msc thesis report estimating the impact of increased ... - wur

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MSc Thesis Report

Estimating the impact of increased mycotoxin control on crop losses

saved in Europe

Manouil Sofoulis (920629784100)

MSc. Food Safety – Applied

Wageningen University and Research

Chair Group Business and Economics

Thesis Supervisor: Ine van der Fels- Klerx

Thesis Code: BEC-80436

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Table of Contents

1. List of Abbreviations ............................................................................................................................................. 3

2. Abstract ................................................................................................................................................................. 4

3. Introduction .......................................................................................................................................................... 5

4. Literature review................................................................................................................................................... 8

4.1. Selection of mycotoxins ........................................................................................................................... 8

4.2. Occurrence of selected mycotoxins on cereals ....................................................................................... 8

4.3. Effects of cereal processing on mycotoxin reduction ............................................................................ 10

4.4. Novel interventions to reduce mycotoxins ............................................................................................ 11

5. Materials & Methods .......................................................................................................................................... 14

5.1. Study Outline ......................................................................................................................................... 14

5.2. Setting the Baseline ............................................................................................................................... 15

5.3. Data collection ....................................................................................................................................... 18

5.4. Cereal Processing ................................................................................................................................... 23

5.5. Cost - Benefit Analysis ........................................................................................................................... 23

6. Results ................................................................................................................................................................. 26

6.1. Setting the Baseline ............................................................................................................................... 26

6.2. Baseline Losses ...................................................................................................................................... 29

6.3. Effect of cereal processing ..................................................................................................................... 32

6.4. Cost Benefit Analysis .............................................................................................................................. 35

6.5. Sensitivity analysis – MCSA .................................................................................................................... 40

7. Discussion ........................................................................................................................................................... 48

7.1. General Remarks - Baseline ................................................................................................................... 48

7.2. Cost – Benefits Analysis & MCSA ........................................................................................................... 50

8. Conclusions ......................................................................................................................................................... 52

9. Annexes ............................................................................................................................................................... 53

A. Total Agricultural and cereal output of EU – 28 ......................................................................................... 53

B. Properties of selected mycotoxins ............................................................................................................. 53

C. Effects of cereal processing ........................................................................................................................ 55

D. Available commercial optical wheat sorters .............................................................................................. 59

10. References .......................................................................................................................................................... 61

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1. List of Abbreviations

CBA Cost – Benefit Analysis

DDGS Dried Distillers Grains with Solubles

DON Deoxynivalenol (Vomitoxin)

EFSA European Food Safety Authority

EU MS European Union Member State (s)

FAO Food and Agriculture Organization

GI (tract) Gastrointestinal (tract)

LIC Low-Income and Developing countries

IARC International Agency for Research on Cancer

MLs Maximum Levels

MCSA Monte Carlo Simulation Analysis

RASFF Rapid Alert System for Food and Feed

OTA Ochratoxin A

ZEA Zearalenone

WHO World Health Organization

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2. Abstract

This report was carried out to estimate the impact of implementing measures of increased mycotoxin

control. in relation with the annual economic losses in Europe (baseline), derived from market rejections

for 3 selected cereals (wheat, maize and durum wheat) and 4 selected mycotoxins (DON, ZEA, Aflatoxins

and Fumonisins). The first part of the study consisted of an estimation of the annual losses from

mycotoxins (establishing baseline). In the second part, cereal processing was examined if was sufficient in

reducing mycotoxin levels in cereals below acceptable levels. Finally, two control measures were

examined via a Cost – Benefit Analysis and a Monte Carlo Simulation Analysis was conducted to examine

the uncertainty and variability of the baseline input variables. The annual economic losses for the selected

regions of Europe were derived using a model (Wu, Liu, & Bhatnagar, 2008) calculating the cost per grower

per hectare and then extrapolated to whole cultivation area. Results showed that losses from

deoxynivalenol in wheat ranged between € 8,8 - € 18,4 ml for a Low Contamination scenario and between

€ 63,9 - € 133,7 ml for a High Contamination scenario. For Zearalenone they ranged between € 2,7- € 3,8

ml for Low Contamination and € 62,5 - € 130,7 ml for High Contamination scenario respectively. Aflatoxins

accounted for € 61,7 - € 102,9 ml (Low Contamination scenario) and € 250,6 - € 417,8 ml (High

Contamination Scenario), while fumonisins for € 42,5 – € 117ml (Low Contamination sc.) and € 467,8 – €

779,9 ml (High Contamination sc.). Durum wheat losses were in the range of € 13,8- € 19,7 (Low

Contamination sc.) and € 64,2- € 92ml, (High Contamination sc.) for DON and € 1,1 - € 1,6 ml (Low

Contamination sc.) and € 5,7 - € 8,2 ml (High Contamination sc.) for ZEA. These losses account for 0.30% -

0.61% (Low Contamination sc.) and 2.11% - 3.61% (High Contamination sc.) of the total cereal production

output of the EU (2015-2016 data). Moreover, cereal processing was moderately sufficient in reducing

mycotoxin levels on cereals as the most frequently used form of mycotoxin reduction, although it fails to

eliminate them completely. Finally, two control measures (Biocontrol and Optical wheat sorter) were

examined and compared via a Cost-Benefit Analysis; Aflatoxin Biocontrol (partially cost beneficial with a

Costs/Benefits ratio of 0.5 – 2.2 depending on the degree of contamination) and Improved Optical Sorting

for Fusarium toxins which was more beneficial (Costs/Benefits ratio 0.04 – 0.74). Finally, due to the high

variance and uncertainty of the input data, a Monte – Carlo sensitivity analysis (MCSA) was conducted to

examine the contribution of each variable to the baseline (economic losses). The MCSA showed that the

price differentiation between the alternative use of cereals had the higher contribution to the baseline

losses with the rejection rate (concentrations > MLs) being the second contributor.

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3. Introduction

Cereal grains are plant produced seeds belonging to the botanological family Gramineae. Cereals are the

main source of nutrition for almost the entire world population since the dawn of the agricultural

revolution. The EU is one of the world’s top cereal producer and trader, with cereal production taking up

to 25% of the total crop production value, 1/8 of the total agricultural value (Stanciu et al., 2015) and

accounting for more than € 300-400 bn turnover, annually. From that cereal production, each year

mycotoxin contaminations claim a considerable portion of the harvested crops with severe economic

consequences. According to FAO, a quarter of the world’s cereal production is contaminated with

mycotoxins. However, reports are rising the percentage up to 50% and also raise the issue of occurrence

of emerging mycotoxins (Kabak, Dobson, & Var, 2006).

Mycotoxins are naturally occurring and have low molecular weight (Mitchell et al., 2017), secondary

fungal metabolites which can have adverse health effects for humans and animals (FAO, 2017). They can

contaminate various food and feed commodities to varying extent. They can occur at all production stages

including pre-harvest, post-harvest, processing and storage conditions (Darwish et al., 2014).

The main types of fungi that are responsible for mycotoxin production are Aspergillus spp., Penicillium

spp. and Fusarium spp. (Streit et al., 2012). According to literature, (Binder et al., 2007); (Neme &

Mohammed, 2017), the most prevalent mycotoxins with regard to occurrence in cereal grains are

aflatoxins, ochratoxin A, deoxynivalenol (DON), zearalenone (ZEA) and fumonisins.

The main setback regarding mycotoxin control is that mycotoxin occurrence on commodities is irreversibly

related to weather conditions, which are by default uncontrollable (Zain, 2011). However, there have been

studies and models developed for predicting mycotoxin prevalence and degree of contamination (van der

Fels-Klerx et al., 2012). Factors affecting fungal growth and subsequent mycotoxin production are mainly

temperature and moisture; with extreme fluctuations contributing the most to mycotoxin occurrence

(Schatzmayr & Streit, 2013). In order for mycotoxins to be produced, the fungus has to be stressed to

some extent, mainly by environmental conditions mentioned above (Rodrigues & Naehrer, 2012).

The EU strictly regulates the MLs of mycotoxins on feed and food. Regarding the presence of DON and

ZEA in wheat, the EC has set a limit 1250 μg/kg and 100 μg/kg respectively for their presence in wheat

and 1750 μg/kg and 100 μg/kg for durum wheat respectively (EC, 2006). For maize, the EC has set the MLs

for aflatoxins to 20 μg/kg (B1 for animal feed) and 10.0 μg/kg for the sum of all aflatoxins for maize

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destined for processing (sorting) before human consumption (EFSA, 2013a). For fumonisins the limit is set

at 4,000 μg/kg for the sum of all fumonisins (EFSA, 2005).

The issue of mycotoxins is a persisting and everlasting one. After the discovery of the numerous adverse

health effects mycotoxins pose to humans and animals worldwide, focus has been made to other aspects

of these hazards. Despite the fact that, in general, mycotoxin concentrations within the EU are below the

regulatory limits, thus complying most of the times for trade and consumption, often economic losses

derived from the exceedance of Maximum Levels (MLs) are an issue (Zain, 2011).

Whereas economic losses derived from mycotoxin contamination in developed countries are more a

result of market losses due to rejection of contaminated commodities due to use, redirection or total

disposal, in developing countries the long term intake of mycotoxins, especially in high doses and in

combination to poor nutrition can lead to severe health implications or even death (Wu, Liu, & Bhatnagar,

2008).

According to literature (Bryden, 2012); (Felicia Wu, Miller, & Casman, 2004); (Wu & Munkvold, 2008) and

Dolhman (2003) in the US, $630 – $2.5 bn are lost each year as a total from the agricultural output, taken

in account all mycotoxins. In more detail, only from aflatoxins, fumonisins and DON, the crop losses

account for $932 ml, while losses related to reduced animal productivity are around $6 ml. In general,

animal production losses are much lower compared to crop losses. For fumonisins they account for

$126.000 while market losses take up $0.92 – $19.3 ml in a low contamination year. When a high

infectious year occurs, the numbers are up to $27,5 – $48,8 ml for market losses and $320,000 animal

losses. For aflatoxins, when applying the EU MLs to African countries, a total of $670 ml can be lost each

year, while exports from US, China and Argentina to the EU (strictest ML worldwide) cause $300 ml in

market losses ( Wu et al., 2004). While estimations and studies have been conducted in the U.S. examining

the economic losses derived from mycotoxins on cereal grains, there is no equivalent study for the

European cereal sector.

This study was the first of its kind, examining the overall economic losses on 3 selected cereals related to

4 major mycotoxins while at the same time estimating the costs and benefits of implementing measures

for increased mycotoxin mitigation. The selection of cereals for this study was based on the fact that cereal

grains (wheat and maize mostly), contribute the most to human/animal food/feed consumption (Cheli

et al., 2013). At the same time according to Pinotti et al (2016), cereals are major food commodities that

are frequently (30% - 100%) contaminated with mycotoxins. Considering also that the EU is a major food

exporter, including cereals, high economic losses are expected. In addition, no other studies have been

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conducted on European level with the aim at estimating the annual losses derived from market rejection

of cereal grains due to mycotoxin contaminations. Furthermore, the widespread application of

commercially available novel measured for mycotoxin control has not been studied either. As the

consumers demand even more safe food and mycotoxin contaminations tend to be more persistent, as a

result of climate change, this report would allow growers and relevant stakeholders to consider applying

increased control measures while maintaining a profit.

The aim of this study was first to estimate the economic losses derived solely from the qualitative

degradation of cereal grains (unprocessed) and market rejection due to mycotoxin contaminations as a

baseline. Although animal losses and human health loss do take place, according to literature, in the

developed countries (like EU and US) market rejections are the major cause of economic losses, as human

health and animal derived losses are negligible compared to market losses, (Wu et al., 2004). During the

second part of this study, the effect of cereals processing on mycotoxin reduction was assessed. In

addition, a Cost-Benefit Analysis was conducted for two control measures that were selected and were

examined on their efficacy on reducing mycotoxin concentrations while presenting economic incentives

to growers; biocontrol using atoxigenic fungal strains and improved vision sorting after harvest and before

cereal processing. A 10-year period was considered to account for yearly variation.

The main objective of this study was to estimate the impact of increased mycotoxin control on the crop

losses saved, in Europe. This overall objective was broken down into the following sub-objectives:

➢ To estimate the current economic losses due to mycotoxin contamination in cereals in Europe.

➢ To identify the effectiveness of cereal processing in reducing mycotoxin levels below regulatory

limits.

➢ To investigate the costs and benefits of increased mycotoxin control (novel interventions) in terms

of volume and quality of the cereals, and (expected) reduced economic losses.

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4. Literature review

4.1. Selection of mycotoxins

According to literature, the main and most troublesome mycotoxins with regard to adverse health effects,

economic significance and European safety regulations are aflatoxins, fumonisins, Deoxynivalenol (DON),

Zearalenone (ZEA), and Ochratoxin A (OTA), (Streit et al., 2012); (Lee & Ryu, 2017); (Rodrigues & Naehrer,

2012), and due to their frequent presence in commodities are among those which are regulated by the

authorities (EU and US). There are more mycotoxins with adverse effects that are also regulated by the

authorities (H2,HT2, Nivalenol, ergot alkaloids), (Binder et al., 2007) but for the scope of this report only

those 4 (Aflatoxins, Fumonisins, DON & ZEA) with major examined and researched economic as well as

adverse health implications will be examined, mainly due to their high occurrence as well as due to limited

data regarding commercial application of novel strategies to control the rest of the mycotoxins.

Note: All adverse health effects and mode of action of the selected mycotoxins above can be seen on

Annex - Table B.

4.2. Occurrence of selected mycotoxins on cereals

Mycotoxin prevalence on cereals is extensively studied and researched. One recent and extensive study

is that of Streit and Schatzmayr (2013), who have published an almost 10 - year survey with 19,000

samples and 70,000 individual analyses. According to this study over 70% (72%) of the samples were

contaminated and detected mycotoxins included aflatoxins, fumonisins, deoxynivalenol and zearalenone

in amounts above the limit of detection. In detail, prevalence of DON and ZEA on Northern Europe was

about 64% and 22% respectively, while aflatoxins and fumonisins were not so prevalent. In Central

Europe, Fumonisins were detected in 51% of the time, while aflatoxins 19%. In addition, almost 4 out of

10 (38%) of the samples were co – contaminated with more than one toxin. In another long term study,

(Rodrigues & Naehrer, 2012) it was demonstrated that the prevalence of fumonisins and aflatoxins on

corn samples was 60% and 31% respectively, while DON and ZEA in wheat samples were present 55% of

the time and 15% in Northern Europe respectively. More studies were examined to derive to accurate

prevalence rates for the mycotoxins in wheat and maize, and they are presented on the SPREADSHEET.

Regarding durum wheat the Italian field study of Bertuzzi (2014) was examined, presenting higher

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incidence of DON and ZEA for durum wheat than normal wheat. DON was detected in most of the samples

(>97%) with various rejection rates (3.9% - 39.6%) and some spiked concentrations were also detected

(>25.000 μg.kg). ZEA had much lower rejection rates (<3%) with prevalence being also low. Another study

on Italian durum wheat samples (Alkadri et al., 2014), showed higher prevalence rates for DON and ZEA

(59% and 35% respectively), however the rejection rates (exceedance of MLs) was relatively low, in

accordance with EFSA’s report (EFSA, 2011) and (EFSA, 2013b) reporting low non-compliance rates ( 1-

3%). Non-compliance rates were varying for DON on soft wheat, with EFSA (2013b) reporting around 2%

non-compliance, but Meister (2009) reporting 17% for DON and 9-10% for ZEA. In fact, Meister noted that

a high exceedance rate (>10%) occurs every 5 years and with extrapolation to each year it was assumed a

2% annually. Rejection rates were higher for fumonisins on corn and DDGS (3 – 9.5%), although for

aflatoxins were relatively lower (2-4%).

Regarding DON contamination of wheat, according to relevant literature, there was a fluctuation in regard

with extreme weather conditions. Streit et al., (2013) noted that the all-time low of the survey detected

a 38% occurrence in 2007 an all-time high of 70% in 2008 followed by a drop again to 46% in 2011. The

latter result was in accordance with van der Fels (2012) that notes that the overall DON concentration in

North Europe is around 50% (45.5%) The occurrence of the samples above the LOD for DON was 45.2%

but still lower than samples originated from regional countries (Lithuania, Germany almost 100%).

Regarding DON levels, they were in general below the maximum permitted levels (1250 / 1750 μg/kg for

wheat and durum wheat respectively). In the main surveys the majority of the 75th percentile was lower

than the limits mentioned above, thus complying with regulatory levels. Even if the maximum limits were

much higher than the MLs (>50,000 μg/kg) this was a result of extremely contaminated – spiked samples,

taken from regions that have previously suffered from extreme weather conditions in the relevant

grow/harvest period. However, there were still samples that exceeded the maximum levels pointing that

the maximum limits can and are, indeed being exceeded.

Assumption

It was assumed that occurrence and concentration values are homogenous within each region cluster and

they do not vary significantly. In addition, data below the LOD were considered as contamination free.

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4.3. Effects of cereal processing on mycotoxin reduction

Regarding the initial control measures, GAPs and GMPs are assumed to be incorporated within the

baseline. The reason for this assumption was that as the EU has one of the highest level on agricultural

technology, infrastructure and level of food safety, thus and the strictest MLs worldwide as well

compared to non-EU countries and the most important factor regarding mycotoxin contamination is

prevention in the first place in the field (Jard et al., 2011) Furthermore, as GMPs and GAPs are

prerequisites set by European Law (GFL 178/2002) it was assumed that all growers / producers (should)

comply with these standards. Concerning the effect of the currently applied methods for mycotoxin

reduction, food processing can decrease the mycotoxin presence in food and feed materials, by physical

and/or chemical and/or enzymatic degradation into less toxic metabolites. It is stated that physical

removal of contaminated parts during cereal processing is effective enough for complying with regulatory

limits, however, it does not eliminate completely the presence of mycotoxins (Karlovsky et al., 2016). The

reduction potential of grain processing varies according to factors which include the type of commodity,

the type of mycotoxin and most importantly, the type of mycotoxin (and concentration) under scope. For

chemical detoxification, it used to be prohibited by the EC but from July 1st 2017, it is allowed as long as

EFSA has concluded that the particular detoxification method complies with certain safety criteria (EC,

2015).

In Karlovsky et al., (2016), the effect of cereal processing on mycotoxin concentrations before and after

processing was examined and stated that cereal processing for the bulk of cereal production is the main

route to mycotoxin mitigation. Through cereal processing, there can be a significant reduction of

mycotoxin concentration. Depending on the type of processing, the parts of the grains that are being

processed and the nature of the mycotoxin (as mycotoxin categories consist of similar compounds) there

can be a varied range of reduction.

Note: Detailed effects of cereal processing and the literature used can be found on Annex – Section D.

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4.4. Novel interventions to reduce mycotoxins

According to literature, (Kabak, Dobson, & Var, 2006); (Karlovsky et al., 2016); (Milani & Maleki, 2014), a

series of strategies have been or being developed to prevent the growth of mycotoxigenic fungi as well as

to decontaminate and/or detoxify mycotoxin contaminated foods and animal feeds. The mentioned

control measures, or “strategies” include measures that aim on preventing mycotoxin formation and,

subsequently, contamination of commodities as well the detoxification of mycotoxins present in food and

feed and the reduction of mycotoxin absorption in the GI tract. In this report, the first two strategies are

considered, namely the prevention of contamination (pre-harvest) with application of biocontrol for

aflatoxins in maize, and the effort to reduce mycotoxins’ concentrations on a post-harvest storage level

with an improved vision sorting machine for effective sorting and removal of fusarium damaged kernels

(FDK).

Pre-harvest Aflatoxin Bio-control

Biocontrol with atoxigenic fungal strains is a promising method for effective mycotoxin mitigation (Wu et

al., 2008); (Wu & Khlangwiset, 2010). It has also been experimentally demonstrated. Dorner (2010)

conducted a 2-year field study examining the efficacy of applying an atoxigenic fungal strain (A. flavus

NRRL 21882) in contaminated maize. The mode of action is that strains that do not produce aflatoxins

compete with toxigenic producing fungi for the same ecological niche, thus excluding in the long/mid -

term strains responsible for aflatoxin production. These fungal strains have been commercialized in the

U.S. by Syngenta Crop Protection launching Alfa-Guard® a product containing seeds coated with

atoxigenic fungal strains. Dorner (2010) conducted his study in Texas, U.S., between 2007-2008. During

his field tests, although aflatoxin mean concentration was low in 2007, after applying a 11.2 and 22.4

kg/ha of biopesticide, the average reduction was almost on the same level (85%, 88%, average 86,5%),

however, there was not a significant difference in results depending on the dose between the two

applications (11.2 & 22.4 kg/ha) resulting in the same level of reduction. The application area varied

between 0 and 100 ha. After the field experiment was over, 450 samples were sent for analysis and

aflatoxin content determination was determined. The mean concentrations reduction for those years

were 85% and 88% respectively, showing that aflatoxins and mycotoxin mitigation in general with

biocontrol methods like the above, offer great reduction potential. However, in a more recent study

conducted by Abbas et al., (2011) the reduction of mycotoxin after the first 20 days of the inoculation was

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98% showing increased efficacy compared to Dorner et al. In that study Abbas et al. (2011) demonstrated

a comparison of the capabilities of NRRL 21882, AF36, and K49 strains to reduce aflatoxins in corn tested

with equal numbers of conidia of toxigenic A. flavus strains (F3W4 and K54), aflatoxins were finally

reduced by 83 and 98% by K49 and NRRL 21882, respectively, while AF36 reduced aflatoxins by 80%.

Post-harvest FDK removal with Optical Sorter

According to Delwiche, Pearson, & Brabec's study (2005), on the efficiency of high-speed optical sorter in

wheat grain kernels, soft wheat (32 samples of red winter, 3 of white wheat and 7 from rejected batches)

with high conenctration of DON (600 – 2000 μg/kg) from US mills. The sorter that was used was a

bichromatic (two wavelengths were used; 675 and 1480 nm) with a rejection rate of approximately 10%.

The mean reduction, expressed as ratio of DON conenctration sorted/unsorted was 51%, however there

was a great variation (18% - 112%). Two more sortings were applied after the first one (three in total) and

the overall final DON conenctration was 16%-69% (mean 42.5%) the original material. These reduction

rates may seem high, but as the researchers note, optical sorting for wheat has some drawbacks, mainly

the high input volume of grains and the high initial cost of invenstment. Based on (Delwiche et al., 2005)

a high speed optical sorter (Scan Master II, SATAKE), with a single pass sorting resulted in a 49% reduction

of DON levels (initial <1000 μg/kg and >20.000 μg/kg). Another optical sorter with an abrasive laboratory

mill gave a 64% and 69% reduction for initial levels of 382 and 4203 μg/kg respectively. The mean

reduction for optical sorter was the average of the above reductions at 60% (58.3%).

Figure 1. Appearance of non-contaminated (left) and strongly contaminated (right, 1400 ppm) maize. Source: (Sosa et al., 2013)

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Other means of mycotoxin reduction through cereal processing

Ammonization in maize decreases aflatoxin levels in feed and has effectively several years of use (Park et

al., 1988), being particularly effective against AFB1 in high temperature and pressure conditions. However,

is costly and can degrade food quality due to ammonia presence in food (Huwig et al.,2001). Chemical

treatment is not allowed unrestrictedly in the EU, based on Regulation (EU) 2015/786 as of 19 May 2015

noting criteria for detoxification processes. It is stated that “a detoxification process shall only be applied

if the EFSA has performed, on request of the Commission, a scientific assessment of the detoxification

process, concluding that the detoxification process complies with the acceptability criteria.”

The use of alkaline (or acidic) solutions can have a great effect on water solubility of the mycotoxins and

for that reason it is often preferred as a way for mycotoxin mitigation, although chemical decontamination

of foods, as long the specific detoxification process, should comply with certain criteria. Torres, Guzmán-

Ortiz, & Ramírez-Wong, (2001) point out that the use of nixtamalization (addition of alkaline solution) can

result in a 64.5% (51-78%) reduction of aflatoxin levels, while Hammed (1993) rises that reduction to 95%

if during extrusion ammonia is added (alkaline solution). Extrusion without alkaline addition results in 65%

(50-80%) reduction of aflatoxins according to Bullerman & Bianchini, (2007).

Irradiation is a physical process of food decontamination used for more than two decades., The main

principle behind irradiation is that high energy rays (γ), emitted from a beam source are shoot to the

sample, affecting the DNA of organisms that should not be on the sample, like insects and microorganisms.

This technique is examined for years and WHO has concluded from 1997 that irradiation poses no threat

to human health nor to the nutritional value of the commodities. Markov et al., (2015) looked into the

potential of inactivation of aflatoxin producing fungi via irradiation with γ rays regarding the germination,

spore formation and growth of strains responsible for production of aflatoxins, notably aflatoxin B1

(AFB1). The samples were maize both artificially and naturally contaminated with toxigenic strains (A.

parasiticus, A. flavus, A. niger). Following an application of 5kGy and 10kGy dose there was a mean

reduction of 69.8% and 94.5% for naturally and 60% and 85% for artificially contaminated samples

respectively. It is to be noted however, that application of irradiation is highly costly, and consumers tend

to be negatively predisposed with commodities that have been irradiated.

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5. Materials & Methods

5.1. Study Outline

This study consisted of three parts. First, current economic losses due to mycotoxin contamination in

Europe were calculated, using an existing model (Wu, Bhatnagar & Liu 2008) for losses from mycotoxin

contamination per hectare. Given the degree of uncertainty and variability of the input parameters of the

model, the model variables were simulated using the Monte – Carlo approach (Monte-Carlo Simulation

Analysis - MCSA) to examine to what extend each of the input variables of the model affected the

economic losses calculated for the baseline situation.

To identify the effects of cereal processing as a form of mycotoxin control, a literature review was

conducted. This review focused on identifying cereal processing techniques and examining their capacity

to reduce mycotoxin levels below desirable limits. This step constituted the second part of the study; to

what extend can current cereal processing reduce mycotoxin levels in cereals? (effectively meaning below

EU MLs).

Based on the literature review, two available commercial interventions were selected for the third part of

the study. Biocontrol and improved optical cereal sorting were selected based on their commercial

availability and ease of application. More techniques were considered but they were either not fully

commercialized (biodegradation) or were too expensive (ozonation). The third part examined how cost-

beneficial the two interventions for mycotoxin contamination would be if they were applied to one of the

three regions in Europe, using a Cost – Benefit Analysis (with a 10-year horizon). This Cost-Benefit Analysis

allowed to compare the two interventions using the costs / benefits ratio. As benefits were considered

the reduced annual monetary losses from mycotoxin contaminations (as calculated in the first part) and

the costs consisted of the costs of interventions (materials & capital investment costs).

For assessing the economic losses in the baseline situation and for the Cost-Benefit Analysis, two scenarios

were taken into account; a Low Contamination scenario and a High Contamination scenario, coinciding

with a normal and a bad harvesting season, respectively. The reason for these two scenarios was that -

according to literature - environmental factors can cause a considerable rise in mycotoxin contaminations.

For the Monte-Carlo Analysis there was no distinction between the two scenarios and results were

presented as a unified range of total losses per cereal/region and mycotoxin.

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5.2. Setting the Baseline

Before calculating the annual crop losses in Europe, regions were identified. Europe was divided in three

parts (clusters) based on environmental conditions, the most cultivated cereal in each region and the

mycotoxin mostly present in the particular cereal.

Figure 5.2.1 presents the regional clustering for each cereal in Europe. In the North-West, production of

soft wheat is most prevalent, in Central-East the bulk of European maize is produced, and in the South

Europe there is a considerable production of durum wheat. Wheat and maize constitute the bulk of the

European cereal production, making up 46% and 20% of total cereals EU-28 gross production on a 5-year

average, respectively (EC, 2017). Given European production data from 2007-2016 (with 2016 being an

estimation), wheat took up almost 50% (48%) of total cereal production each year. Maize came second

with a 5-year average of 62,650 thousand tones, and barley was close behind with 60,646 thousand tones.

Figure 5.2.1. Harvested production of cereals (including seed) and most commonly grown cereals. (Source: Eurostat, 2015)

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Apart from an analysis on commodities with high production volumes, also a commodity with a lower

production but high market value was included in the study. This commodity was hard wheat – Triticum

durum. The bulk of gross production of durum wheat is located at the northern coasts of the

Mediterranean basin, on the south parts of Europe; almost exclusively in Italy, Spain, Greece and France.

The gross production alone from these 4 countries constitutes 96% of the total European production

(2.368 / 2.475 thousand tons on a 10-year average).

The top 12 producing countries (per volume basis) within each region (North – West, Central and South

Europe) were selected, in addition to the geographical distinction, based on their production of the

selected commodity. For example, as France, Germany, UK and Poland (North – West cluster) take up

approximately 55% of the total EU-28 wheat production, they were included (besides other countries) in

the wheat producing countries. For maize, France, Romania, Hungary and Italy took up 60% of the total

maize production (Eurostat, Annual crop statistics, 2016). Overall, each cluster of wheat, maize and durum

wheat producing countries represents at least the 75% of the total European production of the selected

commodity.

Annual losses per hectare

Economic losses related to mycotoxin contamination in cereals in the EU were estimated using a

modelling approach. The losses were calculated for each combination of cereal, region and the two

mycotoxins, including: DON and ZEA in wheat in North-West Europe, aflatoxins and fumonisins in maize

in Central Europe, and DON and ZEA in durum wheat in South Europe. The original model was that of Wu

et al., (2008) and included the following parameters:

Where

M: the production volume of the cereal under scope

dP: the price differentiation between the 1st and 2nd alternative use of the cereal

R: the volume percentage exceeding regulatory limits and thus is degraded to lower quality

17

Q: the quantities sold as food, feed or other used respectively

For the scope of this study however, it was assumed that the entire production volume was sold.

Moreover, materials destined for equine use were considered also as feed materials. The above model

was transformed to a simpler form, as also demonstrated in a later study by the same authors (Wu et al.,

2008), being:

C = Y * P * r

Where,

C = Losses per hectare absent of all interventions – Baseline Losses per hectare (€/ha)

Y = cereal yield on a per-unit-area basis (t/ha)

P = price difference for the selected commodity used for feed (lowest mycotoxin contamination)

versus other uses (food – feed – industrial use - €/t)

r = percentage (%) of commodity that has mycotoxin levels above the maximum levels and

therefore is rejected or marked as lower quality material, destined for alternative use (e.g. wheat

for food used as feed material).

After the losses per hectare were calculated, the next step was to extrapolate these losses to the whole

Europe region, for each of wheat, maize and durum wheat. For this step, for each cereal - mycotoxin

combination, the losses per hectare were multiplied with the total cultivation area of the particular region

and the prevalence of each mycotoxin in the three regions and cereals under scope.

18

5.3. Data collection

Production data (yield, cultivation area, cereal use)

Data regarding the production volume, yield and cultivation area was collected from Eurostat database

(Annual Crop Statistics – 2015) using 10-year average data (2007-2016). Moreover, data about the cereal

use in Europe was collected from the EC, as shown on Table 5.3.1. Next, using descriptive statistics, the

average of each of the three values (pr. volume, yield, cult. area) was calculated for each of the three

regions and cereals. Values from the top 12 producing countries (on a volume basis) for each region/cereal

were considered (only) on a per volume basis, as they constituted more than 75% for wheat and 90% for

maize and durum wheat (bulk volume). This methodology lead to an underestimation of the baseline

losses as will be explained later in the discussion part.

Rejection of production volume due to exceeding the MLs

Rejection of production volume due to exceeding the ML represent the percentage of samples that

contain mycotoxins exceeding the MLs and thus had to be redirected to an alternative use (with reduced

price) or rejected completely. The rejection rate was one of the most influencing factors contributing to

the baseline losses. Data regarding the exceedance of MLs from mycotoxins in cereals are presented in

Table 6.1.6 (rejection rates - %) and were collected from the literature review – mainly EFSA reports and

recent relevant studies (same region/cereal/mycotoxin). The specificity of the data required was a

drawback for this report as very limited data complied with all the above requirements.

Price differentiation for the alternative use of cereals

Price differentiation between the alternatives use of cereals due to mycotoxin contaminations was the

main contributor for the baseline losses. Starting from a year-by-year basis regarding annual commodity

prices (12-month average), the price differentiation between materials sold as food and sold as feed was

derived from a 10-year average (2007-2016) for all 3 regions (NW Europe, Central Europe, South Europe).

Historic prices on the selected commodities were obtained from Eurostat, based on available datasheets

with historic prices from 2007 to 2016 on producers’ level (the price producers receive for commodities,

excluding all subsidies and any VAT). Table 5.3.1 presents the annual average (12-month average)

difference in prices between food and feed grade wheat for the years 2007-2016.

19

Table 5.3.1. Price differentiation between wheat (Food Wheat/ Feed Wheat) sold as food material minus the price of these cereals sold as feed for the MY 2007-2016. Producers' prices at euro currency (Eurostat)

Year Mean annual diff. (€/tn)

Mean SD

Range 95% (€/tn)

2007 14 5.5 17 11

2008 21 6.9 25 17

2009 16 2.2 17 15

2010 16 8.6 20 11

2011 19 3.6 21 17

2012 7 4 9 5

2013 7 2.2 8 6

2014 13 7.5 17 9

2015 16 2.8 17 14

2016 9 2.0 11 8

The price differentiation for maize) was calculated based on the fact that the majority of maize produced

in the EU was destined not for human consumption (6.5%) but for feedstocks (77.2%) (Verstraete, 2016)

and are showed on Table 5.3.2 below. For that reason, the price differentiation constitutes of the initial

intended use of the commodity (feedstock material) versus the alternative use due to quality degradation

from mycotoxin contamination (industrial use).

Table 5.3.2. Cereal use within the EU for the years 2015-2016. In bold are the 1st and 2nd alternative use for each cereal. Source: Verstraete, (2016), DG-Food & Health Safety, EC.

Cereal Human

Consumption

(%)

Animal

Feed (%)

Industrial Use

(%)

Seed Use

(%)

Total (100%) 23 61.2 11.7 3.4

Wheat (common)

(42.4%)

39 46.4 9.1 4.1

Durum Wheat (3.2%) 87 6.6 1 5.0

Maize (26%) 6.5 77.2 14.8 0.6

Other cereals (28.4%)

20

Regarding maize, the most frequent use was that of feed materials, followed by industrial use (14.8%,

assumed for bioethanol production). As every country had different prices for the bioethanol raw

materials, based on literature (Sosa et al., 2013), the raw material costs of bioethanol production from

maize were assumed to constitute ~33% to ~67% of the original price (P0) of the contamination-free

material depending on the degree of contamination – data on Table 5.3.3 below. The high price for raw

materials (67% of original feed grade maize) was used to calculate the Low Contamination scenario, while

the low price raw materials (33% of original price) was used to calculate the losses on a High

Contamination scenario.

Table 5.3.3. Yearly price differentiation between grain maize sold as feedstock material and maize sold for

bioethanol production. Price differentiation was calculated based on a 12-month average of European producers’

prices for grain forage, Data from (Eurostat), and a negative premium correlated with the degree of contamination.

Grain Maize Prices (€/tn)

Year Non-Infected

P(0)

Low Contamination

scenario (67%*P0)

High Contamination

scenario (33%*P0)

2007 188 126 62

2008 191 128 63

2009 130 87 43

2010 165 111 55

2011 221 148 73

2012 220 147 73

2013 204 137 67

2014 166 111 55

2015 158 106 52

2016 160 107 53

Regarding durum wheat, the assumption that degraded durum wheat was sold at the same price as soft

wheat feed material was made. It should be noted that for some years (2014, 2015, 2008) there was a

great difference between the two prices. Table 5.3.4 depicts the annual average difference (12-month

average) between food quality durum wheat and feed quality soft wheat.

21

Table 5.3.4. Yearly price differentiation between durum wheat sold as food and durum wheat sold as plain wheat

feed material. Price differentiation was calculated based on a 12-month average of European producers’ prices

(Eurostat) in durum wheat and soft wheat.

Year Mean annual diff. (€/tn)

Mean SD

Range 95% (€/tn)

2016 89 20 78 101

2015 158 30 141 174

2014 119 60 86 153

2013 54 17 44 64

2012 60 21 48 72

2011 80 33 62 99

2010 40 6 37 44

2009 89 13 82 96

2008 175 39 153 197

2007 77 59 43 110

Sensitivity Analysis – Monte Carlo Simulation Analysis (MCSA)

Whenever results are derived from data that include uncertainty and variability, results also are expected

to be uncertain and variable. A sensitivity analysis was conducted with a Monte Carlo Sensitivity Analysis

– MCSA (using XLStat, a free trial software program) in order to determine the effect of varying input

variables, due to the uncertainty and unpredictability of the input data. A Monte-Carlo Simulation is a

probabilistic approach for an estimation (cost, risk, outcome estimation in general) using distribution

variables which also gives distributions as a result. For this study, the input variables of the model derived

baseline used, were the production yield per cluster, the price differentiation of the commodities, the

rejection rate of cereals that exceeded the maximum limits and the specific mycotoxin prevalence. Those

variables follow distributions that they best describe (based on historical data) how they can change and

with what probability. Using a probabilistic approach – a simulation of theoretically near countless

repetitions of the same calculations, we can come to results that incorporate this uncertainty and

variability of the input data and derive to results that are most probable to come if the experiment could

be repeated multiple times.

22

The results were distributions (same as input variables) estimating a range of annual losses of each

mycotoxin on the selected commodity for the three regions-cereals and 4 mycotoxins. The type of

distributions used for each variable are presented below.

Distribution Cereal Yield: As a natural positive number, a production yield would follow a normal

distribution with no negative values and in the simulation would have the following form: [Truncated

Normal ("Cereal Yield", mean, SD), Truncate (0)]

Distribution Rejection rate (r): Percentage exceeding MLs, uniform distribution between the min and

MAX value as obtained from section 6.1: SimUniform ("Rejection rate", min, MAX).

Distribution Mycotoxin Prevalence with truncation; As prevalence rates are positive values within a

range – min, mean, max a Triangular distribution (moved to the left) was used: [(SimTriangular

["Prevalence – mycotoxin – Cereal: (min, mean value, MAX)]

Distribution Price differentiation: Uniform, with truncation the min and MAX of the 95% range of the

historic 10-year differentiated average prices. SimUniform ["Cereal-price diff.", min, MAX]

Number of iterations: 5000

23

5.4. Cereal Processing

After the calculation of the annual losses (baseline), the next part of the study investigated the effect of

cereal processing on mycotoxins. The effects of the main and most frequently used cereal processing

methods on mycotoxin reduction were examined and compared with each other. As most cereals undergo

some sort of processing before consumption, cereal processing was considered as a widely applied

method of mycotoxin control, even if the purpose of processing is not mycotoxin mitigation in the first

place.

The data necessary was collected through the literature review and included processes like

cleaning/washing, sorting, milling, thermal processing and extrusion of cereals. An average reduction

percentage number (%) for each process was derived, using data gathered from literature. The reduction

potential was compared with the initial levels of mycotoxins before processing (calculated in the first part

of the study) for each of the cereals. After the processing, the new reduced mycotoxin levels were

compared with the MLs for each cereal and mycotoxin to conclude whether the processing method was

sufficient in reducing mycotoxin levels below EU MLs.

5.5. Cost - Benefit Analysis

In the final part of the study, a comparison of the Costs and expected Benefits of the two selected

interventions was conducted to evaluate which one of the two is preferable. To assess whether the two

interventions were economically feasible and whether the expected gains outweigh the costs of the

interventions, the effects of the measures were compared with the effects of the currently applied

measures (Section 6.2) comparing the percentage of mycotoxin present in the finished grains. The

measures were applied directly to cereal grains both pre-harvest and post-harvest. All results were

converted to monetary units (€).

Benefits were accounted as the economic losses (baseline) averted due to implementation of the

interventions from mycotoxin contaminations and subsequent rejection of cereal grains. Costs were

compiled of the implementation of the interventions (materials for biocontrol in maize and initial capital

investment for the optical sorters in wheat).

24

Costs of interventions

The cost of materials as shown in (Wu et al., 2008) was $16-30 USD (2008) for the first intervention

(biocontrol - purchase costs and application costs) per acre which can be converted to € - 2018 per

hectare; € 15.41 – € 29.0 per acre or € 38.8 – € 71.6 per hectare. However, it is stated in literature (F. Wu

& Khlangwiset, 2010) that after the initial application of biocontrol, due to the gradual replacement of

toxigenic with atoxigenic strains in the soil (2-3 years), there is no need for annual reapplication of the

inoculum and gradually less material is needed. For that reason and for the scope of this report, an annual

33% reduction in material costs takes place when applying biocontrol (Tables 6.3.1 & 6.3.2). Table 5.5.1

below presents the total costs (range) of application of biocontrol for the Central Europe – maize region.

Table 5.5.1. Biocontrol materials and applications costs per country for maize in Central Europe

BIOCONTROL – ANNUAL COSTS - PER CULTIVATION AREA

Alfa-Guard ® TM

Cluster Country Cultivation Area

(*1000 ha)

Costs range (38,8-71,6 €/ha)

France 1,696 € 64,543,967 € 121,543,834

Romania 2,781 € 105,810,168 € 199,252,913

Italy 976 € 37,149,166 € 69,956,221

Hungary 1,144 € 43,549,938 € 82,009,624

Serbia 1,050 € 39,956,879 € 75,243,473

Germany 421 € 16,024,900 € 30,176,760

Poland 314 € 11,940,150 € 22,484,699

Bulgaria 410 € 15,600,992 € 29,378,491

Austria 182 € 6,908,445 € 13,009,410

Croatia 285 € 10,836,234 € 20,405,895

Slovakia 160 € 6,074,709 € 11,439,387

Czech Rep. 76 € 2,882,421 € 5,427,935

Total Area (*1000 ha) 9,494 € 361,277,968 € 680,328,642

The initial capital investment costs for the second intervention (optical sorter - Table 5.5.2) were derived

from commercially available optical sorters for cereals. Prices varied between $20,000 – $50,000 (USD

2018 – €16,386 – €40,965). Some samples of commercial sorters are presented in Annex – Section D. Two

scenarios were taken into account; a low initial capital investment ($20,000) and a higher investment

25

($50,000). With an assumption of 3,000 salvage value there was a €2,677.2 to €1,338.6 annual

depreciation if assumed 5 years of use for the machinery, or €7,593.0 - €3,796.5 annual depreciation if

assumed a 10-year use period. Based on the number of active mills in Europe (~3800, European Flour

Millers) the total initial investment costs are derived for either low or high initial investment. The total

purchase costs vary between € 62,266,800 - € 155,667,000 depending on the initial investment.

Table 5.5.1. Initial Capital Investment costs for the optical sorters

Depreciation Linear Costs Range Source

Initial Capital Investment € 16,386 € 40,965 Market prices

(Annex - B)

Salvage value € 3,000.00 assumption

Years of use 5 10 assumption

Depreciable asset cost € 13,386.00 € 37,965.00

Depr. / year (5 years use) € 2,677.20 € 1,338.60 per mill

Depr. / year (10 years use) € 7,593.00 € 3,796.50 per mill

26

6. Results

The following chapter presents the results for the baseline situation – first part (annual economic losses),

the efficiency of cereal processing - second part , the Cost – Benefit Analysis for the two interventions -

third part, and finally the Monte Carlo Simulation Analysis regarding the baseline losses.

6.1. Setting the Baseline

Tables 6.1.1, 6.1.2 and 6.1.3 present the model input parameters for the calculation of the annual losses

for the three regions/cereals. Production yield was used for the baseline model, while the total cultivation

area was used for the extrapolation, from losses per hectare to total losses per region/cereal and per

mycotoxin. Production volume was used in the making of the country clusters for each of the three

regions/cereals.

Table 6.1.1. Selection of countries and annual cereal volume gross production mean values from a 10 – year average, per country cluster. Data: Eurostat, Annual crop statistics 2015.

WHEAT Vol. (1000* tn)

MAIZE

DURUM WHEAT

North West Low Medium High Central Low Medium High South Low Medium High

France 35,175 France 15076 Italy 4,198

Germany 24,407 Romania 8757 France 1,990

UK 14,719 Italy 8472 Greece 1,177

Poland 9,764 Serbia 7377 Spain 989

Denmark 4,842 Hungary 7170

Lithuania 2,607 Germany 4587

Serbia 2,885 Poland 2966

Sweden 2,448 Bulgaria 1974

Latvia 1,352 Austria 2029

Austria 1,554 Croatia 1894

Netherlands 1,258 Slovakia 1184

Finland 882 Czech R. 784

Conf. Int.

95% MAX 1500 4140 30546 1974 6100 12881 1175

Mean 1,262 3,196 21,016 1473 4174 9921 1,083 1,990 4,198

Conf. Int.

95% min 1,023 2,251 11,487 972 2,249 6,960 991

SD 244 963 9724 511 1965 3021 94

27

Table 6.1.2. Cultivation Area per region and country cluster. Data from Eurostat (2015), Annual crop statistics. Descriptive statistics and calculations are available on the SPREADSHEET.

Cultivation Area (*1000 ha)

France 4,962 12,261 France 1,667 6,217 Italy 1,327

Germany 3,173

Romania 2,492

France 400

United Kingdom 1,879

Italy 903

Spain 420

Poland 2,247

Hungary 1,155

Greece 421

Denmark 664 2,259 Serbia 1,050 2,359

Lithuania 604

Germany 472

Sweden 396

Poland 462

Serbia 595

Bulgaria 375

Latvia 341 1,004 Austria 198 772

Austria 287

Croatia 286

Netherlands 147

Slovakia 184

Finland 229

Czech R. 104

Sum (*1000 ha) 15,524

9,348

2,568

Table 6.1.3. Selection of countries and cereal yield per country cluster. Data: Eurostat, Annual crop statistics 2015. Descriptive statistics and relevant calculations for deriving to the yield ranges are available on the SPREADSHEET.

Yield (Y) (tn/ha)

Region min MAX min MAX min MAX

NW-Wheat 4.8 7.0

Central-Maize

6 8.6

South-Durum w.

2.4 5

28

Tables 6.1.4, 6.1.5 and 6.1.6 present all collected data that were used for the setting the baseline situation

for estimation of the annual economic losses. Those parameters were the mycotoxin prevalence,

production yield and cultivation area of the selected cereals, and the price differentiation for the

alternative use of each cereal. The initial mycotoxin concentrations of mycotoxins in cereals, were used

for comparison of cereal processing and their reduction capacities. Finally, the parameters used for the

calculation of the baseline losses were also used as input variables in the Monte-Carlo Simulation Analysis.

Table 6.1.4. Input variables for the baseline calculations. Data about mycotoxin prevalence, max concentration and range were calculated from dats literature review and are available on the SPREADSHEET.

Commodity Wheat Maize Durum W.

Mycotoxin DON ZEA Aflatoxins Fumonisins DON ZEA

Prevalence; mean (SD) - (%) 66.14 (24.97) 45.08 (34.90) 23.43 (9.72) 62.23 (22.87) 79.37 (21.75) 26.77 (18.52)

95% Range 52.31 - 79.97 15.89 - 74.26 16.23 - 30.63 45.87 - 78.58 65.89 - 92.85 12.54 - 41.00

MAX Concentration ; (SD), (μg/kg)

3485 (5255) 626,38 (452.85) 73.7 (21.51) 8650.08 (10400) 2111.92 (2878.52) 82.54 (82.95)

95% Range 685.3 - 6286.6 247.78 - 1004.97 49.3 - 98.0 2042 - 15258 483.27 - 3740.57 26.81 - 138.27

Table 6.1.5. Input variables for the baseline calculations. Data about cereal yield- mean (SD) were calculated from the Eurostat Database.

Yield; mean (SD) (tn/ha)

5.91 (1.71) 7.3 (2.00) 2.4 - 5

95% Range 4.8 - 7.0 6.0 - 8.6 - -

Reference Eurostat, Annual crop Statistics 2015

Table 6.1.6. Cultivation area and rejection rates for the selected mycotoxins and cereals.

Cultivation area (1000 ha)

Mean (SD) Wheat Maize Durum wheat

Low Production 251 (71.93) 193 (65) 841 (420 + 421)

Medium Production 565 (100.97) 590 (268) 400

High Production 3065 (1192) 1554 (607) 1327

Rejection rate (%) 2% - 9.5% 2% - 10% 2% - 4% 3% - 9.5% 3.20% - 10.60% 1.37% - 2.13%

Reference (EFSA, 2013b)/(Meister, 2009)

(Meister, 2009)

(I. Rodrigues & Chin, 2012)

(EFSA, 2014)

(EFSA, 2013b) /(Bertuzzi et al., 2014)

(Bertuzzi et al., 2014)

Price differentiation

Table 5.3.1 Table 5.3.4 Table 5.3.5

29

6.2. Baseline Losses

Table 6.2.1 presents the estimated annual economic losses due to mycotoxin contamination of wheat,

maize and durum wheat for the countries selected for each of the 3 regions – country clusters. Results are

presented for a “good” cultivation year, without extreme weather conditions (Low Contamination

scenario) with relatively low mycotoxin production as well as for a “bad” year, with extreme bad weather

conditions and subsequently high contamination rates (High Contamination scenario).

Table 6.2.1. Range of annual economic losses derived from mycotoxin contaminations in cereals for the selected regions and mycotoxins

Mycotoxin Wheat Low Contamination High Contamination

DON € 8,809,989 € 18,419,114 € 63,967,125 € 133,736,577

ZEA € 2,676,739 € 3,837,449 € 62,524,567 € 130,720,611

Maize Low Contamination High Contamination

Fumonisins € 42,486,742 € 117,029,547 € 467,763,691 € 779,858,694 Aflatoxins € 61,709,139 € 102,881,882 € 250,576,505 € 417,762,793

Durum Wheat Low Contamination High Contamination

DON € 13,762,054 € 19,736,278 € 64,237,569 € 92,123,645 ZEA € 1,121,080 € 1,607,751 € 5,694,531 € 8,166,576

For DON derived losses in wheat, results, for the Low Contamination scenario annual loses ranged

between € 8,8 - € 18,5 ml whereas the range for the High Contamination scenario was between € 64 - €

133,7 ml. For ZEA, the annual losses were lower: € 2,7 – € 3,8 ml in a normal year (Low Contamination Sc.)

and € 62,5 – € 130,7 ml in a High Contamination Sc.). Economic losses for fumonisins in maize were

estimated to range from € 42,5 – € 117 ml annually (Low Contamination Sc.) and € 467,8 – € 779,9 ml

(High Contamination Sc.). As for aflatoxin derived losses in maize, they were almost similar to those for

fumonisins, ranging, for the Low Contamination scenario, between € 61,7 – € 102,9 ml and for the High

Contamination scenario (€ 250,6 – € 417,8 ml). Finally, losses due to mycotoxin contamination of durum

wheat were € 13,8 – € 19,7 ml (Low Contamination sc.) and € 64,2 - € 92,1 ml (High Contamination sc.)

for DON, and € 1,1 ml – € 1,6 ml for the Low Contamination scenario and € 5,7 - € 8,1 ml for the High

Contamination scenario for ZEA. In total, each year € 130,6 to € 263,5 ml are lost due to mycotoxin

contamination in the EU on a low infectious year. These numbers are much higher in a highly infectious

30

year, when occurrence and degree of contamination could be much higher; € 864,2 - € 1.456,8 could be

lost in one year. Results for total losses are presented in Table 6.2.2. Worth noting is that as these results

are derived only from a percentage of the European producing countries (see country clusters in Materials

& Methods), if accounted for the whole cereal sector, namely all cereals, all mycotoxins and all producing

countries, it can be assumed that the losses are much higher.

Table 6.2.2. Annual economic losses from the 4 selected mycotoxins in wheat, maize and durum wheat for a low and high contamination scenario.

TOTAL LOSSES (W+M+D) Range

Low Contamination scenario

Range of annual losses € 130,565,744 € 263,512,021

High Contamination scenario

Range of annual losses € 864,263,627 € 1,456,787,388

Total Agricultural Output of EU 28 (1) € 405,000,000,000

Low Contamination scenario

Range of annual losses 0.03 % 0.07 %

High Contamination scenario


Total Cereal Output (2) = 10.7% * (1) € 43,335,000,000



When compared with the agricultural output and cereal output of the EU (2015 - 2016 data), the losses

constituted less than 0.1% of the total market volume in case of the Low Contamination scenario while in

a High Contamination scenario annual losses could account for less than 1% of the total agricultural

turnover respectively. When compared only to the total cereal output, which accounts for 10.7% of the

total agricultural output the losses are 0.30% -0.61% and 2% - 3.36%, for the low and High Contamination

scenarios respectively.

Comparing a low and a high contamination scenario (Tables 6.2.1 & 6.2.2), it can be concluded that the

total annual losses differ one order of magnitude (x10) on a “bad” year compared to a “normal” – low

contamination year. As shown on Tables 6.2.1 and 6.2.2, the losses for the Low Contamination scenario

range from some millions to tens of millions, however, the losses for the high contamination scenario

varied from hundreds of millions and reaching 1,5 billion euros as a sum of all mycotoxins and cereals

examined in this report.

31

Figure 6.1.1. Total annual economic losses derived from contaminations in wheat, maize and durum wheat for a Low Contamination scenario

Figure 6.1.2. Total annual economic losses derived from contaminations in wheat, maize and durum wheat for a high contamination scenario

Legend Series

1 Wheat – DON 4 Maize – Aflatoxins

2 Wheat – ZEA 5 Durum wheat – DON

3 Maize – Fumonisins 6 Durum wheat – ZEA

€ 1,000,000

€ 10,000,000

€ 100,000,000

0 1 2 3 4 5 6

Total Losses - Low Contamination scenario

€ 1,000,000

€ 10,000,000

€ 100,000,000

€ 1,000,000,000

0 1 2 3 4 5 6

Total Losses - High Contamination scenario

32

6.3. Effect of cereal processing

In this section, effects of cereals processing methods are presented based on their capacity to reduce

mycotoxins levels below the MLs. Values in bold (Tables 6.3.1 to 6.3.4) indicate effective reduction (<MLs)

for the selected processes and mycotoxins. Starting from the initial mycotoxin level (μg/kg) every process

has a reduction value (reduction %) and the results are given with a mean and a 95% range.

Tables 6.3.1 and 6.3.2. present the effects of cereals processing on DON and ZEA levels of wheat and

durum wheat respectively, given the initial concentrations calculated in the Baseline section. While most

of the bottom 95% range reduction was enough to comply with MLs, the mean and upper 95% range were

still above the limits.

Table 6.3.1. Effect of cereals processing on DON levels in wheat and durum wheat.

Initial concentration Mean value - (μg/kg)

3485 ML 1250/1750 μg/kg (wheat/durum wh.)

95% Range 685.3 - 6286.6 Concentration After process (μg/kg)

Reduction percentage Mean 95% Range

Cleaning - Sorting 55% 1568 308 2829

Milling (end-product flour) 62% 1342 264 2420

Thermal 47% 1851 364 3338

Sorting & Washing 56% 1523 299 2747

Washing & Milling (durum-pasta)

77% 802 158 1446

Improved Optical Sorting before processing

60% 1394 274 2515

33

Table 6.3.2. Effect of cereals processing on ZEA levels in wheat and durum wheat.

Initial concentration

Concentration After process (μg/kg)

Mean 626.4 ML 100 μg/kg

95% Range 247.8 - 1004.97 Mean 95% Range


Milling (end-product flour) 54% 288 114 462

Thermal 55% 280 111 449

In Tables 6.3.3 and 6.3.4 depict the effects of cereals processing of maize in the reduction of Aflatoxin

and Fumonisins levels. It is to be noted that in some cases, especially in feedstock production there can

be a repartitioning of mycotoxins on the parts of the grain that are used for feed. This can result in an up

to threefold increase of the mycotoxin concentrations, which for aflatoxins was much higher than the

ML, however for fumonisins was well below the ML of 60,000 μg/kg.

Table 6.3.3. Effect of cereals processing on Aflatoxin levels in maize.

Process

Initial Concentration (μg/kg)


Mean 73.7 ML = 10 μg/kg

95% Range 43.9 - 98.0 Mean 95% Range

Cleaning - Sorting 67.50% 24.0 14.3 31.9 After Process redistribution factor

(x3) ML = 20 μg/kg

Milling (end-product flour)

75% 18.4 11.0 24.5 55.275 32.925 73.5

Thermal 47% 39.1 23.3 52.0

Alkaline 64.50% 26.2 15.6 34.8

Extrusion 65% 25.8 15.4 34.3

Alkaline & Extrusion

95% 3.7 2.2 4.9

Biocontrol 90% 7.4 4.4 9.8

34

Table 6.3.4. Effect of cereals processing on Fumonisins levels in maize.


Mean 8650 ML 4000 μg/kg

95% Range 2042 - 15258 Mean 95% Range After Process redistribution factor (x3)

ML=60,000 μg/kg


Milling (end-product flour)

75% 2163 511 3815 6488 1532 11444

Thermal 41% 5077 1198 8955

Extrusion 42% 5017 1184 8850

35

6.4. Cost Benefit Analysis

In the following section are presented the results of the Cost-Benefit Analysis for the aflatoxin Biocontrol

(Tables 6.4.1 - Low Contamination scenario and 6.4.2 – High Contamination scenario) and the Optical

Sorter (Tables 6.4.3 – Low Contamination scenario and 6.4.4 – High Contamination scenario). Regarding

Biocontrol with aflatoxigenic strains, it was not very Cost-Beneficial as for the Low Contamination scenario

there was a CBA ratio >1 (2.2) and a NPV < 0 (€ 740,615,050) indicating that within a 10-year time, this

investment would not pay out. In fact, the mean annual benefits were almost half of the annual costs in

the Low Contamination scenario. However, for the High Contamination scenario the expected gains from

decreased economic losses outmatched the funds necessary to implement biocontrol in C. Europe – Maize

region. That is indicated by the CBA ratio of <1 (0.5) and a positive NPV of € 1,216,284,290.

For the optical sorter, as depicted on Tables 6.4.3 and 6.4.4, in both scenarios (High – Low Contamination)

the initial investment was considered as cost-beneficial within 10 years with the Payback Period not

exceeding 5 years (Low Contamination scenario) or 2 years (High Contamination scenario). Regarding the

CBA ratio, it was dependent on the level of initial capital investment, meaning that in the case of a high

investment (more expensive machinery) and for the Low Contamination scenario the CBA ratio was 0.29

whether on a lower investment it reached 0.74, making the investment less cost beneficial. As for the High

Contamination scenario the CBA ratio dropped to 0.04 Low Initial Capital Investment and at 0.10 for the

high Initial Capital Investment indicating that this measure, same as biocontrol would pay back more in a

highly infectious region.

36

Cost Benefit Analysis – Aflatoxin Biocontrol – Low Contamination scenario

Discount factor (r=5%)

Actual values Discounted

Year Annual Coasts Range (with 33% reduction each year*)

Annual Benefits Range Annual Costs Range Annual Benefits Range Accumulative Benefits

1 € 361,277,968 € 680,328,642 € 61,709,139 € 71,778,057 € 361,277,968 € 680,328,642 - € 61,709,139 € 102,881,882 € 61,709,139 € 102,881,882

2 € 242,056,239 € 455,820,190 € 228,161,220 € 429,654,246 0.9070 € 55,972,008 € 93,316,900 € 117,681,148 € 196,198,782

3 € 162,177,680 € 305,399,527 € 148,415,551 € 279,483,830 0.8638 € 53,306,675 € 88,873,238 € 170,987,823 € 285,072,019

4 € 108,659,046 € 204,617,683 € 96,542,155 € 181,800,161 0.8227 € 50,768,262 € 84,641,179 € 221,756,084 € 369,713,198

5 € 72,801,561 € 137,093,848 € 62,799,266 € 118,258,357 0.7835 € 48,350,725 € 80,610,647 € 270,106,810 € 450,323,845

6 € 48,777,046 € 91,852,878 € 40,850,008 € 76,925,339 0.7462 € 46,048,310 € 76,772,044 € 316,155,120 € 527,095,889

7 € 32,680,621 € 61,541,428 € 26,572,335 € 50,038,813 0.7107 € 43,855,533 € 73,116,233 € 360,010,653 € 600,212,122

8 € 21,896,016 € 41,232,757 € 17,284,917 € 32,549,519 0.6768 € 41,767,175 € 69,634,507 € 401,777,827 € 669,846,629

9 € 14,670,331 € 27,625,947 € 11,243,587 € 21,172,988 0.6446 € 39,778,261 € 66,318,578 € 441,556,089 € 736,165,207

10 € 9,829,121 € 18,509,385 € 7,313,789 € 13,772,720 0.6139 € 37,884,059 € 63,160,551 € 479,440,147 € 799,325,758

Total Costs Range

€ 957,295,912 -

€ 1,802,700,093 Total Benefits

Range

€ 479,440,147 -

€ 799,325,758

Total Costs Mean

€ 1,379,998,003

Total Benefits

Mean

€ 639,382,953

NPV € (740,615,050)

CBA Ratio

2.2

Table 6.4.1. Annual Costs, Expected economic gains and CBA Ratios for aflatoxin biocontrol for the Low Contamination scenario.

37

Cost Benefit Analysis – Aflatoxin Biocontrol – High Contamination scenario

Actual values

Discounted

Year Annual Coasts Range (with 33% annual reduction*)

Annual Benefits Range

Annual Costs Range

Discount factor (r=5%)

Annual Benefits Range

Cumulative Benefits

1 € 361,277,968 € 680,328,642 € 250,576,505 € 417,762,793 € 361,277,968.29 € 680,328,641.59 - € 250,576,505 € 417,762,793 € 250,576,505 € 417,762,793

2 € 242,056,239 € 455,820,190 € 219,552,144.00 € 413,442,349.09 0.9426 € 227,280,277 € 378,923,168 € 477,856,782 € 796,685,961

3 € 162,177,680 € 305,399,527 € 140,095,177.60 € 263,815,594.18 0.9151 € 216,457,407 € 360,879,208 € 694,314,189 € 1,157,565,169

4 € 108,659,046 € 204,617,683 € 89,394,065.71 € 168,339,474.38 0.8885 € 206,149,911 € 343,694,484 € 900,464,100 € 1,501,259,653

5 € 72,801,561 € 137,093,848 € 57,041,927.64 € 107,416,616.99 0.8626 € 196,333,249 € 327,328,080 € 1,096,797,348 € 1,828,587,733

6 € 48,777,046 € 91,852,878 € 36,398,182.40 € 68,542,031.79 0.8375 € 186,984,046 € 311,741,028 € 1,283,781,395 € 2,140,328,761

7 € 32,680,621 € 61,541,428 € 23,225,506.86 € 43,736,344.10 0.8131 € 178,080,044 € 296,896,217 € 1,461,861,439 € 2,437,224,978

8 € 21,896,016 € 41,232,757 € 14,820,085.33 € 27,907,952.90 0.7894 € 169,600,042 € 282,758,302 € 1,631,461,481 € 2,719,983,281

9 € 14,670,331 € 27,625,947 € 9,456,625.88 € 17,807,931.85 0.7664 € 161,523,849 € 269,293,621 € 1,792,985,330 € 2,989,276,902

10 € 9,829,121 € 18,509,385 € 6,034,227.94 € 11,363,156.51 0.7441 € 153,832,238 € 256,470,115 € 1,946,817,568 € 3,245,747,018

Total Costs Range

€ 957,295,912 - € 1,802,700,093 Total Benefits Range

€ 1,946,817,568 - € 3,245,747,018

Total Costs Mean

€ 1,379,998,003 Total Benefits Mean

€ 2,596,282,293

NPV € 1,216,284,290

CBA ratio 0.5

Table 6.4.2. Annual Costs, Expected economic gains and CBA Ratios for aflatoxin biocontrol for the High Contamination scenario.

38

Table 6.4.3. Initial Capital Investment Costs, Expected Benefits, PP, NPV and CBA Ratios for Improved optical sorting for DON in wheat for the Low Contamination scenario for Low and High initial investment costs.

Cost Benefit Analysis – Optical Sorter – Low Contamination scenario

Actual values

Discounted

Year Initial Capital Investment Costs Low Contamination Benefits Range

Discount

factor (r=5%)

Annual Low Contamination Benefits Range

Low Contamination- High Investment cumulative benefits

1 € 31,133,400 € 77,833,500 € 8,809,989 € 18,419,114 - € 8,809,989 € 18,419,114

€ 8,809,989 € 18,419,114

2 € 17,619,978 € 36,838,229 0.9070 € 7,990,920 € 16,706,680

€ 16,800,909 € 35,125,794

3 € 26,429,967 € 55,257,343 0.8638 € 7,610,400 € 15,911,124

€ 24,411,309 € 51,036,918

4 € 35,239,957 € 73,676,458 0.8227 € 7,248,000 € 15,153,451

€ 31,659,309 € 66,190,369

5 € 44,049,946 € 92,095,572 0.7835 € 6,902,857 € 14,431,858

€ 38,562,166 € 80,622,227

6 € 52,859,935 € 110,514,687 0.7462 € 6,574,150 € 13,744,627

€ 45,136,315 € 94,366,854

7 € 61,669,924 € 128,933,801 0.7107 € 6,261,095 € 13,090,121

€ 51,397,410 € 107,456,974

8 € 70,479,913 € 147,352,915 0.6768 € 5,962,947 € 12,466,782

€ 57,360,357 € 119,923,756

9 € 79,289,902 € 165,772,030 0.6446 € 5,678,998 € 11,873,125

€ 63,039,355 € 131,796,882

10 € 88,099,891 € 184,191,144 0.6139 € 5,408,569 € 11,307,738

€ 68,447,924 € 143,104,620

Total Benefits Range

€ 68,447,924 - € 143,104,620 CBA Ratio (low Inv./High Inv.)

0.29 - 0.74 PP 4 - 5 years

Total Benefits Mean € 105,776,272 NPV € 51,292,822

39

Table 6.4.4. Initial Capital Investment Costs, Expected Benefits, PP, NPV and CBA Ratios for Improved optical sorting for DON in wheat for the High Contamination scenario for Low and High initial investment costs.

Cost Benefit Analysis – Optical Sorter – High Contamination scenario

Actual values

Discounted

Year Initial Capital Investment Costs High Contamination Benefits Range

Discount

factor (r=5%)

High Contamination - Low Investment annual benefits

High Contamination - High Investment cumulative benefits

1 € 31,133,400 € 77,833,500 € 63,967,125 € 133,736,577

-

€ 63,967,125 € 133,736,577 € 63,967,125 € 133,736,577

2 € 17,619,978 € 36,838,229 0.9070

€ 58,020,068 € 121,303,018 € 121,987,192 € 255,039,595

3 € 26,429,967 € 55,257,343 0.8638

€ 55,257,207 € 115,526,684 € 177,244,400 € 370,566,278

4 € 35,239,957 € 73,676,458 0.8227

€ 52,625,912 € 110,025,413 € 229,870,312 € 480,591,691

5 € 44,049,946 € 92,095,572 0.7835

€ 50,119,916 € 104,786,108 € 279,990,228 € 585,377,799

6 € 52,859,935 € 110,514,687 0.7462

€ 47,733,253 € 99,796,293 € 327,723,481 € 685,174,092

7 € 61,669,924 € 128,933,801 0.7107

€ 45,460,241 € 95,044,088 € 373,183,722 € 780,218,180

8 € 70,479,913 € 147,352,915 0.6768

€ 43,295,468 € 90,518,180 € 352,512,065 € 736,999,783

9 € 79,289,902 € 165,772,030 0.6446

€ 41,233,779 € 86,207,790 € 457,712,969 € 956,944,150

10 € 88,099,891 € 184,191,144 0.6139

€ 39,270,266 € 82,102,657 € 496,983,235 € 1,039,046,807

Total Benefits Range

€ 68,447,924 - € 143,104,620 CBA Ratio (low Inv./High Inv.)

0.04 – 0.10 PP 1 – 2

years

Total Benefits Mean

€ 105,776,272 NPV € 693,896,438

40

6.5. Sensitivity analysis – MCSA

In this section are presented the results of the Monte-Carlo Simulation Analysis conducted for the variable

distributions of the baseline, the yield of the cereals on the specific region, the rejection rate (%) of the

contaminated materials, the price differentiation between the alternative uses of the commodity and the

overall field prevalence of each mycotoxin on each of the 3 regions under scope.

Overall, a strong correlation between annual loses, rejection rates and price differentiation was observed.

Both distributions were contributing more than 40% (each) – except maize where the price differentiation

had a higher contribution.

When compared to the results in section 6.2 - Results, MCSA shows that the average annual losses range

were within the range of section 6.2 with the exception of aflatoxins and fumonisins in maize, where

MCSA showed a lower upper limit for the High Contamination scenario. One possible explanation could

be that the assumption for the alternative prices for feed maize (industrial use – bioethanol production)

as 33% - 67% of the original price of food maize was not completely accurate and it presented a large

variation. That was probably reflected on MCSA and for that reason the annual losses for aflatoxins and

fumonisins in maize were considerably lower than those of section 6.2.

41

Losses - Wheat – DON

For DON derived losses in wheat the range shown in the Tables 6.5.1a & 1b below varied between €

4,940,370 and € 202,752,620 (€ 8,8 – € 133,7 ml in section 6.2). The mean and median values of MCSA

were € 53,3 ml and € 46 ml respectively (Data not shown). There was no distinction between Low and

High Contamination scenario for the MCSA, however the range of the annual losses was in partial

accordance with section 6.2. Price differentiation and rejection rate were the highest contributors to the

overall losses - 46.64% and 41.61% respectively.

Figure 6.5.1. Histogram of annual losses in wheat due to DON contaminations

Tables 6.5.1a, 1b. Correlation matrix and Sensitivity results of MCSA for Annual Losses in wheat from DON

Correlation matrix (Spearman): Variables Prevalence Rejection rate Yield Price differentiation Losses - DON

Prevalence DON 1 0.007 -0.035 0.009 0.120

Rejection rate 0.007 1 -0.013 -0.010 0.627

Yield -0.035 -0.013 1 0.025 0.311

Price differentiation 0.009 -0.010 0.025 1 0.663

Annual Losses 0.120 0.627 0.311 0.663 1

Sensitivity (Losses Wheat DON): Distributions Correlation Contribution Contribution (Absolute)

Price differentiation 0.663 46.64% 46.64%

Rejection rate 0.627 41.61% 41.61%

Yield 0.311 10.22% 10.22%

Prevalence 0.120 1.53% 1.53%

0

0.000002

0.000004

0.000006

0.000008

0.00001

0.000012

0.000014

0.000016

0.000018

0 50000 100000 150000 200000 250000

Den

sit

y

Losses Wheat DON

Histogram Losses Wheat DON - € 1000

42

Losses - Wheat – ZEA

For ZEA losses from wheat, MCSA results varied from (min) €2,219,787 to (Max) €192,335,586 and

compared to the main results (€2,7 - €130,7 ml) were within the same level not varying substantially.

Mean and median values were € 38,2 and € 31 ml respectively, lower than the DON derived losses in

wheat. Regarding the contribution of the distributions, rejection rate and price differentiation affected

the results the most with 38.65% and 37.91% contribution respectively.

Figure 6.5.2. Histogram of annual losses in wheat due to ZEA contaminations

Tables 6.5.2a, 2b. Correlation matrix and Sensitivity results of MCSA for Annual Losses in wheat from ZEA.

Correlation matrix (Spearman)

Variables Rejection rate Prevalence Wheat Price differentiation

Losses - ZEA

Rejection rate 1 0.010 -0.001 0.033 0.613

Prevalence ZEA 0.010 1 0.029 -0.004 0.384

Yield -0.001 0.029 1 0.000 0.284

Price differentiation 0.033 -0.004 0.000 1 0.607

Annual Losses 0.613 0.384 0.284 0.607 1

Values in bold are different from 0 with a significance level alpha=0.05

Sensitivity (Losses Wheat ZEA):

Distributions Correlation Contribution Contribution (Absolute)

Rejection rate 0.613 38.65% 38.65%


Prevalence 0.384 15.15% 15.15%

Yield 0.284 8.29% 8.29%

0

0.000005

0.00001

0.000015

0.00002

0.000025

0 50000 100000 150000 200000 250000

Den

sit

y

Losses Wheat ZEA

Histogram Losses Wheat ZEA - € 1000

43

Losses – Maize - Fumonisins

Results of MCSA in maize annual losses from Fumonisins are shown below on Tables 6.5.3, 6.5.3a & 6.5.3b.

They showed that the min and Max values were € 30,4 and € 316,9 ml respectively. Although the main

results showed a higher upper limit for the losses (High Contamination scenario range: € 467,7 – € 779,9

ml) compared to MCSA results, they were in compliance with the fact that in case of a “bad” infectious

year, the losses are approximately ten times higher than a “normal” year supporting the conclusion that

there was an order of magnitude difference in annual losses from a low and a high contamination year.

Moreover, price differentiation and rejection rates were the variables most influencing annual losses, with

the case of price differentiation of maize materials explained earlier.

Figure 6.5.3. Histogram of annual losses in maize due to Fumonisins contaminations

Tables 6.5.3a, 3b. Correlation matrix and Sensitivity results of MCSA for Annual Losses in maize from Fumonisins

Correlation matrix (Spearman) – Fumonisin Annual Losses

Variables Yield Prevalence Price differentiation Rejection rate Annual Losses

Yield 1 -0.007 -0.007 -0.003 0.244

Prevalence -0.007 1 0.013 -0.030 0.264

Price differentiation -0.007 0.013 1 -0.010 0.759

Rejection rate -0.003 -0.030 -0.010 1 0.490

Annual Losses 0.244 0.264 0.759 0.490 1


Sensitivity (Losses Maize Fumonisins)

0

0.000001

0.000002

0.000003

0.000004

0.000005

0.000006

0.000007

0.000008

0.000009

0.00001

0 50000 100000 150000 200000 250000 300000 350000

Den

sit

y

Losses - Maize Fumonisins

Histogram Losses - Fumonisins - € 1000

44

Variables Correlation Contribution Contribution (Absolute)


Rejection rate 0.490 25.42% 25.42%

Prevalence 0.264 7.36% 7.36%

Yield 0.244 6.31% 6.31%

Losses – Maize – Aflatoxins

Concerning the Annual Losses in maize from Aflatoxins, the min and Max values were €11,145,620 and

€119,729,700 respectively. The mean and median values were €45,728,650 and €43,116,527. These

results contradict the results from section 6.2 that showed a range of annual losses due to aflatoxins of €

61,709,139 - €102,881,882 for the Low Contamination scenario and €250,576,505 - € 417,762,793 for the

High Contamination scenario. This difference in the High Contamination scenario could be a result of the

methodology followed regarding the price differentiation for maize as feedstock and as bioethanol raw

materials. Considering also the high correlation and contribution of the “price differentiation” for both

aflatoxins and fumonisins on the overall annual losses it could be assumed that the price difference of

33% - 67% of the original price of feedstocks as raw bioethanol materials had great influence on the

variation of the annual losses. Concerning the sensitivity analysis again price differentiation and rejection

rate contributed the most to the uncertainty of the results with the former having an 0.789 correlation

while the latter had a 0.455.

Figure 6.5.4. Histogram of annual losses in maize due to Aflatoxin contaminations

0

0.000005

0.00001

0.000015

0.00002

0.000025

0 20000 40000 60000 80000 100000 120000 140000

Den

sit

y

Losses Maize Afla

Histogram Losses Maize Afla - € 1000

45

Tables 6.5.4a, 4b. Correlation matrix and Sensitivity results of MCSA for Annual Losses in maize from Aflatoxins


Variables Yield Prevalence Price differentiation Rejection rate Annual Losses

Yield 1 0.018 -0.026 0.010 0.217

Prevalence 0.018 1 -0.032 -0.004 0.261

Price differentiation -0.026 -0.032 1 0.001 0.789

Rejection rate 0.010 -0.004 0.001 1 0.455

Annual Losses 0.217 0.261 0.789 0.455 1


Sensitivity (Losses – Maize - Aflatoxins)


Price diff - Afla 0.789 65.90% 65.90%

Rejection rate-afla 0.455 21.90% 21.90%

Prevalence aflatoxins 0.261 7.22% 7.22%

Maize Yield=afla 0.217 4.98% 4.98%

Losses - Durum wheat- DON

The results of MCSA regarding DON annual losses in durum wheat showed a mean and median of

€47,481,170 and €42,484.710 respectively. The min and Max value were €7,77 ml and €163,3 ml

respectively (€13,7 - €92 ml when compared to results of section 6.2). Once again price differentiation

and rejection rate were the highest contributors to the annual losses (44.2% and 39.3% respectively).

Figure 6.5.5. Histogram of annual losses in durum wheat due to DON contaminations

0

0.000005

0.00001

0.000015

0.00002

0.000025

0 20000 40000 60000 80000 100000 120000 140000 160000 180000

Den

sity

Losses Durum DON

Histogram (Losses Durum DON * € 1000)

46

Tables 6.5.5a, 5b. Correlation matrix and Sensitivity results of MCSA for Annual Losses in durum wheat from DON

Sensitivity (Losses Durum DON)


Price diff. durum-DON 0.651 44.19% 44.19%

Rejection rate durum - DON 0.614 39.29% 39.29%

Yield - Durum-DON 0.356 13.21% 13.21%

Prevalence - durum - DON 0.178 3.32% 3.32%


Variables Prevalence Price differentiation Rejection rate Yield Annual Losses

Prevalence 1 0.025 0.035 0.034 0.178

Price differentiation 0.025 1 -0.008 -0.012 0.651

Rejection rate 0.035 -0.008 1 0.005 0.614

Yield 0.034 -0.012 0.005 1 0.356

Annual Losses 0.178 0.651 0.614 0.356 1


Losses – Durum – ZEA

Results for ZEA losses in durum wheat came up with a mean and median of €4,262,728 and €3,921,841

respectively with a min and Max value of €782,130 and €14,394,600 (€1,1 ml – €8,1 at section 6.2). For

ZEA in durum wheat, after price differentiation (54.3%) the second highest contributor on the overall

losses was the prevalence of the mycotoxin on the cereal (22.4%) instead of the rejection rate as the rest

of MCSA results. That can be supported by the initial hypothesis that a highly valued commodity is

considerably affected by the price fluctuations. However, that can be also explained from the very low

prevalence of ZEA in durum wheat (1-2%) or it was due to a combination of both.

Figure 6.5.6. Histogram of annual losses in durum wheat due to ZEA contaminations

0

0.00005

0.0001

0.00015

0.0002

0.00025

0 2000 4000 6000 8000 10000 12000 14000 16000

Den

sit

y

Losses Durum ZEA

Histogram Losses Durum ZEA - € 1000

47

Tables 6.5.6a, 6b. Correlation matrix and Sensitivity results of MCSA for Annual Losses in durum wheat from ZEA

Sensitivity Losses Durum ZEA


Price diff. durum-ZEA 0.732 54.28% 54.28%

Prevalence durum - ZEA 0.468 22.24% 22.24%

Yield - Durum-ZEA 0.397 16.02% 16.02%

Rejection rate - durum - ZEA 0.271 7.46% 7.46%


Variables Prevalence Price differentiation Rejection rate Yield Losses - ZEA

Prevalence durum - ZEA 1 0.018 0.010 -0.005 0.468

Price diff. durum-ZEA 0.018 1 0.019 0.022 0.732

Rejection rate - ZEA 0.010 0.019 1 0.006 0.271

Yield - Durum-ZEA -0.005 0.022 0.006 1 0.397

Losses Durum ZEA 0.468 0.732 0.271 0.397 1


48

7. Discussion

7.1. General Remarks - Baseline

The economic losses derived from mycotoxin contaminations can be of tens of millions when each

mycotoxin and cereal combination was examined separately, reaching up to a few hundreds of millions if

added altogether for a normal year, or in a bad-case scenario (high contamination) in the order several

hundreds of millions, even exceeding 1€ billion as a total in extreme cases. These results are in partial

accordance with literature (Felicia Wu et al., 2004) who estimates the annual losses (in the US) due to

fumonisins on corn are between $ 14 – $ 86 ml (€ 42 – € 117 for this report - for the Low Contamination

scenario) and $17 - $120 ml for DON (€64 – €134 ml for wheat and €64 - €92 for durum wheat οn this

report). It must be noted, however, that these annual loses do not represent the whole image, as due to

the selection of only 3 cereals and 4 mycotoxins and the inclusion of the top producing European countries

for each of the three cereals, a small part of the production and thus of the losses were left out. Moreover,

due to the assumption that all produced cereals are to be sold, the case of volume rejections due to any

other reason (e.g. quality degradation from other factors) was left out. That case is expected to represent

a considerable portion of the total cereal output and thus the economic losses from mycotoxin quality

degradation are to a degree, underestimated. Moreover, animal health and human life health losses were

also not considered as they represent a negligible amount next to market losses, however they do

underestimate the total losses to some degree. In addition, the inclusion of only 12 countries (or 4 for

durum wheat) in each of the regions/cereal left a part of the production out. Assuming that the rest of

the production can get affected and given that the part that was left out constituted 10%-25% of the total

production, the annual losses are underestimated proportionally. On the other hand, though, it was

assumed that all produced volume is to be sold regardless of any other reason that may lead to rejection.

In addition, for the High Contamination scenario, it was assumed that the whole regions were highly

contaminated to the highest degree. These two factors overestimated considerably the overall losses.

Regarding cereals processing, it was supported that it is ineffective in completely eliminating mycotoxins

as literature notes (Karlovsky et al., 2016) and that conclusion is in accordance with the results of this

report, as it was showed that in many cases the reduction was sufficient but some in some cases was not

(particularly in the upper limit of concentrations – spike concentrations). The degree of reduction of

49

mycotoxin levels depends on many factors, mainly the type of toxin and processing method and on the

initial level of contamination. For that reason, there cannot be a definite conclusion, however it is true

that the reduction potential of processing is satisfactory when there is not an unusual spike in mycotoxin

concentrations (Schatzmayr & Streit, 2013); (Karlovsky et al., 2016).

The difference in concentrations of mycotoxins between food and feed materials is mainly a result of

cereals processing. In principle, due to repartitioning of mycotoxins during cereal processing – mainly

milling, feedstuff materials bear a higher degree of contamination and thus bear higher economic losses,

although regulatory limits are much higher for feedstock materials. As the partitioning is related to the

initial degree of contamination of grains, it is imperative that mycotoxins levels on the raw materials

(grains) should be kept as low as possible to avoid batch rejections at the end. That conclusion is in

accordance with literature citing that the most effective measure for mycotoxins reduction is prevention

in the first place (Jard et al., 2011). As a result, biocontrol in the field can have significant effects on

preventing such incidents, despite being considerably costly. It is recommended that biocontrol should be

considered in cases with extreme and persisting mycotoxin contaminations. Moreover, improved sorting

before milling – or other processes may have the same effect, or even greater if combined with other

processing techniques to obtain a decreased level of mycotoxins in the final products through an

integrated mitigation strategy. If the initial degree of contamination is kept reasonably low (correlated

with weather conditions) and the storage/transport conditions are within an acceptable range, then it is

more unlikely for a future rejection incident to occur and thus economic losses from mycotoxin

contaminations can be minimized. Concluding, any high initial capital investment costs could be repaid as

the expected gains from improved control exceeded these costs in the long term. On possible solution

could be the investment in that type of machinery from local groups of growers/millers. That could

optimize the process as it is more likely that growers within a particular agronomical region would have

the same extend of mycotoxin contamination (same environmental conditions) and thus they could

choose the investment most appropriate to their case and spread out the investment costs.

During this report a series of drawbacks appeared. Due to the division of Europe in regions and each region

been assigned with a cereal and two mycotoxins, very specific data were necessary. Recent data about

the occurrence and rejection rates of each mycotoxin on the specific region and cereal was required.

Moreover, many control measures for mycotoxins were considered but not all of them are

commercialized and easily available to stakeholders or presented very high costs (e.g. ozonation). In

addition, price volatility of marketed commodities affected the baseline (as explained below) and the

50

heterogenicity of European agricultural sector render the impact estimations less accurate as measures

that are beneficial for one country (or even whole regions within the same country) might not be for

others.

7.2. Cost – Benefits Analysis & MCSA

As it is shown on the CBA for the biocontrol, the CBA ratio is higher for the High Contamination scenario

than for the Low Contamination scenario (2.2 < 0.5). That could mean that the biocontrol intervention is

probably more beneficial for regions with a higher burden of aflatoxins (and possible for any other

mycotoxin). In fact, a CBA ratio of >1 renders that control measure incapable of mitigating aflatoxins while

having an economic feasible application. Even if the annual application costs decrease over the years

(explained in the relevant chapter), the expected benefits on a low infectious region might not overcome

the costs. Farmers in that case should be critical and well informed regarding whether they should invest

in biocontrol. Mostly regions with severe and persisting mycotoxin contaminations would profit more

from that investment. On the other hand, investing in an optical sorter, with a lower or higher initial capital

investment costs, will render the investment economically feasible with relatively low payback period (4-

5 years and 1-2 years for high and low initial investment respectively). The expected annual benefits

although have quite a different order of magnitude (€ 51,3 ml & € 693.9 ml for high and low capital

investment respectively). On the other hand, though, optical sorter presented a lower reduction of

mycotoxins when compared to biocontrol. That conclusion is reflected on the CBA ratio (0.29 and 0.74 for

the Low Contamination scenario and low/high initial investment, and 0.04 – 0.10 for the High

Contamination scenario and low/high initial investment respectively). Considering also that technology

advances and newer models come in market relatively fast, investing in a sorter with low initial purchase

cost and using for a limited amount of time would be the best option. The main point of interest is that

regardless of the initial cost of investment the efficiency of the interventions is more related to the degree

of contamination of the region under scope.

Considering also the Monte-Carlo Analysis, it is demonstrated that in general the price differentiation (P)

and rejection rate (r) are strongly correlated with the baseline losses (in general correlation of >0.5).

Looking into the high correlation and contribution of the “price differentiation” for both aflatoxins and

fumonisins on the overall annual losses, it could be assumed that the price difference of 33% - 67% of the

original price of feedstocks as raw bioethanol materials had great influence in the variation of the annual

losses. In addition, the assumption of the annual 33% discount on material costs for biocontrol may have

affected also the results of CBA. The CBA results were also affected by the exclusion of other types of costs

51

(testing, monitoring, transportation etc.). These costs were not accounted as there was a considerable

variation from region to region and from country to country.

Regarding durum wheat, price differentiation seemed to have a great effect on the losses (correlation

0.651 – 44.2% for DON losses and 0.732 – 54.3% for ZEA losses), which is in accordance with the initial

hypothesis (see cereal selection) that a highly valued commodity bears economic losses regardless of the

degree of contamination or/and production volume. As explained earlier, there was a considerable

difference in soft and durum wheat prices for some years which may have affected the results as well. It

is worth noting that for durum wheat, the losses were higher than those for soft wheat for the Low

Contamination scenario. Comparing the two scenarios, a High Contamination scenario can bear 5 to 7

times the losses (0.03/0.23 – 0.39/0.07 as % of the total agricultural output) and when compared to the

total cereal output the losses can be 10 times higher if both scenarios with the widest range are

considered (0.3% - 3.61%).

52

8. Conclusions

Mycotoxin associated market rejections on cereals can result in several millions of euros lost each season

in a Low Contamination scenario and in hundreds of millions (or billions in extreme cases) of euros in an

unfavorable season. The intendent use of the cereal has paramount importance for these losses as

volumes destined for animal consumption tend to present higher contamination rates than those destined

for human consumption. Regarding the factors influencing those losses, the major finding is that the

production yield and occurrence rate of mycotoxins has a less important role when compared to

difference in prices depending on the intended use of the commodities and the percentage exceeding

MLs when calculating these losses. That conclusion points out that economic losses from mycotoxins also

follow the price fluctuations of the cereal market to some degree. The variation range is quite wide (also

when taken into account the importance of environmental conditions) and precise estimations about

losses from mycotoxin contaminations, even if all the other variables are generally stable, are difficult on

a year-to-year basis.

Based on the second part of the study, it is concluded that cereal processing reduces the concentration of

mycotoxins and in most of the cases they can be sufficient, with the exception of an unnaturally highly

contaminated batch, resulting from extreme weather or bad storage conditions. Considering the CBA

results, it is concluded that in general biocontrol is less cost effective (while being much more efficient in

reducing mycotoxin levels) when compared to improved optical sorting due to higher implementation

costs. Growers should consider carefully whether it is beneficial for them to invest on control measures,

especially when there is no severe contamination.

Application of biocontrol could lead countries with higher yield to be much more beneficed. This is a result

of the cost of the intervention, increasing linearly with an increase in the cultivation area (as the cost is

per hectare) but the net benefits are on a per volume basis. That points out that interventions might tend

to have optimum results as the level of agriculture, infrastructure and overall economy of a country/region

are higher. This is also the case for the optical sorter, as more advanced agricultural economies tend to

have higher sizes of agricultural holdings – higher average farm sizes so an initial capital investment tends

to be paid back quicker (lower PP Period). Based on the above results and discussion, it is supported that

the mycotoxin mitigation need a holistic and integrated approach because as cereal processing showed

there is no one sole solution for all. Methods and strategies that work for one situation might not work

for another.

53

9. Annexes

A. Total Agricultural and cereal output of EU – 28 (Data Eurostat, Annual Agricultural statistics, 2015)

B. Properties of selected mycotoxins

(Adverse health effects, affecting commodities, producing fungi and IARC classification)

IARC

Classification

Producing Fungi Affecting

commodities

Adverse effects

Aflatoxins (AFB1,

AFB1, AFG1, AFG2,

AFM1 – milk)

1 Aspergillus (A.

flavus, A.

parasiticus)

Maize, (wheat,

barley, nuts

and peanuts,

cotton, dried

fruits and

spices, cocoa

Carcinogenic, genotoxic,

acute hepatotoxicity,

immunosuppression

54

Fumonisins (FB1,

FB2, FB3)

2B Fusarium

(F.verticillioides, F.

proliferatum, A.

niger)

Maize, grapes

Carcinogenicity (possible),

tumor inducing in rodents,

sphingolipid biosynthesis

inhibitor, equine

leukoencephalomalacia

(ELEM)

Deoxynivalenol 3 F. graminearum

F. culmorum

Wheat (maize

and other

cereals –

barley, oats,

rye, triticale

Feed refusal, diarrhea,

vomiting (vomitoxin),

reduced growth, thymus,

spleen, heart, liver and

immunosuppression in

high doses

Zearalenone 3 Wheat and

other cereals

a- and β- estrogenic

disruptor

Section B. Overview of mycotoxins adverse health effects on humans and animals.

Data from: (Zain, 2011); (EFSA, 2013b); (EFSA, 2011); (Streit et al., 2013); (Karlovsky et al., 2016); (EFSA, 2005)

Aflatoxins

Aflatoxins are considered as the most potent and hazardous of all mycotoxins and as a result they have

been extensively researched and strictly regulated. Aflatoxins (B1, B2, G1, G2) are one of the most

important mycotoxin groups both from health and economic perspective. For that reason, it is the most

studied group of mycotoxins especially after some major incidents regarding their adverse effects they

bear. Such regions are traditionally tropical and/or sub-tropical (Streit et al., 2012), but the undisputable

climate change pushes the aflatoxin “danger zone” more into the North with South and Central Europe

being affected the most. Moreover, long stress periods, such as extended droughts and a temperature

above 30οC also increase the chance of aflatoxin contaminations. Regulation (EC) 1881/2006 was issued

for setting the maximum permitted levels for aflatoxins (B1, B2, G1, G2, M1, M2) containing foodstuffs

and Regulation (EC) 401/2006 setting the official control of aflatoxin levels in foodstuffs (EFSA). Directive

2002/32/EC was also issued regarding undesirable substances in animal feed followed by

Recommendation 2006/576/EC regarding specific aflatoxins in animal feed.

Fumonisins

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Fumonisins are a group of mycotoxins produced by the Fusarium genre and mainly associated with cereal

contaminations. Fumonisins are mainly produced by F. verticillioides, F. proliferatum and Aspergillus niger

and this group includes fumonisins B1, B2 and B3 (FB1, FB2, FB3), (EFSA, 2005). Fumonisins are often

associated with fungal contaminations and diseases and are particularly notorious for causing FDK

(Fusarium Damaged Kernel disease) which leads to extensive cereal contaminations, damage and

subsequent rejection of contaminated batches or qualitative degradation, which along with the former

leads to substantial economic losses. They are relatively not as potent as other mycotoxins (e.g. AFB1,

OTA) for so they have higher maximum limits than those mentioned above. Regarding RASFF’s latest

report only six notifications occurred due to fumonisins and out of those 6, 5 were for maize (RASFF,

2016).

Deoxynivalenol (DON) - Vomitoxin

Deoxynivalenol is part of the trichothecenes group, a group of mycotoxins produced by species of the

Fusarium genre and mainly F. graminearum and F. culmorum (EFSA, 2013b). They infect mainly small grain

cereals, as maize, wheat and barley and cereal derived products. Nonetheless, they also affect other

commodities such as figs and/or vegetables. The most frequently observed and potent toxin from the

trichothecenes group is Deoxynivalenol (DON) and its acetylated derivatives – 3-acetyl-DON and 15-

acetyl-DON (EFSA, 2011). Streit et al., (2012) note that despite being the least toxic mycotoxin, DON, its

high prevalence renders it one of the most troublesome toxins encountered when analyzing wheat and

maize samples. Deoxynivalenol production occurs in warm and humid conditions (Redman & Noleppa,

2017) and its correlated with extreme weather conditions during growth period.

Zearalenone (ZEA)

Zearalenone is a fungal metabolite, produced by the Fusarium genre. Fusarium graminearum is one of the

major zearalenone producing species, it affects maize, wheat and other small grains and producing the

toxin pre-harvest. Zearalenone’s metabolites can interact with the α- and β - estrogen receptors, meaning

it bears high resemblance with estrogen receptors, as it poses as a weak estrogen and acts as a hormone

disruptor in the body of humans and animals. (IARC, 2012).

C. Effects of cereal processing

56

Sorting & Cleaning

Initial processes, like sorting, and cleaning, they are in effect many decades, if not centuries, although not

with the scope of mycotoxin mitigation. Sorting is usually the first processing step for cereals. The bulk

volume of cereals contains a variety of undesired “particles”, insects, broken parts of the grains and dust

among else. Abbas et al., (1985) reported only a 12.5% (6-19%) reduction in DON levels with cleaning and

sorting. On the contrary, according to Neuhof et al., (2008) a 63% reduction of DON can occur by manual

sorting according to visual characteristics, but manual cleaning has increased labor costs. Sieving and

scouring, as Lancova et al., (2008) showed, can be effective as 48% in reducing DON levels. As the

concentrations of some mycotoxins is the maximum in the outer layers of the grains or damaged kernels,

an appropriate sorting process can reduce mycotoxin concentration to a significant degree. Moreover,

the parts that are to be sorted out make up only a small fraction of the total volume (3-6%); (Whitaker et

al.,2003). With regard on aflatoxins, due to the heterogenous distribution on samples, once the damaged

parts are effectively separated, mycotoxin concentration can be reduced (Kabak et al., 2006). Park, (2002),

gives a 60% reduction (40% - 80%) on aflatoxin levels via physical removal of mold damaged kernels.

According to Sydenham et al., (1994), cleaning corn can result in a 26-69% (mean of 47.5%) reduction of

fumonisins levels. However, Karlovsky et al., (2016) note that as a result of fumonisins contamination on

cereals, which has often no visible defects on the grain, optical sorters are not always sufficient as an

contamination from fumonisins bears no visible symptoms, sorting and even optical sorting application is

limited. Finally ZEA reduction as a result of cleaning process gave a 27% reduction Tribola et al., (2015),

while Neuhof et al., (2008) found a 33-40% reduction (initial level 77μg/kg) of ZEA.

Washing

Washing can be an effective control measure for mycotoxin reduction, but it is correlated with the water

solubility of each toxin (Karlovsky et al., 2016). Mycotoxins with high solubility in water can effectively be

removed from the bulk. Regarding durum wheat, Visconti & Pascale, (2010), note that during the whole

pasta production chain, results in reduction in DON levels, as the mycotoxin is localized in wheat bran,

which is to be removed during semolina and pasta production. That is the reason why the fraction of bran

wheat after processing results in a 159% partitioning while all others fraction of cereals processing varying

between uncleaned wheat (100%), to 23% at final cooked pasta (77% reduction). In more detail, from

uncleaned wheat to cleaned wheat there is a reduction of 77% in DON levels, to 64% DON present in fine

57

middling and to 37% and 33% to uncooked spaghetti respectively. ZEA on the other hand has limited water

solubility (117 mg/l), same as Aflatoxins (B1-233 mg/l, B2-392 mg/l, G1-424 mg/l, M1-994 mg/l). Contrary,

DON and fumonisins have much higher degree of solubility with 36,000 mg/l and >20,000 mg/l

respectively (Karlovsky et al., 2016). This is in agreement with Milani & Maleki (2014) citing that washing

after kernel cleaning resulted in 74% reduction of DON, which is higher than the degree obtained from

just cleaning (various, with a mean of 55%)

Milling

Milling of cereals, is the oldest and most frequently applied form of cereal processing (Kaushik, 2015). In

fact, most cereal derived products are a result of milling, as they are flour-based. As milling separates the

parts of the grain and each part has different levels of mycotoxin concentration, the mycotoxins are not

eliminated rather than redistributed to the end/by-products of milling, especially for DON and fumonisins

dependently on which part of the grains the mycotoxin resides. During wet milling, as Bennett & Richard

(1996) note there is a 75% (60-90%) reduction of Fumonisin levels with the addition of glucose and a mean

reduction of 25% without glucose. They conclude that with higher glucose content the reduction of

fumonisins is higher. For DON, Lee et al., (1987) reported a 36% (range of 24-48%) reduction, while Rio et

al., (2009) reported a 64% and 69% reduction using a commercial (SATAKE ™) abrasive mill with an initial

level of contamination 382 & 4203 μg/kg respectively. Regarding aflatoxins, Halt, (1994), reported a 75%

reduction via industrial milling of AF1, while for ZEA using an experimental lab mill Buhler (MLU-202) while

Edwards, (2011) reported a 60% and 55% reduction with an initial level of 5-470 μg/kg and 37-7290 μg/kg

respectively, while in the same study and with an initial level of 6-34 μg/kg came up with an average 44%

(range of 14-85%) reduction. Other results are in accordance with the above results, as Lee et al., (1987)

with the experimental mill and an initial concentration of 2047 μg/kg had a reduction of 48-66%. Last,

DON levels in durum wheat were reduced as described at washing a total of 77% through milling and

washing during semolina and pasta production, according to (Visconti et al.,2004).

According to literature, (Karlovsky et al., 2016); (Milani & Maleki, 2014), the milling process results in

mycotoxin reduction due to fractioning of the compounds during milling for commodities destined for

human consumption. However, it concentrates mycotoxins level for selective parts of the grain which are

then usually used for feedstocks. It may be supported that currently physical processing is the most

frequently used post-harvest intervention (Karlovsky et al., 2016), but biotechnology and novel

techniques offer great potential.

58

During dry milling, aflatoxins concentration increases in the outer parts of the grains, in bran and germ

for wheat and in the outer layers in maize. This results in a decrease in aflatoxin concentration in maize

products of milling. DON, accumulates in bran and middle parts of wheat grain, so as a result there are

lower levels in wheat flour and corn germ. ZEA concentrates in bran and in middling fractions. As for the

fate of fumonisins in maize dry milling they are reduced in flour, but on the contrary the concentration

rises in bran and germ which as they are used for feedstock production they just relocate the problem

(Bullerman & Bianchini, 2007).

Heating

Heating is the most prevalent process of cereal processing, as the majority of cereal derived products

occur via heating (frying, baking, boiling). El-Banna, Lau, & Scott, (1983) give a mean reduction of 47.5%

(24-761%) of DON during bread baking and a 35% during cookies and biscuits making. According to Dupuy

et al. (1993), fumonisins are quite heat stable compounds and are eliminated around 220oC. That property

renders thermal destruction of fumonisins more difficult. As Jackson et al., (1997) note, baking at 175oC,

and 200oC only resulted in a 16% and 28% reduction respectively. The same authors point out that during

tortillas frying (made from corn) there is a 67% reduction, while (Jackson et al.,1997) note that frying corn

grits at 190oC gave a 50% reduction. Castelo (1998) examined the corn flake process and came up with a

48.7% and 53.5% reduction if the process included toasting and cooking respectively. The same author

notes that with the addition of glucose, a higher reduction (87.5%) was achieved. Finally, El Sayed et al.,

(2003) reported a 28% and 43% reduction during pita bread making at 450oC (1 min) and French bread

making at 250oC (20 mins) respectively. Meister, (2001), cites a 30-55% reduction of FB1 and FB2 with

extrusion cooking followed by gelatinization and cornflake making process. Aflatoxins are very heat stable

compounds (degradation at 230 – 306oC; (Milani & Maleki, 2014), thus decontamination through heating

is not very effective. For that reason, any aflatoxin contamination that can go unnoticed in the supply

chain would result in consumption of aflatoxin by animals and humans. G. Kaushik (2015), demonstrated

that cooked corn grits resulted in 64% reduction of aflatoxins, while addition of sugars had a slightly higher

effect (67%), while boiling the grits only resulted in a 28% reduction of aflatoxins. Stoloff & Trucksess,

(1981) noted that frying after boiling resulted in a 43.5% (34-53%) aflatoxin reduction, baking flour at

220oC for 35 mins for bread production resulted in 36% aflatoxin mitigation, while baking for 20 mins gave

a 40% reduction. Moreover, baking cake for 20 mins at 220oC had a greater effect (50%) according to Bot

et al., (2016). Regarding thermal reduction of ZEA, a relatively thermo-stable compound Smith et al.,

59

(1994) achieved during experimental baking, a 23% (T<125oC); a 51% (34-68%) at 150oC and a >92% at

T>175oC reduction of ZEA levels from flour. In addition, Ryu et al., (2003) cite that for ZEA, thermal

treatment is also not very effective as ZEA is also a relatively thermostable compound with a degradation

temperature of 165oC.

D. Available commercial optical wheat sorters

SATAKE Automatic color sorter model Sanli – HL ($15,000 - $20,000)

WENYO CCD optical wheat sorter machine, model WYCS7-448 ($20,000 - $40,000)

60

TAIHO Wheat separation machine optical sorter camera CCD color sorter, model 6SXZ-420

($46,000 - $56,000)

61

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