Karl-Heinz Engel

Anja Demmel

Alexandra ; Ehlert

Christine Hupfer

Ulrich Busch

Simultaneous detection of DNA from ten food allergens by ligation-dependent probe amplification

Keywords: Food allergen, tree nuts, peanut, sesame, detection, ligation, PCR 19

published or not. The documents may come

Introduction 20

In industrialized countries 1-2 % of adults and up to 8 % of children and 21 adolescents are affected by food allergies [START_REF] Sicherer | 9. Food allergy[END_REF]. The 22 symptoms may range from skin irritations to severe anaphylactic reactions with 23 fatal consequences. Approximately 90% of the adverse reactions observed 24 have been associated with eight food groups: cow´s milk, eggs, fish, 25 crustaceans, peanuts, soybeans, tree nuts and wheat. In addition to these 26 major food allergens, a broad spectrum of fruits, vegetables, seeds, spices and 27 meats have been reported to possess allergenic potential [START_REF] Ellman | Food 444 hypersensitivity in two groups of children and young adults with atopic 445 dermatitis evaluated a decade apart[END_REF], 28 Poms et al. 2004[START_REF] Breiteneder | Molecular and biochemical classification of plant-400 derived food allergens[END_REF]. 29

Taking into account the recommendations of the Codex Alimentarius 30

Commission (Codex Alimentarius Commission 2007), the European 31

Commission amended the European Food Labelling Directive 2000/13/EC by a 32 list of ingredients to be labeled [START_REF] Ec | Directive 2000/13/EC of the European Parliament and of the Council 425 of 20 March 2000 on the approximation of the laws of the Member States 426 relating to the labelling[END_REF]. Currently, Annex IIIa of Directive 33 2007/68/EC [START_REF] Ec | Commission Directive 2007/68/EC of the European Parliament and 429 of the Council of 27 November 2007 amending Annex IIIa to Directive 430 2000/13/EC as regards certain food ingredients[END_REF] comprises gluten-containing cereals, crustaceans, 34 molluscs, fish, peanuts, soybeans, eggs, milk and dairy products (including 35 lactose), nuts, celery, mustard, sesame seeds, lupine, sulfite and products 36 thereof. To protect the health of consumers, the declaration of these ingredients 37 has been made mandatory regardless of their amounts in the final product. 38

Allergens are proteins whose routine food analysis is based on 39 immunological detection using either specific IgE from human serum or 40 antibodies raised in animals. Major challenges are the needs to check for the 41 presence of food allergens at extremely low levels and to detect trace amounts 42 of hidden allergens in composite and processed foods (Poms et al. 2004). PCR-43 based methods amplifying specific DNA sequences offer alternative tools to the 44 simultaneous detection of peanut and hazelnut or wheat and barley (Ronning et 61 al. 2006;[START_REF] Rossi | A 512 PNAarray platform for the detection of hidden allergens in foodstuffs[END_REF]. 62

The aim of this study was therefore to develop and to validate a multi-63 target method for the simultaneous detection of allergens in different food 64 matrices. The application of a ligation-dependent probe amplification (LPA) 65 technique for the simultaneous detection of DNA from peanut, cashew nut, 66 pecan nut, pistachio nut, hazelnut, sesame seeds, macadamia nut, almond, 67 walnut and brazil nut in a single reaction is described. Ligation-dependent PCR 68 was originally introduced to allow the detection of nucleic acid sequences 69 [START_REF] Belgrader | Detection of nucleic acid sequence 397 differences using coupled ligase detection and polymerase chain reactions. 398[END_REF][START_REF] Carrino | Multiplex ligations-dependent amplification using split probe 412 reagents containing common primer binding sites[END_REF][START_REF] Hsuih | Novel, ligation-dependent PCR assay for detection of hepatitis C in 478 serum[END_REF]. First applications in the 70 of up to 40 -50 target sequences in a single assay (Eldering et al. 2003, Gille 72 et al. 2002, Hogervorst et al. 2003, Schouten et al. 2002[START_REF] Taylor | Genomic 534 deletions in MSH2 or MLH1 are a frequent cause of hereditary non-polyposis 535 colorectal cancer: Identification of novel and recurrent deletions by MLPA[END_REF]. 73

The suitability of this method for the event-specific detection and relative 74 quantification of DNA from two genetically modified organisms (GMO) has been 75 demonstrated using commercially available maize and soya standards 76 [START_REF] Moreano | Ligation-dependent probe 490 amplification for the simultaneous event-specific detection and relative quantification of DNA from two genetically modified organisms[END_REF]). The technique does not amplify the target sequences, 77 but is rather based on the amplification of products resulting from the ligation of 78 bipartite hybridization probes. The use of this analytical strategy results in a 79 flexible system that can be complemented with further hybridization probes to 80 broaden the range of target sequences to be detected. This approach has been 81

realized by the development of a modular system allowing the simultaneous 82 detection of several GMO targets corresponding to different levels of specificity 83 in a one-tube assay (Ehlert et al. 2008b). 84

Materials and Methods

86

Materials and samples

87

Nut materials, sesame seeds, ingredients of self-prepared walnut cookies 88 and commercial food samples were purchased from local grocery stores. DNA 89 plant and animal materials used for testing the specificity of the method and 90 spiked samples of chocolate, cookies and pesto used to determine the 91 sensitivity were obtained from the Bavarian Health and Food Safety Authority 92 (Oberschleißheim, Germany). Chocolate samples had been spiked with 93 peanuts (100, 10, 5, 1 and 0.5 mg/kg) and hazelnuts (20, 10 and 5 mg/kg). 94 supernatant was transferred to a 1.5 mL tube and centrifuged again at 14000 g 120 for 5 min. 500 µL chloroform/isoamylalcohol (Ready Red™, MP Biomedicals, 121 Heidelberg, Germany) was mixed with 700 µL supernatant and centrifuged at 122 16000 g for 15 min. 500 µL supernatant was added to 500 µL isopropanol 123 (stored at -20°C) and incubated at room temperature (RT) for 30 min. After 124 15 min centrifugation at 16000 g the supernatant was removed, the pellet was 125 washed with 500 µL ethanol (70 %; stored at -20°C) and centrifuged 5 min at 126 16000 g. After removal of ethanol the pellet was dried 1 h at 50°C and 127 afterwards diluted in 100 µL TE-buffer (1x). Additionally, the DNA extracts were 128 purified using spin filter columns (Qiagen, Hilden, Germany) The LPA reaction was essentially carried out as described by Schouten 147 et al. [START_REF] Schouten | Relative quantification of 40 nucleic acid sequences by multiplex ligation-519 dependent probe amplification[END_REF]. Hybridization was performed overnight in 0.5 mL 148 reaction vessels using a thermocycler (Mastercycler Gradient, Eppendorf, 149

Hamburg, Germany) and 100 ng DNA sample. After 5 min of DNA denaturation 150 at 98°C, 1.5 µL of MLPA buffer and 1.5 µL of a mixture of the synthesized 151 probes containing 1 fmol of each LPA probe for the detection of peanut, 152 cashew, pecan nut, pistachio, hazelnut, sesame, macadamia nut, almond, 153 walnut and brazil nut, respectively, were added and held at 60°C for 16 h. 154

Ligation reaction was performed at 54°C for 15 min after adding 3 µL Ligase-65 155 buffer A, 3 µL Ligase-65 buffer B, 25 µL H 2 O and 1 µL Ligase-65 (MRC-156

Holland, Amsterdam, The Netherlands). After ligation, reaction mixes were 157 heated for 5 min at 98°C to inactivate the enzyme. 158

For amplification of the ligation products 10 µL Polymerase mix with 159 primers, dNTPs, buffer and Polymerase enzyme of the MLPA kit (MRC-Holland, 160 Amsterdam, The Netherlands) were added to 40 µL ligation reaction mixture at 161 60°C. 35 amplification cycles at 95°C for 30 sec, 60°C for 30 sec and 72°C for 162 60 sec were followed by a final step of 20 min at 72°C and cooled down to 4°C. 163

Fragment length analysis 164

Fragment length analysis was performed on an ABI PRISM ® 310 Genetic 165 Analyzer using capillaries (47 cm), polymer (POP-6™ Performance Optimized 166 Polymer); reagents were obtained from Applied Biosystems (Forster City, USA). sequencing. Primers used for sequencing were identical to those listed in Table 176 1. FAM-labeling did not interfere with cycle sequencing. Amplified products 177 were cleaned up using a PCR purification kit (QIAquick' Quiagen GmbH, Hilden, 178

Germany) and used as template for the sequencing PCR. This reaction was 179 performed using a BigDye' Terminator v1.1 Cycle Sequencing Kit (Applied 180 Biosystems, Foster City, USA). One reaction mix (20 µL) contained 2 µL 5x 181 buffer, 4 µL RR-mix, 2 µL primer (10 pmol), 8 µL H 2 O and 4 µL template. 182

Reaction conditions were as follows: initial denaturation (1 min at 96°C), 30 183 cycle denaturation steps (10 sec at 96°C) and primer annealing (5 sec at 56°C), 184 and a final step (4 min at 60°C). 185

Purification of the PCR products was carried out following amplification. 186 PCR products (10 µL) were mixed with 16 µL H 2 0, 4 µL Na-acetate (3M) and 187 50 µL EtOH (100%) in a 1.5 mL reaction vessel and centrifuged at 15.000 rpm 188 for 15 min. EtOH was removed carefully without damaging the precipitated DNA 189 pellet. The pellet was vortexed with 50 µL EtOH and centrifuged at 15.000 rpm 190 for 5 min. After carefully removing EtOH, the pellet was allowed to dry at 50°C were used for Real-time PCR analysis. The limits of detection for both kits were 201 indicated to be 10 copies of genomic DNA. For the detection of cashew nut a 202 recently described Real-time PCR system was used (Ehlert et al. 2008a). 203

Ridascreen ® enzyme immunoassays for the quantitative analysis of hazelnut 204 and peanut were obtained from r-biopharm AG (Darmstadt, Germany). The limit 205 of detection as indicated by the manufacturer were 2.5 mg/kg hazelnut and 206 peanut, respectively (r-biopharm; 2006, 200x). 207 208

Results and Discussion

209

Design of LPA probes and Choice of target sequences 210

The principle of ligation-dependent probe amplification reaction as well 211 as hints and rules for the design of LPA probes have been specified in detail 212 [START_REF] Schouten | Relative quantification of 40 nucleic acid sequences by multiplex ligation-519 dependent probe amplification[END_REF], Moreano et al. 2006, MRC Holland). The detection of 213 each target sequence requires the design of two probes containing the 214 respective target-specific hybridization sites as well as identical primer binding reaction (data not shown). The region that was also used for specific detection 266 of cashew DNA by Real-time PCR analysis (Ehlert et al. 2008a) was therefore 267 chosen as hybridization site for the cashew LPA probes. 268 Subsequently, the specificity of the method was tested by analyzing DNA 269 from organisms related to the selected targets and from other organisms that 270 are to be expected as ingredients of composed foods. Non-coding regions of 271 chloroplast DNA were amplified for verification [START_REF] Taberlet | Universal primers for 531 amplification of three non-coding regions of chloroplast DNA[END_REF] The fragment lengths determined for each target were highly 286 reproducible (SD ± 0.2 nt). The observed slight shifts (≤ 1 nt) of the absolute 287 length values (Table 1) are well-known effects arising from differences in the 288 mobilities of the length standards and of the analyzed fragments caused by 289 different labeling dyes and by differences in the structures and base 290 compositions of the DNA (Magnuson et al. 1996[START_REF] Wenz | High-precision 539 genotyping by denaturing capillary electrophoresis[END_REF]. were chosen to evaluate the sensitivity of the LPA system. Chocolate was 296 spiked with peanuts and hazelnuts, pesto with cashew nuts, and cookies with 297 peanuts. In addition, walnut cookies spiked with peanuts, hazelnuts, pecan and 298 macadamia nuts were self-prepared. These examples were selected to simulate 299 fraudulent labeling or the unintended contamination of foods with different nuts 300 (Table 3). Starting materials and ingredients were checked for the absence of 301 any target of the LPA system. 302 303 [Insert Table 3 about here] 304 305 Two independent DNA extractions of the spiked samples were analyzed 306 in duplicate by LPA to determine the limits of detection (LOD). Fragment length 307 analysis was performed with the pure and 1:20 diluted PCR product. The LOD 308 was assessed as the least concentration for which all results were consistently 309 positive. For peanut, hazelnut and cashew nut, limits of detection of 5 mg/kg 310 were determined in the matrices chocolate, cookie and pesto, respectively. 311

To study the influence of unequal proportions of allergenic components in 312 a food, i.e. high excess of one of the LPA targets in the sample, on the 313 sensitivity of the method, cookies containing 25% (250,000 mg/kg) walnuts 314 were spiked with defined amounts (1 -10,000 mg/kg) of four other nuts. Due to 315 competitive amplification of the probes during PCR, under these conditions the 316 products. Consequently, the sensitivity for the nuts contained as trace amounts 318 is reduced, resulting in LODs of 1000 mg/kg for peanut, hazelnut, pecan, 319 macadamia and 100 mg/kg for hazelnut, respectively (Table 3). 320

This inherent feature of the multi-target LPA method has to be taken into 321 account, when traces of targets are to be simultaneously detected in the 322 presence of a high amount of one of the other LPA targets. 323

The samples used to determine the limits of detection of the LPA method 324 were also analyzed by Real-time PCR and ELISA (Table 3). For the samples 325 analyzed by the commercially available ELISA kits, the LODs determined for 326 hazelnut and peanut were slightly higher that those given by the manufacturer 327 (r-biopharm 2006, 2007). For the LODs determined for these targets by the 328 Real-time PCR methods, such a comparison was not possible because the 329 information given by the manufacturer refers to copies of genomic DNA 330 (Congen, 2006a(Congen, , 2006b)). The limits of detection observed for peanuts, 331 hazelnuts and cashew nuts in chocolate and pesto by LPA were similar to those 332 determined for the tested ELISA and Real-time PCR approaches. The Real-333 time PCR method applied for the detection of peanuts showed higher 334 sensitivity; however, the application of this kit also resulted in positive signals for 335 the non-spiked walnut reference cookie. Therefore, the reliability of these 336 results remains questionable. The hazelnut ELISA test also showed cross 337 reactivity to walnut (0.001 -0.036 %); owing to the false-positive results a 338 detection of hazelnut traces in the presence of walnut is not possible with this kit 339 (r-Biopharm 2006, [START_REF] Kniel | Nachweis allergener Haselnußspuren in Backwaren 483 und in den dafür verwendeten Rohstoffen und Zwischenprodukten[END_REF]. 340 LPA analysis by amplification of non-coding regions of chloroplast DNA 346 [START_REF] Taberlet | Universal primers for 531 amplification of three non-coding regions of chloroplast DNA[END_REF]. Results obtained for spreads, sausages, dressings, dairy 347 products and sweets are shown in Table 4. 348 349 [Insert Table 4 about here] 350

351

The results obtained for the samples "organic mixed nut butter" and 352 "gingerbread" demonstrate the advantages of the developed multi-target LPA 353 method. In both cases the simultaneous detection of the allergenic ingredients 354 declared on the label could be achieved. Figure 1B shows the results obtained 355 for the sample "organic mixed nut butter". 356

Except for walnuts in a Thuringian sausage, the presence of ingredients 357 that had been declared on the label could be confirmed by LPA analysis in all 358 samples. In addition, information given as part of a precautionary labeling in 359 some of the retail samples could be confirmed or specified. Considering the 360 described reduction in sensitivity for targets contained as trace amounts in the 361 presence of a high amount of one of the other LPA targets, the detection of 362 undeclared allergenic ingredients may be even further improved by analyzing 363 samples with probe mixes that exclude the probes that would amplify the 364 quantitatively dominating and declared allergenic ingredients. 365 A still existing limitation of the method is the missing differentiation of 366 almond from apricot, nectarine, peach and plum. This became obvious for the 367 sample "gingerbread": The signal detected for almond could not be assigned 368 unequivocally to almond, because apricot kernels had also been declared in the 369 list of ingredients. 370

Quantitative conclusions on the contaminations found in the retail samples are 371 difficult, but concentrations at the lower ppm range are likely. 372 373 Conclusions 374

The LPA system for the detection of food allergens was shown to be a 375 specific and sensitive detection method suitable for the simultaneous detection 376 of peanut, cashew, pecan nut, pistachio nut, hazelnut, sesame seeds, 377 macadamia nut, walnut and brazil nut in the lower mg/kg range. The modular 378 system allows the extension to further target sequences of interest. LPA 379 analysis of different food matrices resulted in LODs in the lower ppm range, 380 thus confirming the suitability of the method for allergen detection (Poms et al. 381 2004[START_REF] Goodwin | Food allergen detection methods: a coordinated approach[END_REF], Poms and Anklam 2004). The specificities of the probes 382 targeting almond DNA have to be improved in order to be able to differentiate 383 almond from phylogenetically related species. 384

The lack of certified reference materials in the field of allergen detection 385

  present, only a few Duplex-PCR systems are known allowing the 60

  diagnostics allowed the detection and the relative quantification 71

  peanuts contained 100, 10, 5, 1 and 0.5 mg/kg of peanuts 95 and pesto spiked with cashew nuts contained 100, 20, 10, 5, 2 and 1 mg/kg of 96 cashew nuts. In all cases samples of the unspiked material were included in the 97 analysis. 98 99 Preparation of walnut cookies spiked with different nuts 100 Nuts ground with a Thermomixer (Vorwerk, Wuppertal, Germany) as well 101 as the other cookie ingredients were analyzed by LPA to ensure the purities of 102 the starting materials. Two doughs containing 25 % wheat flour, 25 % sugar, 103 25 % butter and 25 % ground nuts (either walnuts only or walnuts spiked with 104 10 % of peanut, hazelnut, pecan and macadamia) were prepared using a food 105 processor (Braun, Germany). The reference cookies containing only walnuts 106 and the cookies with all five nuts were baked separately at 180°C for 10 min 107 and ground afterwards. The concentrations of peanut, hazelnut, pecan and 108 macadamia were adjusted in the spiked cookies to 10000, 1000, 100, 10 and 109 1 mg/kg, respectively, by mixing the corresponding amounts of ground spiked 110 cookies and ground walnut reference cookies in a food processor. The mixtures 111 became fluid due to the high fat contents; however the procedure resulted in 112 visually homogeneous dispersions. 113 114 DNA extraction 115 Each sample (2 g) was mixed with 10 mL CTAB-extraction buffer [2 % 116 (w/v) cetyltrimethylammoniumbromide, 1.4 M NaCl, 20 mM EDTA, 100 mM Tris-117 overnight. After 5 min centrifugation at 5000 g, 1000 µL 119

  the PCR product (pure or diluted) was mixed with 0.3 µL size 168 standard (GeneScan ® -500 [TAMRA]™) and 14.7 µL Hi-Di™ formamide. Prior to 169 analysis, DNA was denatured at 94°C for 3 min and cooled down on ice. 170 Electrokinetic injections were performed at 15 kV for 5 sec. Electrophoretic 171 separations were run at 60°C and 15 kV. 172 173 Sequencing 174 Ligation products were generated and amplified separately prior to 175

  ) at their 5'-or 3'-ends. The LPA system designed in this study uses 216 the advantages of synthetic oligonucleotides for hybridization as described by 217Moreano et al.[START_REF] Moreano | Ligation-dependent probe 490 amplification for the simultaneous event-specific detection and relative quantification of DNA from two genetically modified organisms[END_REF]. 218Different tree nuts(macadamia, cashew, pecan, walnut, brazil nut, 219 pistachio, almond and hazelnut) as well as peanut and sesame were chosen as 220 examples for the detection of allergenic components in foods by LPA. They are 221 often contained as hidden allergens in pastries, candies or chocolate and 222 present potential health risks for allergic persons. 223 Details of the selected target sequences and the GenBank accession 224 numbers of the LPA primers and probes are listed in Table 1. The probes 225 mostly detect genes encoding for plant food proteins of the cupin and prolamin 226 superfamily, respectively, which are known to cause IgE-mediated allergic 227reactions. The simultaneous detection of all targets is shown in Figure 1. 228 The primer sequences described by Schouten et al. (Schouten et al. 229 2002) for MLPA analysis were also used in this study. They were tested 230 regarding their suitability for analyzing foods by database enquiry via NCBI 231 GenBank and PCR using DNA of different plant species. The use of spacer 232 sequences between PBS and hybridization sites rendered ligation products with 233 lengths characteristic for each of the target DNA. Differences of 4 nucleotides 234 (nt) in length have been shown sufficient for unequivocal determination of the 235 amplification products using POP-6™ polymer (Ehlert et al. 2008b). Simple 236 repeats of GT bases were used for the spacer sequences to avoid intra-and 237 intermolecular hybridizations. 238 Using this approach, all target signals were sufficiently separated. The 239 signal obtained for pistachio nut overlapped with one of the standard signals 240 ); however, owing to the use of different fluorescent dyes for standard 241 and target probes a discrimination of the two signals is possible. homologies the specificities of the probe target 247 sequences were first evaluated by BLAST search within NCBI GenBank by 248 Beacon Designer software. Additionally, each probe was checked with DNA 249 extracted from the other target species. The performance of the LPA system 250 was examined using DNA extracts of the ten targets adjusted to 20 ng/µL and 251 different mixtures prepared to simulate composed food products with different 252 nut proportions. No unspecific signals were observed for almond, peanut, 253 pecan, pistachio, hazelnut, sesame, macadamia, walnut and brazil nut probes. 254 Furthermore, the differentiation between the phylogenetically closely related 255 tree nuts pecan and walnut could be achieved. Published sequencing data were 256 used to design the ligation probes with only four different DNA bases (Brezna 257 2005, Brezna and Kuchta 2007). Cross-reactivity of the cashew probes with 258 other members of the Anacardiaceae family, specifically pistachio and mango, 259 could be eliminated by detailed characterization of the target and a new design 260 of the probes. Different pairs of primer were designed to amplify the target 261 region of the 2s albumin gene (ana o3 allergen). PCR analysis using one of the 262 primer pairs and DNA of cashew, mango and pistachio resulted in specific 263 amplification of a 103 bp DNA section of cashew DNA (Ehlert et al. 2008a). No 264 signals were observed when DNA of pistachio or mango was used in the PCR 265

  lack of appropriate reference materials for the detection of 294 allergens, different food matrices in which the selected plants typically occur 295

  mainly used to amplify the excess of walnut ligation 317

  total of 39 samples from different food categories were analyzed. All 344 DNA extracts were checked for PCR inhibition and amplifiability of DNA prior to 345

Figure 1

 1 Figure legends

  191 for 1 hr. Finally, DNA was dissolved in 20 µL H 2 O. Sequencing of the diluted PCR products (10 µL H 2 O + 6 µL purified DNA) 193was carried out on an ABI PRISM ® 310 Genetic Analyzer(Applied Biosystems, 194 Forster City, USA) using 47 cm capillaries and POP-6™ Performance 195

	196	Optimized Polymer. Electrokinetic injections were performed at 2 kV for 30 sec.
	197	Runs were carried out at 50°C and 15 kV.
	198 199	Real-time PCR and ELISA assays F o r SureFood ® Allergen Kits for the qualitative detection of DNA from
	200	P hazelnut and peanut from Congen Biotechnology GmbH (Berlin, Germany)
		e
		e r
		R
		e
		v i e
		w
		O n l
		y
		9

  . The 272 species tested with all LPA probes are listed in Table2. No false positive 273 signals were observed for peanut, cashew, pecan, pistachio, hazelnut, sesame, 274 macadamia, walnut and brazil nut. The probes developed for the detection of 275 almond DNA also yielded positive results in the presence of DNA from apricot, 276 nectarine, peach and plum due to their phylogenetical relation. Further 277 characterization of the target by sequencing will be necessary to increase the 278

	279	specificity of the almond detection.
	280
	281	[Insert Table 2 about here]
	282
	283	Slight peaks at n-1 and n-2 nucleotides are caused by impurities of the
	284	synthesized oligonucleotides that could be reduced by a new synthesis but
	285	could not be eliminated completely (data not shown).

Table 2

 2 Species used for the determination of the target specificity of the LPA method

	plants

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Table 3

 3 Comparison of limits of detection

	Detection limit [mg/kg]

Table 4

 4 Analysis of commercially available samples by LPA

	Product	Declared allergenic ingredients	Precautionary labeling	Detection
	Spreads
	organic mixed nut butter	peanuts, hazelnuts,	-	peanut, cashew,
		cashews, almonds		hazelnut, almond
	nutella	hazelnut 13 %	-	hazelnut
	Sausages
	original Thuringian sausage	walnuts	-	n.d.
	mortadella with pistachios	pistachios 1%	-	pistachio
	Convenience food dressings
	pesto alla Genovese	cashew nuts	-	cashew
	saté dressing	peanuts 21%, peanut flavor -	peanut
	pesto goutweed (Aegopodium	macadamia nut	-	macadamia nut
	podagraria)
	Dairy products
	yoghurt with almonds	almonds 2%	-	almond
	milkshake pistachio-cocos	pistachio pulp	-	pistachio
	Sweets/ Cookies
	chocolate bar	hazelnut 5 %	traces of almond,	hazelnut
			peanut and other nuts
	hazelnut bar	hazelnut mark 3,4%	traces of nuts and	hazelnut
			other seeds
	hazelnut bar with honey	hazelnut 66 %	traces of peanut,	hazelnut, peanut
			sesame or other nuts
	peanut bar with honey	peanut 68 %	traces of sesame or	peanut, hazelnut
			nuts
	sesame seed bar with honey	sesame 68 %	traces of peanut or	sesame
			other nuts
	praline hazelnut	hazelnut 30,5 %, almonds	traces pistachio	hazelnut, almond
	praline pistachio	almonds 12,5 %, pistachio	traces hazelnut	almond, pistachio
		6,5 %
	praline coconut	almonds 5 %	traces egg, hazelnut,	almond, pistachio
			pistachio
	gingerbread	hazelnuts, walnuts,	traces of other nuts	cashew, hazelnut,
		almonds, cashew nuts,	and kernels	almond, walnut
		apricot kernels

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Journal of Agricultural and Food Chemistry 53(18):7003-9.


Grażyna Balcerowska

Ryszard Siuda

Joanna Skrzypczak

Alaksander £ukanowski

Czeslaw Sadowski

Graŝyna Balcerowska

Aleksander Łukanowski

Czesław Sadowski

Graŝyna Balcerowska

Effect of particle size and spectral sub-range within the UV-VIS-NIR range using diffuse reflectance spectra on multivariate models in evaluating the severity of fusariosis in ground wheat *)

Keywords: Mycotoxins -fusarium, Mycotoxins -trichothecenes fusarium head blight, scab, wheat, reflectance spectroscopy

Effect of particle size and spectral sub-range within the UV-VIS-NIR range using diffuse reflectance spectra on multivariate models in evaluating the severity of fusariosis in ground wheat

Introduction

The quality of cereal grains, primarily their possible contamination with harmful substances, is of key importance for a safe human diet. An obvious strategy in delivering safe grain is the detection of possible contamination at the earliest possible stage in the "farm-to-table" chain of food production. One of the problems that arise here is the protection of grain, before its storage and/or processing, from contamination by any grain that has been infected with soil fungi, since developing fungi can produce harmful secondary metabolites (Mirocha et al. 2003). In countries with a temperate climate, the soil fungi most frequently infecting flowering cereals and then their grain are Fusarium spp. Fusarium spp. infecting cereals are known to cause a disease called fusarium head blight (FHB) (or scab, or fusariosis). FHB causes considerable economic loss (Johnsson et al. 2003) and often causes contamination of damaged grain with mycotoxins (Dexter and Nowicki 2003), first of all with deoxynivalenol (DON, vomitoxin). DON contaminated food and feed can cause severe health problems, both in humans and livestock (see Dexter and Nowicki 2003, and references therein). Regulations for DON content specify different limits for unprocessed wheat in different countries (see, e.g., Dexter and Nowicki 2003). These limits are mostly 1 mg/kg or higher (with the exception of Austria). There are reports of the occurrence of DON in cerealbased commodities on the market in many countries [START_REF] Gareis | Collection of Occurrence Data of Fusarium Toxins in Food and Assessment of Dietary Intake by the Population of EU Member States[END_REF]. For these reasons considerable effort has been made to develop quick and cost effective methods that could be useful in the detection of fusariosis in cereal grain and that could replace tedious chemical methods, which are mostly based on chromatographic analysis (Mirocha et al. 2003). Of such methods the most popular ones seem to be those fully, or in part based on optical measurements. Quantitative evaluation of change in kernels colour and other visible symptoms supplemented with evaluations extended into near infrared region was found useful for detection of scab/DON damaged single kernels [START_REF] Dowell | Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy[END_REF], 2002[START_REF] Delwiche | Hyperspectral imaging for detection of scab in wheat[END_REF][START_REF] Delwiche | Classification of scab-and other mold-damaged wheat kernels by near-infrared reflectance spectroscopy[END_REF][START_REF] Delwiche | Detection of Scab-Damaged Hard Red Spring Wheat Fusarium Head Blight in Wheat[END_REF][START_REF] Polder | Detection of Fusarium in[END_REF], and can be also used for development of automatic systems for [START_REF] Mc | Sorting systems based on optical methods for detecting and removing seeds infested internally by insects or fungi: a review[END_REF][START_REF] Pearson | Reduction of aflatoxin and fumonisin contamination in yellow corn by high-speed bi-chromatic sorting[END_REF][START_REF] Dowell | An Automated Near-Infrared System for Selecting Individual Kernels Based on Specific Quality Characteristics[END_REF].

Another approach that can be useful in the detection of scab/DON damaged grain is based on optical measurements of ground grain followed with multivariate calibration [START_REF] Abramović | Detection of Deoxynivalenol in Wheat by Fourier Transform Infrared Spectroscopy[END_REF][START_REF] Kos | Fourier transform mid-infrared spectroscopy with attenuated total reflection (FT-IR/ATR) as a tool for the detection of Fusarium fungi on maize[END_REF][START_REF] Delwiche | Classification of scab-and other mold-damaged wheat kernels by near-infrared reflectance spectroscopy[END_REF], 2004, Siuda et al. 2006, 2008). Errors of calibration models are a key question in such an approach as they determine the ability of the method to detect fusariosis. Our previous papers (Siuda et al. 2006(Siuda et al. , 2008) ) showed that the most promising PLS models for the fine fraction (particle size less than 0.180 mm) of ground winter wheat were the models based on spectra recorded within the UV range, or when the measurement covered additionally the VIS range. Errors of these models when applied to DON content resulted in a limit of detection below 1 ppm (Siuda et al. 2008). This paper aims to check whether that finding is valid when other fractions of ground winter wheat are used in diffuse reflectance measurements and to find which fraction ensures the lowest error (highest accuracy) in the calibration model.

Experimental

Material

Two lots of winter wheat (Triticuum aestivum ssp. vulgare, cv. Turnia harvested in 2005) grain were obtained from two plots. One lot (referred to as control) was harvested from a plot where plants grew in natural conditions, while the other one (damaged) was from a plot where heads were inoculated with a suspension of Fusarium culmorum conidia (1•10 6 •ml -1 ) during the flowering stage.

The F. culmorum isolate used for inoculation was previously classified as DON chemotype on the basis of the presence and functioning of Tri7 and Tri13 genes detected with a PCR assay. After harvesting, control and inoculated lots were dried in air at room temperature to a moisture content of ca. 11% and then stored in a refrigerator at temperature 4 o C. Grain from both categories was ground (1.0 mm screen, rotor speed 7000 rpm, ZM1, Retsch, Haan, Germany) and sieved. Three fractions of particle size were sieved: fine (<0.180mm), medium (>0.180 and <0.25mm) and coarse (>0.25). Within the fractions control and damaged material was blended in different proportions ranging from 0% to 100% of damaged constituent in the mass of the sample. Essentially, the same procedure for gaining and processing the material was repeated the following year (2006) with cvs.

Tonacja, Satyna and Trend. Grain was ground with a ZM200 Retsch grinder (screen and rotor speed the same as above), while the set of screens used for sieving was: fine (<0.18mm), medium (>0.180 and <0.355mm) and coarse (>0.355 and <0.710mm). Sieving was done with a sieve shaker (AS20, Retsch) operating at maximum amplitude in a sequence (up to five) of 10 min time intervals, interrupted for manual manipulations necessary to prevent clogging of the material. As a result, three series of blended samples of different fractions, each consisted of ca. 30 samples were obtained for each analysed cultivar.

Measurements

A UV-VIS-NIR commercial spectrophotometer Cary 5000 (Varian Inc., Mulgrive, Australia) was used to record diffuse reflectance spectra in log(1/R) mode in the range 200 to 2500 nm, every 2 nm. For each sample, 5 scans at two crossed positions of the cuvette were taken and averaged. Each spectrum was scaled with reference to a base line spectrum recorded from the PTFE reference plate (a part of internal DRA accessory, Varian Inc.). An equal volume of approximately 1-ml of the material was taken from each blended sample and put into a quartz cuvette of diameter 17-mm equipped with a spring driven piston (a part of internal DRA accessory, Varian Inc.) that pressed the material. No special circumstances, such as air-and/or temperature conditioning in the laboratory during measurements were maintained.

Multivariate calibration

The PLS (Partial Least Squares) foundations have been broadly presented elsewhere (see eg. [START_REF] Geladi | Partial Least-squares regression: a tutorial[END_REF]Kowalski 1986, Vandeginste et al. 1997;[START_REF] Massart | Handbook of Chemometrics and Qualimetrics: Part A and B[END_REF]. Briefly PLS tries to find a relationship between the latent structure in spectra set and the latent structure in responses. It is carried out by finding specific directions in data space, the so-called latent variables (LV). In practical use of modelling by latent variables methods such as PLS, first of all, the number of significant latent variables (components) has to be determined for each calibration model. Cross-validation approach provides a very reliable way for this (Wold 1978). A number of alternations is performed accordingly to some permutations schemes. Parameters used for assessment of PLS models are then the root mean square error of calibration (RMSEC) and cross-validation (RMSECV). It is commonly accepted that the number of LV giving the lowest RMSECV is used as an optimal. Besides other statistical parameters are used (eg. the coefficient of multiple determination (R 2 ), for more complete review see ASTM Standards 2000 ).

Calibration models for the percentage of damaged material in blended samples (assumed as a measure of the severity of fusariosis) were built using commercial software PLS_Toolbox3.0 (Eigenvector Research, Inc., Wenatchee, USA). Different pre-processing of spectra were tested, including MSC, SNV and differentiation. As the models based on differentiation (eleven-points Savitzky-Golay first derivative smoothing filter) and centring of the spectra give the best performance, these models were studied. Additional manipulations on spectra mentioned in the following sections were performed with our own codes written in Matlab® software package (Mathworks Inc., Natick, MA, USA).

Results and discussion

Calibration models for differing levels of severity of fusariosis within each cultivar and fraction were built in the UV, VIS and NIR spectral ranges and for all possible combinations. The results obtained are presented in Table 1. R 2 is not shown in the results of calibration, since it was always satisfactory (better than 0.9). Model parameters presented in Table 1 show noticeable differences when moving from one cultivar to another within a fraction, and from fraction-to-fraction within a cultivar. In order to make these results more easily visible, the RMSECV model errors were averaged over cultivars within each fraction and spectral range, and plotted in Figure 1. It can be seen from Figure 1 that independently of spectral range, model errors tend to increase considerably with an increase of particle size. This error enlargement is especially prominent when the UV range is included in the measurements and the NIR range excluded. The former finding can be supposed to be caused by changes in path length of diffusely reflected radiation with the change of mean particle size. This effect is well known in NIR reflectance spectroscopy (see, [START_REF] Osborne | Near infrared reflectance spectroscopy for online particle size analysis of powders and ground materials[END_REF][START_REF] Mc | Sorting systems based on optical methods for detecting and removing seeds infested internally by insects or fungi: a review[END_REF]. In some publications a tendency has been reported towards model errors being dependent on particle size that was qualitatively similar to that seen in Figure 1, despite the subject of their investigations being different from those reported here. [START_REF] Hareland | Evaluation of particle size distribution by laser diffraction, sieve analysis and Fusarium Head Blight in Wheat[END_REF] used NIR reflectance spectroscopy to predict the percent volume of wheat flour particles of three size ranges: <10, 10-41 and 41-300 µm. The authors obtained an increase of both standard errors of crossvalidation (0.26, 0.87 and 1.11) and errors of prediction (0.35, 1.17 and 1.13) with increasing particle size. [START_REF] Pasikatan | Granulation sensing of firstbreak ground wheat using a near-infrared reflectance spectrometer: studies with soft red winter wheat[END_REF] investigated the possibility of application of PLS models based on reflectance spectra (recorded in the 400-1700 nm wavelength range) to predict the granulation of wheat ground with an experimental roller mill. The authors found SEP equal to 3.53, 1.83, 1.43 and 1.30 for the >1041, >375, >240 and >136 µm size fractions respectively. [START_REF] Kos | Optimisation of a sample preparation procedure for the screening of fungal infection and assessment of deoxinivalenol content in maize using mid-infrared attenuated total reflection spectroscopy[END_REF] reported results on optimizing particle size for ATR-MIR measurements used as a tool for screening fungal infection and assessment of DON content in ground maize. The authors compared the repeatability of MIR spectra at several wavelengths for fractions of particle size >0.71, 0.71-050, 0.50-0.25, 0.25-0.10 and <0.10 mm, and they found an increase of repeatability with decreasing particle size.

The results presented here together with the results from the cited reports suggest that a general rule may be that an increase of errors in multivariate calibration models based on reflectance spectra of ground wheat follows from an increase in the mean particle size of analysed samples. In addition, our results suggest that this dependence holds its validity also in the spectral ranges other than NIR, in particular in the UV.

As regards the improvement of the models observed when the UV range is included in modelling, this finding is in line with our previous results obtained for fine fraction only and a lower number of cultivars (Siuda et al. 2006;2008). Therefore, considering the results presented here, there now exists better evidence to suggest that improved results can be obtained from the UV sub-range than from the two other sub-ranges considered. However, even though better results can be obtained when UV is merged with other spectral ranges, it is worth noting that the NIR range, which is the most commonly used range for the analysis of food and grain properties, provided results of noticeably poorer quality (unless it was merged with other sub ranges) especially in the case of fractions other than fine. Hence, one can state that inclusion of the UV sub-range improves the quality of PLS calibration. This observation remains in accordance with the fact that the main constituents contributing to the chemical composition of wheat have absorption bands located in the UV (for references see Siuda et al. 2008). Also, general properties of the spectra support this opinion about the importance of this spectral range. Exemplary spectra (shown for cultivar Tonacja only, since the general features of the spectra discussed below are similar for the other cultivars) presented in Figure 2a show vertical shifts caused by differences in particle size not only in the NIR range but also in the UV range. Plots presented in Figure 2b show that the variance in sets of spectra depends considerably on both the sub range of wavelength and on the particle size, nevertheless they illustrate the considerable amount of variance present in the UV range. In the NIR range, the variance due to the presence of spectral bands is superimposed on a monotonic background that contributes more strongly for coarser fractions. In the UV range, a strong contribution to variance from a background of perhaps non-monotonic character, can also be seen. This considerable contribution from the background to the variance present in sets of spectra seems to explain why PLS models based on differentiated spectra were found to be better than models based on non differentiated spectra (Siuda et al. 2006), since differentiation of the spectra reduces the variance coming from broad shaped features present in the structure of the spectra. Careful inspection of the curves which have been presented seems to suggest that after subtraction of the background, variance caused by changes in intensity of absorption bands could appear to be most prominent in the UV sub range, especially for fine fractions. Additional insight into the information that spectra contain in particular spectral sub ranges can be found from inspecting the values of the regression coefficient of PLS model. This coefficient, usually denoted as b, has a greater absolute value at wavelengths that contribute more to a calibration model. In Figure 3 are shown plots of absolute values of b versus wavelength for the best PLS models based on the whole spectral range for all 12 series of blended samples. Although the details of the superimposed plots cannot clearly be seen, the figure demonstrates that the largest absolute values of regression coefficients appear in the UV sub region. In order to evaluate quantitatively the importance of particular spectral ranges for PLS modelling, mean squared values of regression coefficients, defined as

∑ = = N 1 i i fraction cultivar, range, b N 1 b ,
where b i is the value of the coefficient at the i-th wavelength, and N is the number of wavelengths in the spectral range, were calculated for all cultivars, fractions and the UV, VIS and NIR spectral sub ranges. These values were next averaged over cultivars within fractions and ranges, and the results relevant for particular spectral sub ranges and fractions are presented in Figure 4. These values plotted against the errors of relevant PLS models presented in Figure 1 resulted in the data points shown in Figure 5. The data points in Figure 5 demonstrate regularity within a fraction: the larger is the mean correlation coefficient, the lower is the error of the model. This regularity also supports what has been stated above, i.e. that the UV sub-range can be considered as more informative than the other two.

Conclusions

In conclusion, the results presented suggest that the most promising approach for detecting fusariosis in winter wheat grain with diffuse reflectance spectroscopy seems to be that using of fine fractions of ground material and performing the measurements in the UV-VIS spectral range.

However, as the measurements made solely in the UV range provide results of almost the same quality as those in the UV-VIS range, such measurements can be recognised as an even more advantageous alternative because they need a narrower spectral range and less data to be processed.

It is worth noting that the NIR spectral range, the most commonly used range in an evaluation of grain quality, when applied to an evaluation of the severity of fusariosis in ground wheat provides models of considerably poorer performance, even although the NIR range provides measurements from a number of wavelengths that much exceed the number in the UV.

F

Figure 1 .

 1 Figure Captions

Figure 2

 2 Figure 2. a) Exemplary (for cv. Tonacja) spectra averaged within fractions of particle size, b)

Figure 3 .

 3 Figure 3. Absolute values of regression coefficients in best PLS models for all cultivars and

Figure 4 .

 4 Figure 4. Averaged over cultivars mean values of regression coefficients of PLS models for

Figure 5 .

 5 Figure 5. Plots of averaged over cultivars RMSECV of PLS models (cf. Figure 1) versus mean

Table 1 .

 1 Calibration (RMSEC) and cross-validation (RMSECV) errors and number of latent variables of the best PLS models for all cultivars, spectral ranges and particle size fractions. Errors values are given as percentage of damaged material in blended samples.

				Food Additives and Contaminants				Page 16 of 23
	Cultivar	Range		Fine			Medium			Coarse
			RMSEC RMSECV	LV	RMSEC RMSECV	LV	RMSEC RMSECV	LV
		UV	1.77	2.52	4	2.63	4.34	5	3.12	6.42	7
		VIS	3.86	4.30	2	4.37	7.98	4	3.77	8.70	6
		NIR	4.95	8.10	5	5.45	7.44	3	1.09	3.10	6
	TURNIA	UV-VIS	1.45	2.43	5	2.54	4.41	5	1.75	5.07	8
		VIS-NIR	2.44	5.06	6	5.46	7.40	3	1.23	3.39	6
		UV-NIR	1.61	2.70	5	1.60	3.65	6	1.82	4.10	6
		Whole	1.56	2.76	5	1.37	3.55	6	0.99	3.80	6
		UV	1.92	3.62	6	4.61	6.31	5	8.18	11.3	4
		VIS	3.04	3.98	3	4.52	5.48	3	5.02	6.41	4
		NIR	3.25	7.38	5	3.26	7.55	5	4.43	13.4	5
	SATYNA	UV-VIS	2.00	3.41	5	3.67	4.81	4	5.76	8.25	4
		VIS-NIR	1.87	3.97	5	4.06	6.14	4	5.96	7.11	2
		UV-NIR	1.21	3.72	8	3.52	6.29	6	8.39	12.4	4
		Whole	2.62	4.04	4	3.11	5.33	5	5.30	8.79	4

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

Acknowledgments

The work was supported from 2P06R 11326, 2P06R 04030 and BS-16/2005 research projects.


Keith A Scudamore

Susan Patel

Fusarium mycotoxins in milling streams from the commercial milling of maize 1 imported to the UK, and relevance to current

Keywords: Mycotoxins -fumonisins, Mycotoxins -fusarium, Mycotoxinstrichothecenes, Mycotoxins -zearalenone Food Types: Cereals maize, deoxynivalenol, zearalenone, fumonisins, nivalenol, milling, flour, 32 grits, bran 33 34

Fusarium mycotoxins in milling streams from the commercial milling of maize imported to the UK, and relevance to current legislation.

such as deoxynivalenol, zearalenone and fumonisins present at mill intake are 13 distributed in milling streams approximately according to their occurrence in the 14 maize seed structure. Fractions derived from the endosperm tended to contain the 15 lowest levels of mycotoxins. Concentrations of mycotoxins within the endosperm are 16 also related to the particle size. However, the products derived from the embryo or 17 outer seed layers contained the highest mycotoxin levels being concentrated up to 5 18 times or more, although these components are normally used for animal feed or 19 industrial use. The general pattern of mycotoxin distribution found when milling 20 French and Argentinean maize was similar although very variable and it is concluded 21 that this variability stems from different milling strategies used at each mill and from 22 the nature and condition of each consignment of maize.. Mycotoxins in maize grits 23 (particle sizes >500 µm) were usually reduced by the greatest amount when compared 24 to the whole maize, while flour (≤500 µm) could be both reduced or increased 25 Introduction 37 38 Maize often contains a range of mycotoxins that sometimes occur in combination. 39

These include aflatoxins (Andersen et al. 1975), ochratoxin A [START_REF] Shotwell | Ochratoxin A: Occurrence as a 491 natural contaminant of a corn sample[END_REF], a 40 number of Fusarium mycotoxins, deoxynivalenol (DON) [START_REF] Gilbert | A survey of the occurrence of the 439 trichothecene mycotoxin deoxynivalenol (vomitoxin) in United Kingdom 440 grown barley and in imported maize by combined gas chromatography-mass 441 spectrometry[END_REF], 41 nivalenol (NIV) and other trichothecenes [START_REF] Vesonder | Isolation of the emetic principle from 495[END_REF], zearalenone (ZON) 42 [START_REF] Shotwell | Survey of corn for 489 aflatoxin, zearalenone, and ochratoxin[END_REF], fumonisins [START_REF] Gelderblom | Fumonisins -novel mycotoxins with cancer-promoting 436 activity produced by Fusarium moniliforme[END_REF], moniliformin (Sharman 43 et al. 1991[START_REF] Scudamore | Occurrence of 462 mycotoxins in raw ingredients used for animal feeding stuffs in the United 463 Kingdom in 1992[END_REF][START_REF] Scudamore | Mycotoxins in ingredients of animal 465 feeding stuffs: II. Determination of mycotoxins in maize and maize products[END_REF] and beauvaricin, fusaproliferin and fumaric 44 acid [START_REF] Ritieni | Occurrence of Fusaproliferin, Fumonisin B 1 , and 453 Beauvericin in Maize from Italy[END_REF]). In the UK, occurrence of mycotoxins in imported maize has 45 been reported previously during a 10-month period in 2000 (Scudamore and Patel 46 2000) and more recently from the 2004-2007 harvests (Scudamore and Patel,in 47 press). 48

In order to protect the consumer, EC legislation relevant to mycotoxins in raw 49 maize and derived products now applies to aflatoxin, ochratoxin A, DON, ZON and 50 3 the sum of fumonisins B 1 and B 2 (FB 1 + FB 2 ). Limits have been set within the EC for 51 DON, ZON and fumonisins in maize (EC 2006, EC 2007) and are under consideration 52 for Fumonisins, first reported in 1988, have been shown to occur widely and 54 sometimes in high concentrations in maize [START_REF] Shephard | World-wide survey 482 of fumonisin contamination of corn and corn--based products[END_REF] but less commonly 55 and in much lower amounts in other commodities. Legislation for Fusarium toxins 56 applies to the unprocessed maize, which includes both in-take maize and whole maize 57 after cleaning. Maize mills use comprehensive cleaning regimes to remove stones, 58 metal objects and other such contaminants and also dust, straw, maize cobs and 59 broken maize seeds. Because mycotoxins are often concentrated in the latter 60 impurities, overall concentrations can be reduced during cleaning although the extent 61 to which this occurs in cereals can be very variable [START_REF] Brekke | Cleaning trials for corn containing 421 aflatoxin[END_REF], Scott et al. 62 1984[START_REF] Abbas | Effect of cleaning, milling 416 and baking on deoxynivalenol in wheat[END_REF][START_REF] Seitz | Distribution 477 of deoxynivalenol in soft wheat mill streams[END_REF], Lauren et al. 2006, Scudamore and Patel in 63 press). 64

The human consumer however is not exposed directly to mycotoxins in raw 65 maize but to the mycotoxins remaining in the food products manufactured after 66 milling and processing of the raw cereal. Maize processing is a complex technology 67 (e.g. [START_REF] Kent | Kents Technology of Cereals[END_REF] and the levels of mycotoxins in the finished product may 68 not reflect the concentrations in raw maize as the amounts surviving through the food 69 processing chain will depend on the properties of the mycotoxin and on the nature of 70 the process(es). The distribution and survival of mycotoxins in products obtained by 71 milling and subsequent processing has been reported but often in a piecemeal fashion 72 and rarely at full commercial production scale. This paper reports how mycotoxins present in whole maize are distributed in 75 the milling streams obtained in typical commercial UK maize mills and forms part of 76 a more extensive project studying the fate of Fusarium mycotoxins occurring during 77 cereal processing. The occurrence of mycotoxins in the maize samples studied here, 78 including the effects of cleaning, has already been reported (Scudamore and Patel 79 2008a), while their fate during processing is to be reported elsewhere (e.g. Scudamore 80 and Patel 2008b). These studies should assist industry in managing the EC regulations 81

for mycotoxins and provides data to the UK Government and to the EC to assist with 82 assessing current legislation and setting future legislation as necessary. 83 84

Materials and methods 85

Maize and milling streams 86 82 consignments of cleaned maize collected during the period 2004-2007 from three 87 major UK maize mills were studied following normal commercial milling practice at 88 each mill. The commercially important fractions for food production are the different 89 grades of grits and flour. Thus the flaking grit stream was collected from each 90 consignment of Argentinean maize because this is the ingredient used for cornflake 91 manufacture and the flour from each French maize consignment as the important 92 ingredient in many foods such as snack products. For approximately one in every 93 three consignments, all the main milled fractions were collected to include the flour, 94 polenta (flour), flaking grits, coarse grits, fine grits, grits recovered by reprocessing 95 bran, broken maize, germ, meal, bran and so on. 96 97

Sampling and analysis 98

Sampling at maize mills 99 Sampling followed the principles described previously for raw and cleaned maize 100 resulting in bulk samples of several kilograms (Scudamore and Patel, 2008a). Samples 101 collected were kept cool and dry out of direct sunlight before sending to the analytical 102 laboratory. These were examined over an approximate 3-year period spanning four 103 different harvest years. The quantity of sample taken was 4 to 10 kg depending on the 104 agreed method of sampling used (2008a) and the mill. Each operated on a different scale, 105 that milling Argentine grain milled on a very large scale, while the throughput of the 106 smaller of the two French mills operated with a much smaller throughput. Each 107 aggregate sample was well mixed and a sub sample sent for analysis. On receipt, the 108 whole sample was ground to pass through a 0.8 mm screen and mixed for 30 minutes in 109 a Gardner horizontal mixer to ensure homogeneity. Samples were stored at -20°C if not 110 analysed immediately. 111

All mills have in-house sampling protocols at in-take to their mills and 112 supplied samples based on the internal quality control system in place, or adaptations 113 Typical mean recovery was 86% with a coefficient of variability of 6.5% for 10 151 replicates. For ZON, ground maize in house reference material contaminated at 50 152 µg/kg of ZON was spiked at 53 µg/kg. Typical mean recovery was 88% with a 153 coefficient of variability of 7.2 % for 10 replicates. 154 155 The limit of detection and limit of determination respectively for each mycotoxin 156 were 5 µg/kg and 10 µg/kg for DON and each fumonisin mycotoxin and 1.5 µg/kg and 157 3 µg/kg respectively for ZON. The limit of detection is defined as 3 times the 158 electronic baseline noise and the limit of determination as 6 times baseline noise. 159

Calibration curves for each mycotoxin were plotted with the lowest calibration points 160 respectively being equivalent to 10, 3 and 10 µg/kg for DON, ZON and each 161 fumonisin. After analysis samples were retained and stored at -20°C. An important initial stage in the maize milling process after cleaning is to de-200 germ the maize and the efficiency with which this can be carried out affects the yield 201 and composition of the grits produced. As mycotoxins are associated with different 202 parts of cereal seeds the distribution into the milling streams will vary considerably 203 with batch and mill. Complex milling systems also result in overlap between the 204 different fractions. The feed product for example may contain both the bran from the 205 pericarp, extracted germ from the embryo, and some endosperm. The precise 206 composition of the different products can thus vary from mill to mill or mill 207 configurations can be changed on demand to alter the nature of the raw ingredients 208 produced. The use of particle size at least in relation to legislation is thus helpful in 209 attempting to simplify a complex situation. Thus statutory levels can be applied to 210 products with particle size >500 µm and those with particle size ≤500 µm together 211 and to with fractions used for animal feed, see table 1 212 The products of highest value are grits, which are used to produce breakfast 213 cereals such as cornflakes while maize 'flour', is the basis of many foods and of 214 maize-based snack products. The terminology used to describe the products from dry 215 maize milling is often unclear to users in different countries and comparison of data 216 for the concentrations of mycotoxins in milled maize products is difficult and 217 sometimes conflicting. Recently the EC agreed that maize fractions can be usefully 218 considered on the basis of their particle size and this has simplified comparison of 219 such data. Table 1 gives the current EC limits for Fusarium mycotoxins in maize and 220 products derived from maize. Only guideline limits exist for animal feed and the 221 values of these are included for reference for the most sensitive species Legislation is 222 now given partly on the basis of particle size and the the situation where polenta meal is a corn meal, in which case it is not pre-cooked and 225 thus not for human consumption. Depending on particle size range its maximum 226 allowed levels of fumonisin for example could be 1400 µg kg -1 or 2000µg kg -1 . If 227 however, it is pre-cooked/pre-boiled polenta corn meal that can be consumed directly 228 as such as polenta, the level of 1000 µg kg -1 is applied. Previous studies of the distribution of mycotoxins from raw cereals into milling 249 streams have shown in most instances that there is a reduction in concentration in the 250 endosperm-derived milling streams and a concentration in the products derived by the 251 outer layers of the grain seeds [START_REF] Scudamore | Control of mycotoxins: secondary processing[END_REF]. 252

The distribution pattern of mycotoxins in each maize milling streams produced 253 at the three mills studied here is shown in Figures 234. The concentrations of 254 mycotoxins found in the grits and flours, which are mostly derived from the 255 endosperm typically, contain the lowest mycotoxin levels and concentrations are 256 further closely related to particle size (see Table 1). Thus flaking and coarse grits 257 (those with the largest particles) are much reduced in mycotoxin concentration 258 compared with the raw maize, while flour (containing the finest particles) has higher 259 levels. This can be seen clearly e.g. in figure 3 where the mycotoxin concentrations 260 increase from being lowest in the coarse grits, through fine grits, polenta flour and 261 flour as particle size is reduced. However, all these milling streams are much lower in 262 mycotoxins than the bran/meal/germ-derived fractions in which they are concentrated. 263

This pattern is the same at all 3 mills although the three grit products from mill 264 3 which used Argentinean flint maize have the lowest mycotoxin levels in the grits of 265 all relative to the intake maize. The proportion of DON found in grits (particle size 266 >500 µm) tended to be higher than the fumonisins while the situation was reversed in 267 flour so that a higher proportion of the fumonisins compared to DON were found in 268 flour (≤500 µm particles). The situation for ZON was unclear, as this was only found 269 intermittently throughout the study and usually in low concentrations in raw maize so 270 could not be detected in a significant number of grit samples. 271 During the study, one 'grit' product (bran grits) from mill 2 did not fit the expected 275 pattern as it contained much higher levels of mycotoxins (particularly fumonisins) 276 despite being composed of particles of a size similar to other grits. In addition, DON, 277 ZON and NIV (not shown in figure 3) were much higher than expected in grits but 278 were still considerably less so than the fumonisins. This product was produced by re-279 processing bran to extract residual grit particles. 280

INSERT FIGURE 4 281 282

The milled products most used for the manufacture of human foods are the flour and 283 grits of various grades as discussed previously. The number of commercial 284 consignments in which the change in mycotoxin concentration from whole maize to 285 ingredient were measured is given in the title for each table. Mycotoxin levels were 286 both reduced and increased in flour compared with the intake maize and the 287 proportions were different in each mill. French maize was examined at mills 1 and 2 288 and Argentinean flint maize at mill 3. It is likely that these differences are due not 289 only due to the maize type, history and condition of the maize of each consignment 290 but also to different machinery configurations set at each mill. Hard flint maize 291 produces a larger yield of large flaking grits that reduces the yield of flour while the 292 softer varieties of French maize result in a smaller proportion of coarse grits and a 293 higher yield of flour. 294

The efficiency of the initial de-germing stage also determines the yields of 295 flaking grits and flour. The range of relative amounts of mycotoxins in individual 296 consignments of raw maize and flour processed were also very variable so that 297 sometimes concentrations were increased in the flour while on other occasions 298 reduced, even in a single mill. The number of consignments examined at each mill 299 was sufficient to be confident that there is a real difference between mills. Each mill 300 also produces a variety of different milled products varying in their individual 301 properties and nature so that differences between mills should not be unexpected. 302

303

INSERT TABLE 2 304 305

Tables 2-4 show the mean, median and range of concentrations of each mycotoxin in 306 the whole maize and flour and also the mean, median and range of values found for 307 the proportion of mycotoxins in the maize flour after milling compared with that in 308 intake maize. Fumonisins were present in virtually every sample in contrast to DON 309 and particularly ZON where concentrations were lower and less frequent. As a result, 310 the number of times when it was possible to determine the change from intake maize 311 to flour varied and this is given by 'n' for each mycotoxin. The % change in 312 concentration for each mycotoxin transferred to the flour is very variable as shown by 313 the values for % SD in Tables 2345. Within each mill these values are quite similar for 314 the mycotoxins and this perhaps suggests that the variability is in large part due to 315 sampling as the coefficients of variability for the analytical methods are small in 316 comparison. The least variable results were obtained from mill 1 which operates on a 317 much smaller scale than mills 2 and 3 and sampling the milling streams was much 318 easier. 319 320 Applying a paired T-test to each of the pairs of mycotoxins DON/FB 1 , DON/ZON and 321 ZON/FB1 showed that there was no significant difference in the relative 322 concentrations in intake maize and flour for each mycotoxin in the two larger mills, 2 323 (p=0.8, 0.7 and 0.8) and mill 3 (p= 0.4, 0.7 and 0.7). In the smallest mill 1, there was 324 a significant difference between DON and FB 1 but not between DON and ZON (p = 325 <0.05,0.7 and 0.22). When the relative concentration of each mycotoxin between 326 intake maize and flour were compared between mills the difference was significant 327 between the smaller mill 1 and the larger mills 2/3 (p for each = <0.05, <0.05 and 328 <0.05 for DON, ZON and FB 1 ) but somewhat less so at least for DON and ZON 329 between the two larger mills (p= 0.3, 0.3 and <0.05 for DON, ZON and FB 1 ). 330 EC legislation has set 1400 µg kg -1 as the limit for FB 1 +FB 2 for processed 331 maize with particle size >500 micron whereas the limit for unprocessed maize is 4000 332 µg kg -1 . In addition there is a limit of 2000 µg kg -1 for processed maize of particle size 333 </= 500 micron that includes maize flour. However on the basis of the mean change 334 calculated from all consignments at each site, this required reduction for flour was not 335 achieved in any of the mills. In mills 2 and 3, Tables 3 and4, the mean figures 336 showed increased concentration in the flour. It has been speculated that the amount 337 remaining in the various milled fractions might also depend on the actual 338 concentration in the raw maize so that a larger reduction occurs at higher 339 concentrations although it is difficult to find firm evidence for this. 340

INSERT TABLE 3 341

This relationship is examined in Figure 5 for each mill by plotting the flour : raw 342 maize concentration ratio of FB 1 +FB 2 using the mean of the highest 5 concentrations, 343 the mean of the highest 10 concentrations and mean of all samples against the ratio of 344 flour : raw maize concentrations. This indicates clearly that the relative amount of 345 FB 1 +FB 2 in flour decreases with higher intake contamination. However even in mill 3 346 where the five highest intake concentrations in maize consignments gave a mean 347 concentration of 2873 µg kg -1 , the value of 0.75 would still result in a level of 2155 348 From the buyer's and the miller's viewpoint, this means that either stricter purchasing 350 requirements must be enforced for the maximum concentrations of fumonisins 351 acceptable, that the subsequent maize flour must be monitored closely or its further 352 use carefully considered. 353

INSERT TABLE 4 354

A rather lower proportion of DON, ZON and NIV were found in flour than for the 355 fumonisins. In addition, the concentrations of these mycotoxins found usually 356 occurred well below the EC limits, so that if transfer of mycotoxins to flour are less at 357 higher concentrations, any problem appears much less for the fumonisins. During the 358 period of this study no sample approached the legislative limits for these other 359

mycotoxins. 360

The mycotoxin concentrations remaining in flaking grits after maize milling at 361 mill 3 that uses Argentinean Flint maize, are given in Table 5. The mycotoxin 362 concentrations remaining in flaking grits are much lower than in flour. Thus the mean 363 reduction for all consignments for FB 1 +FB 2 is 93%. In contrast to the regulations 364 pertaining to flour this is a much greater reduction than is anticipated by the limits set 365 and should present no problem for the miller. Using the approach as for flour by 366 taking the mean of the 5 and 10 most contaminated samples and the mean of all, 367 showed that the reduction achieved for the more contaminated maize increased to 94 368 % and 95.5 % respectively for the 10 and 5 most highly contaminated samples 369 respectively suggesting that the reduction was again related to concentration although 370 it is not clear whether these small changes are statistical significant (figure not given). 371 Fusarium mycotoxins other than DON, ZON, NIV and fumonisins were sometimes 373 detected in intake maize. These were low levels of 3-Ac DON, 15-Ac DON, 374 fusarenone-X, HT2 and T2 although T2 and HT2 were never detected in Argentinean 375 maize. When these trace concentrations were found in intake maize, their presence 376 was confirmed after milling by the higher levels in the milling streams such as bran, 377 meal and germ in which mycotoxins are concentrated. 3-and 15-Ac-DON and FUS-378 X when detected usually correlated to the concentration of DON present. 379 Because the distribution of mycotoxins into different milling streams from 380 different consignments is very variable, % SD values being typically around 50%, 381 accurate predictive models are extremely difficult to design especially as the 382 distribution pattern of the mycotoxins in the milling streams is related to some extent 383 to the mycotoxin concentration in the raw maize so that care must be taken in 384 extrapolation from one level to another. Variability occurs as the result of many 385 factors such as analysis and sampling. These are well recognised but there are also 386 many other factors that could include the variability in the physical structure and 387 chemical composition of the maize seeds resulting from different growing conditions; 388 temperature, rainfall and drought, inadvertent loss of contaminated dust or physical 389 damage to seeds during transport, handling and storage, variety or to changes in 390 milling machinery settings in order to maximise the production of required maize 391 Universally applicable predictive models are difficult to design because so many 397 factors influence the mycotoxin distribution pattern in milling streams. However, 398 some relationships are consistent in the mills covered by this study so that maize grits 399 always contain reduced levels of all the mycotoxins studied, and mycotoxins were 400 always concentrated in the feed components such as the maize germ/meal/bran, 401 broken maize and similar products. Mycotoxin concentrations in maize flour could be 402 similar to, reduced or somewhat increased compared with those present in the raw 403 maize. It is concluded that if whole maize complies with legal limits, maize users 404 should in most situations be able to meet those set for maize ingredients destined for 405 food purposes although this might not always be achievable particularly for 406 fumonisins in products with particle size ≤500

INSERT TABLE 5 372

  thereof. Mills 1 and 2 sampled 26 and 30 consignments respectively of French maize, 114 supplied by lorry. Mill 3 supplied 30 consignments of raw Argentinean maize other related trichothecene mycotoxins) was determined following the 119 method of[START_REF] Patel | Survey of ethnic foods for 448 mycotoxins[END_REF] as used byScudamore et al, 2007. Ground samples (20 g) 120 were extracted with 100 ml acetonitrile/water (84:16). An aliquot of the extract was 121 cleaned using a charcoal/alumina column. After taking to dryness and re-dissolving 122 in acetonitrile DON was derivatised to form the trichothecene -trimethyl silyl (TMS) 123 derivatives and determined by GC/MS operating in selected ion mode, using 4 ZON was determined by HPLC with fluorescence detection as used by 125Scudamore et al, 2007. Ground samples (25 g) were extracted with 125 ml of 126 acetonitrile/water (75/25). The extract was cleaned-up using an immunoaffinity 127 column and determined by HPLC with fluorescence detection. The method for 128 fumonisins was a modification of the method ofShephard et al. 1990 as used by 129 Scudamore and Patel 2000. A ground sample (25 g) was extracted with 100 ml 130 acetonitrile:water (50:50) and filtered. After adjusting an aliquot of the filtrate to pH 5.8 131 -6.5 it was cleaned-up using a Bond-Elut strong anion exchange (SAX) cartridge. The 132 cartridge was washed with methanol:water (75:25), methanol and finally the fumonisins 133 were eluted with 10 ml 1% acetic acid in methanol. The eluate was evaporated to 134 dryness and re-dissolved in acetonitrile:water (50:50). The fumonisins were determined 135 using HPLC with fluorescence detection after forming the ortho-ophthaldialdehyde 136 derivatives. 137 138 Recovery, Limit of Detection and Determination 139 All analyses were conducted with a spiked sample, i.e. a known amount of toxin was 140 added to a sample of ground wheat or maize each day prior to extraction, clean-up and 141 HPLC determination for each batch of 1-5 samples. These results were used to assess 142 recovery and all reported results were corrected using the values obtained. Recoveries 143 in the range 70-110% were considered acceptable. The spike level was 200 µg/kg for 144 DON, 50 µg/kg for ZON and 400 µg/kg for each fumonisin. 145 146In house reference material (wheat and maize) contaminated at 220 and 550 µg/kg of 147 DON was spiked with 200 and 500 µg/kg respectively. Typical mean recovery was 148 89% with a coefficient of variability of 8.1% for 10 replicates. For fumonisins, maize 149 material contaminated at 498 µg/kg was then spiked at 400 µg/kg. 150

162 163

 162 schemes such as FAPAS (www.fapas.co.uk/fapas.cfm) and Certified Reference 170 Materials (CRM's), intermittently. Acceptable results have been achieved in the 171 FAPAS Scheme for DON, ZON and FB 1 and FB 2 in cereals during rounds during the 172 past 8 years. 173 174

  as bran, germ, meal or mixtures of these can thus vary 233 enormously in the parts and proportions of the maize seed incorporated but in 234 practice, most of these fractions are used for animal feed or other industrial uses and 235 so for practical purposes can be considered together. 236Milled products from maize are used in a wide range of processes to give the 237 range of foods consumed by the public. The fate of mycotoxins from raw maize to 238 consumer product thus depends on two distinct phases, the redistribution during 239 milling which is a physical process and changes occurring during food processing. 240 These changes will depend on the chemical properties of the mycotoxin, the 241 conditions of processing including temperature, pressure, moisture, pH and enzyme 242 activity, or dilution effects occurring because of the inclusion of other components. 243 Small scale or pilot scale studies of cereals mimicking industrial situations do 244 not always reflect accurately what happens in the full commercial situation as for 245 example operating scale, mill set up and commercial product will differ from mill to

  1 +FB 2 in maize exceeding the 2000 µg kg -1 needed to meet legislation. 349

Page

  schematic view of a maize seed. Origin of different milled products 543 Relative amount of mycotoxins in each milling stream compared to whole 545 maize (=1) in French maize used at Mill 1Relative amount of mycotoxins in each milling stream compared to whole 550 maize (=1) in French maize used at Mill 2Relative amount of mycotoxins in each milling stream compared to whole 553 maize (=1) in Argentinean Flint maize used at Mill 3Change in the proportion of FB 1 + FB 2 with concentration in intake maize = based on consignments with the 5 highest 562 concentrations in intake maize , = based on 10 highest, = based on all consignments 563

table gives

 gives

		F o r
		P
		e
		e r
			R
			e
			v i e
			w
			O n l
	223		examples of the y
	224	milling streams falling within these ranges. 'Polenta' emphasises the complexity of
		03/03/2011	10

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	496 497	Fusarium-infected corn. Applied Microbiology 26:1008-1010. Table 1. EC, Maximum Permissible limit for mycotoxins in maize, (EC 2006, EC
	498	2007), related to product examples
			Mycotoxin, µg kg -1	Milling stream
			DON ZON FB 1 +FB 2	examples
		Unprocessed maize with the exception of that intended to be processed by wet milling	1750	350	4000	intake and cleaned maize
		Milling fractions of maize with particle size >500 µm falling within CN code 1103 13 or 1103 20 40	750	200	1400	coarse grits, flaking grits
		Milling fractions of maize with particle				flour, polenta
		F o r F o r F o r F o r F o r F o r size ≤500 µm falling within CN code 1102 20 Maize or maize-based foods intended for direct human consumption, (with the exception of the 2 categories below) F o r	1250 750	300 75	2000 1000	flour/meal, fine grits pre-cooked/pre-boiled polenta meal
	407 408 409 410 499 500 501 502	micron such as flour r e P e P e e r P e e r P e e r P e e r P e e r 500 100 800 Processed cereal based foods for infants Acknowledgements Breakfast cereals, etc. 200 20 200 P and young children Animal feed * 900 a 100 b 5000 c e * = guideline limits depend on feed/animal. Lowest guideline values cited here (see finished products finished products bran, meal, germ, EC, 2006a for all limits) a = complementary and complete feedingstuffs for pigs, b = complementary and complete feedingstuffs for piglets and gilts e r
	411 412 413 503 504 505 506 507	These studies have been supported by the UK DEFRA, LINK Programme UK, Food Standards Agency, and the UK maize milling industry. R e v i e R e v i e R e v i e R e v i e R e v i e R e v i e c = complementary and complete feedingstuffs for pigs, horses (Equidae), rabbits and pet animals CN= Custom Tariff Code R e v i e
					w w w w w w w
					O n l O n l O n l O n l O n l O n l O n l
						y y y y y y y
		03/03/2011 03/03/2011 03/03/2011 03/03/2011 03/03/2011 03/03/2011	21 23 24

Table 2 :

 2 Mycotoxin content of maize flour milled from French maize at mill 1

	508
	509	2004-2007 compared with that in intake maize, n = 25 (DON), n = 12 (ZON),
	510	n = 29 (FB 1 ), n = 28 (FB 2 ), n =23 (FB 3 ), n = 29 (FB 1 +FB 2 ), n = 11 (NIV)
				mycotoxin concentration, µg kg -1		relative % in flour
		mycotoxin sample	mean	median	range	mean median	range	SD, %
		DON	intake flour	139 58	63 31	<10-444 <10-290	45	40	14-85	45.0
		ZON	intake flour	16 5	<3 <3	<3-86 <3-24	43	34	2-84	54.3
		FB 1	intake flour	320 234	267 215	<10-1110 16-654	75	67	35-134	40.9
		FB 2	intake flour	80 58	57 48	<10-269 <10-167	78	71	32-177	40.5
		FB 3	intake flour	49 40	30 31	<10-180 <10-133	90	86	46-141	31.4
		FB 1 +FB 2	intake flour	400 292	324 263	<10-1279 <10-821	74	67	34-143	41.2
		NIV	intake flour	40 13	28 5	<10-134 <10-55	43	38	17-77	37.2
	511	n= number of pairs on which relative % values are calculated
	512
	513
	514
	515
	516

Table 4 :

 4 Mycotoxin content of maize flour milled from Argentine maize at mill 3

	522
	523	2004-2007 compared with that in intake maize, n = 15 (DON), n = 9 (ZON), n
	524	= 3 (FB 1 ), n = 13 (FB 2 ), n =13 (FB 3 ), n = 13 (FB 1 +FB 2 ), n = 7 (NIV)
				mycotoxin concentration µg kg -1		relative % in flour
		mycotoxin sample	mean	median	range	mean median	range	SD, %
		DON	intake flour	89 68	74 51	16-220 22-132	92	79	27-188	61.5
		ZON	intake flour	15 15	10 7	<3-42 <3-101	127	107	44-263	50.4
		FB 1	intake flour	1426 1405	1405 1510	345-3813 228-2810	131	99	39-288	56.6
		FB 2	intake flour	484 440	408 493	101-1230 <10-701	123	113	34-244	55.0
		FB 3	intake flour	249 385	229 361	74-711 37-706	185	132	49-354	61.3
		FB 1 +FB 2	intake flour	1990 1846	1661 2003	505-5002 612-3511	129	102	34-278	56.3
		NIV	intake flour	53 28	13 8	<10-496 <10-131	65	45	6-118	69.6
	525	n= number of pairs on which relative % values are calculated

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