Exposure assessment of mycotoxins in dairy milk

Food Control 20 (2009) 239–249

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Exposure assessment of mycotoxins in dairy milk Rory Coffey *, Enda Cummins, Shane Ward School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland

a r t i c l e

i n f o

Article history: Received 29 January 2008 Received in revised form 22 April 2008 Accepted 13 May 2008

Keywords: Mycotoxins Milk Exposure assessment

a b s t r a c t The objective of this study was to develop a quantitative Monte Carlo exposure assessment model for mycotoxins in dairy milk and to assess the potential human exposure levels. Mean concentrations of mycotoxins in milk were estimated using the simulation model (Aflatoxin M1 = 0.0161 lg/kg, Ochratoxin A = 0.0002 lg/kg, Deoxynivalenol = 1 lg/kg, Fumonisin B1 = 0.36 lg/kg, Zearalenone = 0.39 lg/kg, T-2 = 0.0722 lg/kg) while the simulated tolerable daily intakes (TDIs) from milk for males and females all fell below European Union guidelines. Aflatoxin M1 was the toxin of greatest concern as it had potential to exceed the EU limit of 0.05 lg/kg in milk. The sensitivity analysis identified the concentration of toxins in maize as the area which needs most attention in relation to crop management and agricultural practice. The sensitivity analysis assessed also identified the carry over rate as a factor closely related to risk and as a factor which required further research. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction Quantitative Exposure Assessment is a methodology used to analyse scientific information in order to estimate the probability and severity of an adverse event. This methodology was applied to model the human exposure to mycotoxins resulting from mycotoxin contamination of dairy feed, subsequently carried over to dairy milk for human consumption. Mycotoxins are secondary metabolites of fungi and are produced when cereals or animal feed are colonised by moulds. Excretion of such toxins in bovine milk has been documented (Blüthgen, Hammer, & Teufel, 2004; Yiannikouris & Jouany, 2002) and their carryover to dairy produce represents a potential threat to human health. Studies have demonstrated that human dietary exposure to mycotoxins may lead to severe illness and can lead to liver cancer (Marquardt, 1996; Notermans, 2003). The mycotoxins which are focussed on in this study and their effects on human health are displayed in Table 1. This assessment specifically focused on six mycotoxins of concern to humans (Aflatoxin B1/M1, Ochratoxin A, Deoxynivalenol, Fumonisin B1, Zearalenone and T-2 toxin) and involved analysing data on the occurrence of these mycotoxins in three dairy feed ingredients (barley, wheat and maize), inclusion rates in dairy feed, carryover rates to milk and subsequent human exposure. By combining the estimated individual mycotoxin concentrations in milk with available consumption data for the Irish population, the daily intake of mycotoxins from milk by individuals was calculated. The exposure was characterised by the probability that viable mycotoxin concentrations were in milk at the time of consumption. * Corresponding author. Tel.: +353 1 7162164. E-mail address: [email protected] (R. Coffey). 0956-7135/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodcont.2008.05.011

Information and data for the development of the model were obtained from Irish studies and expert opinion and, when not available, from research in other countries. The basic model structure is given in Fig. 1. 2. Materials and methods The developed model relies upon the generation of random variables from input probability distributions and these are represented in the model equations by the name of the probability distribution (e.g. Poisson, triangular etc.) followed by the parameters in brackets. The model used Monte Carlo simulation techniques (Vose, 2000) to create the output distributions. Monte Carlo methods repeatedly select values randomly from distributions to create multiple scenarios of a problem. Together, these scenarios give a range of possible solutions, some of which are more probable and some less probable, resulting in a probability distribution for the solution parameter. 2.1. Model inputs Individual levels of mycotoxins simulated in the model are discussed. A summary of data and model inputs is given in Table 2. 2.1.1. Mycotoxin contamination in feed ingredients 2.1.1.1. Aflatoxin B1. From the literature reviewed, it was considered that there was only a remote chance of Aflatoxin B1 (AFB1) contamination in feed ingredients (barley, wheat) produced within Ireland (D’Mello, Placinta, & Macdonald, 1999; Larsen, Hunt, Perrin, & Ruckenbauer, 2004; Placinta, D’Mello, & Macdonald, 1999). This was due to the fact that the Irish climate does not favour

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Table 1 Mycotoxins and their effects on human health Mycotoxin

Possible risk to human

Reference

Aflatoxin B1/M1 Ochratoxin A Deoxynivalenol Fumonisin B1 Zearalenone T-2 toxin

Liver cancer Possible carcinogen, kidney damage Nausea, diarrhoea, vomiting and headache Possible carcinogen, kidney/liver damage Natural oestrogen (effects undefined) Nausea, diarrhoea, vomiting and headache

IARC (1987), Creppy (2002) Petzinger and Weidenbach (2002), Creppy (2002) Tritscher and Page (2004), Meky et al. (2003) Turner et al. (1999), Quillien (2002) Kuiper-Goodman et al. (1987), Quillien (2002) Tritscher and Page (2004), Meky et al. (2003)

the formation of Aflatoxins in cereals. However, a lot of the components of bovine concentrate feed are imported. For example, maize from other countries was identified as a possible source of Aflatoxin B1 contamination of dairy cow concentrate feeds. To assess the presence of Aflatoxin B1 in maize, data from a survey by The Ministry for Agriculture, Food and Fisheries (MAFF, 1999), which analysed maize imported into the UK intended for use in animal feed, was used. 139 samples in total were analysed with the limit of detection (LOD) being 0.1 lg/kg. Any samples below the limit of detection (i.e. value was between 0 and 0.1 lg/kg) were assumed to be equal to 0 lg/kg (see [i] in Section 2.1.6). Out of the 139 samples, 51 proved positive for Aflatoxin B1 with the highest concentration being 16.4 lg/kg. The uncertainty about the probability of contamination (PAmz) was modelled using a beta distribution (n = 139, s = 51) to assess the probability of Aflatoxin B1 being present in maize. A beta distribution can be used to model the confidence one has about the probability of success of a binomial trial p, where one has observed n independent trials of which s were successes with the formula p = Beta (s + 1, n  s + 1) (Vose, 2000). A beta distribution with uniform prior is therefore used to model uncertainty about contamination probability estimates in this study. A Boolean flag was used to determine if a sample was contaminated, with 0 indicating no contamination and 1 indicating contamination. Level ranges (LRPAmz) of 0–0.1 lg/kg (range 1: 88 samples), 0.1–1 lg/kg (range 2: 36 samples), 1.1–2 lg/kg (range 3: 5 samples) and >2 lg/kg (range 4: 10 samples) were used for the detection of Aflatoxin B1 in maize in the survey. The probability within each range was also modelled using discrete distributions. To model the level of Aflatoxin B1 in maize the sequence of events is as follows:

but rather the model relies upon data from scientific sources (e.g. MAFF, 1999 [for Aflatoxin in maize]) to represent the level of mycotoxins. Thus, any distribution will be inherent in the form of the probability of each contaminant range level. Hence, the resulting distribution for the level of mycotoxins will follow the distribution of the data and hence inherently be distributed accordingly, whether it is negatively binomial or otherwise distributed. Data and model inputs are summarised in Table 2.

1. A Boolean flag indicates if mycotoxins are present or not. 2. Should mycotoxins be present (boolean flag = 1), it is assigned one of the 4 level ranges using a discrete probability distribution based on data by MAFF. 3. An individual level is then assigned by using a uniform distribution with the minimum equal to the minimum value of the range and the maximum equal to the maximum within the range. The sequence of events can be seen in Fig. 2.

2.1.1.2. Ochratoxin A. To estimate the typical concentrations of Ochratoxin A (OTA) in barley, a survey of stored grain by Prickett, Macdonald, and Wildley (1999) was examined. Out of 106 barley samples analysed, 20 proved positive for Ochratoxin A with a minimum concentration of 0.3 lg/kg and a maximum concentration of 117 lg/kg. The probability of Ochratoxin A being present in barley (PBb) was modelled using a beta distribution (n = 106, s = 20). To account for the uncertainty in the level of Ochratoxin A in barley (LBb) a cumulative distribution based on Prickett et al. data was used (Fig. 3). The same study also surveyed wheat for Ochratoxin A. In all 201 samples were examined. 32 were positive for Ochratoxin A with concentrations ranging from 0.3 to 231 lg/kg. The probability of Ochratoxin A occurring in wheat (PBw) was again modelled using a beta distribution (n = 201, s = 32) and the variability of Ochratoxin A levels in wheat (LBw) was represented by a cumulative distribution based on the concentration of individual samples recorded. To model the level of Ochratoxin A in maize the survey by MAFF (1999) was used. Out of the 139 samples, only 14 were found to contain traces of Ochratoxin A. This uncertainty was modelled using a beta distribution to assess the probability of Ochratoxin A being present in maize (PBmz). A boolean flag was used to determine if a sample was contaminated, with 0 indicating no contamination and 1 indicating contamination. The maximum concentration found was 1.5 lg/kg. The level ranges (LRPBmz) within the survey were 0–0.1 lg/kg (125 samples), 0.1–1 lg/kg (12 samples), 1.1–1.4 lg/kg (2 samples) and >1.5 lg/kg (0 samples). The same procedure as used for Aflatoxin was used to assign levels of Ochratoxin A to maize samples (LBmz) (inputs and data are given in Table 2).

For example to illustrate this, from 139 samples 51 were positive for Aflatoxin B1. Hence a Boolean flag will indicate the presence or absence e.g. Binomial[1, beta(51 + 1, 139  51 + 1)] mean = 0.38981. Should this be a positive result (i.e. = 1) then a level range is assigned based on the following probabilities: probability in range 1 = beta (88 + 1, 139  88 + 1); probability in range 2 = beta (36 + 1, 139  36 + 1); probability in range 3 = beta (5 + 1, 139  5 + 1); probability in range 4 = beta (10 + 1, 139  10 + 1). These probabilities are normalised in the model so they sum to 1 (Vose, 2000). When a range is assigned the actual level is modelled using a uniform distribution giving concentrations for Aflatoxin B1 in maize (LAmz). A negative binomial distribution has previously been used to represent the level of mycotoxins in foods (Knutti & Schlatter, 1982; Whitaker, 1977). However, the model developed in this study does not assume any distribution for the level of mycotoxins

2.1.1.3. Deoxynivalenol. The survey carried out by Prickett et al. (1999) in the UK also tested stored barley and wheat for Deoxynivalenol. In relation to barley there were 75 positive samples out of the total of 106. The probability of Deoxynivalenol being present in barley (PCb) was modelled using a beta distribution (n = 106, s = 75). The minimum recorded positive concentration was 20 lg/ kg and the maximum was 370 lg/kg. Any samples below the limit of detection were assumed to be 0 lg/kg. To account for this variability, the level of Deoxynivalenol in barley (LCb) was modelled using a cumulative distribution based on the data collected by Prickett et al. (1999) and is displayed in Fig. 4. There were 198 positive samples out of 201 for wheat. Again a beta distribution represented the probability of Deoxynivalenol occurring in wheat (PCw) (n = 201, s = 198). The positive values ranged between 24 lg/kg and 600 lg/kg (Table 3). A cumulative distribution

241

Probability density

R. Coffey et al. / Food Control 20 (2009) 239–249

Concentration in cereals at harvest

Concentration (µg/kg)

Bovine exposure

Probability density

• Feed inclusion rate

Probability density

Carry over rate to milk

Concentration in milk

Carry over percentage

Concentration in milk (µg/kg)

Human consumption

Probability density

• Consumption data

Exposure assessment

Exposurefrom milk (Logµg/kg bw/day)

0.881 0.264 0.214 0.133

Policy change

0.103 0.065

Risk management

0.005

-0.5

-0.25

0

0.25

Rank Correlation 0.5

0.75

Sensitivity analysis Fig. 1. Model structure for simulating feed to food transfer of mycotoxins in bovine milk.

1

242

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Table 2 Model distributions and inputs for mycotoxin contamination level, carry over rate and concentration in milk Symbol

Description

Distribution

Units

Mycotoxin A = Aflatoxin M1/B1 (AFM1) Probability of AFB1 presence or absence in maize Level range probability for maize (lg/kg) P1 probabiliy of level range L1 (0–0.1) P2 probabiliy of level range L2 (0.1–1) P3 probabiliy of level range L3 (1.1–2) P4 probabiliy of level range L4 (>2) Level of AFB1 contamination in maize Average AFB1 concentration in feed AFM1 carry over rate to milk Concentration of AFM1 in milk

Beta (n = 139, s = 51) Discrete (L1, L2, L3, L4; P1, P2, P3, P4) Beta (n = 139, s = 88) Beta (n = 139, s = 36) Beta (n = 139, s = 5) Beta (n = 139, s = 10) Uniform (min = range min, max = range max  from LRP) ImzLAmz Exponential AVA  COA

Fraction

Beta (n = 106, s = 20) Beta (n = 201, s = 32) Beta (n = 139, s = 14) Cumulative (see text) Cumulative (see text) Discrete (L1, L2, L3, L4; P1, P2, P3, P4) Beta (n = 139, s = 125) Beta (n = 139, s = 12) Beta (n = 139, s = 2) Beta (n = 139, s = 0) Uniform (min = range min, max = range max  from LRP) (Ib  LBb) + (Iw  LBw) + (Imz  LBmz) Fixed value AVB  COB

Fraction Fraction Fraction ug/kg ug/kg

LBmz AVB COB CMB

B = Ochratoxin A (OTA) Probability of OTA presence or absence in barley Probability of OTA presence or absence in wheat Probability of OTA presence or absence in maize Level of OTA contamination in barley Level of OTA contamination in wheat Level range probability for maize (lg/kg) P1 probabiliy of level range L1 (0–0.1) P2 probabiliy of level range L2 (0.1–1) P3 probabiliy of level range L3 (1.1–1.4) P4 probabiliy of level range L4 (>1.5) Level of OTA contamination in maize Average OTA concentration in feed OTA carry over rate to milk Concentration of OTA in milk

PCb PCw PCmz LCb LCw mnLCmz LCmz AVC COC CMC

C = Deoxynivalenol (DON) Probability of DON presence or absence in barley Probability of DON presence or absence in wheat Probability of DON presence or absence in maize Level of DON contamination in barley Level of DON contamination in wheat Mean Level of DON contamination in maize Level of DON contamination in maize Average DON concentration in feed DON carry over rate to milk Concentration of DON in milk

Beta (n = 106, s = 75) Beta (n = 201, s = 198) Beta (n = 41, s = 32) Cumulative (see text) Cumulative (see text) Triangular (100, 400, 630) Triangular (3, mnLCmz, 3700) (Ib  LCb) + (Iw  LCw) + (Imz  LCmz) Fixed value AVC  COC

Fraction Fraction Fraction ug/kg ug/kg

D = Fumonisin B1 (FB1) Probability of FB1 presence or absence in barley Probability of FB1 presence or absence in wheat Probability of FB1 presence or absence in maize Level of FB1 contamination in barley Level of FB1 contamination in wheat Level range probability maize (lg/kg) P1 probabiliy of level range L1 (0–10) P2 probabiliy of level range L2 (10–100) P3 probabiliy of level range L3 (101–500) P4 probabiliy of level range L4 (501–1000) P5 probabiliy of level range L5 (1001–5000) Level of FB1 contamination in maize Average FB1concentration in feed FB1 carry over rate to milk Concentration of FB1 in milk

Fixed value Fixed value Beta (n = 139, s = 139) Fixed value Fixed value Discrete (L1, L2, L3, L4, L5; P1, P2, P3, P4, P5) Beta (n = 139, s = 0) Beta (n = 139, s = 30) Beta (n = 139, s = 42) Beta (n = 139, s = 28) Beta (n = 139, s = 39) Uniform (min = range min, max = range max  from LRP) (Ib  LDb) + (Iw  LDw) + (Imz  LDmz) Fixed value AVD  COD

Fraction Fraction Fraction ug/kg ug/kg

E = Zearalenone (ZEN) Minimum probability of ZEN presence or absence in barley Probability of ZEN presence or absence in barley Probability of ZEN presence or absence in wheat Probability of ZEN presence or absence in maize Uncertainty about the mean level of ZEN contamination in barley Level of ZEN contamination in barley Level of ZEN contamination in wheat Level range probability maize (lg/kg) P1 probabiliy of level range L1 (0–4) P2 probabiliy of level range L2 (4–200) P3 probabiliy of level range L3 (21–100) P4 probabiliy of level range L4 (101–500) P5 probabiliy of level range L5 (>500) Level of ZEN contamination in maize Average ZEN concentration in feed ZEN carry over rate to milk Concentration of ZEN in milk

Beta (n = 34, s = 4) Uniform (min = minPEb, max = 1) Beta (n = 317, s = 164) Beta (n = 139, s = 135) Triangular (1, 3, 9) Triangular (1, mnLEb, 21) Cumulative, based on data Discrete (L1, L2, L3, L4, L5; P1, P2, P3, P4, P5) Beta (n = 139, s = 4) Beta (n = 139, s = 13) Beta (n = 139, s = 63) Beta (n = 139, s = 58) Beta (n = 139, s = 1) Uniform (min = range min, max = range max  from LRP) (Ib  LEb) + (Iw  LEw) + (Imz  LEmz) Cumulative, based on data AVE  COE

PAmz LRPAmz

LAmz AVA COA CMA PBb PBw PBmz LBb LBw LRPBmz

PDb PDw PDmz LDb LDw LRPDmz

LDmz AVD COD CMD minPEb PEb PEw PEmz mnLEb LEb LEw LRPEmz

LEmz AVE COE CME

Fraction Fraction Fraction Fraction ug/kg % ug/kg

Fraction Fraction Fraction Fraction ug/kg ug/kg percentage ug/kg

ug/kg ug/kg Percentage ug/kg

Fraction Fraction Fraction Fraction Fraction ug/kg ug/kg Percentage ug/kg

Fraction Fraction Fraction ug/kg ug/kg ug/kg Fraction Fraction Fraction Fraction Fraction ug/kg ug/kg Percentage ug/kg

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R. Coffey et al. / Food Control 20 (2009) 239–249 Table 2 (continued) Symbol

Description

Distribution

Units

PFb PFw PFmz LFb LFw LFmz AVF COF CMF

F = T-2 toxin (T-2) Probability of T-2 presence or absence in barley Probability of T-2 presence or absence in wheat Probability of T-2 presence or absence in maize Level of T-2 contamination in barley Level of T-2 contamination in Wheat Level of T-2 contamination in Maize Average T-2 concentration in feed T-2 carry over rate to milk Concentration of T-2 in milk

Beta (n = 70, s = 21) Beta (n = 921, s = 321) Beta (n = 57, s = 53) Lognormal (12, 15) Exponential (16, 137) Uniform (min = range min, max = range max) (Ib  LFb) + (Iw  LFw) + (Imz  LFmz) Cumulative, based on data AVF  COF

Fraction Fraction Fraction ug/kg ug/kg ug/kg ug/kg Percentage ug/kg

Boolean flag

Mycotoxin present

Mycotoxin not present

Range selection

a triangular distribution with a minimum equal to 3 lg/kg, a maximum equal to 3700 lg/kg as suggested by the CAC (2002) and uncertainty about the mean (mnLCmz) was modelled using a trianmode = 400 lg/kg, gular distribution (min = 100 lg/kg, max = 630 lg/kg) in line with published data (inputs and data are given in Table 2). A triangular density distribution is used as a modelling tool where the range and most likely value within a range can be estimated. The triangular distribution offers considerable flexibility in its shape while accounting for the uncertainty within the given range (Vose, 2000). 2.1.1.4. Fumonisin B1. From the literature reviewed, it was concluded that the only real risk from Fumonisin B1 in bovine feeds

1

Cumulative probability

modelled this uncertainty for the level of Deoxynivalenol in wheat (LCw), using the concentrations of Deoxynivalenol recorded in each sample. Data from Yiannikouris and Jouany (2002) looked at the natural distribution of Fusarium mycotoxins in maize in France during the years 1996 and 1997. In total there was 41 samples with 79.3% (32 samples) proving positive for traces of Deoxynivalenol. The probability of Deoxynivalenol occurring (PCmz) was modelled using a beta distribution (n = 41, s = 32). A mean concentration of 400 lg/ kg was reported for 17 of the samples and 100 lg/kg for the remaining 24 samples. Other data by Veldman, Borggreve, Mulders, and van de Lagemaat (1992) found a mean level of 630 lg/ kg for Deoxynivalenol in maize in line with published data. The worldwide contamination of maize by Deoxynivalenol ranges from 3 to 3700 lg/kg according to the Code Alimentarious Commission (CAC, 2002). To model the level of Deoxynivalenol in maize (LCmz),

0.8

0.6

0.4

0.2

(L 1 ,L 2 ,L 3 ,L 4 ,L 5 )

0 0

Level within range

100

150

200

250

300

350

400

Concentration (µg/kg) Fig. 4. Concentration of deoxynivalenol in barley (derived from data by Prickett et al., 1999).

Fig. 2. Sequence of events to model the level of AFB1 in maize.

1

Cumulative probability

50

Table 3 Concentrations of ochratoxin A and deoxynivalenol found in wheat samples (data by Prickett et al., 1999)

0.8 Mycotoxin

Range (ug/kg)

No. of samples

Ochratoxin A

0.6

0.4

0.2

0–10 10–20 20–30 30–40 40–50 >50

27 3 0 0 1 1

0–50 50–100 100–150 150–250 250–450 450–600 >600

49 97 34 13 4 1 0

Deoxynivalenol

0 0

20

40

60

80

100

120

140

Concentration (µg/kg) Fig. 3. Concentration of Ochratoxin A in barley (derived from data by Prickett et al., 1999).

R. Coffey et al. / Food Control 20 (2009) 239–249

is from maize and maize products. This was due to the fact that the occurrence of Fumonisin B1 in barley and wheat was rare and represented a negligible risk. Data from the MAFF survey (1999) was used to assess the risk of Fumonisin B1 occurring in maize. From the 139 samples examined, all contained concentrations of Fumonisin B1. A beta distribution (n = 139, s = 139) was used to represent the probability of Fumonisin B1 being present in maize (PDmz). Concentrations recorded were high with values varying between 10 and 3406 lg/kg. The ranges of concentrations with the survey on Fumonisin B1 (LRPDmz) were 0–10 lg/kg (0 samples), 10–100 lg/kg (30 samples), 101–500 lg/kg (42 samples), 501–1000 lg/kg (28 samples) and 1001–5000 lg/kg (39 samples). The same procedure as used for Aflatoxin B1 and Ochratoxin A was used to initially assign level ranges and then individual levels of Fumonisin B1 to maize samples (LDmz) (inputs and data are given in Table 2). 2.1.1.5. Zearalenone. Zearalenone in barley was surveyed in the UK by Tanaka, Hasegawa, Matsuki, Lee, and Ueno (1986). Four out of 31 samples (13%) contained traces of Zearalenone with the mean concentration being 1 lg/kg. A beta distribution (n = 34, s = 4) was used to represent this uncertainty surrounding the minimum probability of Zearalenone being present or absent in these samples (min PEb). The probability of the presence of Zearalenone in barley (PEb) was subsequently modelled using a uniform distribution (minimum = min PEb, maximum = 1). Additional surveys on 10 samples in Scotland revealed that 100% of samples were contaminated with Zearalenone. The mean value recorded was 9 lg/ kg. A study by the Home Grown Cereals Authority UK (HGCA) in 2004 revealed a mean concentration of 3 lg/kg, a maximum surveyed value of 21 lg/kg and a 95th percentile of 10 lg/kg. The level of Zearalenone in barley (LEb) was represented by a triangular distribution with a minimum value of 1 lg/kg (Tanaka et al., 1986) and a maximum value equal to that as reported by the HGCA (2004) (21 lg/kg). Uncertainty about the mean level of Zearalenone in barley (mnLEb) was modelled using a triangular distribution with the minimum and maximum values corresponding to those given by Tanaka et al. (1986) (i.e. minimum = 1 lg/kg, maximum = 9 lg/kg) and the mean equal to 3 lg/kg as given in survey work by the HGCA (2004). Subsequently the standard deviation was adjusted until the 95th percentile recorded by the HGCA (2004) (i.e. 10 lg/kg) was reached. The occurrence of Zearalenone in wheat has been studied by Vrabcheva, Gessler, Usleber, and Martlbauer (1996). 140 samples of Bulgarian wheat were tested post harvest for Zearalenone with a contamination frequency of 69%. The average level of contamination in positive samples was calculated at 17 lg/kg with a maximum recorded value of 120 lg/kg. A similar study of Russian wheat for Zearalenone (Tutelyan, 2004) found 41 out of 60 samples contaminated in a survey carried out in 1992 and 13 out of 56 positive results for the year 1997. The mean concentration of Zearalenone found in the wheat was 180 lg/kg and 10 lg/kg, respectively, for each survey. Wheat samples for the years 1996 and 1997 in France were both reported to be 12% positive for Zearalenone (46 samples and 69 samples, respectively) (Yiannikouris & Jouany, 2002). The mean concentration in wheat was 9 lg/kg in 1996 and 7 lg/kg in 1997. Studies by the HGCA of wheat in the UK estimated mean concentrations of 10.6 lg/kg, 7 lg/kg and 65 lg/kg for the years 2002, 2003 and 2004, respectively. The sampling data by Vrabcheva et al. (1996), Tutelyan (2004) and Yiannikouris and Jouany (2002) was combined giving a total number of samples equal to 370 with 164 (44%) of these being positive for Zearalenone. This uncertainty in the probability of Zearalenone being present in wheat (PEw) was accounted for using a beta distribution (n = 370, s = 164). Uncertainty about the mean concentration of Zearalenone in wheat was given by a cumulative

1

Cumulative probability

244

0.8

0.6

0.4

0.2

0 0

10

20

30

40

50

60

70

Concentration (µg/kg) Fig. 5. Mean concentrations of Zearalenone in wheat (derived from data by HGCA, 2004; Tutelyan, 2004; Vrabcheva et al., 1996; Yiannikouris and Jouany, 2002).

distribution based on data (Fig. 5). A 95th percentile equal to 304 lg/kg was estimated for the year 2004 in a study by the HGCA. Uncertainty about the standard deviation was modelled by means of a back calculation. This was achieved by modelling the 95th percentile with a uniform distribution (minimum = cumulative based on mean values, maximum = 304; in line with published data). A standard deviation was thus selected to give the required 95th percentile value (304 lg/kg) for the distribution. The variability in the level of Zearalenone in wheat (LEw) was then modelled using a log normal distribution with the mean equal to that in the cumulative distribution and the standard deviation corresponding to the value estimated previous. The MAFF survey of mycotoxins in maize imported into the UK in 1999 revealed that 135 out of the 139 samples taken were positive for Zearalenone. A beta distribution (n = 139, s = 135) was used to represent the probability of Zearalenone being present in maize (PEmz). The ranges of concentrations with the survey on Zearalenone (LRPEmz) were 0–4 lg/kg (4 samples), 4–20 lg/kg (13 samples), 21–100 lg/kg (63 samples), 101–500 lg/kg (58 samples) and >500 lg/kg (1 sample = 584 lg/kg). The same procedure as used for Aflatoxin B1, Ochratoxin A and Fumonisin B1 was used to assign level ranges and individual levels of Zearalenone to maize samples. These figures (normalised probability and individual concentrations) were then used in a discrete distribution giving concentrations for Zearalenone in maize (LEmz). Inputs and data are given in Table 2. 2.1.1.6. T-2 toxin. Seventy samples of barley were collected in the UK in 2004 and tested for the presence of T-2 toxin (HGCA, 2004). Seventy percent of the samples (49) were less than the limit of quantification (<10 lg/kg) with 30% (21) proving to have T-2 contamination. This uncertainty in the probability of a positive sample of T-2 in barley (PFb) was modelled using a beta distribution (n = 70, s = 21). The average concentration of T-toxin in the barley was 12 lg/kg, the 95th percentile was 37 lg/kg and the mean was 12 lg/kg. To account for this uncertainty, the level of T-2 in barley (LFb) was modelled using a normal distribution with a mean of 12 lg/kg and a standard deviation calculated such that the 95th percentile corresponded to 37 lg/kg as suggested by the HGCA (2004). Another survey by the HGCA (2004) examined T-2 contamination of wheat for the years 2002, 2003 and 2004. 921 samples were collected over the three years harvest period with 321 of the samples containing T-2 toxin contamination. The uncertainty in the probability of T-2 occurring in wheat samples (PFw) was based on this data using a beta distribution (n = 921, s = 321). For the years

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2002 and 2004 the mean concentration found was less than 20 lg/ kg (where the mean was found to be less than 20 lg/kg, a value of 10 lg/kg was used) and in 2004 the mean value was 22 lg/kg. The maximum values for each year were 75 lg/kg and 199 lg/kg. Uncertainty about the mean levels was modelled with a uniform distribution (minimum = 10 lg/kg, maximum = 22 lg/kg), while the uncertainty about the maximum level was also modelled using a uniform distribution (minimum = 75 lg/kg, maximum = 199 lg/ kg). The level of T-2 in wheat (LFw) was subsequently modelled using an exponential distribution with the mean and maximum levels corresponding to the previously modelled uniform distributions for each parameter, respectively. In relation to T-2 toxin in maize, its occurrence was looked at in countries in the EU in the SCOOP (Reports on tasks for Scientific Cooperation) task report 3.2.10 – Part A trichothecenes (2001). In France, one survey of 17 samples resulted in 13 positive contaminations. A second survey of 40 samples found that all the samples had T-2 present in them. When combined there was a total number of samples equal to 57 with 53 positives. This uncertainty was modelled using a beta distribution (n = 57, s = 53) to account for the probability of T-2 occurring in maize (PFmz). All of the 53 positive samples were within the range 10–29.9 lg/kg. The other 4 samples were below the limit of detection (<10 lg/kg) and taken as 0 lg/kg. The normalised probability for each range (<10 lg/kg and 10–29.9 lg/kg) was subsequently calculated along with the uncertainty in the level of T-2 contamination for each range (modelled using a uniform distribution similar to that used for Aflatoxin B1, Ochratoxin A, Fumonisin B1 and Zearalenone). The uncertainty in the level of T-2 in maize (LFmz) was then modelled using a discrete distribution. The inputs and data are summarised in Table 2. 2.1.2. Cereal inclusion rates in feed In order to gain a comprehensive insight into the inclusion rates of barley, wheat and maize/maize products in dairy cow concentrate feeds, various feed formulations used within Ireland were examined and assessed. This included feeding studies conducted by Teagasc (2001) on sample dairy cow concentrate rations, maximum inclusion levels of feed components in concentrate mixes and concentrate feeding to balance grass/silage based diets. Also contact was made with GAIN feeds (the animal feed manufacturing sector of Glanbia) to obtain information on their feed ingredient usage by percentage for dairy cow consumption. Overall it was noted that feeding formulations change regularly depending on ingredient availability and pricing. Model and distribution inputs for feed inclusion rates are displayed in Table 4. After assessing all collected data it was decided on the following as fixed inclusion rates for dairy cow concentrate feeding: Barley: 28% (Ib) Wheat: 7% (Iw) Maize and maize products: 41% (Im)

2.1.3. Mycotoxin carry over rates The extent to which AFM1 is carried over to milk can vary greatly. Jones et al. (1994) reported the carry over rate to be circa 1.7% while studies by the European Food Safety Authority (EFSA) in 2004 suggested a mean carry over rate of 2% increasing to 6% for high yielding cows. Henry et al. (2004) suggested ranges from 0.2% to 4%. More recent studies estimate a wider carry over range from 0.3% to 6.2% (Henry et al., 2004). An approximate carry over of 0.1% was suggested by Blüthgen et al. (2004). To account for the uncertainty in the percentage carry over of AFM1 to milk (COA), the individual rates (Table 5) described above were fitted to an exponential distribution. Due to the fact that Ochratoxin A is degraded by rumen microflora in bovines, it has been suggested that the carry over rate to milk is minimal. Few studies have been carried out in this area. However, a study by Galtier (1998) calculated that if a cow was fed an oral dose of 1 g/day, it would result in 100 lg/kg of Ochratoxin A in milk. This results in a carry over rate of 0.01% and was taken as a fixed value to represent Ochratoxin A carry over to milk (COB) in the model. Similarly with Deoxynivalenol, transfer to bovine milk is estimated to be small; however, there is very little research in the area. A carry over of 0.22% was calculated from figures of dose and resulting concentration in milk (Galtier, 1998). Again this (COC) was taken as a fixed value in the model. For the carry over of Fumonisin B1 to milk (COD), two values were identified. The first was a value of 0.11% reported by the EFSA (2005). A transfer rate of 0.05% was reported as the average carry over rate for a single administration of 3 mg of toxin per kg of feed by Yiannikouris and Jouany (2002). A uniform distribution (minimum = 0.05, maximum = 0.11) was used to account for this uncertainty. Transfer of Zearalenone to milk has revealed varying carry over rates. Yiannikouris and Jouany (2002) reported transfer rates of 0.06%, 0.016% and 0.008%, depending on the dose of the toxin administered. Rates of 0.00625% and 1.924% were estimated from other feeding studies (Galtier, 1998). A cumulative distribution was used to model the uncertainty surrounding Zearalenone carry

Table 5 Percentage carry over rates of AFM1, ZEN and T2 to bovine milk Mycotoxin

Carry over (%)

Probability f(x)

Reference

AFM1 0.1 0.2 0.3 0.3 0.3 1.7 2 2.2 4 6

Table 4 Model and distribution inputs for feed inclusion rates

Blüthgen et al. (2004) Henry et al. (2004) Henry et al. (2004) Yiannikouris and Jouany (2002) Creppy (2002) Jones et al. (1994) EFSA (2004) Yiannikouris and Jouany (2002) Henry et al. (2004) EFSA (2004)

ZEN Symbol

Ib Iw Io

Imz

Description

Mean value

Feed ingredient inclusion rates Inclusion rate of barley in 0.28 feed Inclusion rate of wheat in 0.07 feed Inclusion rate of other 0.41 ingredients In feed Inclusion rate of maize in feed

0.24

Distribution

From text From text From text From text From text

Units

data, see

Percentage

data, see

Percentage

data, see

Percentage

data, see

Percentage

data, see

Percentage

0 0.00625 0.008 0.016 1.924 2

(Best guess min) 0.2 0.4 0.6 0.8 (Best guess max)

Galtier (1998) Yiannikouris and Jouany (2002) Yiannikouris and Jouany (2002) Galtier (1998)

0.01 0.02 0.05 0.32 2.00 2.50

(Best guess min) 0.2 0.4 0.6 0.8 (Best guess max)

Galtier (1998) Yiannikouris and Jouany (2002) Galtier (1998) Yiannikouris and Jouany (2002)

T2

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Table 6 Model distributions and inputs for consumption data (based on IUNA, 2001) Symbol

Description

WMm

Consumption data Wholemilk consumption (males)

WMf LFSm LFSf PMm PMf

Wholemilk consumption (females) Low fat skimmed milk consumption (males) Low fat skimmed milk consumption (females) Processed milk consumption (males) Processed milk consumption (females)

Mean value

Distribution: lognormal

Units

195

Standard deviation = 220 Standard deviation = 141 Standard deviation = 149 Standard deviation = 132 Standard deviation = 32 Standard deviation = 31

g/ day g/ day g/ day g/ day g/ day g/ day

110 80 95 5 5

over to milk (COE) using the transfer rates reported by Yiannikouris and Jouany (2002) and Galtier (1998) and is given in Table 5. The presence of T-2 residues in cows milk was reported to have been found in the range of 0.05–2% (Yiannikouris & Jouany, 2002). Research by Galtier (1998) found a carry over rate in milk of between 0.02 and 0.32% for dairy cows fed 50,000 lg/kg of body weight. The variability in the transfer of T-2 toxin to milk (COF) was modelled using a cumulative distribution fitted to the carry over data suggested in the literature (given in Table 5). 2.1.4. Consumption data Data on the quantities of milk consumed by Irish consumers was obtained from the Irish Universities Nutritional Alliance Survey (IUNA, 2001). The mean consumption of wholemilk (WMm), low fat skimmed (LFSm) and processed milks (PMm) for adult males (18–64 years) was 195 g/day, 80 g/day and 5 g/day, respectively. The standard deviations were 220 g/day, 149 g/day and 32 g/day, respectively. To account for the variability in wholemilk, low fat skimmed and processed milk consumption, a lognormal distribution was used with the mean and standard deviation for each product from the IUNA survey (2001). For adult females, average consumption of wholemilk (WMf) was equal to 110 g/day, low fat skimmed (LFSf) was equal to 95 g/day and processed milk (PMf) was estimated at 5 g/day. Standard deviations were 141 g/ day, 132 g/day and 31 g/day, respectively. Similar to male consumption, a lognormal distribution using the given mean and standard deviation for female consumption of each product was used to account for the uncertainty in consumption levels for females. The consumption data used in the model is given in Table 6. 2.1.5. Product exposure assessment The product exposure assessment provides an estimate of how likely it is for an individual to be exposed to mycotoxin residues

and in what quantities they are likely to be ingested. In order to calculate the human exposure to a mycotoxin from wholemilk, low fat skimmed milk or processed milks, firstly the concentration of the mycotoxin in milk was calculated. This is estimated using the equation

ðCMx =1000Þ  P=M where CMx is the concentration of mycotoxin in milk (ug/kg) (subscript x = mycotoxin A, B, C, D, E or F), P is the milk product consumption; male or female (g/day) (wholemilk/low fat skimmed milk/processed milk), M is the mass of individual (kg): male or female, assumed to be 82.9 kg for males and 67.5 kg for females as given in IUNA study (2001). The model equations are summarised in Table 7. Different exposure levels for male and female were identified due to differences in consumption data of individual products and average body weights for genders. A total exposure (TE) for male/ females to each toxin was calculated by adding the exposure levels for wholemilk, low fat skimmed milk and processed milk together. 2.1.6. Model assumptions Simulation models frequently need to use necessary subjective assumptions. Such assumptions can have an impact on the results obtained in risk and exposure assessments. Consequently, assumptions made must be taken in context when considering model outputs. The following modelling assumptions have been made in the development of this exposure assessment: (i) Any mycotoxin contamination levels found in grains which were below the sampling limit of detection were assumed to be equal to 0. This may not be the case due to censored data; however, there is limited data to quantify this value. It is perceived that contamination values below the limit of detection would have little or no effect on overall human exposure levels. (ii) Feed production processes have little or no effect on initial mycotoxin concentrations in grain. (iii) Feed is not pelleted and is fed in ration form. (iv) Due to the fact that comprehensive data on mycotoxins in feed grains was unavailable for Ireland, data from other countries is representative of what may occur in Ireland. The authors acknowledge that this may not be the case in all circumstances but believe pessimistic values have been used therefore representing the upper end of risk. (v) Dairy cows are fed a fixed feed formulation. (vi) Milk production processes (such as pasteurisation) have no effect on mycotoxin concentration in milk. This is due to the fact that the majority of mycotoxins are heat stable (Bata & Lásztity, 1999).

Table 7 Model calculations for exposure assessment Symbol

Description

Mm EWMm,x ELFSm,x EPMm,x TEm,x Mf EWMf,x ELFSf,x EPMf,x TEf,x

Exposure assessment Males mean weight (18–64 yrs) Exposure to mycotoxin x from whole milk for males Exposure to mycotoxin x from low fat, skimmed milk for males Exposure to mycotoxin x from processed skimmed for males Total exposure from mycotoxin x (males) Females mean weight (18–64 yrs) Exposure to mycotoxin x from whole milk for females Exposure to mycotoxin x from low fat, skimmed milk for females Exposure to mycotoxin x from processed skimmed for females Total exposure from mycotoxin x (females)

a

Where subscript x, x is equal to mycotoxin A, B, C, D, E or F (bw = body weight).

Mean value

Distribution

Units

82.9

Fixed value ðCMx =1000Þ  WMm =Mm a (CMx/1000)  LFSm/Mm (CMx/1000)  PSm/Mm EWMm + ELFSm + EPMm Fixed value (CMx/1000)  WMf/Mf (CMx/1000)  LFSf/Mf (CMx/1000)  PSf/Mf EWMf + ELFSf + EPMf

kg g/kg g/kg g/kg g/kg kg g/kg g/kg g/kg g/kg

67.5

bw/day bw/day bw/day bw/day bw/day bw/day bw/day bw/day

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(vii) Mycotoxins were assumed to be uniformly distributed throughout the milk.

mation was displayed on a bar chart and can be seen in Fig. 6. The sensitivity analysis may be the most important result of the risk assessment. It can be used to identify factors for which risk management strategies can be based in order to reduce the overall exposure to mycotoxins. Rank order correlation determines the correlation between input variables and outputs. The correlation coefficient lies between 1 (direct negative correlation) and +1 (direct positive correlation). Correlation values in the vicinity of zero indicate a weak predictive value of the variable (Cassin, Lammerding, Todd, Ross, & McColl, 1998).

2.2. Model simulation The exposure model was developed using Monte Carlo simulation techniques and probability distributions to account for model uncertainty and variability. The @RISK software package, version 4.0 (Palisade, USA), in combination with Microsoft Excel 2000 (Microsoft, USA) was used to run the simulation. The simulation was run for 10,000 iterations and reflects the inherent uncertainty in the production of bovine feed, in milk consumption and in the uncertainty of the mathematical process. The probability of a toxin in milk, the level of the toxin in milk and the probability of human exposure were outputs of the mathematical model. Monte Carlo simulation was also used to perform a sensitivity analysis of the model to assist in the identification of critical points in the process. 3. Results and discussion For each toxin, the risk assessment model produced:  A probability density distribution representing the potential level of bovine milk contamination with mycotoxin x (CMx; where x is equal to mycotoxin A, B, C, D, E or F). This represents the uncertainty about the true mean value. A summary of the simulation results, including uncertainty analysis and comparison with EU regulations for each analysed mycotoxin, is given in Table 8.  A probability-exposure distribution for males and females in relation to milk consumption (EWMf/m,x). Table 9 displays a summary of these results together with existing legislation in the EU.  A sensitivity analysis was subsequently conducted to provide a measure of the most important factors affecting the risk to human health from an individual mycotoxin in milk. This infor-

Mean levels for the mycotoxins assessed in milk all fell below EU limits (Table 8). Aflatoxin M1 was singled out as the toxin of greatest concern. In certain circumstances its concentration exceeded the EU limit of 0.05 lg/kg in milk. This data was in line with the literature reviewed which also found Aflatoxin M1 occurring at potentially high levels in milk (EFSA, 2004). However, it should be noted that for all other mycotoxins assessed in this study, EU limits only exist for their presence in cereal based foods and are thus not strictly comparable to this risk model which focused on milk. It has not been possible to estimate a tolerable daily intake (TDI) for Aflatoxin which is a carcinogen. The estimated TDI’s for all other mycotoxins by the exposure assessment model were below those estimated by the EU indicating negligible risk to humans. It is suggested that the regulatory limits for Aflatoxin in milk are set to ensure that any risk from total dietary intake is very low. Occasionally eating foods containing Aflatoxin at levels marginally above the regulatory limit will not increase that very low risk (UK food standards agency, 2005). On examination of the sensitivity results for all the assessed mycotoxins, it was clear that in most cases risk estimates were very sensitive to the initial concentration of each toxin in maize followed by the level in barley and wheat. This concurs with the literature reviewed. The sensitivity analysis also singles out the concentration of toxins in maize as the area which needs most attention in relation to crop management and agricultural practice. The carry over rate also warrants further investigation.

Table 8 Simulated uncertainty distribution about individual mycotoxin levels in milk

Mean 5th Percentile 95th Percentile Limit a b c d e

EU EU EU EU No

AFM1(lg/kg)

OTA (lg/kg)

DON (lg/kg)

FB1 (lg/kg)

ZEN (lg/kg)

T-2 (lg/kg)

0.0161 0.0002 0.0834 0.05a

0.0002 8.84  107 0.0009 3b

1.0000 0.0049 4.0047 500c

0.3600 0.0093 1.4391 400d

0.3900 0.0002 2.5570 50c

0.0722 0.0006 0.2881 e

limit for AFM1 in milk (Commission Regulation 2003/2174/EC). limit for cereal based food (Commission Regulation (EC) 472/2002). limits for cereal based foods (no limits exist for milk) (Commission Regulation (EC) No. 856/2005). limits for maize based products (no limits exist for milk) (Commission Regulation (EC) No. 856/2005). limit for the presence of T-2 in milk/food products exists (Commission Regulation (EC) No. 856/2005).

Table 9 Simulated exposure to individual mycotoxins from milk for males and females AFM (pg/kg bw/ day)

OTA (pg/kg bw/ day)

DON (pg/kg bw/day)

FB1 (pg/kg bw/day)

ZEN (pg/kg bw/day)

T-2 (pg/kg bw/day)

Male

Female

Male

Female

Male

Female

Male

Female

Male

Female

Male

Female

Mean 5th Percentile 95th Percentile

8.619 0.356 212.559

9.371 0.381 238.037

0.046 0.002 2.353

0.049 0.002 2.426

608.520 9.961 13580.226

650.738 10.293 13833.657

327.097 16.960 4650.418

351.125 18.253 5021.688

43.558 0.478 6553.209

46.827 0.479 6895.027

32.492 0.927 973.727

34.992 0.988 1168.956

Tolerable daily intake (pg/ kg bw/day)

–a

–a

5000b

5000b

1,000,000b

1,000,000b

2,000,000b

2,000,000b

200,000b

200,000b

60,000b

60,000b

a b

No tolerable daily intake has been estimated (see text) (UK food standard agency, 2004). Tolerable daily intake estimated by the EU (Commission Regulation (EC) No. 856/2005).

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1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Concentration in maize

Carry over rate

AFM1

0 .9 0 1

0 .1 6 7 0.806

ZEN

0.412

OTA

0 .4 4 8

FB1

0 .8 8 6

DON

0 .6 7 8

T-2

0.214

Concentraion Concentration in barley in wheat

Low fat skimmed milk consumption

Processed milk consumption

0 .0 5 5

0 .0 1

0 .0 1 4

0.039

0.013

0.21

0.115

0.022

0 .6 2 9

0 .4 9

0 .0 2 6

0 .0 5 1

0 .3 3 9

0 .1 9 8

0 .1 4 3 0.881

Whole milk consumption

0 .0 2 2

0 .0 5 6

0 .0 1 3

0 .2 8 9

0 .1 6

0 .0 2

0.103

0.065

0.264

0.133

0.005

RANK CORRELATION Fig. 6. Sensitivity analysis for human exposure to mycotoxins in bovine milk.

4. Conclusion As natural and unavoidable contaminants of important agricultural commodities, mycotoxins have continued to severely impact animal health and consequently human health, which may have implications for livestock production, crops production and the economy. The risk is well recognised, but at present it has not been quantified accurately. Exposure modelling and risk assessment can be valuable tools in assessing risks to humans and animal from mycotoxins in the feed/food chain. The quantitative exposure assessment developed in this study tries to address the deficiency in scientific literature on the estimation of risk from feedborne hazards (Hinton, 2000; Notermans, 2003) and serves as an initial attempt to link the animal feed chain and the human food chain. The model assesses the potential human exposure to six mycotoxins in dairy milk. There has been no published attempt in scientific literature to simultaneously assess human exposure to these mycotoxins from milk consumption. Results fit well with observed data, suggesting that the mathematical approximations of all real life variables are justified. Except for Aflatoxin M1, the simulated exposure levels for mycotoxins in milk are below the limits set by the EU. Under certain conditions Aflatoxin M1 exceeded EU limits. A sensitivity analysis also suggests that the key to reducing mycotoxin contamination is at the field level prior to the harvesting of grain for feed production. Results from the exposure assessment model suggested that the presence of mycotoxins in bovine feed at normal contamination levels should not give rise to significant mycotoxin concentrations in milk. Evidence suggests that mycotoxins may never be completely removed from the feed-tofood chain but that current exposure levels are likely to be small in dairy milk and well below EU guidelines. It can be concluded that, from a risk perspective, the presence of mycotoxins in bovine milk poses little risk to man. If more accurate data becomes available on the biological fate and carry over of mycotoxins to bovine

food products, the developed model should be updated and developed further. The model identified data gaps in these areas while directing future research efforts to fill these gaps.

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