide range of mycotoxins and other secondary metabolites to be determined. On the negative side, DaS has the risk of having excessive and unpredictable interference from the matrix which is a limitation as it can potentially overwhelm the sensitivity of the instrument [82,87,104,155].

6. Final Considerations

The world demand for commodities commonly used in the manufacture of animal feed, such as maize, wheat, and soybeans has been steadily rising in the last years, driven by higher demands for livestock production. This has led to an increased awareness of animal feed safety issues due to the fact that feed consumption is a potential route for chemical hazards to enter the human food chain. Within these hazards, mycotoxins deserve some prominence and AFs, FMs, OTs, TRCs, and ZEN are the most prevalent and worrying classes of compounds.

Mycotoxins represent a serious threat to the feed supply chain, animal health, and, in the limit, human health. So, regulatory agencies established limits to keep their levels in animal feeds under control. In this way, the protection of all parts likely affected by the presence of these toxins is somehow assured. The legislation (regulation or recommendation) applicable in the EU to products intended for livestock feed is very strict and can block exportation of feed commodities from developing countries to their European trading partners. A verified limitation in the legislation on mycotoxins is the fact that it does not consider the frequently reported and worrying scenario of multi-mycotoxin contamination of single commodities and animal feed.

The review of published reports from 2016 to 2018 on contamination of maize, wheat, soybeans, their by-products, and animal feed with legislated mycotoxins and their metabolites, made us realize that this is an issue that is increasingly relevant. In general, it was verified that the common association of maize with AFs and FMs, and of wheat with DON, favored the investigation of these mycotoxins. However, mycotoxin formation is a complex and multifactor phenomenon whose worldwide contamination and distribution patterns are predicted to be significantly affected by climate change because of the appearance of favorable environmental conditions for fungal proliferation in uncommon places. Therefore, the presence of mycotoxins is unpredictable, and therefore multi-mycotoxins surveys end become more realistic and preferred, since the study of only some of these contaminants provides insufficient information about the risks associated with a respective feedstuff. In addition, since co-occurrence was commonly reported in the years under review, it is expected that this phenomenon will be further addressed in the coming years. Specifically, regarding soybean and their by-products, they are less targeted as compared with other matrices because these fungal toxins are not considered to be very problematic in this commodity.

With respect to testing methods, in the future, it is expected that there will be an expansion of sample pretreatment techniques that are aimed at the minimization and automation of these procedures, although classical methods like SLE will probably still be applied prior to some detection approaches, as verified in this review. Concerning LC, similar to what happened in last years, the use of the HPLC and LC-MS methodologies to quantify mycotoxins in animal feed, will perhaps continue side-by-side. Furthermore, detection methodologies that target several mycotoxins will surely gain ground, and, probably, developments will occur in screening methods that allow analysis in the field.

Finally, in our point of view, the mycotoxins field of analysis within the matrices in review is not expected to decline and the industries of animal production systems will become even more aware of the relevance of these contaminants in order to improve the quality and safety of products intended for animal feed.

Acknowledgments

S.C.C. acknowledge FCT for the IF/01616/2015 contract. Authors would like to thank Rotoquimica Lda for technical support in graphical preparation.

Appendix A

This appendix includes tables of mycotoxins occurrence in maize, wheat, soybeans, and their by-products.

Table A1.

Occurrence of mycotoxins concerned in the EU legislation and its metabolites in maize and in the derived by-products.

Sample Country of Origin Year of Collection Mycotoxin/ Metabolite Total Samples Positive Samples Year of Publication Reference
% Range (µg/kg) Mean (µg/kg)
Corn Argentine NM T-2 1 100 NM NM 2016 [63]
Corn South Korea 2014 DON 40 22.5 ≥3.3–232.56 190.78 2016 [143]
3-AcDON ND ND ND
Corn China 2013–2015 AFB1 220 80 ≥0.5–25.5 Φ 3.9 2016 [97]
ZEN 96 ≥10–1442.5 251.5
DON 98 ≥100–4320.9 755.1
Corn germ meal AFB1 34 76 ≥0.5–14.1 7.4
ZEN 85 ≥100–1518.2 495.7
DON 91 ≥100–4402.7 1549.6
Corn grain Brazil 2013 FB1 15 80 16–1732 289 2016 [156]
FB2 47 32–743 254
Corn grits FB1 15 100 88–2727 719
FB2 100 48–1454 386
Corn hybrid 30V46 Mexico NM FMs NM NM NM 370 2016 [135]
AFs NM NM 2.0
Corn hybrid Oso FMs NM NM 250
AFs NM NM 13.0
Corn hybrid Leopardo Mexico NM FMs NM NM NM 660 2016 [135]
AFs NM NM 7.5
Corn meal Brazil 2013 FB1 15 100 75–5439 1305 2016 [156]
FB2 93 52–1481 651
Corn hybrids 2B688RR and 30K73Hx—winter storage Brazil 2012 AFs 22 68 2.8–14.5 76.1 2016 [157]
AFB1 9 0.49–6.5 NM
AFB2 55 8.8–14.5 NM
AFG1 ND ND ND
AFG2 23 2.9–4.1 NM
Corn hybrids 2B688RR and 30K73Hx—summer storage 2012/13 AFs 82 85 3.0–197.5 45.8
AFB1 35 0.6–76.5 Φ NM
AFB2 62 9–169.2 NM
AFG1 28 2.1–17.7 NM
AFG2 66 2.8–96.1 NM
Crushed yellow corn Iran NM AFB1 16 87.50 NM–45.46 Φ 9.94 2016 [158]
Domestic DDGS China 2013–2015 AFB1 24 100 ≥0.5–13.6 10.4 2016 [97]
ZEN 100 ≥100–529.6 351.9
DON 96 ≥100–2146.8 1319.5
Imported DDGS AFB1 37 86 ≥0.5–15.2 9.3
ZEN 95 ≥100–510.3 325.3
DON 97 ≥100–3561.0 1483.6
DDGS NM NM DON 5 40 435–724 579.5 2016 [159]
15-AcDON ND ND ND
3-AcDON ND ND ND
AFB1 ND ND ND
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
FB1 20 80 -
FB2 ND ND ND
FB3 ND ND ND
HT-2 ND ND ND
T-2 ND ND ND
OTA ND ND ND
ZEN 20 1 -
α-ZEL ND ND ND
β-ZEL ND ND ND
Ground maize South Africa NM AFB1 3 ND ND ND 2018 [160]
AFB2 100 0.474; 3.648 1.674
AFG1 33 3.479 -
AFG2 100 2.805; 98.486 34.892
ZEN 100 <LOQ; 0.680 0.448
α- ZEL 100 1.329; 6.765 3.556
β- ZEL 100 2.159; 3.118 2.602
FB1 100 26.036; 379.242 147.236
DON 100 4.339; 81.612 36.347
3-AcDON + 15-AcDON 67 0.802; 2.177 1.489
HT-2 100 8.576; 312.952 162.564
OTA ND ND ND
Maize NM NM AFB1 6 NM NM 18 2016 [161]
Maize Tanzania 2012 AFs 120 45 0.1–269 NM 2016 [73]
FB1 + FB2 85 49–18273
Maize Tanzania 2010 FB1 72 100 63.26–213.15 157.88 2016 [162]
Maize Kenya 2014 AFB1 497 76 ≥1.0–1137.4 Φ 16.0 2016 [61]
Maize China 2012–2014 AFB1 98 69 ≥0.5–300.0 Φ 47.9 2016 [138]
ZEN 72 85 ≥10–1613.7 260.6
DON 45 84 ≥100–19811.0 Φ 1394.4
Maize Serbia 2013 DON 600 2.5 260.1–1388 642.3 2017 [163]
2014 600 96.0 260.4–9050 Φ 363.3
2015 600 15.5 252.3–6280 921.1
Maize Zambia NM AF 250 NM 1.3–107.6 Φ 25 2017 [164]
Maize Norway NM AFB1 13 46 0.13–100.4 Φ 31.1 2016 [165]
AFB2 15 7.3–17.4 12.4
AFG1 46 0.10–0.10 0.10
AFG2 ND ND ND
AFs 15 107.88–114.95 111.4
FB1 100 31–8750 1001
FB2 100 5–3540 354
FB1 + FB2 100 36–12290 1355
Maize Poland 2011–2014 DON 295 88 ≥1.0–6688 766 2016 [15]
T-2 67 ≥0.2–550 Φ 22.8
HT-2 68 ≥0.7–1583 Φ 37.6
ZEN 92 ≥0.07–521 75.3
FMs 83 58 ≥1.6–1885 272
OTA 113 11 ≥0.13–86.0 13.9
AFs 45 2 0.18 -
Maize Qatar NM AFs 10 70 NM–120 33 2018 [166]
OTA 40 NM–350 Φ 181
Maize Serbia 2015 AFB1 180 57.2 1.3–88.8 Φ 11.4 2017 [167]
AFB2 13.9 0.60–2.8 1.3
AFG1 5.6 1.8–28.5 8.6
AFG2 2.8 2.2–7.5 3.8
AFs 57.2 1.3–91.4 12.7
Maize NM NM DON 5 40 410–686 548.0 2016 [159]
15-AcDON ND ND ND
3-AcDON 20 12 -
AFB1 ND ND ND
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
FB1 20 43 -
FB2 ND ND ND
FB3 ND ND ND
HT-2 ND ND ND
T-2 ND ND ND
OTA ND ND ND
ZEN 20 2 -
α-ZEL ND ND ND
β-ZEL ND ND ND
Maize Egypt 2014–2015 AFB1 79 16 0.3–197.5 Φ NM 2017 [168]
AFB2 5 0.42–9.8
DON 8 26–807
FB1 51 1–2453
FB2 18 1.3–386
FB3 8 1.5–286
OTA 3 2.8–11
ZEN 13 0.46–184
Maize Pakistan 2012–2013 OTA 120 69.7 5.18–198.68 118.23 2017 [169]
Maize Croatia 2014–2015 T-2/HT-2 38 57.9 31.2–336.2 Φ 101 2017 [170]
Bosnia and Herzegovina 30 53.3 28.7–321.2 Φ 125.2
Maize China NM AFB1 41 39.0 <0.03–>30 33.0 2018 [171]
Maize Egypt 2014–2015 AFB1 79 16 0.3–197.5 Φ NM 2017 [168]
AFB2 5 0.42–9.8
DON 8 26–807
FB1 51 1–2453
FB2 18 1.3–386
FB3 8 1.5–286
OTA 3 2.8–11
ZEN 13 0.46–184
Maize South Africa 2006–2017 AFs 282 9.6 >0.5–14 NM 2018 [172]
ZEN 308 47.1 >LOD–6276
DON 314 80.6 >LOD–9176
T-2 273 0.7 >LOD–80
FB1 + FB2 281 80.1 >LOD–16932
OTA 269 7.4 >LOD–95
Maize and maize-based products Tanzania 2013/14 AFs 160 32 2.1–16.2 3.4 2016 [173]
FMs 39 0.4–62.0 5.6
Maize kernel China 2012–2014 FB1 225 74 ≥4–28285 1878 2016 [94]
FB2 ≥3–11809 853
FB1 + FB2 ≥3–37653 2728
Maize meal NM NM DON 5 40 412–787 599.5 2016 [159]
15-AcDON ND ND ND
3-AcDON 20 13 -
AFB1 ND ND ND
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
FB1 20 45 -
FB2 ND ND ND
FB3 ND ND ND
HT-2 ND ND ND
T-2 ND ND ND
OTA ND ND ND
ZEN 20 2
α-ZEL ND ND ND
β-ZEL ND ND ND
Maize panel NM NM AFB1 24 29.2 ≥0.005–75.0 Φ 22.1 2016 [39]
Silage Iran NM AFB1 103 94.17 NM–71.57 Φ 3.86 2016 [158]
Silage Brazil NM FM 36 ND ND ND 2016 [117]
DON 2.7 300
AF 77.7 <2.0–7.3 NM
OTA 33.3 <2.0–6.9 NM
ZEN 22.2 <25.0–91.3 NM
T-2/HT-2 ND ND ND
Silage Iran 2014 AFB1 70 25.7 2.53–18.65 10.98 2016 [174]
Silage Spain NM DON 148 10.8 143.1–6685.6 1685.4 2016 [71]
FB1 9.5 10.4–994.1 212.4
FB2 22.3 10.7–137.0 50.9
ZEN 21.6 63.5–820.2 221.1
α-ZEL 2.0 606.6–2889.4 1833.3
β-ZEL 2.7 326.1–1721.1 779.3
3-/15-AcDON ND ND ND
HT-2 ND ND ND
T-2 ND ND ND
OTA ND ND ND
AFB1 ND ND ND
AFG1 ND ND ND
Silage Italy 2011–2013 DON NM NM NM 49 2016 [175]
Silage England 2014 DON 29 70 ≥10.0–7111 603 2016 [176]
ZEN 55 ≥10.0–3901 Φ 209
FB1 24 ≥1.0–107 10.4
FB2 24 ≥1.0–24 2.50
T-2 ND ND ND
HT-2 ND ND ND
AFB1 ND ND ND
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
OTA ND ND ND
Silage Poland 2011–2014 DON 143 86 ≥1.0–7860 853 2016 [15]
T-2 48 ≥0.2–31.2 2.21
HT-2 73 ≥0.7–204 35.9
ZEN 87 ≥0.07–1133 84.4
FMs 21 52 ≥1.6–108 23.8
OTA 61 36 ≥0.13–10.2 2.16
AFs 26 4 0.15 -
Fresh silage Lithuania NM AFs 20 15 NM 0.94 2016 [72]
ZEN 100 206.88
DON 100 1640.0
T-2/HT-2 45 40.21
OTA ND ND
Silage after 3 months of storage AFs 20 8 NM 16.86
ZEN 100 880.04
DON 100 2600.0
T-2/HT-2 100 141.48
OTA NM 29.15
Silage after 8 months of storage AFs 20 75 NM 20.05
ZEN 100 380.42
DON 100 1118.3
T-2/HT-2 100 147.25
OTA NM 18.95
Silage Iran NM AFB1 40 40 0.3–8.24 4.47 2016 [177]
AFB2 32 0.015–7.24 3.53
AFG1 28 0.05–6.04 2.60
AFG2 28 0.03–2.9 1.30
WDG NM NM DON 5 40 218–276 247.0 2016 [159]
15-AcDON ND ND ND
3-AcDON ND ND ND
AFB1 ND ND ND
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
FB1 20 35 -
FB2 ND ND ND
FB3 ND ND ND
HT-2 ND ND ND
T-2 ND ND ND
OTA ND ND ND
ZEN 20 1 -
α-ZEL ND ND ND
β-ZEL ND ND ND

Φ—Exceeds the EU legislative level; ND—not detected; NM—not mentioned; LOD—limit of detection.

Table A2.

Occurrence of mycotoxins concerned in the EU legislation and its metabolites in wheat and in the derived by-products.

Sample Country of Origin Year of Collection Mycotoxin/ Metabolite Total Samples Positive Samples Year of Publication Reference
% Range (µg/kg) Mean (µg/kg)
Silage Iran NM FMs 35 9 NM–0.4 0.034 2016 [178]
ZEN 88 NM–10.40 3.77
Spring wheat Lithuania 2013/14 DON 114 99 ≥100–10644.0 Φ 798.77 2016 [179]
Wheat Pakistan 2014 AFB1 185 26.0 0.05–4.78 0.51 2016 [180]
AFB2 7.0 0.02–0.48 0.02
AFG1 ND ND ND
AFG2 ND ND ND
AFs 26.0 0.02–5.26 0.53
Wheat Norway NM DON 25 84 5–94 28.3 2016 [165]
HT-2 36 10–23 15.0
T-2 16 11–12 11.5
HT-2 + T-2 24 20–34 19.38
Wheat China 2012–2014 AFB1 27 63 ≥0.5–54.5 Φ 11.0 2016 [138]
ZEN 36 83 ≥10–1278.9 215.0
DON 29 69 ≥100–3536.2 1262.5
Wheat China 2013–2015 AFB1 24 50 ≥0.5–4.0 1.1 2016 [97]
ZEN 92 ≥10–161.8 120.2
DON 100 ≥100–1048.1 647.1
Wheat Belgium and Hungary NM DON 16 100 NM–1113 244 2016 [181]
Wheat Croatia 2014–2015 T-2/HT-2 24 33.3 32.5–123.4 55.8 2017 [170]
Bosnia and Herzegovina 28 21.4 31.5–105.0 59.0
Wheat China 2013 DON 1 100 1690 - 2016 [140]
Wheat and wheat bran Qatar NM AFs 10 40 NM–14 9 2018 [166]
OTA 60 NM–45 3
Wheat bran Iran NM AFB1 41 97.56 NM–56.13 Φ 2.94 2016 [158]
Wheat bran NM NM AFB1 35 NM 9–31 Φ 15 2016 [161]
Wheat bran China 2013–2015 AFB1 55 73 ≥0.5–10.9 2.6 2016 [97]
ZEN 98 ≥10–329.0 148.1
DON 98 ≥100–3503.2 951.2
Wheat bran China 2013 DON 1 100 2050 - 2016 [140]
Wheat bran China NM AFB1 30 10.0 <0.03–19.9 9.8 2018 [171]
Wheat dust Belgium and Hungary NM DON 16 100 607–14043 Φ 5012 2016 [181]
Wheat grains Slovakia 2013 DON 178 82.0 NM–5100 740 2016 [182]
Wheat shorts China 2013 DON 1 100 2940 - 2016 [140]
Wheat shorts and red dog China 2013–2015 AFB1 20 90 ≥0.5–10.5 5.3 2016 [97]
ZEN 100 ≥10–280.3 207.7
DON 100 ≥100–1319.5 572.0
Winter wheat Lithuania 2013/14 DON 30 67 ≥100–1393.0 383.98 2016 [179]

Φ—Exceeds the EU legislative levels; NM—not mentioned; ND—not detected.

Table A3.

Occurrence of mycotoxins concerned in the EU legislation and its metabolites in soybeans and in the derived by-products.

Sample Country of Origin Year of Collection Mycotoxin/Metabolite Total Samples Positive Samples Year of Publication Reference
% Range (µg/kg) Mean (µg/kg)
Processed soya bean powder Nigeria NM AFs 20 45 NM–813 Φ NM 2016 [77]
FB1 100 NM–4286 NM
OTA 40 NM–125 NM
Soy Croatia 2014–2015 T-2/HT-2 7 28.6 32.3–33.8 33.1 2017 [170]
Bosnia and Herzegovina 5 40.0 30.6–42.5 36.6
Soya bean meal USA NM FB1 1 ND ND ND 2016 [63]
NM AFB2 1 ND ND ND
Soya bean meal Pakistan 2012–2013 OTA 120 63.3 4.33–211.16 113.43 2017 [169]
Soya bean seeds Nigeria NM AFs 21 100 111 Φ–3430 Φ NM 2016 [77]
FB1 100 33–2270 NM
OTA 23.8 NM–51 NM
Soybean and soybean by-products Brazil 2010–2011 AFB1 30 43.3 LOQ–7.9 0.5 2018 [183]
ZEN 80 LOQ–104 16.7
Soybean meal Pakistan 2012/13 AFB1 14 64 0.09–105.9 Φ 4.90 2016 [184]
AFs LOQ–135.3 Φ 5.20
ZEN 71 0.15–120.89 18.90
Soybean meal China 2013–2015 AFB1 29 90 ≥0.5–9.8 3.9 2016 [97]
ZEN 97 ≥10–332.5 189.5
DON 97 ≥100–786.4 457.5
Soybean meal Iran NM AFB1 7 71.43 NM–11.46 6.62 2016 [158]
Soybean meal China NM AFB1 34 29.4 <0.03–9.9 1.7 2018 [171]
Soybeans Qatar NM AFs 6 100 5–150 55 2018 [166]
OTA ND ND ND

Φ—Exceeds the EU legislative levels; LOQ—limit of quantification; ND—not detected; NM—not mentioned.

Table A4.

Occurrence of mycotoxins concerned in the EU legislation and its metabolites in animal feed.

Sample Country of Origin Year of Collection Mycotoxin/ Metabolite Total Samples Positive Samples Year of Publication Reference
% Range (µg/kg) Mean (µg/kg)
Broiler feed India NM OTA 50 42 10.13–14.23 11.69 2016 [139]
Broiler feeds Thailand NM AFB1 100 93 0.47–8.52 2.02 2016 [79]
AFB2 20 0.79–3.30 1.87
AFG1 7 0.66–1.89 1.30
AFG2 ND ND ND
T-2 1 1.15 -
OTA ND ND ND
ZEN 63 2.22–263.51 84.27
DON 9 33.58–60.81 45.05
Broiler finisher feed Egypt NM FB1 2 50 NM NM 2016 [63]
Broiler starter feed AFB1 1 ND ND ND
AFB2
Calves feed Croatia 2014–2015 T-2/HT-2 17 47.1 26.3–129.3 65.1 2017 [170]
Bosnia and Herzegovina 12 58.3 25.7–70.5 42.7
Cattle complete feed China 2013–2015 AFB1 6 100 ≥0.5–8.3 4.5 2016 [97]
ZEN ND ND ND
DON ND ND ND
Cattle compound feed Spain 2012–2014 AFB1 6 33 <2 - 2018 [185]
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
ZEN 11 88.2 -
OTA 33 <25 -
DON 11 289.9 -
3-AcDON + 15-AcDON ND ND ND
FB1 67 <375–863.9 697.6
FB2 67 <125–276.1 215.2
T-2 ND ND ND
HT-2 ND ND ND
Cattle feed China NM AFB1 20 30 NM–28.27 Φ 3.96 2016 [78]
AFB2 25 NM–22.43 2.98
AFG1 15 NM–12.37 1.24
AFG2 5 NM–1.84 0.09
OTA 25 NM–15.64 1.53
ZEN 20 NM–14.43 1.45
T-2 30 NM–8.23 2.07
Cattle feed Egypt NM AFB1 60 18.3 1.5–72.4 Φ 24.15 2016 [136]
Cattle feed South Korea 2014 DON 60 100.0 91.65–950.25 602.51 2016 [143]
3-AcDON 3.3 ≥8.3–52.10 32.75
Cattle feed NM NM ZEN 14 NM <1.1 - 2018 [186]
α-ZEL ND ND ND
β-ZEL ND ND ND
Cattle feeds Korea 2009–2016 ZEN 174 97.7 NM 134.23 2017 [187]
Chicken complete feed China 2012–2014 AFB1 290 57 ≥0.5–187.5 Φ 25.4 2016 [138]
Chicken feed China NM AFB1 20 30 NM–21.27 Φ 2.68 2016 [78]
AFB2 25 NM–15.33 1.56
AFG1 10 NM–8.36 0.43
AFG2 5 NM–1.64 0.08
OTA 25 NM–10.55 1.09
ZEN 25 NM–61.59 4.84
T-2 15 NM–5.28 0.32
Chicken feed NM NM ZEN 13 ND ND ND 2018 [186]
α-ZEL ND ND ND
β-ZEL NM <0.6 -
Chicken feeds Korea NM AFs 20 100 0.10–1.86 0.56 2016 [141]
AFB1 NM 0.09–1.70 0.38
OTA 100 0.14–2.24 0.77
ZEN 85 5.17–147.53 35.02
Chicken feed South Korea 2014 DON 50 94.0 ≥3.3–603.10 258.36 2016 [143]
3-AcDON 2.0 ≥8.3–29.70 -
Complementary dairy cow feed NM NM AFB1 31 71.0 ≥0.005–51.4 Φ 10.1 2016 [39]
Complete farm-mixed wet feed for pigs Norway NM DON 15 27 10–34 354 2016 [165]
HT-2 7 10 -
T-2 ND ND ND
T-2 + HT-2 ND ND ND
Complete feed for pigs DON 13 100 20–289 117.0
HT-2 100 10–94 47.0
T-2 73 10–60 23.4
T-2 + HT-2 97 22–140 66.7
ZEN 97 1.5–217.2 37.8
OTA 80 0.1–1.44 0.32
Complete feed samples for swine, poultry and cattle Poland 2011–2014 DON 480 99 ≥1.0–5478 Φ 4689 2016 [15]
T-2 97 ≥0.2–185 8.19
ZEN 99 ≥0.07–349 35.6
HT-2 479 97 ≥0.7–276 Φ 16.7
FMs 14 86 ≥1.6–1063 209
OTA 412 69 ≥0.13–88.0 Φ 3.14
AFs 241 12 NM–1.31 0.47
Complete feed China 2012–2014 ZEN 147 95 ≥10–3261.2 Φ 221.0 2016 [138]
DON 116 77 ≥100–2611.4 626.8
Compound feeds South Africa NM AFB1 5 40 <0.06 - 2018 [160]
AFB2 100 0.551; 1.365 0.871
AFG1 20 <0.15 -
AFG2 100 7.848; 31.748 17.589
ZEN 80 0.562; 1.853 1.127
α-ZEL 100 0.975; 3.391 2.711
β-ZEL 100 1.776; 3.801 2.875
FB1 100 494.409; 1389.624 805.677
DON 100 3.225; 56.520 33.154
3-AcDON + 15-AcDON 20 >0.27 -
HT-2 80 >0.21; 5.061 2.972
OTA ND ND ND
Concentrate cow feed Iran 2014 AFB1 70 44.3 2.08–19.41 9.77 2016 [174]
Concentrated feed China NM DON 8 75 11.6–277.6 NM 2016 [93]
3-AcDON 63 5.6–56.4
15-AcDON 63 5.7–160.2
Dairy cattle CFM Egypt NM AFB1 1 100 NM NM 2016 [63]
AFB2
Dairy cattle feed Brazil 2011–2014 AFB1 160 100 0.2–50.0 Φ NM 2016 [84]
Dairy concentrate feed Kenya NM AFB1 NM NM 21.33–147.86 Φ 47.84 2016 [188]
DON ≥18.53–179.89 86.95
Dairy feed Kenya 2014 AFB1 277 73 ≥1–9661 Φ 154.5 2016 [189]
Dairy feeds NM NM AFB1 156 100 7–419 Φ 97 2016 [161]
Duck complete feed China 2012–2014 AFB1 282 52 ≥0.5–150.0 Φ 22.6 2016 [138]
Duck complete feed China 2013–2015 AFB1 6 100 ≥0.5–8.84 6.4 2016 [97]
ZEN 86 ≥10–357.9 307.0
DON 100 ≥100–2613.7 1718.3
Duck feed NM NM ZEN 15 7 39.08~47.61 - 2018 [186]
α-ZEL 7 4.19 -
β-ZEL ND ND ND
Feed and raw materials Italy 2010–2014 AFB1 919 68 ≥1–18.37 NM 2016 [190]
Finished feed South Africa 2006–2017 AFs 310 5.8 >LOD–232 NM 2018 [172]
ZEN 301 57.5 >LOD–386
DON 311 67.2 >LOD–9805
T2 301 1.3 >LOD–4.5
FB1 + FB2 306 83.3 >LOD–7578
OTA 259 3.1 >LOD–6.0
Feed materials Spain 2012–2014 AFB1 3 ND ND ND 2018 [185]
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
ZEN ND ND ND
OTA 33 <25 -
DON ND ND ND
3-AcDON + 15-AcDON ND ND ND
FB1 67 <375 -
FB2 67 <125 -
T-2 ND ND ND
HT-2 ND ND ND
Formula feed China NM DON 11 82 47.1–864.5 NM 2016 [93]
3-AcDON 73 5.1–221.8
15-AcDON 55 5.0–350.4
Finished feed for poultry, swine and ruminant, maize and maize silage 44 countries 2012–2015 AFB1 1113 4.9 ≥1.5–1077 Φ NM 2016 [43]
AFB2 1.4 ≥1.5–112
AFG1 1.9 ≥1.5–95
AFG2 0.80 ≥1.5–12
ZEN 88 ≥1–11192 Φ
DON 79 ≥1.5–13488
3-AcDON 7.1 ≥15–527
15-AcDON 13 ≥15–2177
T-2 10 ≥10–852 Φ
T-2 Tetraol 1.3 ≥15–290
T-2 Triol 0.10 ≥15–93
HT-2 19 ≥10–2328 Φ
FB1 67 ≥4.0–31784
FB2 58 ≥4.0–12968
FB3 40 ≥4.0–3345
FB4 28 ≥4.0–4341
FB6 0.10 ≥4.0–30
OTA 4.5 ≥1.5–67
Full ration pellet for dairy cow Iran NM AFB1 64 100.00 0.02–36.07 Φ 3.64 2016 [158]
Feed Egypt 2014–2015 AFB1 77 4 NM–11 NM 2017 [168]
DON 71 NM–1516
FB1 77 NM–2409
FB2 69 NM–260
FB3 55 NM–310
HT-2 13 NM–32.3
T-2 25 NM–39.5
ZEN 92 NM–791
α-ZEL 6 NM–8
β-ZEL 36 NM–60
Layer feed India NM OTA 50 46 12.33–15.20 13.22 2016 [139]
Layer poultry feed Egypt NM AFB1 1 ND ND ND 2016 [63]
AFB2
Mixed dairy cow feeds Turkey 2012–2015 AFB1 76 26.3 0.278–6.89 Φ 2.25 2016 [191]
AFB2 23.7 0.081–0.752 0.231
AFG1 22.4 0.207–0.788 0.334
AFG2 ND ND ND
Mixed ruminant feed Turkey 2012/13 AFs 88 81.81 NM–33.90 5.22 2016 [192]
AFB1 81.81 NM–19.24 Φ 2.85
OTA 95.45 NM–79.10 30.45
FMs 94.31 NM–1600 307.5
Pig complete feed China 2012–2014 AFB1 802 30 ≥0.5–111.0 Φ 12.6 2016 [138]
Pig complete feed (powder) China 2013–2015 AFB1 25 96 ≥0.5–9.1 13.7 2016 [97]
ZEN 96 ≥10–835.4 Φ 290.4
DON 96 ≥100–2767.6 Φ 999.2
Pig complete feed (pellet) AFB1 90 78 ≥0.5–18.1 5.8
ZEN 82 ≥10–329.0 Φ 291.4
DON 81 ≥100–3346.0 Φ 642.5
Pig feed China NM AFB1 20 40 NM–32.12 Φ 4.29 2016 [78]
AFB2 25 NM–21.53 2.34
AFG1 20 NM–7.35 1.01
AFG2 10 NM–5.08 0.31
OTA 20 NM–13.22 1.23
ZEN 30 NM–18.78 1.87
T-2 35 NM–1.55 35
Pigs feed Croatia 2014–2015 T-2/HT-2 24 53.3 24.7–93.4 39.9 2017 [170]
Bosnia and Herzegovina 16 50 25.6–118.1 45.9
Pig feeds Korea 2009–2016 ZEN 160 95.0 NM 31.70 2017 [187]
Pig feed NM NM ZEN 17 6 124.78 - 2018 [186]
α-ZEL NM <0.6 -
β-ZEL ND ND ND
Pig feed South Korea 2014 DON 50 100.0 32.38–932.48 Φ 164.74 2016 [143]
3-AcDON ND ND ND
Poultry compound feed Spain 2012–2014 AFB1 9 11 <2 - 2018 [185]
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
ZEN 11 <50 -
OTA 11 <25 -
DON 11 <250 -
3-AcDON + 15-AcDON ND ND ND
FB1 11 <375 -
FB2 11 139.2 -
T-2 ND ND ND
HT-2 ND ND ND
Poultry feed 1 Pakistan 2012/13 AFB1 11 82 0.09–145.7 Φ 6.20 2016 [184]
AFs LOQ–165.5 9.30
ZEN 82 0.15–125.20 15.80
Poultry feed 2 AFB1 13 54 0.09–98.3 Φ 4.97
AFs LOQ–103.1 7.89
ZEN 77 0.15–118.42 19.45
Poultry feed Pakistan 2012–2013 OTA 120 68.6 2.88–178.78 93.03 2017 [169]
Poultry feed Croatia 2014–2015 T-2/HT-2 13 53.9 30.0–63.7 44.6 2017 [170]
Bosnia and Herzegovina 9 66.7 32.6–52.3 42.6
Poultry feeds Morocco 2013/14 OTA 62 30.6 0.24–26.8 7.1 2016 [90]
Poultry feeds Korea 2009–2016 ZEN 160 96.3 NM 37.93 2017 [187]
Poultry, swine, cattle, horse and lamb feed Spain NM DON 32 NM NM NM 2016 [104]
AFG2 ND ND ND
AFG1 ND ND ND
AFB2 ND ND ND
AFB1 ND ND ND
T-2 ND ND ND
ZEN 6 13.8–14.8 14.3
OTA ND ND ND
FB1 NM NM NM
FB2 NM NM NM
Premixed feed China NM DON 12 67 97.4–776.3 NM 2016 [93]
3-AcDON 42 26.5–135.1
15-AcDON 17 99.5–332.8
Rabbit feed China NM AFB1 20 30 NM–12.22 Φ 1.56 2016 [78]
AFB2 25 NM–9.31 1.28
AFG1 10 NM–6.37 0.51
AFG2 5 NM–1.46 0.07
OTA 25 NM–15.21 1.44
ZEN 40 NM–10.46 2.25
T-2 25 NM–7.49 0.86
Sheep compound feed Spain 2012–2014 AFB1 17 6 <2 - 2018 [185]
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
ZEN 18 <50–104,4 79,5
OTA 29 <25 -
DON 12 <250 -
3-AcDON +15-AcDON ND ND ND
FB1 53 <375 -
FB2 53 <125 -
T-2 ND ND ND
HT-2 ND ND ND
Starter feed India NM OTA 50 32 5.13–6.73 5.78 2016 [139]
Silage, corn dust, commercial concentrate Thailand 2011 AFB1 125 NM 3.95–114.9 NM 2017 [193]
Swine compound feed Spain 2012–2014 AFB1 20 15 <2 - 2018 [185]
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
ZEN 10 <50 -
OTA 40 <25 -
DON 5 254,9 -
3-AcDON + 15-AcDON ND ND ND
FB1 45 <375 -
FB2 45 <125 -
T-2 ND ND ND
HT-2 ND ND ND
Swine feed Hungary NM DON 45 NM 137–997 Φ 261 2016 [194]
ZEN 18–192 39
T-2 18–55 40
Total mixed ration for dairy England 2014 DON 38 66 ≥10.0–1654 154 2016 [176]
ZEN 39 ≥10.0–1431 Φ 84.2
FB1 NM ≥1.0–119 11.5
FB2 NM ≥1.0–48.0 3.95
T-2 ND ND ND
HT-2 ND ND ND
AFB1 ND ND ND
AFB2 ND ND ND
AFG1 ND ND ND
AFG2 ND ND ND
OTA ND ND ND
Total mixed rations South Africa NM AFB1 5 100 <0.06; 0.463 0.299 2018 [160]
AFB2 100 0.903; 5.339 3.070
AFG1 80 <0.15; 2.655 1.049
AFG2 100 31.307; 50.199 40.708
ZEN 100 0.325; 28.040 7.191
α-ZEL 100 2.913; 5.637 3.723
β-ZEL 100 1.445; 3.356 2.708
FB1 100 134.231; 1067.822 542.589
DON 100 <1.62; 18.038 10.255
3-AcDON + 15-AcDON 80 0.507; 2.634 1.281
HT-2 100 0.834; 48.268 22.970
OTA ND ND ND

Φ—Exceeds the EU legislative levels; ND—not detected; NM—not mentioned; LOQ—limit of quantification.

Appendix B

This appendix includes tables of ELISA and chromatographic methods applied to detect mycotoxins in maize, wheat and soybeans and their by-products.

Table A5.

Overview of ELISA methods in mycotoxins analysis.

Mycotoxin/ Metabolite Matrix Sample Pre-Treatment ELISA Year of Publication Reference
Extraction Format Detection Method LOD; LOQ (µg/kg) or (µg/L)
AFB1 Maize; wheat bran and dairy feeds MeCN 80% Direct competitive Optical density NM 2016 [161]
AFB1 Corn silage; crushed yellow corn; wheat bran; soybean meal and full ration pellet for dairy cow MeOH 70% Competitive Absorbance 1; NM 2016 [158]
AFB1 Dairy concentrate feed MeOH 70% Competitive Absorbance 1.75; 3.61 2016 [188]
AFB1 Feed and raw materials 1 g of NaCl and MeOH 70% Competitive Absorbance 1; NM 2016 [190]
AFs Distiller’s dried grains with solubles MeOH 80% Direct competitive Absorbance NM 2016 [195]
AFB1 Commercial concentrate Methyl alcohol 70% Direct competitive Absorbance NM; 3.43 2018 [193]
Corn dust NM; 3.12
Silage NM; 6.93
DON Dairy concentrate feed Distilled water Competitive Absorbance 18.5; 21.68 2016 [188]
DON Wheat Water Direct competitive Absorbance 233; NM 2016 [181]
Wheat dust 458; NM
DON Maize Distilled water Direct competitive Optical density 100; 250 2017 [163]
DON Swine feed NM Competitive Absorbance 13; 200 2016 [194]
DON Cereals and feedstuff Double-distilled water Direct competitive Absorbance 300; NM 2017 [196]
Wheat and feedstuff 480; NM
FMs Wheat silage MeOH 80% Competitive Absorbance NM 2016 [178]
OTA Poultry feed and poultry feed ingredients MeOH 70% Direct Competitive Absorbance 1.9; 2.0 2017 [169]
T-2 Swine feed NM Competitive Absorbance 13; 200 2016 [194]
T-2/HT-2 Maize MeOH 70% Competitive Absorbance 9.1; 14.6 2017 [170]
Wheat 14.6; 20.1
Pig feed 14.8; 21.5
ZEN Swine feed NM Competitive Absorbance 13; 200 2016 [194]
ZEN Wheat silage MeOH 60% Competitive Absorbance 12.5; NM 2016 [178]

NM—Not mentioned.

Table A6.

Overview of GC-MS methods in mycotoxins analysis.

Mycotoxin/ Metabolite Matrix Sample Pre-Treatment GC-MS Year of Publication Reference
Extraction Clean-Up Derivatization Ionization/ Ion Selection Scan Mode LOD; LOQ (µg/kg) or (µg/L)
DON Wheat; complete feed for pigs; complete farm-mixed wet feed for pigs NM NM NM NM NM NM; 10 2016 [165]
DON Durum wheat MeCN 82% Charcoal/Alumina/Celite column TMSIM-TMCS (100/1 v/v) NM SIM 0.01; NM 2016 [197]
3-AcDON
15-AcDON
T-2 Wheat; complete feed for pigs; complete farm-mixed wet feed for pigs NM NM NM NM NM NM; 10 2016 [165]
HT-2 NM; 10
T-2 + HT-2 NM; 10

NM—Not mentioned; TMSIM—trimethylsilylimidazole; TMCS—trimethylchlorosilane.

Table A7.

Overview of HPLC methods coupled to classical detectors and DAD in mycotoxins analysis.

Mycotoxin/ Metabolite Matrix Sample Pre-Treatment HPLC Year of Publication Reference
Extraction Clean-Up Derivatization Detection Method Column LOD; LOQ (µg/kg) or (µg/L)
AFB1 Wheat MeOH 80% Easi-Extract® AF IAC Post-column derivatization Fluorescence LiChroCART 100Å RP-18 (5 mm, 250 × 4.0 mm) 0.031; 0.093 2016 [180]
AFB2 0.022; 0.066
AFG1 0.032; 0.096
AFG2 0.028; 0.084
AFs 0.091; 0.273
AFB1 Maize NM IAC Post-column derivatization Fluorescence NM NM; 0.1 2016 [165]
AFB2 NM; 0.1
AFG1 NM; 0.1
AFG2 NM; 0.1
AFs NM; 0.1
AFB1 Corn silage MeOH 80% C18 SPE column Electrochemical post-column derivatization Fluorescence NM 0.12; 0.4 2016 [177]
AFB2 0.015; 0.05
AFG1 0.05; 0.16
AFG2 0.03; 0.1
AFB1 Maize; wheat; pig, chicken and duck complete feed MeOH 80% CF AFLA IAC - Fluorescence C18 (250 × 4.6 mm, 5 μm) 0.5; 1.5 2016 [138]
AFB1 Soybean kernels MeCN 75% IAC AlfaStarTM Fit Post-column photochemical derivatization Fluorescence X-Terra RP18 (4.6 × 150 mm, 5 µm) 0.13; 0.37 2018 [183]
AFB1 Maize panel and complementary dairy cow feed NM AflaPrep® IAC SPE Electrochemical post-column derivatization with potassium bromide Fluorescence NM 0.005; 0.014 2016 [39]
AFB1 Maize; maize silage and complete feed samples for swine, poultry, and cattle MeOH 80% AflaTest® IAC Post-column derivatization Fluorescence Shimadzu Nexera with Gemini-NX-C18 (150 × 4.6 mm, 3 μm) 0.05; 0.15 2016 [15]
AFB2 0.02; 0.06
AFG1 0.25; 0.75
AFG2 0.08; 0.24
AFB1 Dehulled yellow corn MeOH 70% with 1% NaCl AflaTest® WB IAC Pre-column derivatization with trifluoroacetic acid Fluorescence Synergi Hydro-RP (250 mm × 4.6 mm, 4 μm) 0.08; 0.25 2016 [142]
AFB2 0.03; 0.11
AFG1 0.13; 0.39
AFG2 0.09; 0.27
AFB1 Post-column photochemical derivatization (PHRED cell) C18 (150 mm × 4.6 mm, 3.5 μm) 0.02; 0.06
AFB2 0.01; 0.02
AFG1 0.02; 0.05
AFG2 0.01; 0.02
AFB1 Electrochemical post-column bromination derivatization (Kobra cell) C18 (150 mm × 4.6 mm, 3.5 μm) 0.04; 0.11
AFB2 0.02; 0.05
AFG1 0.05; 0.14
AFG2 0.01; 0.04
AFB1 Mixed dairy cow feeds MeOH 80% with 5 g NaCl AflaTest® IAC - Fluorescence Reversed phase inertsil® ODS-3 (5 μm, 250 × 4.6 mm i. d.) 0.054; 0.181 2016 [191]
AFB2 0.046; 0.153
AFG1 0.059; 0.197
AFG2 0.050; 0.168
AFB1 Dairy cattle feed 1 g NaCl AflaTest® IAC - Fluorescence NM NM 2016 [84]
AFB1 Corn; domestic and imported distiller’s dried grains with solubles; corn germ meal; wheat; bran; wheat shorts and red dog; soybean meal; pig complete feed (powder and pellet); duck and cattle complete feed MeOH 80% MycoSep® 226 column Post-column photochemical derivatization Fluorescence C18 (4.6 mm × 150 mm, 5 μm) 0.5; 1.5 2016 [97]
AFB1 Animal feedstuffs IL-DLLME coupled to magnetic SPE - - Fluorescence RP C18 analytical (150 × 4.6 mm, 5 μm) 0.632; NM 2016 [106]
AFB2 0.087; NM
AFG1 0.422; NM
AFG2 0.146; NM
AFB1 Maize MeCN 84% MycoSep® 224 AflaZon SPE column Post-column derivatization with iodine Fluorescence ZORBAX Eclipse Plus C18 (4.6 × 100 mm, i.d. 3.5 µm) 0.4; 1.3 2017 [167]
AFB2 0.20; 0.60
AFG1 0.40; 1.4
AFG2 0.60; 1.8
DON Maize; wheat and complete feed MeOH 60% CF DON IAC - UV mm × 4.6 mm × C18 5 μm reverse-phase 100; 260 2016 [138]
DON Milled wheat; bran Water DON-Test IAC - DAD C18 reversed-phase (250 × 4.6 mm, 4 μ) 22; 77 2016 [144]
DON Corn; domestic and imported distiller’s dried grains with solubles; corn germ meal; wheat; bran; wheat shorts and red dog; soybean meal; pig complete feed (powder and pellet); duck and cattle complete feed MeOH 60% CF AFLA IAC - UV C18 (4.6 mm × 150 mm, 5 μm) 0.02; 0.06 2016 [97]
DON Wheat; wheat shorts; wheat bran MeCN 84% MycoSep® 227 column - UV C18-HL (250 mm × 4.6 mm, 5 μm) NM 2016 [140]
FB1 Maize kernel Ultrapure water and MeCN SAX column Post-column derivatization with o-phthaldialdehyde Fluorescence ZORBAX SB-C18 reversed-phase (250 mm × 4.6 mm, 5 μm) 4; 13 2016 [94]
FB2 3; 10
FB1 Corn grain; corn grits; corn meal MeOH 80% SPE cartridge - Fluorescence C18 reversed-phase (150 × 4.6 mm, 5 µm) 2.5; 12.5 2016 [156]
FB2 6; 31.3
OTA Complete feed for pigs NM IAC - Fluorescence NM NM; 0.1 2016 [165]
OTA Maize; maize silage and complete feed samples for swine, poultry, and cattle MeCN 60% OCHRAPREP® IAC - Fluorescence Shimadzu Nexera with Gemini-NX-C18 (150 × 4.6 mm, 3 μm) 0.13; 0.40 2016 [15]
ZEN Complete feed for pigs NM IAC - Fluorescence NM NM; 3.0 2016 [165]
ZEN Maize; wheat and complete feed MeCN 84% ZearaStar IAC - UV 150-mm × 4.6-mm × C18 5-μm reverse-phase 10; 24 2016 [138]
ZEN Soybean kernels MeCN 75% IAC NeoColumnTM 8140 - Fluorescence ODS Purospher (4.0 × 250 mm × 5 μm) 2.0; 6.0 2018 [76]
ZEN Corn; domestic and imported distiller’s dried grains with solubles; corn germ meal; wheat; bran; wheat shorts and red dog; soybean meal; pig complete feed (powder and pellet); duck and cattle complete feed MeOH 60% CF AFLA IAC - Fluorescence C18 (4.6 mm × 150 mm, 5 μm) 1.5; 4 2016 [97]

NM—not mentioned.

Table A8.

Overview of LC-MS methods in mycotoxins analysis.

Mycotoxin/ Metabolite Matrix Sample Pre-Treatment LC-MS Year of Publication Reference
Extraction Clean-Up Ionization/ Ion Selection Scan Mode LOD; LOQ (µg/kg) or (µg/L)
AFB1 Distiller’s dried grains with solubles QuEChERS-like approach UHPLC ESI (±) QTRAP MRM NM; 1 2016 [159]
AFB2 NM; 1
AFG1 NM; 1
AFG2 NM; 1
AFB1 Pig, cattle, chicken and rabbit feed MeCN/water/acetic acid (80:18:2) mIAC ESI (±) QqQ SRM 0.02; 0.06 2016 [78]
AFB2 0.02; 0.06
AFG1 0.04; 0.12
AFG2 0.03; 0.09
AFB1 Animal feed MeCN/water/acetic acid (79:20:1) AflaTest® IAC UPLC ESI (±) QqQ MRM 0.50; 1.0 2016 [198]
AFB2 0.50; 1.0
AFG1 0.50; 1.0
AFG2 0.50; 1.0
AFB1 Finished feed for poultry, swine and ruminant, maize and maize silage MeCN/water/acetic acid (79:20:1) - ESI IT NM NM 2016 [43]
AFB2
AFG1
AFG2
AFB1 Poultry, swine, cattle, horse and lamb feed QuEChERS-based approach UHPLC HESI (±) Orbitrap Full scan NM; 2.5 2016 [104]
AFB2 NM; 2.5
AFG1 NM; 2.5
AFG2 NM; 2.5
AFB1 Maize silage MeCN 84% with 1% of acetic acid - HESI (±) QqQ SRM 0.05; 0.17 2016 [71]
AFG1 0.05; 0.17
AFB1 Feed MeCN/water/acetic acid (79:20:1) - HPLC ESI (±) MRM 0.72; 2.4 2017 [168]
Maize 0.3; 0.98
AFB2 0.42; 1.4
AFB1 Compound feed for swine, sheep, poultry, cattle, equine and feed materials MeCN/water/formic acid (80:19:1) - UPLC ESI (+) MRM 1; 2 2018 [185]
AFB2 2; 4
AFG1 2; 4
AFG2 2; 4
AFB1 Corn and feed MeOH and sodium chloride AOF-MS-PREP and DZT-MS-PREP multiantibody IAC in tandem ESI (+) QTRAP MRM 0.2; 0.7 2018 [199]
AFB2 0.2; 0.5
AFG1 0.4; 1.1
AFG2 0.1; 0.3
AFB1 Ground maize, compound feeds, total mixed rations MeCN/water/acetic acid (79:20:1) - UHPLC ESI (+) QTOF NM 0.02; 0.06 2018 [160]
AFB2 0.05; 0.16
AFG1 0.05; 0.15
AFG2 0.06; 0.19
DON Maize MeCN/water/acetic acid (79:20:1) C18 SPE column ESI (+) QqQ SRM 7; 14 2016 [70]
15-AcDON 5; 10
DON Distiller’s dried grains with solubles QuEChERS-like approach UHPLC ESI (±) QTRAP MRM NM; 100 2016 [159]
15-AcDON NM; 50
3-AcDON NM; 25
DON Formula feed MeCN 50% GPD HLB SPE cartridge UHPLC ESI (±) QqQ MRM 0.08; 0.10 2016 [93]
3-AcDON 2.09; 4.17
15-AcDON 0.57; 1.21
DON Concentrated feed 0.23; 0.52
3-AcDON 2.31; 4.85
15-AcDON 0.98; 1.86
DON Premixed feed 0.12; 0.24
3-AcDON 1.32; 2.98
15-AcDON 0.74; 1.86
DON Corn silage MeCN with 1% of acetic acid and deionized water with sodium acetate trihydrate - ESI (+) QqQ SRM NM; NM 2016 [175]
DON Maize; maize silage and complete feed samples for swine, poultry, and cattle MeCN 80% Bond Elut® Mycotoxin column API NM 1.0; 3.0 2016 [15]
DON Maize silage MeCN 84% with 1% of acetic acid - HESI (±) QqQ SRM 34.2; 113.9 2016 [71]
3-/15-AcDON 1.6; 5.2
DON Animal feed QuEChERS UPLC ESI (±) QqQ MRM 50; 100 2016 [198]
3-AcDON 10; 50
DON Finished feed for poultry, swine and ruminant, maize and maize silage MeCN/water/acetic acid (79:20:1) - ESI IT NM NM 2016 [43]
3-AcDON
15-AcDON
DON Poultry, swine, cattle, horse and lamb feed QuEChERS-based approach UHPLC HESI (±) Orbitrap Full scan NM; 450 2016 [104]
DON Feed MeCN/water/acetic acid (79:20:1) - HPLC ESI (±) MRM 9.5; 31 2017 [168]
Maize 26; 86
DON Compound feed for swine, sheep, poultry, cattle, equine and feed materials MeCN/water/formic acid (80:19:1) - UPLC ESI (+) MRM 125; 250 2018 [185]
DON Corn and feed MeOH and sodium chloride AOF-MS-PREP and DZT-MS-PREP multiantibody IAC in tandem ESI (+) QTRAP MRM 12.1; 36.8 2018 [199]
3-/15-AcDON Ground maize, compound feeds, total mixed rations MeCN/water/acetic acid (79:20:1) - UHPLC ESI (+) QTOF NM 0.08; 0.27 2018 [160]
DON 0.49; 1.62
FB1 Maize 0.4 M phosphate buffer - ESI (+) QqQ NM 10; 30 2016 [200]
FB2 10; 30
FB1 + FB2 10; 30
FB1 Maize MeCN/water/acetic acid (79:20:1) C18 SPE column ESI (+) QqQ SRM 8.2; 16.4 2016 [70]
FB2 11.5; 23
FB3 14; 28
FB1 Maize silage MeCN 84% with 1% of acetic acid - HESI (±) QqQ SRM 1.7; 5.8 2016 [71]
FB2 3.9; 12.9
FB1 Distiller’s dried grains with solubles QuEChERS-like approach UHPLC ESI (±) QTRAP MRM NM; 25 2016 [159]
FB2 NM; 25
FB3 NM; 25
FB1 Animal feed QuEChERS UPLC ESI (±) QqQ MRM 10; 50 2016 [198]
FB2 10; 50
FB1 Maize; maize silage and complete feed samples for swine, poultry, and cattle MeCN 80% MultiSep® 211 SPE column API NM 1.6; 5.0 2016 [15]
FB2 1.6; 5.0
FB3 1.6; 5.0
FB1 Poultry, swine, cattle, horse and lamb feed QuEChERS-based approach UHPLC HESI (±) Orbitrap Full scan NM; 2500 2016 [104]
FB2 NM; 2500
FB1 Finished feed for poultry, swine and ruminant, maize and maize silage MeCN/water/acetic acid (79:20:1) - ESI IT NM NM 2016 [43]
FB2
FB3
FB4
FB6
FB1 Feed MeCN/water/acetic acid (79:20:1) - HPLC ESI (±) MRM 2.6; 8.5 2017 [168]
FB2 1; 3.3
FB3 3.8; 11
FB1 Maize 1; 3.3
FB2 1.3; 4.3
FB3 1.5; 4.9
FB1 Compound feed for swine, sheep, poultry, cattle, equine and feed materials MeCN/water/formic acid (80:19:1) - UPLC ESI (+) MRM 187.5; 375 2018 [185]
FB2 62.5; 125
FB1 Corn and feed MeOH and sodium chloride AOF-MS-PREP and DZT-MS-PREP multiantibody IAC in tandem ESI (+) QTRAP MRM 39.2; 118.7 2018 [199]
FB2 28.0; 84.9
FB1 Ground maize, compound feeds, total mixed rations MeCN/water/acetic acid (79:20:1) - UHPLC ESI (+) QTOF NM 3.46; 11.52 2018 [160]
OTA Maize silage MeCN 84% with 1% of acetic acid - HESI (±) QqQ SRM 0.29; 0.97 2016 [71]
OTA Distiller’s dried grains with solubles QuEChERS-like approach UHPLC ESI (±) QTRAP MRM NM; 1 2016 [159]
OTA Pig, cattle, chicken and rabbit feed MeCN/water/acetic acid (80:18:2) mIAC ESI (±) QqQ SRM 0.12; 0.36 2016 [78]
OTA Animal feed QuEChERS UPLC ESI (±) QqQ MRM 1.0; 5.0 2016 [198]
OTA Finished feed for poultry, swine and ruminant, maize and maize silage MeCN/water/acetic acid (79:20:1) - ESI IT NM NM 2016 [43]
OTA Poultry, swine, cattle, horse and lamb feed QuEChERS-based approach UHPLC HESI (±) Orbitrap Full scan NM; 25 2016 [104]
OTA Maize MeCN/water/acetic acid (79:20:1) - HPLC ESI (±) MRM 2.8; 9.4 2017 [168]
OTA Ground maize, compound feeds, total mixed rations MeCN/water/acetic acid (79:20:1) - UHPLC ESI (+) QTOF NM 0.08; 0.26 2018 [160]
OTA Corn and feed MeOH and sodium chloride AOF-MS-PREP and DZT-MS-PREP multiantibody IAC in tandem ESI (+) QTRAP MRM 0.7; 2.0 2018 [199]
OTA Compound feed for swine, sheep, poultry, cattle, equine and feed materials MeCN/water/formic acid (80:19:1) - UPLC ESI (+) MRM 12.5; 25 2018 [185]
T-2 Layer feed MeCN 84% MycoSep® 227 column ESI (+) QqQ MRM 0.9; 2.9 2016 [201]
HT-2 7.1; 23.8
T-2 triol 1.0; 3.4
T-2 tetraol 7.5; 25
HT-2 Maize silage MeCN 84% with 1% of acetic acid - HESI (±) QqQ SRM 4.9; 16.2 2016 [71]
T-2 0.29; 0.96
T-2 Distiller’s dried grains with solubles QuEChERS-like approach UHPLC ESI (±) QTRAP MRM NM; 2.5 2016 [159]
HT-2 NM; 25
T-2 Pig, cattle, chicken and rabbit feed MeCN/water/acetic acid (80:18:2) mIAC ESI (±) QqQ SRM 0.12; 0.36 2016 [78]
T-2 Maize; maize silage and complete feed samples for swine, poultry, and cattle MeCN 80% Bond Elut® Mycotoxin column API NM 0.2; 0.6 2016 [15]
HT-2 0.7; 2.0
T-2 Animal feed QuEChERS UPLC ESI (±) QqQ MRM 6.0; 25 2016 [198]
HT-2 10; 25
HT-2 Maize MeCN/water/acetic acid (79:20:1) C18 SPE column ESI (+) QqQ SRM 6.5; 13 2016 [70]
T-2 Finished feed for poultry, swine and ruminant, maize and maize silage MeCN/water/acetic acid (79:20:1) - ESI IT NM NM 2016 [43]
T-2 Tetraol
T-2 Triol
HT-2
T-2 Poultry, swine, cattle, horse and lamb feed QuEChERS-based approach UHPLC HESI (±) Orbitrap Full scan NM; 500 2016 [104]
HT-2 Feed MeCN/water/acetic acid (79:20:1) - HPLC ESI (±) MRM 1.7; 5.7 2017 [168]
T-2 1.05; 3.5
T-2 Compound feed for swine, sheep, poultry, cattle, equine and feed materials MeCN/water/formic acid (80:19:1) - UPLC ESI (+) MRM 12.5; 25 2018 [185]
HT-2 12.5; 25
T-2 Corn and feed MeOH and sodium chloride AOF-MS-PREP and DZT-MS-PREP multiantibody IAC in tandem ESI (+) QTRAP MRM 1.0; 2.9 2018 [199]
HT-2 2.2; 6.6
HT-2 Ground maize, compound feeds, total mixed rations MeCN/water/acetic acid (79:20:1) - UHPLC ESI (+) QTOF NM 0.06; 0.21 2018 [160]
ZEN Animal feed QuEChERS UPLC ESI (±) QqQ MRM 5.0; 10 2016 [198]
ZEN Pig, cattle, chicken and rabbit feed MeCN/water/acetic acid (80:18:2) mIAC ESI (±) QqQ SRM 0.25; 0.75 2016 [78]
ZEN Maize MeCN/water/acetic acid (79:20:1) C18 SPE column ESI (+) QqQ SRM 3.25; 6.5 2016 [70]
α-ZEL 4.6; 9.2
β-ZEL 5; 10
ZEN Maize silage MeCN 84% with 1% of acetic acid - HESI (±) QqQ SRM 3.4; 11.2 2016 [71]
α-ZEL 17.3; 57.7
β-ZEL 10.4; 34.6
ZEN Distiller’s dried grains with solubles QuEChERS-like approach UHPLC ESI (±) QTRAP MRM NM; 0.5 2016 [159]
α-ZEL NM; 2.5
β-ZEL NM; 2.5
ZEN Maize; maize silage and complete feed samples for swine, poultry, and cattle MeCN 80% Bond Elut® Mycotoxin column API NM 0.07; 0.20 2016 [15]
ZEN Finished feed for poultry, swine and ruminant, maize and maize silage MeCN/water/acetic acid (79:20:1) - ESI IT NM NM 2016 [43]
ZEN Maize MeCN 75% Magnetic SPE with magnetic nanoparticles API (+) UV-Vis DAD coupled with a MS detector SIM 0.8; 2.5 2016 [202]
α- ZEL 1.0; 3.3
β- ZEL 0.6; 1.9
ZEN Poultry, swine, cattle, horse and lamb feed QuEChERS-based approach UHPLC HESI (±) Orbitrap Full scan NM; 10 2016 [104]
ZEN Feed MeCN 75%, sodium chloride, Tween 20 IAC HPLC ESI (±) MRM 0.1-3; 0.3-8 2017 [187]
ZEN Feed MeCN/water/acetic acid (79:20:1) - HPLC ESI (±) MRM 0.64; 2.1 2017 [168]
α- ZEL 1.3; 4.5
β- ZEL 1.2; 3.5
ZEN Maize 0.46; 1.5
ZEN Feed MeCN 80% IAC-ZER HPLC ESI (+) MRM 1.1; 3.1 2018 [186]
α- ZEL 0.6; 2.2
β- ZEL 0.6; 2.1
ZEN Compound feed for swine, sheep, poultry, cattle, equine and feed materials MeCN/water/formic acid (80:19:1) - UPLC ESI (+) MRM 25; 50 2018 [185]
ZEN Corn and feed MeOH and sodium chloride AOF-MS-PREP and DZT-MS-PREP multiantibody IAC in tandem ESI (+) QTRAP MRM 14.7; 44.5 2018 [199]
ZEN Ground maize, compound feeds, total mixed rations MeCN/water/acetic acid (79:20:1) - UHPLC ESI (+) QTOF NM 0.04; 0.12 2018 [160]
α- ZEL 0.19; 0.63
β- ZEL 0.19; 0.64

MRM—Multiple reaction monitoring; NM—not mentioned; HESI—Heated electrospray ionization; SRM—selective reaction monitoring.

Author Contributions

All authors participated in the creation and conceptualization of the article. C.S.P. and S.C.C. conducted the literature search and wrote the manuscript. J.O.F controlled and critically reviewed language and manuscript content during preparation. All authors read and approved final manuscript.

Funding

C.S.P., S.C.C. and J.O.F. thanks REQUIMTE, FCT (Fundação para a Ciência e a Tecnologia) and FEDER through the project UID/QUI/50006/2013-POCI/01/0145/FEDER/007265 with financial support from FCT/MEC through national funds and co-financed by FEDER, under the Partnership Agreement PT2020.

Conflicts of Interest

The authors declare no conflict of interest.

Key Contribution

This review gives an overview of scientific data about feed contamination with different mycotoxins and mycotoxin producing fungi. Additionally; analytical methods on mycotoxin in feed will be discussed.

References

  • 1.European Commission Comission recomendation of 14 January 2011 establishing guidelines for the distinction between feed materials, feed additives, biocidal products and veterinary medicinal products. Off. J. Eur. Union. 2011;2011:75–79. [Google Scholar]
  • 2.Food Standards Agency Food.gov.uk. [(accessed on 4 December 2016)]; Available online: https://www.food.gov.uk/business-industry/farmingfood/animalfeed/what-farm-animals-eat.
  • 3.Adams C.A. Nutrition-based health in animal production. Nutr. Res. Rev. 2006;19:79–89. doi: 10.1079/NRR2005115. [DOI] [PubMed] [Google Scholar]
  • 4.GRACE Foundation GRACE Communications Foundation. [(accessed on 2 December 2016)]; Available online: http://www.sustainabletable.org/260/animal-feed.
  • 5.Pinotti L., Ottoboni M., Giromini C., Dell’Orto V., Cheli F. Mycotoxin Contamination in the EU Feed Supply Chain: A Focus on Cereal Byproducts. Toxins. 2016;8:45. doi: 10.3390/toxins8020045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Wilkinson J.M. Re-defining efficiency of feed use by livestock. Animal. 2011;5:1014–1022. doi: 10.1017/S175173111100005X. [DOI] [PubMed] [Google Scholar]
  • 7.Awika J.M. Major Cereal Grains Production and Use around the World. In: Awika J.M., Piironen V., Bean S., editors. Advances in Cereal Science: Implications to Food Processing and Health Promotion. American Chemical Society; Washington, DC, USA: 2011. pp. 1–13. [Google Scholar]
  • 8.Oliveira P.M., Zannini E., Arendt E.K. Cereal fungal infection, mycotoxins, and lactic acid bacteria mediated bioprotection: From crop farming to cereal products. Food Microbiol. 2014;37:78–95. doi: 10.1016/j.fm.2013.06.003. [DOI] [PubMed] [Google Scholar]
  • 9.Capper J.L., Berger L., Brashears M.M., Jensen H.H. Animal Feed vs. Human Food: Challenges and Opportunities in Sustaining Animal Agriculture Toward 2050. Council for Agricultural Science and Technology; Ames, IA, USA: 2013. [Google Scholar]
  • 10.FAO . Protein Sources for the Animal Feed Industry. FAO’s Animal Production and Health; Rome, Italy: 2002. [Google Scholar]
  • 11.Ray D.K., Mueller N.D., West P.C., Foley J.A. Yield Trends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE. 2013;8:e66428. doi: 10.1371/journal.pone.0066428. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.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. 2013;93:2892–2899. doi: 10.1002/jsfa.6225. [DOI] [PubMed] [Google Scholar]
  • 13.Perry T.W. In: Animal life-Cycle Feeding and Nutrition. Cunha T.J., editor. Academic Press, Inc.; London, UK: 1984. [Google Scholar]
  • 14.Heuzé V., Tran G. Maize Grain. [(accessed on 6 December 2016)]; Available online: http://www.feedipedia.org/node/556.
  • 15.Kosicki R., Błajet-Kosicka A., Grajewski J., Twaruzek M. Multiannual mycotoxin survey in feed materials and feedingstuffs. Anim. Feed Sci. Technol. 2016;215:165–180. doi: 10.1016/j.anifeedsci.2016.03.012. [DOI] [Google Scholar]
  • 16.Cowieson A.J. Factors that affect the nutritional value of maize for broilers. Anim. Feed Sci. Technol. 2005;119:293–305. doi: 10.1016/j.anifeedsci.2004.12.017. [DOI] [Google Scholar]
  • 17.FAO . Food Outlook—Biannual Report on Global Food Markets. FAO; Rome, Italy: 2016. [Google Scholar]
  • 18.Heuzé V., Tran G., Renaudeau D., Lessire M., Lebas F. Wheat Grain. [(accessed on 6 December 2016)]; Available online: http://www.feedipedia.org/node/223.
  • 19.Heuzé V., Tran G. Wheat (General) [(accessed on 6 December 2016)]; Available online: http://www.feedipedia.org/node/6435.
  • 20.Heuzé V., Tran G. Soybean (General) [(accessed on 6 December 2016)]; Available online: http://www.feedipedia.org/node/753.
  • 21.Martín-Pedrosa M., Varela A., Guillamon E., Cabellos B., Burbano C., Gomez-Fernandez J., De Mercado E., Gomez-Izquierdo E., Cuadrado C., Muzquiz M. Biochemical characterization of legume seeds as ingredients in animal feed. Span. J. Agric. Res. 2016;14:0901. doi: 10.5424/sjar/2016141-7450. [DOI] [Google Scholar]
  • 22.Newkirk R. SOYBEAN Feed Industry Guide. Canadian International Grains Institute; Winnipeg, MB, Canada: 2010. [Google Scholar]
  • 23.Tilman D., Balzer C., Hill J., Befort B.L. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. USA. 2011;108:20260–20264. doi: 10.1073/pnas.1116437108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kruse J. Estimating Demand for Agricultural Commodities to 2050. Global Harvest Initiative; Washington, DC, USA: 2010. pp. 1–26. [Google Scholar]
  • 25.FAO . The State of Food and Agriculture. FAO; Rome, Italy: 2009. [Google Scholar]
  • 26.Krska R., Richard J.L., Schuhmacher R., Slate A.B., Whitaker T.B. In: Romer Labs Guide to Mycotoxins. 4th ed. Binder E.M., Krska R., editors. Romer Labs Inc.; Leicestershire, England: 2012. [Google Scholar]
  • 27.FAO & WHO . Animal Feed Impact on Food Safety. FAO; Rome, Itlay: 2007. [Google Scholar]
  • 28.EU Commission The European Parliament and The Council of the European Union Directive 2002/32/EC of the European Parliament and of the Council of 7 May 2002 on undesirable substances in animal feed. Off. J. Eur. Union. 2015 May 7;L 32:1–30. [Google Scholar]
  • 29.Tima H., Brückner A., Mohácsi-Farkas C., Kiskó G. Fusarium mycotoxins in cereals harvested from Hungarian fields. Food Addit. Contam. Part B. 2016;9:127–131. doi: 10.1080/19393210.2016.1151948. [DOI] [PubMed] [Google Scholar]
  • 30.Binder E.M., Tan L.M., Chin L.J., Handl J., Richard J. Worldwide occurrence of mycotoxins in commodities, feeds and feed ingredients. Anim. Feed Sci. Technol. 2007;137:265–282. doi: 10.1016/j.anifeedsci.2007.06.005. [DOI] [Google Scholar]
  • 31.Dzuman Z., Zachariasova M., Veprikova Z., Godula M., Hajslova J. Multi-analyte high performance liquid chromatography coupled to high resolution tandem mass spectrometry method for control of pesticide residues, mycotoxins, and pyrrolizidine alkaloids. Anal. Chim. Acta. 2015;863:29–40. doi: 10.1016/j.aca.2015.01.021. [DOI] [PubMed] [Google Scholar]
  • 32.CAST . Mycotoxins: Risks in Plant, Animal, and Human Systems. CAST; Ames, IA, USA: 2003. [Google Scholar]
  • 33.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. 2016;8:94. doi: 10.3390/toxins8040094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Bordin K., Sawada M.M., da Costa Rodrigues C.E., da Fonseca C.R., Oliveira C.A.F. Incidence of Aflatoxins in Oil Seeds and Possible Transfer to Oil: A Review. Food Eng. Rev. 2014;6:20–28. [Google Scholar]
  • 35.Sirhan A.Y., Tan G.H., Wong R.C.S. Determination of aflatoxins in food using liquid chromatography coupled with electrospray ionization quadrupole time of flight mass spectrometry (LC-ESI-QTOF-MS/MS) Food Control. 2013;31:35–44. doi: 10.1016/j.foodcont.2012.09.016. [DOI] [Google Scholar]
  • 36.da Rocha M.E.B., da Freire F.C.O., Maia F.E.F., Guedes M.I.F., Rondina D. Mycotoxins and their effects on human and animal health. Food Control. 2014;36:159–165. doi: 10.1016/j.foodcont.2013.08.021. [DOI] [Google Scholar]
  • 37.Piotrowska M., Śliżewska K., Biernasiak J. In: Soybean—Pest Resistance. El-Shemy H.A., editor. InTech; Rijeka, Croatia: 2013. [Google Scholar]
  • 38.IARC . Agents Classified by the IARC Monographs, Volumes 1–117. IARC; Lyon, France: 2016. [Google Scholar]
  • 39.Dimitrieska-Stojković E., Stojanovska-Dimzoska B., Ilievska G., Uzunov R., Stojković G., Hajrulai-Musliu Z., Jankuloski D. Assessment of aflatoxin contamination in raw milk and feed in Macedonia during 2013. Food Control. 2016;59:201–206. doi: 10.1016/j.foodcont.2015.05.019. [DOI] [Google Scholar]
  • 40.Groopman J.D., Kensler T.W., Wu F. Mycotoxins—Occurrence and Toxic Effects. Encycl. Hum. Nutr. 2013;2:337–341. [Google Scholar]
  • 41.Marin S., Ramos A.J., Cano-Sancho G., Sanchis V. Mycotoxins: Occurrence, toxicology, and exposure assessment. Food Chem. Toxicol. 2013;60:218–237. doi: 10.1016/j.fct.2013.07.047. [DOI] [PubMed] [Google Scholar]
  • 42.Streit E., Schatzmayr G., Tassis P., Tzika E., Marin D., Taranu I., Tabuc C., Nicolau A., Aprodu I., Puel O., et al. Current Situation of Mycotoxin Contamination and Co-occurrence in Animal Feed—Focus on Europe. Toxins. 2012;4:788–809. doi: 10.3390/toxins4100788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Kovalsky P., Kos G., Nährer K., Schwab C., Jenkins T., Schatzmayr G., Sulyok M., Krska R. Co-Occurrence of Regulated, Masked and Emerging Mycotoxins and Secondary Metabolites in Finished Feed and Maize—An Extensive Survey. Toxins. 2016;8:363. doi: 10.3390/toxins8120363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Pitt J.I., Taniwaki M.H., Cole M.B. Mycotoxin production in major crops as influenced by growing, harvesting, storage and processing, with emphasis on the achievement of Food Safety Objectives. Food Control. 2013;32:205–215. doi: 10.1016/j.foodcont.2012.11.023. [DOI] [Google Scholar]
  • 45.Anukul N., Maneeboon T., Roopkham C., Chuaysrinule C., Mahakarnchanakul W. Fumonisin and T-2 toxin production of Fusarium spp. isolated from complete feed and individual agricultural commodities used in shrimp farming. Mycotoxin Res. 2014;30:9–16. doi: 10.1007/s12550-013-0182-y. [DOI] [PubMed] [Google Scholar]
  • 46.Marroquín-Cardona A.G., Johnson N.M., Phillips T.D., Hayes A.W. Mycotoxins in a changing global environment—A review. Food Chem. Toxicol. 2014;69:220–230. doi: 10.1016/j.fct.2014.04.025. [DOI] [PubMed] [Google Scholar]
  • 47.Murugesan G.R., Ledoux D.R., Naehrer K., Berthiller F., Applegate T.J., Grenier B., Phillips T.D., Schatzmayr G. Prevalence and effects of mycotoxins on poultry health and performance, and recent development in mycotoxin counteracting strategies. Poult. Sci. 2015;94:1298–1315. doi: 10.3382/ps/pev075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Milani J.M. Ecological conditions affecting mycotoxin production in cereals: A review. Vet. Med. 2013;58:405–411. doi: 10.17221/6979-VETMED. [DOI] [Google Scholar]
  • 49.Juan C., Ritieni A., Mañes J. Occurrence of Fusarium mycotoxins in Italian cereal and cereal products from organic farming. Food Chem. 2013;141:1747–1755. doi: 10.1016/j.foodchem.2013.04.061. [DOI] [PubMed] [Google Scholar]
  • 50.Rodríguez-Carrasco Y., Ruiz M.J., Font G., Berrada H. Exposure estimates to Fusarium mycotoxins through cereals intake. Chemosphere. 2013;93:2297–2303. doi: 10.1016/j.chemosphere.2013.07.086. [DOI] [PubMed] [Google Scholar]
  • 51.Ran R., Wang C., Han Z., Wu A., Zhang D., Shi J. Determination of deoxynivalenol (DON) and its derivatives: Current status of analytical methods. Food Control. 2013;34:138–148. doi: 10.1016/j.foodcont.2013.04.026. [DOI] [Google Scholar]
  • 52.González Peyera M.L., Sulyok M., Baralla V., Dalcero A.M., Krska R., Chulze S., Cavaglieri L.R. Evaluation of zearalenone, α-zearalenol, β-zearalenol, zearalenone 4-sulfate and β-zearalenol 4-glucoside levels during the ensiling process. World Mycotoxin J. 2014;7:291–295. doi: 10.3920/WMJ2013.1638. [DOI] [Google Scholar]
  • 53.Grenier B., Applegate T.J. Modulation of intestinal functions following mycotoxin ingestion: Meta-analysis of published experiments in animals. Toxins. 2013;5:396–430. doi: 10.3390/toxins5020396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.The Commission of the European Communities The Commission of the European Communities Comission recomendation of 17 August 2006 on the presence of deoxynivalenol, zearalenone, ochratoxin A, T-2 and HT-2 and fumonisins in products intended for animal feeding. Off. J. Eur. Union. 2006 Aug 17;L 229:7–9. [Google Scholar]
  • 55.FAO . On-Farm Mycotoxin Control in Food and Feed Grain. FAO; Rome, Italy: 2007. [Google Scholar]
  • 56.Alkadri D., Rubert J., Prodi A., Pisi A., Mañes J., Soler C. Natural co-occurrence of mycotoxins in wheat grains from Italy and Syria. Food Chem. 2014;157:111–118. doi: 10.1016/j.foodchem.2014.01.052. [DOI] [PubMed] [Google Scholar]
  • 57.Pereira V.L., Fernandes J.O., Cunha S.C. Mycotoxins in cereals and related foodstuffs: A review on occurrence and recent methods of analysis. Trends Food Sci. Technol. 2014;36:96–136. doi: 10.1016/j.tifs.2014.01.005. [DOI] [Google Scholar]
  • 58.Guerre P. Worldwide Mycotoxins Exposure in Pig and Poultry Feed Formulations. Toxins. 2016;8:350. doi: 10.3390/toxins8120350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Garcia L.P., Savi G.D., Santos K., Scussel V.M. Fumonisins and fungi in dry soybeans (Glycine Max L.) for human consumption. Food Addit. Contam. Part B. 2016;9:79–84. doi: 10.1080/19393210.2015.1135484. [DOI] [PubMed] [Google Scholar]
  • 60.Krnjaja V., Lević J., Stanković S., Petrović T., Tomić Z., Mandić V., Bijelić Z. Moulds and mycotoxins in stored maize grains. Biotechnol. Anim. Husb. 2013;29:527–536. doi: 10.2298/BAH1303527K. [DOI] [Google Scholar]
  • 61.Sirma A., Senerwa D., Grace D., Makita K., Mtimet N., Kang’ethe E., Lindahl J. Aflatoxin B1 occurrence in millet, sorghum and maize from four agro-ecological zones in Kenya. Afr. J. Food Agric. Nutr. Dev. 2016;16:10991–11003. doi: 10.18697/ajfand.75.ILRI03. [DOI] [Google Scholar]
  • 62.Li X., Zhao L., Fan Y., Jia Y., Sun L., Ma S., Ji C., Ma Q., Zhang J. Occurrence of mycotoxins in feed ingredients and complete feeds obtained from the Beijing region of China. J. Anim. Sci. Biotechnol. 2014;5:37. doi: 10.1186/2049-1891-5-37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Abdou D.A.M., Othman R.M., El-Bordeny N.E., Ibrahim N.A., Abouzeid M.A. Monitoring imported grain-based ingredients used in feed processing for toxigenic moulds and naturally occurring mycotoxins. Egypt. J. Exp. Biol. 2016;15:145–154. doi: 10.5455/egyjebb.20160710115119. [DOI] [Google Scholar]
  • 64.Stoev S.D. Foodborne mycotoxicoses, risk assessment and underestimated hazard of masked mycotoxins and joint mycotoxin effects or interaction. Environ. Toxicol. Pharmacol. 2015;39:794–809. doi: 10.1016/j.etap.2015.01.022. [DOI] [PubMed] [Google Scholar]
  • 65.Greco M.V., Franchi M.L., Golba S.L.R., Pardo A.G., Pose G.N. Mycotoxins and Mycotoxigenic Fungi in Poultry Feed for Food-Producing Animals. Sci. World J. 2014;2014:968215. doi: 10.1155/2014/968215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Aiko V., Mehta A. Occurrence, detection and detoxification of mycotoxins. J. Biosci. 2015;40:943–954. doi: 10.1007/s12038-015-9569-6. [DOI] [PubMed] [Google Scholar]
  • 67.The commission of the European Communities The Commission of the European Communities Commission Regulation (EC) No 386/2009 of 12 May 2009 amending Regulation (EC) No 1831/2003 of the European Parliament and of the Council as regards the establishment of a new functional group of feed additives. Off. J. Eur. Union. 2009 May 12;L 188:66. [Google Scholar]
  • 68.Food Standards Agency Food.gov.uk. [(accessed on 3 August 2017)]; Available online: https://www.food.gov.uk/business-industry/farmingfood/crops/mycotoxinsguidance/animalfeed.
  • 69.Chen Y., Chen Q., Han M., Zhou J., Gong L., Niu Y., Zhang Y., He L., Zhang L. Development and optimization of a multiplex lateral flow immunoassay for the simultaneous determination of three mycotoxins in corn, rice and peanut. Food Chem. 2016;213:478–484. doi: 10.1016/j.foodchem.2016.06.116. [DOI] [PubMed] [Google Scholar]
  • 70.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. 2016;8:342. doi: 10.3390/toxins8110342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Dagnac T., Latorre A., Fernández Lorenzo B., Llompart M. Validation and application of a liquid chromatography-tandem mass spectrometry based method for the assessment of the co-occurrence of mycotoxins in maize silages from dairy farms in NW Spain. Food Addit. Contam. Part A. 2016;33:1850–1863. doi: 10.1080/19440049.2016.1243806. [DOI] [PubMed] [Google Scholar]
  • 72.Jovaišienė J., Bakutis B., Baliukonienė V., Matusevičius P., Lipiński K., Antoszkiewicz Z., Fijałkowska M. Mycotoxins and Biogenic Amines Content and Their Changes During Storages in Produced in Lithuania in Maize Silages. Vet. Med. Zoot. 2016;73:58–63. [Google Scholar]
  • 73.Kamala A., Kimanya M., Haesaert G., Tiisekwa B., Madege R., Degraeve S., Cyprian C., Meulenaer B. De Local post-harvest practices associated with aflatoxin and fumonisin contamination of maize in three agro ecological zones of Tanzania. Food Addit. Contam. Part A. 2016;33:551–559. doi: 10.1080/19440049.2016.1138546. [DOI] [PubMed] [Google Scholar]
  • 74.Mngqawa P., Shephard G.S., Green I.R., Ngobeni S.H., de Rijk T.C., Katerere D.R. Mycotoxin contamination of home-grown maize in rural northern South Africa (Limpopo and Mpumalanga Provinces) Food Addit. Contam. Part B. 2016;9:38–45. doi: 10.1080/19393210.2015.1121928. [DOI] [PubMed] [Google Scholar]
  • 75.Murugesan R. Mycotoxin Survey in the 2015 US Corn. BIOMIN; Herzogenburg, Austria: 2016. [Google Scholar]
  • 76.Calori-Domingues M.A., Bernardi C.M.G., Nardin M.S., de Souza G.V., Dos Santos F.G.R., Stein M.D.A., Gloria E.M.D., Dias C.T.D.S., de Camargo A.C. Co-occurrence and distribution of deoxynivalenol, nivalenol and zearalenone in wheat from Brazil. Food Addit. Contam. Part B. 2016;9:142–151. doi: 10.1080/19393210.2016.1152598. [DOI] [PubMed] [Google Scholar]
  • 77.Egbuta M.A., Mwanza M., Phoku J.Z., Chilaka C.A., Dutton M.F. Comparative Analysis of Mycotoxigenic Fungi and Mycotoxins Contaminating Soya Bean Seeds and Processed Soya Bean from Nigerian Markets. Adv. Microbiol. 2016;6:1130–1139. doi: 10.4236/aim.2016.614102. [DOI] [Google Scholar]
  • 78.Hu X., Hu R., Zhang Z., Li P., Zhang Q., Wang M. Development of a multiple immunoaffinity column for simultaneous determination of multiple mycotoxins in feeds using UPLC-MS/MS. Anal. Bioanal. Chem. 2016;408:6027–6036. doi: 10.1007/s00216-016-9626-5. [DOI] [PubMed] [Google Scholar]
  • 79.Kongkapan J., Poapolathep S., Isariyodom S., Kumagai S. Simultaneous detection of multiple mycotoxins in broiler feeds using a liquid chromatography tandem-mass spectrometry. J. Vet. Med. Sci. 2016;78:259–264. doi: 10.1292/jvms.15-0317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Zachariasova M., Dzuman Z., Veprikova Z., Hajkova K., Jiru M. Occurrence of multiple mycotoxins in European feedingstuffs, assessment of dietary intake by farm animals. Anim. Feed Sci. Technol. 2014;193:124–140. doi: 10.1016/j.anifeedsci.2014.02.007. [DOI] [Google Scholar]
  • 81.Gutleb A.C., Caloni F., Giraud F., Cortinovis C., Pizzo F., Hoffmann L., Bohn T., Pasquali M. Detection of multiple mycotoxin occurrences in soy animal feed by traditional mycological identification combined with molecular species identification. Toxicol. Rep. 2015;2:275–279. doi: 10.1016/j.toxrep.2015.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Xie L., Chen M., Ying Y. Development of Methods for Determination of Aflatoxins. Crit. Rev. Food Sci. Nutr. 2016;56:2642–2664. doi: 10.1080/10408398.2014.907234. [DOI] [PubMed] [Google Scholar]
  • 83.Cheli F., Battaglia D., Gallo R., Dell’Orto V. EU legislation on cereal safety: An update with a focus on mycotoxins. Food Control. 2014;37:315–325. doi: 10.1016/j.foodcont.2013.09.059. [DOI] [Google Scholar]
  • 84.Keller L.A.M., Aronovich M., Keller K.M., Castagna A.A., Cavaglieri L.R., da Rocha Rosa C.A. Incidence of Mycotoxins (AFB1 and AFM1) in Feeds and Dairy Farms from Rio de Janeiro State, Brazil. Vet. Med. 2016;1:29–35. doi: 10.17140/VMOJ-1-106. [DOI] [Google Scholar]
  • 85.Bryden W.L. Mycotoxin contamination of the feed supply chain: Implications for animal productivity and feed security. Anim. Feed Sci. Technol. 2012;173:134–158. doi: 10.1016/j.anifeedsci.2011.12.014. [DOI] [Google Scholar]
  • 86.Wagner C. Critical Practicalities in Sampling for Mycotoxins in Feed. J. AOAC Int. 2015;98:301–308. doi: 10.5740/jaoacint.14-235. [DOI] [PubMed] [Google Scholar]
  • 87.Turner N.W., Bramhmbhatt H., Szabo-vezse M., Poma A., Coker R., Piletsky S.A. Analytical methods for determination of mycotoxins: An update (2009–2014) Anal. Chim. Acta. 2015;901:12–33. doi: 10.1016/j.aca.2015.10.013. [DOI] [PubMed] [Google Scholar]
  • 88.The European Commission The European Commission Commission Regulation (EC) No 691/2013 of 19 July 2013 amending Regulation (EC) No 152/2009 as regards methods of sampling and analysis. Off. J. Eur. Union. 2013 Jul 19;L 197:1–12. [Google Scholar]
  • 89.The European Commission The Commission of the European Communities Comission regulation (EC) No 401/2006 of 23 February 2006 laying down the methods of sampling and analysis for the official control of the levels of mycotoxins in foodstuffs. Off. J. Eur. Union. 2006 Feb 23;L 70:12–34. [Google Scholar]
  • 90.Sifou A., Mahnine N., Manyes L., El Adlouni C., El Azzouzi M., Zinedine A. Determination of Ochratoxin A in Poultry Feeds Available in Rabat area (Morocco) by High Performance Liquid Chromatography. J. Mater. Environ. Sci. 2016;7:2229–2234. [Google Scholar]
  • 91.Chen S., Zhang H. Development of a microwave-assisted-extraction-based method for the determination of aflatoxins B1, G1, B2, and G2 in grains and grain products. Anal. Bioanal. Chem. 2013;405:1623–1630. doi: 10.1007/s00216-012-6564-8. [DOI] [PubMed] [Google Scholar]
  • 92.Li C., Wu Y.-L., Yang T., Huang-Fu W.-G. Rapid Determination of Fumonisins B1 and B2 in Corn by Liquid Chromatography-Tandem Mass Spectrometry with Ultrasonic Extraction. J. Chromatogr. Sci. 2012;50:57–63. doi: 10.1093/chromsci/bmr009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Fan Z., Bai B., Jin P., Fan K., Guo W., Zhao Z., Han Z. Development and Validation of an Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry Method for Simultaneous Determination of Four Type B Trichothecenes and Masked Deoxynivalenol in Various Feed Products. Molecules. 2016;21:1–14. doi: 10.3390/molecules21060747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Guo C., Liu Y., Jiang Y., Li R., Pang M., Liu Y., Dong J. Fusarium species identification and fumonisin production in maize kernels from Shandong Province, China, from 2012 to 2014. Food Addit. Contam. Part B. 2016;9:203–209. doi: 10.1080/19393210.2016.1175515. [DOI] [PubMed] [Google Scholar]
  • 95.Binder E.M. Managing the risk of mycotoxins in modern feed production. Anim. Feed Sci. Technol. 2007;133:149–166. doi: 10.1016/j.anifeedsci.2006.08.008. [DOI] [Google Scholar]
  • 96.Zhang Z., Hu X., Zhang Q., Li P. Determination for multiple mycotoxins in agricultural products using HPLC-MS/MS via a multiple antibody immunoaffinity column. J. Chromatogr. B. 2016;1021:145–152. doi: 10.1016/j.jchromb.2016.02.035. [DOI] [PubMed] [Google Scholar]
  • 97.Wu L., Li J., Li Y., Li T., He Q., Tang Y., Liu H., Su Y., Yin Y., Liao P. Aflatoxin B1, zearalenone and deoxynivalenol in feed ingredients and complete feed from different Province in China. J. Anim. Sci. Biotechnol. 2016;7:1–10. doi: 10.1186/s40104-016-0122-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Hietaniemi V., Rämö S., Yli-Mattila T., Jestoi M., Peltonen S., Kartio M., Sieviläinen E., Koivisto T., Parikka P. Updated survey of the Fusarium species and toxins in Finnish cereal grains. Food Addit. Contam. Part A. 2016;33:831–848. doi: 10.1080/19440049.2016.1162112. [DOI] [PubMed] [Google Scholar]
  • 99.Shephard G.S. Current status of mycotoxin analysis: A critical review. J. AOAC Int. 2016;99:842–848. doi: 10.5740/jaoacint.16-0111. [DOI] [PubMed] [Google Scholar]
  • 100.Wang Q., Chen M., Zhang H., Wen W., Zhang X., Wang S. Enhanced electrochemiluminescence of RuSi nanoparticles for ultrasensitive detection of ochratoxin A by energy transfer with CdTe quantum dots. Biosens. Bioelectron. 2016;79:561–567. doi: 10.1016/j.bios.2015.12.098. [DOI] [PubMed] [Google Scholar]
  • 101.Dzuman Z., Zachariasova M., Lacina O., Veprikova Z., Slavikova P., Hajslova J. A rugged high-throughput analytical approach for the determination and quantification of multiple mycotoxins in complex feed matrices. Talanta. 2014;121:263–272. doi: 10.1016/j.talanta.2013.12.064. [DOI] [PubMed] [Google Scholar]
  • 102.Xu J.-J., Zhou J., Huang B.-F., Cai Z.-X., Xu X.-M., Ren Y.-P. Simultaneous and rapid determination of deoxynivalenol and its acetylate derivatives in wheat flour and rice by ultra high performance liquid chromatography with photo diode array detection. J. Sep. Sci. 2016;39:2028–2035. doi: 10.1002/jssc.201501316. [DOI] [PubMed] [Google Scholar]
  • 103.Bryła M., Waśkiewicz A., Podolska G., Szymczyk K., Jędrzejczak R., Damaziak K., Sułek A. Occurrence of 26 mycotoxins in the grain of cereals cultivated in Poland. Toxins. 2016;8:160. doi: 10.3390/toxins8060160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.León N., Pastor A., Yusà V. Target analysis and retrospective screening of veterinary drugs, ergot alkaloids, plant toxins and other undesirable substances in feed using liquid chromatography-high resolution mass spectrometry. Talanta. 2016;149:43–52. doi: 10.1016/j.talanta.2015.11.032. [DOI] [PubMed] [Google Scholar]
  • 105.Ye H., Lai X., Liu C. Determination of Fumonisin B1 and B2 in Corn Using Matrix-Phase Dispersion Coupled to High Performance Liquid Chromatography. Asian J. Chem. 2013;25:6807–6810. doi: 10.14233/ajchem.2013.14711. [DOI] [Google Scholar]
  • 106.Zhao J., Zhu Y., Jiao Y., Ning J., Yang Y. Ionic-liquid-based dispersive liquid-liquid microextraction combined with magnetic solid-phase extraction for the determination of aflatoxins B1, B2, G1, and G2 in animal feeds by high-performance liquid. J. Sep. Sci. 2016;39:3789–3797. doi: 10.1002/jssc.201600671. [DOI] [PubMed] [Google Scholar]
  • 107.The European Commission The European Parliament and The Council of the European Union Regulation (EC) No 882/2004 of the European Parliament and of the Council of 29 April 2004 on official controls performed to ensure the verification of compliance with feed and food law, animal health and animal welfare rules. Off. J. Eur. Union. 2004 Apr 29;L 165:1–141. [Google Scholar]
  • 108.The European Commission The European Commission Commission regulation (EU) No 519/2014 of 16 May 2014 amending Regulation (EC) No 401/2006 as regards methods of sampling of large lots, spices and food supplements, performance criteria for T-2, HT-2 toxin and citrinin and screening methods of analysis. Off. J. Eur. Union. 2014 May 16;L 147:29–43. [Google Scholar]
  • 109.Venkataramana M., Chandranayaka S., Prakash H.S., Niranjana S.R. Mycotoxins Relevant to Biowarfare and Their Detection. Toxinology. 2014:1–22. [Google Scholar]
  • 110.Anfossi L., Giovannoli C., Baggiani C. Mycotoxin detection. Curr. Opin. Biotechnol. 2016;37:120–126. doi: 10.1016/j.copbio.2015.11.005. [DOI] [PubMed] [Google Scholar]
  • 111.Thermo Fisher Scientific Overview of ELISA. [(accessed on 23 August 2017)]; Available online: https://www.thermofisher.com/pt/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-elisa.html#2.
  • 112.R-Biopharm AG . Good ELISA Practice Manual. R-Biopharm AG; Darmstadt, Germany: 2016. [Google Scholar]
  • 113.Robinson R., Pellenz S. An Introduction to ELISA (Part 2) [(accessed on 25 September 2018)]; Available online: https://www.antibodies-online.com/resources/17/1464/an-introduction-to-elisa-part-2/
  • 114.Bio-Rad Laboratories ELISA Basics Guide. Bio-Rad Laboratories; Kidlington, UK: 2017. pp. 1–40. [Google Scholar]
  • 115.Liang Y., Huang X., Yu R., Zhou Y., Xiong Y. Fluorescence ELISA for sensitive detection of ochratoxin A based on glucose oxidase-mediated fluorescence quenching of CdTe QDs. Anal. Chim. Acta. 2016;936:195–201. doi: 10.1016/j.aca.2016.06.018. [DOI] [PubMed] [Google Scholar]
  • 116.AHDB Beef & Lamb . Mycotoxin Contamination in Animal Feed and Forages. Plus; Warwickshire, UK: 2016. pp. 1–12. [Google Scholar]
  • 117.Carvalho B.F., Ávila C.L.S., Krempser P.M., Batista L.R., Pereira M.N., Schwan R.F. Occurrence of mycotoxins and yeasts and moulds identification in corn silages in tropical climate. J. Appl. Microbiol. 2016;120:1181–1192. doi: 10.1111/jam.13057. [DOI] [PubMed] [Google Scholar]
  • 118.Porricelli A.C.R., Lippolis V., Valenzano S., Cortese M., Suman M., Zanardi S., Pascale M. Optimization and Validation of a Fluorescence Polarization Immunoassay for Rapid Detection of T-2 and HT-2 Toxins in Cereals and Cereal-Based Products. Food Anal. Methods. 2016;9:3310–3318. doi: 10.1007/s12161-016-0527-1. [DOI] [Google Scholar]
  • 119.Li C., Wen K., Mi T., Zhang X., Zhang H., Zhang S., Shen J., Wang Z. A universal multi-wavelength fluorescence polarization immunoassay for multiplexed detection of mycotoxins in maize. Biosens. Bioelectron. 2016;79:258–265. doi: 10.1016/j.bios.2015.12.033. [DOI] [PubMed] [Google Scholar]
  • 120.Lin X., Guo X. Advances in Biosensors, Chemosensors and Assays for the Determination of Fusarium Mycotoxins. Toxins. 2016;8:161. doi: 10.3390/toxins8060161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121.Ma H., Sun J., Zhang Y., Bian C., Xia S., Zhen T. Label-free immunosensor based on one-step electrodeposition of chitosan-gold nanoparticles biocompatible film on Au microelectrode for determination of aflatoxin B1 in maize. Biosens. Bioelectron. 2016;80:222–229. doi: 10.1016/j.bios.2016.01.063. [DOI] [PubMed] [Google Scholar]
  • 122.Zhang X., Li C.-R., Wang W.-C., Xue J., Huang Y.-L., Yang X.-X., Tan B., Zhou X.-P., Shao C., Ding S.-J., et al. A novel electrochemical immunosensor for highly sensitive detection of aflatoxin B1 in corn using single-walled carbon nanotubes/chitosan. Food Chem. 2016;192:197–202. doi: 10.1016/j.foodchem.2015.06.044. [DOI] [PubMed] [Google Scholar]
  • 123.Lu L., Seenivasan R., Wang Y.-C., Yu J.-H., Gunasekaran S. An Electrochemical Immunosensor for Rapid and Sensitive Detection of Mycotoxins Fumonisin B1 and Deoxynivalenol. Electrochim. Acta. 2016;213:89–97. doi: 10.1016/j.electacta.2016.07.096. [DOI] [Google Scholar]
  • 124.Plotan M., Devlin R., Porter J., Benchikh M.E.O., Rodríguez M.L., McConnell R.I., FitzGerald S.P. The Use of Biochip Array Technology for Rapid Multimycotoxin Screening. J. AOAC Int. 2016;99:878–889. doi: 10.5740/jaoacint.16-0115. [DOI] [PubMed] [Google Scholar]
  • 125.Wang B., Wu Y., Chen Y., Weng B., Xu L., Li C. A highly sensitive aptasensor for OTA detection based on hybridization chain reaction and fluorescent perylene probe. Biosens. Bioelectron. 2016;81:125–130. doi: 10.1016/j.bios.2016.02.062. [DOI] [PubMed] [Google Scholar]
  • 126.Wang B., Chen Y., Wu Y., Weng B., Liu Y., Lu Z., Li C.M., Yu C. Aptamer induced assembly of fluorescent nitrogen-doped carbon dots on gold nanoparticles for sensitive detection of AFB1. Biosens. Bioelectron. 2016;78:23–30. doi: 10.1016/j.bios.2015.11.015. [DOI] [PubMed] [Google Scholar]
  • 127.De Girolamo A., Cervellieri S., Visconti A., Pascale M. Rapid analysis of deoxynivalenol in durum wheat by FT-NIR spectroscopy. Toxins. 2014;6:3129–3143. doi: 10.3390/toxins6113129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 128.Coufal-Majewski S., Stanford K., McAllister T., Blakley B., McKinnon J., Chaves A.V., Wang Y. Impacts of Cereal Ergot in Food Animal Production. Front. Vet. Sci. 2016;3:15. doi: 10.3389/fvets.2016.00015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 129.Lee K., Herrman T.J., Nansen C., Yun U. Application of Raman spectroscopy for qualitative and quantitative detection of fumonisins in ground maize samples. J. Cereal Sci. 2013;1:1–14. [Google Scholar]
  • 130.Smeesters L., Meulebroeck W., Raeymaekers S., Thienpont H. Non-destructive detection of mycotoxins in maize kernels using diffuse reflectance spectroscopy. Food Control. 2016;70:48–57. doi: 10.1016/j.foodcont.2016.05.039. [DOI] [Google Scholar]
  • 131.Kos G., Sieger M., McMullin D., Zahradnik C., Sulyok M., Öner T., Mizaikoff B., Krska R. A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits. Food Addit. Contam. Part A. 2016;33:1596–1607. doi: 10.1080/19440049.2016.1217567. [DOI] [PubMed] [Google Scholar]
  • 132.Mignani A.G., Ciaccheri L., Mencaglia A.A., De Girolamo A., Lippolis V., Pascale M. Rapid screening of wheat bran contaminated by deoxynivalenol mycotoxin using Raman spectroscopy—A preliminary experiment; Proceedings of the Sixth European Workshop on Optical Fibre Sensors; Limerick, Ireland. 31 May–3 June 2016; pp. 1–4. [Google Scholar]
  • 133.Lee K.M., Herrman T.J., Yun U. Application of Raman spectroscopy for qualitative and quantitative analysis of aflatoxins in ground maize samples. J. Cereal Sci. 2014;59:70–78. doi: 10.1016/j.jcs.2013.10.004. [DOI] [Google Scholar]
  • 134.Lee K.-M., Herrman T.J. Determination and Prediction of Fumonisin Contamination in Maize by Surface-Enhanced Raman Spectroscopy (SERS) Food Bioprocess Technol. 2016;9:588–603. doi: 10.1007/s11947-015-1654-1. [DOI] [Google Scholar]
  • 135.Betancourt P., Denise S. Microbiota and Mycotoxins in Trilinear Hybrid Maize Produced in Natural Environments at Central Region in Mexico. Adv. Microbiol. 2016;6:671–676. doi: 10.4236/aim.2016.69066. [DOI] [Google Scholar]
  • 136.Mona E.-E., Mona M.H.S., Nagwa S.A. Frequency of fungal and aflatoxin B1 contaminants in cattle feed. Int. J. PharmTech. Res. 2016;9:81–88. [Google Scholar]
  • 137.Sigma-Aldrich . Derivatization Reagents—For Selective Response and Detection in Complex Matrices. Sigma-Aldrich; St. Louis, MO, USA: 2011. [Google Scholar]
  • 138.Liu J., Sun L., Zhang J., Guo J., Chen L., Qi D., Zhang N. Aflatoxin B1, zearalenone and deoxynivalenol in feed ingredients and complete feed from central China. Food Addit. Contam. Part B. 2016;9:91–97. doi: 10.1080/19393210.2016.1139003. [DOI] [PubMed] [Google Scholar]
  • 139.Rao V.K., Girisham S., Reddy S.M. Prevalence of toxigenic Penicillium species associated with poultry house in Telangana, India. Arch. Environ. Occup. Health. 2016;71:353–361. doi: 10.1080/19338244.2016.1140627. [DOI] [PubMed] [Google Scholar]
  • 140.Wang L., Shao H., Luo X., Wang R., Li Y., Li Y., Luo Y., Chen Z. Effect of Ozone Treatment on Deoxynivalenol and Wheat Quality. PLoS ONE. 2016;11:e0147613. doi: 10.1371/journal.pone.0147613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 141.Lee M., Seo D.J., Jeon S.B., Ok H.E., Jung H., Choi C., Chun H.S. Detection of Foodborne Pathogens and Mycotoxins in Eggs and Chicken Feeds from Farms to Retail Markets. Korean J. Food Sci. Anim. Resour. 2016;36:463–468. doi: 10.5851/kosfa.2016.36.4.463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 142.Ok H.E., Jung H., Lee S.-E., Peak O., Chun H.S. Three liquid chromatographic methods for the analysis of aflatoxins in for different corn (Zea mays) matrices. J. Food Compos. Anal. 2016;54:20–26. doi: 10.1016/j.jfca.2016.09.010. [DOI] [Google Scholar]
  • 143.Kim D.-H., Hong S.-Y., Jeon M.-H., An J.-M., Kim S.-Y., Kim H.-Y., Yoon B.R., Chung S.H. Simultaneous determination of the levels of deoxynivalenol, 3-acetyldeoxynivalenol, and nivalenol in grain and feed samples from South Korea using a high-performance liquid chromatography-photodiode array detector. Appl. Biol. Chem. 2016;59:881–887. doi: 10.1007/s13765-016-0238-8. [DOI] [Google Scholar]
  • 144.Savi G.D., Piacentini K.C., Tibola C.S., Santos K., Maria G.S., Scussel V.M. Deoxynivalenol in the wheat milling process and wheat-based products and daily intake estimates for the Southern Brazilian population. Food Control. 2016;62:231–236. doi: 10.1016/j.foodcont.2015.10.029. [DOI] [Google Scholar]
  • 145.Trombete F., Barros A., Vieira M., Saldanha T., Venâncio A., Fraga M. Simultaneous Determination of Deoxynivalenol, Deoxynivalenol-3-Glucoside and Nivalenol in Wheat Grains by HPLC-PDA with Immunoaffinity Column Cleanup. Food Anal. Methods. 2016;9:2579–2586. doi: 10.1007/s12161-016-0450-5. [DOI] [Google Scholar]
  • 146.Boyd R.K., Basic C., Bethem R.A. Trace Quantitative Analysis by Mass Spectrometry. John Wiley & Sons, Ltd.; Hoboken, NJ, USA: 2008. [Google Scholar]
  • 147.Zhao Z., Liu N., Yang L., Deng Y., Wang J., Song S., Lin S., Wu A., Zhou Z., Hou J. Multi-mycotoxin analysis of animal feed and animal-derived food using LC-MS/MS system with timed and highly selective reaction monitoring. Anal. Bioanal. Chem. 2015;407:7359–7368. doi: 10.1007/s00216-015-8898-5. [DOI] [PubMed] [Google Scholar]
  • 148.Wang R.-G., Su X.-O., Cheng F.-F., Wang P.-L., Fan X., Zhang W. Determination of 26 Mycotoxins in Feedstuffs by Multifunctional Clean-up Column and Liquid Chromatography-Tandem Mass Spectrometry. Chin. J. Anal. Chem. 2015;43:264–270. doi: 10.1016/S1872-2040(15)60807-6. [DOI] [Google Scholar]
  • 149.Beltrán E., Ibáñez M., Sancho J.V., Hernández F. Determination of mycotoxins in different food commodities by ultra-high-pressure liquid chromatography coupled to triple quadrupole mass spectrometry. Rapid Commun. Mass Spectrom. 2009;23:1801–1809. doi: 10.1002/rcm.4077. [DOI] [PubMed] [Google Scholar]
  • 150.Sulyok M., Krska R., Schuhmacher R. A liquid chromatography/tandem mass spectrometric multi-mycotoxin method for the quantification of 87 analytes and its application to semi-quantitative screening of moldy food samples. Anal. Bioanal. Chem. 2007;389:1505–1523. doi: 10.1007/s00216-007-1542-2. [DOI] [PubMed] [Google Scholar]
  • 151.Hofgaard I.S., Aamot H.U., Torp T., Jestoi M., Lattanzio V.M.T., Klemsdal S.S., Waalwijk C., Van der Lee T., Brodal G. Associations between Fusarium species and mycotoxins in oats and spring wheat from farmers’ fields in Norway over a six-year period. World Mycotoxin J. 2016;9:365–378. doi: 10.3920/WMJ2015.2003. [DOI] [Google Scholar]
  • 152.Åberg A.T., Solyakov A., Bondesson U. Development and in-house validation of an LC-MS/MS method for the quantification of the mycotoxins deoxynivalenol, zearalenone, T-2 and HT-2 toxin, ochratoxin A and fumonisin B1 and B2 in vegetable animal feed. Food Addit. Contam. Part A. 2013;30:541–549. doi: 10.1080/19440049.2012.760208. [DOI] [PubMed] [Google Scholar]
  • 153.Shephard G.S. Aflatoxin analysis at the beginning of the twenty-first century. Anal. Bioanal. Chem. 2009;395:1215–1224. doi: 10.1007/s00216-009-2857-y. [DOI] [PubMed] [Google Scholar]
  • 154.Herebian D., Zühlke S., Lamshöft M., Spiteller M. Multi-mycotoxin analysis in complex biological matrices using LC-ESI/MS: Experimental study using triple stage quadrupole and LTQ-Orbitrap. J. Sep. Sci. 2009;32:939–948. doi: 10.1002/jssc.200800589. [DOI] [PubMed] [Google Scholar]
  • 155.Shephard G.S., Burger H.M., Gambacorta L., Krska R., Powers S.P., Rheeder J.P., Solfrizzo M., Sulyok M., Visconti A., Warth B., et al. Mycological analysis and multimycotoxins in maize from rural subsistence farmers in the former Transkei, South Africa. J. Agric. Food Chem. 2013;61:8232–8240. doi: 10.1021/jf4021762. [DOI] [PubMed] [Google Scholar]
  • 156.Savi G.D., Piacentini K.C., Marchi D., Scussel V.M. Fumonisins B1 and B2 in the corn-milling process and corn-based products, and evaluation of estimated daily intake. Food Addit. Contam. Part A. 2016;33:339–345. doi: 10.1080/19440049.2015.1124459. [DOI] [PubMed] [Google Scholar]
  • 157.Di Domenico A.S., Busso C., Hashimoto E.H., Frata M.T., Christ D., Coelho S.R.M. Ocorrência de Aspergillus sp., Fusarium sp. e aflatoxinas em híbridos de milho submetidos a diferentes acondicionamentos de armazenagem. Acta Sci. Agron. 2016;38:111–121. doi: 10.4025/actasciagron.v38i1.25621. [DOI] [Google Scholar]
  • 158.Hashemi M. Aflatoxin B1 levels in feedstuffs from dairy cow farms in south of Iran. Food Agric. Immunol. 2016;27:251–258. doi: 10.1080/09540105.2015.1086319. [DOI] [Google Scholar]
  • 159.Dzuman Z., Stranska-Zachariasova M., Vaclavikova M., Tomaniova M., Veprikova Z., Slavikova P., Hajslova J. Fate of Free and Conjugated Mycotoxins within the Production of Distiller’s Dried Grains with Solubles (DDGS) J. Agric. Food Chem. 2016;64:5085–5092. doi: 10.1021/acs.jafc.6b00304. [DOI] [PubMed] [Google Scholar]
  • 160.Changwa R., Abia W., Msagati T., Nyoni H., Ndleve K., Njobeh P. Multi-Mycotoxin Occurrence in Dairy Cattle Feeds from the Gauteng Province of South Africa: A Pilot Study Using UHPLC-QTOF-MS/MS. Toxins. 2018;10:294. doi: 10.3390/toxins10070294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 161.Gizachew D., Szonyi B., Tegegne A., Hanson J., Grace D. Aflatoxin contamination of milk and dairy feeds in the Greater Addis Ababa milk shed, Ethiopia. Food Control. 2016;59:773–779. doi: 10.1016/j.foodcont.2015.06.060. [DOI] [Google Scholar]
  • 162.Magembe K.S., Mwatawala M.W., Mamiro D.P., Chingonikaya E.E. Assessment of awareness of mycotoxins infections in stored maize (Zea mays L.) and groundnut (arachis hypogea L.) in Kilosa District, Tanzania. Int. J. Food Contam. 2016;3:12. doi: 10.1186/s40550-016-0035-5. [DOI] [Google Scholar]
  • 163.Kos J., Hajnal E.J., Šarić B., Jovanov P., Nedeljković N., Milovanović I., Krulj J. The influence of climate conditions on the occurrence of deoxynivalenol in maize harvested in Serbia during 2013–2015. Food Control. 2017;73:734–740. doi: 10.1016/j.foodcont.2016.09.022. [DOI] [Google Scholar]
  • 164.Kachapulula P.W., Akello J., Bandyopadhyay R., Cotty P.J. Aflatoxin contamination of groundnut and maize in Zambia: Observed and potential concentrations. J. Appl. Microbiol. 2017;122:1471–1482. doi: 10.1111/jam.13448. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 165.Bernhoft A., Christensen E., Sandvik M. The Surveillance Programme for Mycotoxins and Fungi in Feed Materials, and Complete and Complementary Feed in Norway 2015. Norwegian Veterinary Institute; Oslo, Norway: 2016. [Google Scholar]
  • 166.Hassan Z.U., Al-Thani R.F., Migheli Q., Jaoua S. Detection of toxigenic mycobiota and mycotoxins in cereal feed market. Food Control. 2018;84:389–394. doi: 10.1016/j.foodcont.2017.08.032. [DOI] [Google Scholar]
  • 167.Hajnal E.J., Kos J., Krulj J., Krstović S., Jajić I., Pezo L., Šarić B., Nedeljković N. Aflatoxins contamination of maize in Serbia: The impact of weather conditions in 2015. Food Addit. Contam. Part A. 2017;34:1999–2010. doi: 10.1080/19440049.2017.1331047. [DOI] [PubMed] [Google Scholar]
  • 168.Abdallah M.F., Girgin G., Baydar T., Krska R., Sulyok M. Occurrence of multiple mycotoxins and other fungal metabolites in animal feed and maize samples from Egypt using LC-MS/MS. J. Sci. Food Agric. 2017;97:4419–4428. doi: 10.1002/jsfa.8293. [DOI] [PubMed] [Google Scholar]
  • 169.Abidin Z., Khatoon A., Arooj N., Hussain S., Ali S., Manzoor A.W., Saleemi M.K. Estimation of ochratoxin A in poultry feed and its ingredients with special reference to temperature conditions. Br. Poult. Sci. 2017;58:251–255. doi: 10.1080/00071668.2017.1293797. [DOI] [PubMed] [Google Scholar]
  • 170.Pleadin J., Vasilj V., Kudumija N., Petrović D., Vilušić M., Škrivanko M. Survey of T-2/HT-2 toxins in unprocessed cereals, food and feed coming from Croatia and Bosnia & Herzegovina. Food Chem. 2017;224:153–159. doi: 10.1016/j.foodchem.2016.12.063. [DOI] [PubMed] [Google Scholar]
  • 171.Xiong J., Xiong L., Zhou H., Liu Y., Wu L. Occurrence of aflatoxin B1 in dairy cow feedstuff and aflatoxin M1 in UHT and pasteurized milk in central China. Food Control. 2018;92:386–390. doi: 10.1016/j.foodcont.2018.05.022. [DOI] [Google Scholar]
  • 172.Gruber-Dorninger C., Jenkins T., Schatzmayr G. Multi-mycotoxin screening of feed and feed raw materials from Africa. World Mycotoxin J. 2018;11:369–383. doi: 10.3920/WMJ2017.2292. [DOI] [Google Scholar]
  • 173.Nyangi C., Mugula J., Beed F., Boni S., Koyano E., Sulyok M. Aflatoxins and Fumonisin Contamination of Marketed Maize, Maize Bran and Maize Used As Animal Feed in Northern Tanzania. Afr. J. Food, Agric. Nutr. Dev. 2016;16:11054–11065. doi: 10.18697/ajfand.75.ILRI07. [DOI] [Google Scholar]
  • 174.Ehsani A., Barani A., Nasiri Z. Occurrence of aflatoxin B1 contamination in dairy cows feed in Iran. Toxin Rev. 2016;35:54–57. doi: 10.3109/15569543.2016.1155622. [DOI] [Google Scholar]
  • 175.Gallo A., Bertuzzi T., Giuberti G., Moschini M., Bruschi S., Cerioli C., Masoero F. New assessment based on the use of principal factor analysis to investigate corn silage quality from nutritional traits, fermentation end products and mycotoxins. J. Sci. Food Agric. 2016;96:437–448. doi: 10.1002/jsfa.7109. [DOI] [PubMed] [Google Scholar]
  • 176.Cogan T., Hawkey R., Higgie E., Lee M.R.F., Mee E., Parfitt D., Raj J., Roderick S., Walker N., Ward P., et al. Silage and total mixed ration hygienic quality on commercial farms: Implications for animal production. Grass Forage Sci. 2017;72:601–613. doi: 10.1111/gfs.12265. [DOI] [Google Scholar]
  • 177.Bahrami R., Shahbazi Y., Nikousefat Z. Occurrence and seasonal variation of aflatoxin in dairy cow feed with estim+ation of aflatoxin M1 in milk from Iran. Food Agric. Immunol. 2016;27:388–400. doi: 10.1080/09540105.2015.1109613. [DOI] [Google Scholar]
  • 178.Yazdi H., Joshaghani H.R., Nejabat M., Mostakhdem M., Hashemi N.B., Chogan A., Abbasinejat Z., Niknejad F. Evaluation of fumonisin and zearalenone levels in wheat of silages in Golestan Province, Northeastern Iran. Biosci. Biotechnol. Res. Commun. 2016;9:804–808. [Google Scholar]
  • 179.Supronienė S., Sakalauskas S., Mankevičienė A., Barčauskaitė K., Jonavičienė A. Distribution of B type trichothecene producing Fusarium species in wheat grain and relation to mycotoxins DON and NIV concentrations. Zemdirbyste-Agriculture. 2016;103:281–288. doi: 10.13080/z-a.2016.103.036. [DOI] [Google Scholar]
  • 180.Asghar M.A., Ahmed A., Iqbal J., Zahir E., Nauman H. Fungal flora and aflatoxin contamination in Pakistani wheat kernels (Triticum aestivum L.) and their attribution in seed germination. J. Food Drug Anal. 2016;24:635–643. doi: 10.1016/j.jfda.2016.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 181.Sanders M., McPartlin D., Moran K., Guo Y., Eeckhout M., O’Kennedy R., De Saeger S., Maragos C. Comparison of Enzyme-Linked Immunosorbent Assay, Surface Plasmon Resonance and Biolayer Interferometry for Screening of Deoxynivalenol in Wheat and Wheat Dust. Toxins. 2016;8:103. doi: 10.3390/toxins8040103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 182.Šliková S., Gavurníková S., Hašana R., Mináriková M., Gregová E. Deoxynivalenol in Grains of Oats and Wheat Produced in Slovakia. Agric. For. 2016;62:343–348. [Google Scholar]
  • 183.Calori-Domingues M.A., Iwahashi P.M.R., Ponce G.H., da Gloria E.M., Dias C.T.D.S., Button D.C., De Camargo A.C. Aflatoxin B1 and zearalenone in soybeans: Occurrence and distribution in whole and defective kernels. Food Addit. Contam. Part B. 2018;11:273–280. doi: 10.1080/19393210.2018.1502818. [DOI] [PubMed] [Google Scholar]
  • 184.Iqbal S.Z., Asi M.R., Nisar S., Zia K.M., Jinap S., Malik N. A Limited Survey of Aflatoxins and Zearalenone in Feed and Feed Ingredients from Pakistan. J. Food Prot. 2016;79:1798–1801. doi: 10.4315/0362-028X.JFP-16-091. [DOI] [PubMed] [Google Scholar]
  • 185.Romera D., Mateo E.M., Mateo-Castro R., Gómez J.V., Gimeno-Adelantado J.V., Jiménez M. Determination of multiple mycotoxins in feedstuffs by combined use of UPLC–MS/MS and UPLC–QTOF–MS. Food Chem. 2018;267:140–148. doi: 10.1016/j.foodchem.2017.11.040. [DOI] [PubMed] [Google Scholar]
  • 186.Lee M.J., Kim H.J. Development of an immunoaffinity chromatography and LC-MS/MS method for the determination of 6 zearalenones in animal feed. PLoS ONE. 2018;13:e0193584. doi: 10.1371/journal.pone.0193584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 187.Chang H., Kim W., Park J.-H., Kim D., Kim C.-R., Chung S., Lee C. The Occurrence of Zearalenone in South Korean Feedstuffs between 2009 and 2016. Toxins. 2017;9:223. doi: 10.3390/toxins9070223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 188.Makau C.M., Matofari J.W., Muliro P.S., Bebe B.O. Aflatoxin B1 and Deoxynivalenol contamination of dairy feeds and presence of Aflatoxin M1 contamination in milk from smallholder dairy systems in Nakuru, Kenya. Int. J. Food Contam. 2016;3:6. doi: 10.1186/s40550-016-0033-7. [DOI] [Google Scholar]
  • 189.Senerwa D., Sirma A., Mtimet N., Kang’ethe E., Grace D., Lindahl J. Prevalence of aflatoxin in feeds and cow milk from five counties in Kenya. Afr. J. Food Agric. Nutr. Dev. 2016;16:11004–11021. doi: 10.18697/ajfand.75.ILRI04. [DOI] [Google Scholar]
  • 190.Vita V., Clausi M.T., Franchino C., De Pace R. Aflatoxin B1 contamination in feed from Puglia and Basilicata regions (Italy): 5 years monitoring data. Mycotoxin Res. 2016;32:229–236. doi: 10.1007/s12550-016-0255-9. [DOI] [PubMed] [Google Scholar]
  • 191.Sahin H.Z., Celik M., Kotay S., Kabak B. Aflatoxins in dairy cow feed, raw milk and milk products from Turkey. Food Addit. Contam. Part B. 2016;9:152–158. doi: 10.1080/19393210.2016.1152599. [DOI] [PubMed] [Google Scholar]
  • 192.Ekici H., Yildirim E., Yarsan E. The effect of seasonal variations on the occurrence of certain mycotoxins in concentrate feeds for cattle collected from some provinces in Turkey. Turk. J. Vet. Anim. Sci. 2016;40:298–303. doi: 10.3906/vet-1501-71. [DOI] [Google Scholar]
  • 193.Mongkon W., Sugita-Konishi Y., Chaisri W., Suriyasathaporn W. Aflatoxin B1 Contamination of Dairy Feeds after Storage in Farm Practice in Tropical Environmen. Biocontrol Sci. 2017;22:41–45. doi: 10.4265/bio.22.41. [DOI] [PubMed] [Google Scholar]
  • 194.Tima H., Rácz A., Guld Z., Mohácsi-Farkas C., Kiskó G. Deoxynivalenol, zearalenone and T-2 in grain based swine feed in Hungary. Food Addit. Contam. Part B. 2016;9:275–280. doi: 10.1080/19393210.2016.1213318. [DOI] [PubMed] [Google Scholar]
  • 195.Oplatowska-Stachowiak M., Sajic N., Xu Y., Haughey S.A., Mooney M.H., Gong Y.Y., Verheijen R., Elliott C.T. Fast and sensitive aflatoxin B1 and total aflatoxins ELISAs for analysis of peanuts, maize and feed ingredients. Food Control. 2016;63:239–245. doi: 10.1016/j.foodcont.2015.11.041. [DOI] [Google Scholar]
  • 196.Zhang Y., Yang J., Lu Y., Ma D.Y., Qi M.G., Wang S. A competitive direct enzyme-linked immunosorbent assay for the rapid detection of deoxynivalenol: Development and application in agricultural products and feedstuff. Food Agric. Immunol. 2017;28:516–527. doi: 10.1080/09540105.2017.1306491. [DOI] [Google Scholar]
  • 197.Buśko M., Stuper K., Jeleń H., Góral T., Chmielewski J., Tyrakowska B., Perkowski J. Comparison of Volatiles Profile and Contents of Trichothecenes Group B, Ergosterol, and ATP of Bread Wheat, Durum Wheat, and Triticale Grain Naturally Contaminated by Mycobiota. Front. Plant Sci. 2016;7:1243. doi: 10.3389/fpls.2016.01243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 198.Jedziniak P., Pietruszka K., Burek O. Development of a UPLC-MS/MS Method for Determination of Mycotoxins in Animal Feed. French Agency for Food, Environmental and Occupational Health & Safety; Maisons-Alfort, France: 2016. pp. 63–69. Euroreference 1. [Google Scholar]
  • 199.Solfrizzo M., Gambacorta L., Bibi R., Ciriaci M., Paoloni A., Pecorelli I. Multimycotoxin Analysis by LC-MS/MS in Cereal Food and Feed: Comparison of Different Approaches for Extraction, Purification, and Calibration. J. AOAC Int. 2018;101:647–657. doi: 10.5740/jaoacint.17-0339. [DOI] [PubMed] [Google Scholar]
  • 200.Bertuzzi T., Mulazzi A., Rastelli S., Pietri A. Hidden Fumonisins: Simple and Innovative Extractions for Their Determination in Maize and Derived Products. Food Anal. Methods. 2016;9:1970–1979. doi: 10.1007/s12161-015-0377-2. [DOI] [Google Scholar]
  • 201.Bernhardt K., Valenta H., Kersten S., Humpf H.U., Dänicke S. Determination of T-2 toxin, HT-2 toxin, and three other type A trichothecenes in layer feed by high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS)—Comparison of two sample preparation methods. Mycotoxin Res. 2016;32:89–97. doi: 10.1007/s12550-016-0244-z. [DOI] [PubMed] [Google Scholar]
  • 202.Moreno V., Zougagh M., Ríos Á. Hybrid nanoparticles based on magnetic multiwalled carbon nanotube-nanoC18SiO2 composites for solid phase extraction of mycotoxins prior to their determination by LC-MS. Microchim. Acta. 2016;183:871–880. doi: 10.1007/s00604-015-1722-2. [DOI] [Google Scholar]

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