Characterisation and comparison of selected wheat (Triticum aestivum L.) cultivars and their blends to develop a gluten reference material

Journal Pre-proofs Characterisation and comparison of selected wheat (Triticum aestivum L.) cultivars and their blends to develop a gluten reference material Eszter Schall, Katharina A. Scherf, Zsuzsanna Bugyi, Lívia Hajas, Kitti Török, Peter Koehler, Roland E. Poms, Stefano D'Amico, Regine Schoenlechner, Sándor Tömösközi PII: DOI: Reference:

S0308-8146(19)32195-8 https://doi.org/10.1016/j.foodchem.2019.126049 FOCH 126049

To appear in:

Food Chemistry

Received Date: Revised Date: Accepted Date:

20 May 2019 22 November 2019 10 December 2019

Please cite this article as: Schall, E., Scherf, K.A., Bugyi, Z., Hajas, L., Török, K., Koehler, P., Poms, R.E., D'Amico, S., Schoenlechner, R., Tömösközi, S., Characterisation and comparison of selected wheat (Triticum aestivum L.) cultivars and their blends to develop a gluten reference material, Food Chemistry (2019), doi: https://doi.org/ 10.1016/j.foodchem.2019.126049

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Characterisation and comparison of selected wheat (Triticum aestivum L.) cultivars and their blends to develop a gluten reference material

Eszter Schalla ([email protected]), Katharina A. Scherfb,c ([email protected]), Zsuzsanna Bugyia ([email protected]), Lívia Hajasa ([email protected]), Kitti Töröka ([email protected]), Peter Koehlerd ([email protected]), Roland E. Pomse ([email protected]), Stefano D’Amicof ([email protected]), Regine Schoenlechnerg ([email protected]) and Sándor Tömösközia* ([email protected])

aDepartment

of Applied Biotechnology and Food Science, Research Group of Cereal Science and

Food Quality, Budapest University of Technology and Economics, Budapest, Hungary bLeibniz-Institute

for Food Systems Biology at the Technical University of Munich, Freising,

Germany cDepartment

of Bioactive and Functional Food Chemistry, Institute of Applied Biosciences, Karlsruhe

Institute of Technology (KIT), Karlsruhe, Germany dBiotask

AG, Esslingen am Neckar, Germany

eMoniQA

Association, Güssing, Austria

fDepartment

for Feed Analysis and Quality Testing, Institute for Animal Nutrition and Feed, Division

for Food Security, AGES – Austrian Agency for Health and Food Safety, Vienna, Austria gUniversity

of Natural Resources and Life Sciences, Department of Food Science and Technology,

Vienna, Austria

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*Corresponding author. Tel.: +36-1-463-1419; fax: +36-463-3855

Abbreviations: ANOVA, analysis of variance; CD, celiac disease; ELISA, enzyme-linked immunosorbent assay; GS, glutenin subunits; GxE, genetic and environmental; HMW, highmolecular-weight; HPLC, high-performance liquid chromatography; LMW, low-molecularweight; LSD, Fisher’s Least Significant Difference; PWG-gliadin, gliadin isolate provided by the Working Group on Prolamin Analysis and Toxicity; RM, reference material; RP, reversed-phase; SE, size-exclusion; TFA, trifluoroacetic acid; ωb-gliadins, glutenin-bound ωgliadins

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Abstract The reliability and comparability of gluten analytical results in gluten-free foods is hampered by the lack of reference materials (RM). This is partly caused by the yet incomplete knowledge of the effect of genetic and environmental variability of wheat proteins on immunochemical analyses, which affects the choice of gluten source to be applied for RM production. We investigated the genetic variability and the effect of harvest year on the protein composition of five previously selected wheat cultivars. We also compared the magnitude of these effects on ELISA results to get closer to the question of choosing individual cultivar or a mixture as an RM. Our results proved that the application of a blend for this purpose is advantageous. The candidates were also produced on pilot scale to investigate the feasibility of upscaling. The results of comparison studies showed that the pilot scale blended flour can also be suitable for RM. Keywords celiac disease; gluten; ELISA; reference material

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1. Introduction Gluten, a complex protein group in wheat, rye and barley, causes celiac disease (CD), an autoimmune enteropathy of the small intestine, in susceptible individuals. The only effective treatment is lifelong adherence to a gluten-free diet (Diaz-Amigo & Popping, 2012; Rallabhandi, 2012). In order to help the availability of products for sensitive consumers, the Codex Alimentarius defines the requirements of “gluten-free” (20 mg/kg) and “low gluten content” (100 mg/kg) statements (Codex Stan 118-1979, 2015). Gluten is located in the starchy endosperm of wheat and other cereals. According to the classical Osborne fractionation, gluten includes alcohol-soluble prolamins (referred to as gliadins, secalins and hordeins in wheat, rye, and barley, respectively) and alcohol-insoluble glutelins (referred to as glutenins in wheat) (Koehler & Wieser, 2013; Wieser & Koehler, 2009). The gliadins are mainly monomeric proteins connected through non-covalent interactions. According to their electrophoretic mobility, gliadins could be classified as α-, β-, γ- and ω-gliadins. Glutenins are polymeric proteins stabilized by disulphide bonds. They can be grouped into high- (HMWGS) and low-molecular-weight (LMW-GS) glutenin subunits according to their size (Koehler & Wieser, 2013). Most of the reactive epitopes that are characteristic of CD have been found in the gliadin fraction. The α-gliadin fraction contains a peptide of 33 amino acids that was shown to be highly toxic (Shan et al., 2002) and present in all 40 hexaploid wheat cultivars investigated (Schalk, Lang, Wieser, Koehler & Scherf, 2017a), but immunogenic epitopes have been described in γ- and ω-gliadins, too. A number of immunoreactive epitopes are also present in glutenins (Navarro, del Pilar Fernández-Gil, Simón & Bustamante, 2017; Ozuna & Barrow, 2018).

The determination of the potentially harmful proteins and the gluten content in gluten-free food is a great challenge. Several methods are available for the qualitative or quantitative

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determination of the triggering proteins or other molecules that indicate the presence of the offending food components (Haraszi, Chassaigne, Maquet & Ulberth, 2011). Liquid chromatography- mass spectrometry may be useful for the simultaneous measurement of several proteins by targeting gluten peptides, but the routine utilisation of this technique is limited, because advanced expertise and costly equipment are necessary. Methods of molecular biology, e.g. polymerase chain reaction (PCR), only identify the gene segment encoding the protein. They only indicate the risk of harmful proteins being present, but not actual toxicity (Mujico, Lombardía, Mena, Méndez & Albar, 2011; Scherf & Poms, 2016). Methods based on an immunochemical reaction can be used to quantitate a protein fragment consisting of some amino acids called an epitope, from which the total gluten content can be calculated using a specific factor. The method-of-choice for routine gluten analysis is the specific and sensitive immunoanalytical enzyme-linked immunosorbent assay (ELISA) (Sharma, Rallabhandi, Williams & Pahlavan, 2016). Different ELISA kits are available on the market using antibodies with varying specificity and different sample preparation procedures (Scherf & Poms, 2016). The monoclonal antibodies are characterized by the recognition of one type of epitope. The R5 monoclonal antibody-based ELISA is a widely used method also recommended by the Codex Alimentarius Commission Committee on Methods of Analysis and Sampling for the detection of gluten. The R5 antibody was developed against a rye extract and the key recognition motif is the heat-resistant epitope QQPFP, which is present in α-gliadins, γ-gliadins and ω1,2-gliadins, some LMW-GS and also in the corresponding barley hordeins and rye secalins (Haraszi et al., 2011; Lexhaller, Tompos & Scherf, 2017; Mena, Lombardía, Hernando, Méndez & Albar, 2012; Valdes, García Llorent & Méndez, 2003). The G12 antibody was raised against the immunogenic 33-mer gluten fragment which is highly resistant to degradation of digestive enzymes. The R5 had the highest affinity to γ- and ω1,2gliadins whereas the G12 reacted mostly with α-, γ- and ω1,2-gliadins (Lexhaller et al., 2017).

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Despite multiple steps towards the harmonization of methods in this field, currently there is no generally accepted reference material (RM) available for gluten analysis. A certified RM would provide an opportunity to support the validation of the methods and to identify the factors influencing gluten analysis. A widely used standard-like material is the so-called Prolamin Working Group (PWG)-gliadin isolated from the 28 most commonly grown European wheat cultivars. It is a well-characterized gliadin isolate with good solubility, homogeneity and stability properties (van Eckert et al., 2006; van Eckert, Bond, Rawson, Klein, Stern & Jordan, 2010). PWG-gliadin was proposed for approval as a certified RM, but it did not meet some of the RM requirements for certification, such as reproducibility of production (Diaz-Amigo & Popping, 2013).

There is an ongoing debate in the analytical community about the desirable properties of a gluten RM, including the selection of the gluten protein source to apply (e.g. a single variety or a blend of multiple varieties) and the form (flour or isolated protein). The number and quality of varieties that should be involved in the design of the RM is greatly influenced by the magnitude of genetic (G) and environmental (E) variability of epitopes (and the protein types containing them) on the results of ELISA methods. Many studies have shown that GxE factors have a great impact on the protein content, the composition and the gliadin/glutenin ratio of cereals (Johansson, Prieto-Linde, Svensson & Jönsson, 2003; Johansson et al. 2013; Uhlen et al., 2015; Wieser, 2000). However, only a few studies are available about the investigation of GxE effects on the results of ELISA methods (Hajas et al., 2016; Pahlavan, Sharma, Pereira & Williams, 2016). In order to standardize the results obtained with the various methods, an RM must be selected for validation of the methods which contributes the least to increasing uncertainty. The degree of genetic variability could be reduced by choosing

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a blend or a cultivar with average values. The degree of environmental variability could be critical for a reproducible RM production and can be taken care of by using one cultivar with a stable protein composition or a blend. In the framework of international cooperation, our aim was to develop an appropriate RM candidate for method validation which is supported by experimental results. In previous studies we examined the selection of different wheat cultivars collected from all over the world to investigate the genetic variability and the effect of harvest year on ELISA results (Hajas et al. 2016; Hajas et al.; 2018). As a result of these studies, we established a set of criteria for the selection of cultivars that could be suitable candidates for RM production in terms of protein composition, gluten content, gliadin/glutenin ratio, ELISA response and the availability of the wheat cultivar (Hajas et al., 2018) (Table 1). Based on these criteria, potential RM candidates were reduced to five wheat cultivars. These cultivars were collected from a new harvest year. This work aims to develop the laboratory production of an RM candidate from the selected cultivars and their blend as well as characterisation of their protein content, gluten composition and their response to different ELISA methods. Investigating the degree of genetic variability and the effect of harvest year on the results of the selected cultivars will allow the comparison of candidates (individual cultivars versus blend) in their possible use as an RM. The further goal of the study is the upscaling of the procedure to a level that allows RM production in amounts sufficient for commercialization.

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2. Materials and methods 2.1. Wheat samples The following five wheat (Triticum aestivum L.) cultivars were selected in line with a set of selection criteria described in our previous study (Hajas et al., 2018) and were collected from the harvest year of 2016 for this study: Akteur (Germany); Carberry (Canada); Mv Magvas (Hungary); Yitpi (Australia); Yumai-34 (China). Comparative characterization with the same cultivars from the year of 2014 was also performed.

2.2. Production of wheat flours on laboratory scale Seed moisture content of the five cultivars was determined by an InfratecTM 1241 Grain Analyser (Foss Tecator AB, Höganäs, Sweden). Wheat samples were conditioned prior to milling according to Hungarian Standard MSZ 6367-9:1989. The tempered kernels were milled on a laboratory mill (FQC 109, Metefém, Budapest, Hungary). Then, the whole meal was sieved on a 250 μm sieve for 20 min. (AS 200 basic, Retsch GmbH, Haan, Germany). The milling yields varied between 50–55% based on grain weight after sieving. The blend of the five cultivars was prepared by mixing equal amounts (80 grams each) of grains from the individual cultivars by shaking in a closed container manually for 10 minutes before milling. The homogeneity of the blend was confirmed later by calculations using chemical composition data.

2.3. Production of wheat flours on pilot scale The kernels from the five cultivars were conditioned and wetted to a moisture content of 15.5% 24 hours before milling. The tempered kernels were milled on a roller mill (Bühler MLU-202 Laboratory Flour Mill, Uzwil, Switzerland). In total, four white flour fractions can be obtained after milling and sieving. All white flour fractions were homogenized with a

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drum-mixer (Meyer Maschinen und Mühlenbau AG, Solothurn, Switzerland). The blend of the five cultivars was prepared by mixing equal amounts (1 kg each) of grains from the single cultivars. Three independent batches were prepared to test the reproducibility of the pilot scale production. The identity of the different batches and the homogeneity of the blend were confirmed by chemical composition data.

2.4. Determination of crude protein content The nitrogen content of the flours was determined by a Leco FP 528 nitrogen analyser (Leco Corporation, St. Joseph, USA) in triplicates following the MSZ EN ISO standard 166342:2016. The nitrogen content was multiplied by 5.7 to obtain the crude protein content.

2.5. Determination of ash content The ash content of the flours was determined by incineration following the MSZ EN ISO standard 2171:2010.

2.6. Protein characterization by SE-HPLC Protein extracts were prepared according to Batey, Gupta and MacRitchie (1991) and Gupta, Khan and MacRitchie (1993) with a slight modification. Acetonitrile (50%, v/v) containing 0.1% (v/v) trifluoroacetic acid (TFA) was used as the extraction solvent. Wheat flour (15 mg) was suspended in 1 mL of the extraction buffer and shaken (1,500×rpm, 30 min, RT) followed by centrifugation (4,500×g, 20 min, 20 °C). The supernatant was collected (extractable protein fraction). The remaining pellet was extracted with 1 mL of the same extraction solution using sonication for 40 s with an amplitude of 90%. Then, samples were shaken (1,500×rpm, 30 min, RT) and centrifuged (4,500×g, 20 min, 20 °C) to obtain a supernatant. This protein fraction could be extracted only after sonication, so this was named

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as “unextractable protein fraction”. All supernatants were filtered (Minisart®, 15/0.45 RC, Sartorius AG, Goettingen, Germany) before SE-HPLC analysis. The extractions were done in duplicate for each flour sample. The conditions for the SE-HPLC analyses were the following: instrument: PerkinElmer Series 200 HPLC with TotalChrom Navigator v6.2.1 (PerkinElmer Inc., Shelton, CT, USA); column: BioSep-SEC-s4000 (particle size 5 µm, pore size 50 nm, 300 × 7.8 mm, separation range for proteins 15,000 - 1,500,000, Phenomenex, Torrance, CA, USA); temperature: 25 °C; injection volume: 20 µL; elution solvents: 50% (v/v) acetonitrile/water containing 0.1% (v/v) TFA; flow rate: 1 mL/min; running time: 20 min, detection: UV absorbance at 214 nm. After each run, the column was equilibrated with the elution solvent for 1 min. The chromatograms of the extractable and unextractable proteins were divided into three sections: the proportion of polymeric (peak 1), monomeric (peak 2) and albumin/globulin (peak 3) fractions to ‘total’ protein were calculated from the peak areas as percentage of the total peak area.

2.7. Protein characterization by RP-HPLC Wheat flours (100 mg) were extracted sequentially according to the modified Osborne procedure (Wieser, Antes, & Seilmeier, 1998) by magnetic stirring with salt solution (extraction of albumins/globulins), followed by 60% (v/v) ethanol solution (extraction of gliadins),

and

glutelin

extraction

solution

(containing

propanol,

tris(hydroxymethyl)aminomethane hydrochloride, dithiothreitol and urea for the extraction of glutenins). All suspensions were centrifuged (3550 x g, 25 min, 20 °C), then filtered (WhatmanTM Spartan 13/0.45 RC, GE Healthcare, Freiburg, Germany). The extractions were done in triplicate for each flour sample. The conditions for the reversed-phase highperformance liquid chromatography (RP-HPLC) analyses were the following (Schalk, Lexhaller, Koehler & Scherf, 2017b): instrument: Jasco XLC with Jasco Chrompass

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Chromatography Data System (Jasco, Pfungstadt, Germany); column: AcclaimTM 300 C18 (particle size 3 µm, pore size 30 nm, 2.1 x 150 mm, Thermo Fisher Scientific, Braunschweig, Germany); temperature: 60 °C; elution solvents: TFA (0.1%, v/v) in water (A) and TFA (0.1%, v/v) in acetonitrile (B); linear gradient: 0 min 0% B, 0.5 min 20% B, 7 min 60% B, 7.1–11 min 90% B, 11.1–17 min 0% B for albumins/globulins; 0 min 0% B, 0.5 min 24% B, 20 min 56% B, 20.1–24.1 min 90% B, 24.2–30 min 0% B for gliadins and glutenins; flow rate: 0.2 mL/min; injection volume: 20 µL for albumins/globulins and glutenins, 10 µL for gliadins; detection: UV absorbance at 210 nm. The protein contents of the extracts were calculated from the peak areas using 5, 10, 15 and 20 µL of a PWG-gliadin solution (2.5 mg/mL in 60% ethanol) (van Eckert et al., 2006) as calibration reference. The contents of ω5-, ω1,2-, α- and γ-gliadins were calculated from the peak area of each gliadin type relative to the total gliadin content, as were those of ωb-gliadins (glutenin-bound ω-gliadins), HMW-GS and LMW-GS relative to the total glutenin content.

2.8. Gliadin/gluten quantification with ELISA methods The gliadin/gluten quantification was performed with two commercially available ELISA test kits: the AgraQuant Gluten G12 Assay (COKAL0200, Romer Labs, Tulln, Austria) and the RIDASCREEN Gliadin Assay (R7001, R-Biopharm, Darmstadt, Germany). They apply different antibodies (monoclonal G12 and monoclonal R5, respectively) and are calibrated differently (vital wheat gluten extract and PWG-gliadin, respectively). Three independent extractions were performed for each flour sample. ELISA procedures were carried out according to the kit instructions. The absorbances were determined using a microplate reader (iMarkTM Microplate Absorbance Reader, Bio-Rad, Hercules, CA, USA). The gliadin/gluten concentration was calculated from the absorbance values by the Bio-Rad Microplate Manager 6 software (Bio-Rad, Hercules, CA, USA) using the curve fit suggested by the manufacturer

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(Table 2). The ELISA test kits used for analysis were randomly coded with capital letters (A and B) in section 3.

2.9. Statistical analysis The analytical results were statistically evaluated with the investigation of means, standard deviations, analysis of variance (ANOVA) with Fisher’s Least Significant Difference (LSD) post hoc test and t-test at a confidence level of 0.95 using Statistica 13 software (StatSoft Inc., Tulsa, USA). For statistical evaluation, the results of our previous study (Hajas et al., 2018) were also used. The experimental factors were cultivars (Akteur, Carberry, Mv Magvas, Yitpi and Yumai-34) and harvest years (2014 or 2016). One-way and factorial (with two factors) ANOVA were also performed. In case of factorial ANOVA, the interaction of different factors was also investigated. The homogeneity of variance and the normality of distribution were checked in all examined cases.

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3. Results and discussion 3.1. Protein content of the flours prepared on laboratory scale The five cultivars were selected on the basis of certain selection criteria from the 2014 harvest year (Hajas et al., 2018). The crude protein contents of these five cultivars varied in a range of 12.6-15.6% in 2014 (Table 2). Mv Magvas and Yitpi had the lowest and Akteur had the highest value. By comparison, the crude protein content of the flours from the new harvest year (2016) examined covered a wider range (12.1-18.7%) (Table 2). The lowest value belonged to Mv Magvas and the highest to Carberry. Mv Magvas had the best similarity between the two years, but there was still a significant difference between the two harvest years for all cultivars. Examining the variability of the five cultivars (G) and the two harvest years (E) by ANOVA, the degree of their effect was similar and significant to the protein content. The selection criteria used to choose the five cultivars from the collection of the 2014 harvest year are shown in Table 1. Considering the values of the cultivars from 2016, only Akteur and Mv Magvas met the protein content criterion, because the other cultivars had a higher protein content than in 2014.

The crude protein content of the blend prepared from the five cultivars of 2016 was 15.4%. The measured protein content of the blend did not show significant difference from the mean value of the five cultivars (Table 3). This predicts that the applied laboratory homogenization process was suitable for the production of the blend. Besides, the value of the blend fell within the range of the crude protein content criterion.

3.2. Gluten composition of the flours prepared on laboratory scale The cultivars from 2016 showed significant changes in crude protein content compared to the 2014 harvest year, which may also be reflected in the distribution of different protein types.

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This could be critical due to the change in the amount of target proteins used in different gluten analytical methods. Information on the proportions of monomeric and polymeric proteins can be obtained from SE-HPLC results (Table 2).

The cultivars from 2014 had monomeric protein contents from 46% (Akteur) to 47.5% (Mv Magvas, Yumai-34) and polymeric protein contents between 44.7% (Mv Magvas) and 48% (Akteur, Carberry). The monomeric/polymeric protein ratio was balanced between the cultivars and there was no significant difference between them. Among the cultivars of the 2016 harvest year, Akteur had the lowest monomeric protein proportion (like in 2014) with 42%, while Carberry had the highest value with 47.5%. The polymeric protein contents were between 42.6% (Yumai-34) and 47.5% (Akteur). The monomeric/polymeric protein ratio was in the range of 0.88-1.07 and in contrast to the results of the 2014 year, genetics had an effect on the percentage composition of monomeric and polymeric proteins. According to the comparison of the different cultivars and the two years, the diversity between the two harvest years was higher than the variability of the cultivars.

The monomeric protein content of the blend was 44.1% and the polymeric protein content 43.3%. In case of monomeric proteins, only Carberry differed significantly from the blend, while in case of polymeric proteins, Akteur differed significantly from it. The values of the blend were well reflected in the average values of the five cultivars, since there was no significant difference in the proportion of monomeric and polymeric proteins compared to the calculated values (Table 3).

It is clear that the distribution of monomeric and polymeric protein changes significantly, and in our case, the harvest year had a greater effect on the monomeric/polymeric ratio than the

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cultivar. Since mainly the monomeric proteins contain the protein types and epitopes that are important for celiac disease and ELISA measurements, it is worth characterizing the changes in the proportions of the different protein types in detail. The results of sequential extraction combined with RP-HPLC are shown in Table 4. In case of ω5-gliadins, the trend of the values from the two harvest year was similar and the differences between the two years were very small, but significant. Less diversity was observed in the case of ω1,2-gliadins, but there was a significant difference between both the cultivars and the harvest years. The values of αgliadins were lower for all cultivars in 2016 compared to 2014, but the tendencies were somewhat different. In case of α-gliadins, there were also significant differences between the cultivars and the harvest years. In case of γ-gliadins, there was also a great diversity among the cultivars. The cultivars of 2016 showed very similar tendencies, but significantly lower values of γ-gliadins than in 2014. The values of ωb-gliadins were very small, but the cultivars from 2016 had significantly higher ωb-gliadin proportions than in 2014. The rates of the individual glutenin fractions showed fewer differences between the cultivars than the gliadins. Each cultivar had a very similar HMW-GS value (around 8.5%) in 2014, except Carberry with 4.5%. The HMW-GS typically showed higher values in 2016. In case of LMW-GS, the proportion of these proteins was also higher in 2016 compared to 2014. Despite the comparatively small absolute variations, there were significant differences between the cultivars and the harvest years in each glutenin type. The gliadin/glutenin ratios were in the range of 2.1 and 2.8 in 2014, while they were between 1.6-1.9 in 2016. One of the selection criteria relates to the proper gliadin/glutenin ratio (Table 1), but numerically none of the cultivars from 2016 matched this range, because the proportion of α- and γ-gliadins decreased while glutenins increased in 2016 compared to 2014, resulting in lower gliadin/glutenin ratios. The change in the ratio of gliadins and glutenins and the ratio of subfractions within gliadins may be critical in methods where just gliadins (or some type of gliadins) are measured to

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determine the total gluten content. The ratio of α- and γ-gliadins is also one of the selection criteria and in this case, all of the cultivars from 2016 were in the range.

By comparing the GxE variability by ANOVA, it was found that they have a significant effect on the relative amount of each protein type. The variability coming from the different cultivars was greater than the difference between the two years in case of ω5- and ω1,2gliadins, but the effect of the harvest year was higher than that of the different cultivars in case of HMW-GS. For the other protein types, the extent of the two effects was mostly balanced.

The ω5-gliadin content of the blended flour was 5.7% and the most similar value belonged to Yitpi (Table 4). The proportion of ω1,2-gliadins was also 5.7% and the value of Akteur was most similar to the blend. The α-gliadin content of the blend was 27% and thus close to the value of Mv Magvas, while Yitpi was most similar to the blend (20.5%) in case of γ-gliadins. The HMW-GS proportion of 9.2% of the blend was close to Carberry and in case of LMWGS Yumai-34 was most similar to the blend (20.2%). Based on these results, none of the cultivars can be identified with average protein composition in all respects. Although there were significant differences between the protein types measured for the blend and the calculated averages, the variations were very small (Table 3) and appeared to be negligible compared to the variability between the cultivars and the two years. The measured values for the blend fit in the range of the selection criterion regarding α-gliadin/γ-gliadin ratio and its value for gliadin/glutenin ratio does not differ significantly from the lower limit of the criterion (Table 1). This predicts the possibility that if genetic variability has such an effect on the results of ELISA method, the blend would prove to be a better candidate, since methods validated with its average values could give more accurate results. The best way to reduce the

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extent of the harvest year effect is the use of target epitopes that are less sensitive to the different external environmental factors. It should be examined how the protein types that are important for our applied ELISA methods have changed during the two years in the cultivars (ω1,2- and γ-gliadins in case of R5 antibody and ω1,2-, α- and γ-gliadins in case of G12 antibody). The change in the amount of protein types involved in ELISA methods is higher for each cultivar (except Yitpi) between the two years than the change of the average values for the two years.

3.3. ELISA results of the flours prepared on laboratory scale The GxE variability of protein content and composition of the selected wheat cultivars was examined in detail above. The question is how these effects appear in the results of two ELISA methods using different epitopes.

Gliadin contents measured by method A followed the crude protein content of the cultivars from 2014, as the lowest value belonged to Mv Magvas and the highest to Akteur (Table 5). However, a significantly lower value was measured for Akteur by method B, whereas there were no significant differences between the results for the other samples obtained by the two methods. We also got similar values for Akteur from the 2016 year, but in case of other samples there were significant differences between the two years. Changes in the gliadin content mostly followed the protein content between the two harvest years, because the gliadin content of Carberry, Yitpi and Yumai-34 measured by ELISA got higher with increasing protein content. The lowest gliadin concentration was measured for Mv Magvas and the highest for Carberry from 2016 in case of both kits. There was no significant difference between Akteur and Mv Magvas or between Yitpi and Yumai-34 measured by

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method A. In case of method B, Akteur and Mv Magvas as well as Carberry, Yitpi and Yumai-34 were similar and there were no significant differences between them.

Based on the statistical evaluation of the above results by analysis of variance, we can also answer which and to what extent the examined factors (genetic variability and effect of harvest year) influence the reliability of the results. If the repeatability of the ELISA method is greater than the GxE effect, the issue of cultivar selection is not so critical. If any effect is greater than the random error during the implementation of the method, then it makes sense to choose the right material. If the genetic variability is identifiable, but the effect of harvest year is not, then it is not necessary to use a blend as RM. In that case, one cultivar with stable protein properties against external conditions is sufficient. If the effect of harvest year is also valid, the individual cultivar choice is not appropriate, because it inhibits the possibility of reproducible production. It would be much more difficult to achieve similar parameters for one cultivar in later harvest than for the blend. In case of method A, we found that the greatest individual impact on the results of the ELISA method was the harvest year (35%), and the effect resulting from genetic variability was much lower (17%) for these samples. The value of the interaction between the two effects was 25%, while the repeatability standard deviation was 22%. In case of method B, the highest effect was also the harvest year with 41%, while the effect of genetic variability was only 21%. The value of the interaction between the two effects was 20%, while the standard deviation was 18%, which is comparable with method A. The effect of different cultivars and harvest years each were higher than the repeatability of the methods for both kits, so reducing the GxE effect is required. Using the blend sample could reduce the magnitude of these effects, but it may be important to explore the causes of random errors during ELISA measurements, which we attempted in our previous study (Schall, Bugyi, Hajas, Török & Tömösközi, 2019).

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Only Akteur did not differ significantly from the blend in case of method A and the value of Yumai-34 were the closest to the blend in case of method B. The measured value of the blend and the average of the five cultivars were not significantly different in either method (Table 3) which confirmed the homogeneous distribution of target epitopes for both methods.

The magnitude of the change in ELISA values between the two harvest years varied by cultivar. The lowest difference between the two years belonged to Akteur with an average increase of 10%, while Carberry had an increase of 80% between the two years. The extent and tendency of changes typically do not follow the change in the content of the different protein types relevant in ELISA methods (ω1,2-, α- and γ-gliadins), because the gliadin concentrations measured by ELISA increased in 2016 compared to 2014, while the ratio of gliadins -though not in all cases- decreased. The change in average value of the five cultivars between the two years showed an increase of about 40% in case of method A and about 60% in case of method B, which is still a smaller change than in most individual cultivars.

3.4. Comparison of ELISA and RP-HPLC results Gliadin recovery values (Table 5) of ELISA measurements were calculated by taking the total gliadin content measured by RP-HPLC as basis. The recovery values of the samples from the 2014 harvest year were around 100% and there were no significant differences between the recovery values of the two methods. However, in case of method A, Mv Magvas differed significantly from the other cultivars, while in case of method B, Yumai-34 showed differences to the others. This means that Akteur, Carberry and Yitpi showed similar gliadin values with ELISAs and RP-HPLC methods. The recovery values for the samples of 2016 were in the range of 150-183% in case of method A and only Carberry showed significantly

19

higher value from the other cultivars. The recovery values were between 156-190% in case of method B and Yitpi was significantly different from the others. There was no significant difference between the recovery values of the two methods. The ELISA results followed the results obtained by RP-HPLC, but the values measured with ELISAs were higher. Due to the higher recovery values for the 2016 cultivars, only Mv Magvas met the criterion for gliadin recovery value in the case of method A, while none of the cultivars fit in the range of this criterion for method B (Table 1).

The gliadin recovery value of the blend was 150% in case of method A and 180% in case of method B (Table 5). There was no significant difference between the gliadin recovery value of the blend and the average of the five cultivars (Table 3). The blend suited the criteria of gliadin recovery by method A, while it did not fit in the range for method B, because its gliadin recovery value was too high (Table 1). From a food safety aspect, the overestimation of the real gluten content is more preferred than underestimation, but the results pointed out one of the most sensitive issues regarding the trueness and reliability of the ELISA methods.

Choosing the right gluten RM is not an easy task, because several different aspects have to be fulfilled for the selected material. Some cultivars are more advantageous in some properties than others (e.g. Mv Magvas with good protein composition stability), but none of them can be considered as the most appropriate solution. Furthermore, the standard deviations belonging to the mean values of the blend are not higher than for the individual cultivars, so its homogeneity is not problematic. Therefore -based on the results of the samples produced on laboratory scale- it is advisable to choose the blend of the five cultivars against a single one.

20

3.5. Up-scaling of production In our laboratory experiments we found that the blend of the five cultivars would be a more appropriate gluten RM. In order to be used widely, the amount of the material needs to be increased. Thus, the production of the flours from 2016 was increased to pilot scale, so that the amount would be sufficient for use as RM. However, it should be examined that the statements for all laboratory samples are also valid for samples produced on pilot scale (min. 10 kg/sample). For this reason, all five cultivars and the blend were characterized with the same methods as the laboratory samples to compare the results of laboratory and pilot scale. The range of the crude protein of the pilot scale samples was between 13.6-17.7% and the values were very similar to the laboratory scale samples (Table 2). However, statistically significant differences were observed (except for Mv Magvas) due to variation in the milling methods between the two scales. Based on the selection criteria, there were some differences between pilot and laboratory scale samples (Table 1). Considering the criterion of protein content, more pilot scale samples were suitable than laboratory samples, because Yitpi and Yumai-34 also fitted in the range. The crude protein content of the pilot scale blend (14.7%) was very close to the laboratory scale blend (15.7%), but the difference was significant as in case of the individual cultivars (Table 2). The homogeneity of the pilot scale blend was also sufficient, because there was only a very small difference between the measured value of crude protein content (14.7%) and the calculated mean (14.9%) (Table 3). Consequently, the value of the blend was appropriate for the crude protein content criterion (Table 1).

The range of monomeric proteins measured with SE-HPLC was between 50.7-56.9% and the polymeric proteins were between 38.1-40.5% (Table 2). The monomeric/polymeric protein ratio also showed that the monomeric protein proportion of pilot scale samples was higher than in the laboratory scale samples. The different milling methods can affect the protein

21

composition of the flour (Prabhasankar & Rao, 2001) so the up-scaling and differences in the equipment could induce changes in gluten composition. There was also an increase in the proportion of monomeric proteins in the blend compared to the laboratory scale. However, the blend showed a good match with the mean of the five cultivars as its measured values of monomeric and polymeric protein contents did not differ significantly from the calculated ones (Table 3).

The increase in the proportion of monomeric proteins in the pilot scale samples was also supported by the results of the RP-HPLC method (Table 4). The gliadin/glutenin ratio of pilot scale samples were in the range of 1.9-3.6. Similar to the lab scale samples, the lowest value belonged to Akteur and the highest to Carberry. Among the different protein types, the proportion of α-gliadins increased most, but ω5- and γ-gliadins also had significantly higher values than the laboratory scale samples. The values of the other protein types changed less extent with up-scaling. Due to the increase in the proportions of gliadins, Mv Magvas, Yitpi and Yumai-34 also suited the range of gliadin/glutenin ratio compared to the laboratory samples (Table 1). Akteur and Yumai-34 were not appropriate for the α-gliadin/γ-gliadin ratio criterion.

The measured values of the pilot scale blend and the average of the five cultivars differed significantly, but the differences (similar to the laboratory scale blend) were minor and much smaller than the variability of the cultivars (Table 3). The gliadin/glutenin and α-gliadin/γgliadin ratios of the blend also fit in the range of the selection criteria (Table 1).

Changes in ash content could serve as indirect evidence for the effect of different milling methods. The ash content of the laboratory samples located in the range of 0.42-0.56% (Table

22

2). This range was very similar for the pilot scale samples, but the values changed by samples. The ash content of samples decreased significantly with up-scaling, except Akteur and Yitpi. This may lead to a reduction in the proportion of proteins from the outer layers of the wheat seed, thus affecting the distribution of total protein content.

The values obtained by ELISA method A were typically lower for the pilot scale samples than for the laboratory samples, while in the case of method B, it was the opposite (Table 5). However, the differences were not significant, as in case of gliadin recovery in most cases. Based on the results obtained with ELISA method A, Akteur, Carberry and Mv Magvas were also in the range of the gliadin recovery criterion, while -similarly to laboratory scale samples- none of them was suitable in case of method B (Table 1).

Similar gliadin contents and recovery values were obtained for the pilot scale blend and the laboratory blend for method A, while significant difference could be observed in measured gliadin content in case of method B (Table 5). The pilot scale blend did not show significant differences from the calculated value in case of method A, while the difference was significant between the measured value and the calculated mean with method B, but with low variation (Table 3). In case of gliadin recovery values, there were no significant differences between measured and calculated values. Based on the selection criterion on gliadin recovery, the blend was in the range in case of method A while it did not fitted in the range for method B (Table 1).

There were more pilot scale cultivars meeting the selection criteria than laboratory scale ones, but overall, the blend was the most appropriate among the pilot scale samples as well. The decision about the blend sample is further enhanced by the fact that it was very similar to the

23

mean of the 23 cultivars examined in our previous study in many aspects (Table 3), so its value represents the average value of a larger sample population and also supports the possibility of reproducible production.

3.6. Experiment on stability of the samples To monitor the shelf life of the samples, the gliadin content of an arbitrarily selected cultivar (Carberry) and the blend was tested by ELISA. The gliadin content was measured four times during the experimental period of two years so far. The reproducibility of the gliadin content measurement was 17% obtained for both samples, which is acceptable for these samples (Schall et al., 2019). There were no apparent tendencies in measured gliadin concentrations of the sample at different times.

24

4. Conclusion The protein, gluten and gliadin content and composition of five wheat cultivars and their blend produced on both laboratory and pilot scale were investigated in this work to examine some questions regarding the production of an appropriate gluten RM. We examined the effects of genetic variability and two harvest years on analytical results and their possible reduction by using blended flour. In addition, the homogeneous distribution of the blend was adequate, which is a basic requirement of its potential application. We have found that the GxE effect was significant for the results of the ELISA methods and the blend seems to be the best choice to reduce these effects, because its values are sufficiently average to reduce genetic variability and there is a greater potential for reproducible production due to the effect of harvest year on individual cultivars The production of the homogenous blend was achieved at both laboratory and pilot scale. The pilot scale blend has a sufficient quantity for widespread use as a RM and is ready-to-use. Our further task is the continuous monitoring of flour stability (which is already in progress) and to prepare the value assignment procedure of RM within an international collaborative study.

25

Acknowledgement The authors gratefully acknowledge the contribution of Prof. Ferenc Békés (FBFD Pty. Ltd.), Dr. Terry Koerner (Head, Allergen and Natural Toxin Section, Food Research Division, Bureau of Chemical Safety, Health Canada) and Dr. Marianna Rakszegi (Agricultural Research Institute of the Hungarian Academy of Sciences) in collecting the wheat samples and Dr. Elisabeth Reiter (Institute of Animal Nutrition and Feeding, AGES, Vienna) in the pilot scale milling of the wheat samples. The authors wish to thank Dr. Szilveszter Gergely (Budapest University of Technology and Economics) for his assistance in NIR measurements. This research is related to the scientific goals of the MoniQA Association, the OTKA ANN 114554 project (“Improving gluten-free dough by a novel hemicellulose network”) and the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Biotechnology research area of Budapest University of Technology and Economics (BME FIKP-BIO).

26

Conflict of interest The authors declare no conflict of interest.

27

References Batey, I. L., Gupta, R. B., & MacRitchie, F. (1991). Use of size-exclusion high-performance liquid chromatography in the study of wheat flour proteins: an improved chromatographic procedure. Cereal Chemistry, 68, 207–209. Codex Stan 118-1979 (2008). Codex standard for foods for special dietary use for persons intolerant to gluten. Codex Alimentarius Commission. Diaz-Amigo, C., & Popping, B. (2012). Gluten and gluten-free: issues and considerations of labeling regulations, detection methods and assay validation. Journal of AOAC International, 95, 5–24. Diaz-Amigo, C., & Popping, B. (2013). Accuracy of ELISA detection methods for gluten and reference materials: a realistic assessment. Journal of Agricultural and Food Chemistry, 61, 5681–5688. http://dx.doi.org/10.1021/jf3046736. Gupta, R. B., Khan, K., & MacRitchie, F. (1993). Biochemical basis of flour properties in bread wheats. I. Effects of variation in the quantity and size distribution of polymeric proteins.

Journal

of

Cereal

Science,

18,

23–41.

http://dx.doi.org/10.1006/jcrs.1993.1031. Hajas, L., Scherf, K. A., Bugyi, Zs., Török, K., Schall, E., Köhler, P., & Tömösközi, S. (2016). ELISA response and gliadin composition of different wheat cultivars grown in multiple

harvest

years.

Acta

Alimentaria,

46,

187-195.

http://dx.doi.org/10.1556/066.2016.0019. Hajas, L., Scherf, K. A., Török, K., Bugyi, Z., Schall, E., Poms, R. E., Koehler, P., & Tömösközi, S. (2018). Variation in protein composition among wheat (Triticum aestivum L.) cultivars to identify cultivars suitable as reference material for wheat gluten analysis. Food Chemistry, 267, 387-394. http://dx.doi.org/10.1016/j.foodchem.2017.05.005.

28

Haraszi, R., Chassaigne, H., Maquet, A., & Ulberth, F. (2011). Analytical methods for detection of gluten in food - method developments in support of food labeling legislation. Journal of AOAC International, 94, 1006–1026. Johansson, E., Hafeez Malik, A., Hussain, A., Rasheed, F., Newson, W. R., Plivelic, T., Hedenqvist, M. S., Gällstedt, M., & Kuktaite, R. (2013). Wheat gluten polymer structures: The impact of genotype, environment, and processing on their functionality in various applications. Cereal Chemistry, 90, 367–376. http://dx.doi.org/10.1094/CCHEM08-12-0105-FI. Johansson, E., Prieto-Linde, M. L., Svensson, G., Jönsson, J. Ö. (2003). Influences of cultivar, cultivation year and fertilizer rate on amount of protein groups and amount and size distribution of mono- and polymeric proteins in wheat. Journal of Agricultural Science, 140, 275–284. http://dx.doi.org/10.1017/S0021859603003162. Koehler, P., & Wieser, H. (2013). Chemistry of Cereal Grains. In: Gobbetti M., Gänzle M. (eds)

Handbook

on

Sourdough

Biotechnology.

Springer,

Boston,

MA.

http://dx.doi.org/10.1007/978-1-4614-5425-0_2. Lexhaller, B., Tompos, C., & Scherf, K. A. (2017). Fundamental study on reactivities of gluten protein types from wheat, rye and barley with five sandwich ELISA test kits. Food Chemistry, 237, 320-330. http://dx.doi.org/10.1016/j.foodchem.2017.05.121. Mena, M. C., Lombardía, M., Hernando, A., Méndez, E., & Albar, J. P. (2012). Comprehensive analysis of gluten in processed foods using a new extraction method and a

competitive

ELISA

based

on

the

R5

antibody.

Talanta,

91,

33–40.

http://dx.doi.org/10.1016/j.talanta.2011.12.073. MSZ 6367-9:1989 (1989). Edible, fodder and industrial seeds and husked products. Quality tests. Production of wheat flour in laboratory. Hungarian Standard Institution.

29

MSZ EN ISO 2171:2010 (2010). Cereals, pulses and by-products. Determination of ash yield by incineration. Hungarian Standard Institution. MSZ EN ISO 16634-2:2016 (2016). Food products. Determination of the total nitrogen content by combustion according to the Dumas principle and calculation of the crude protein content. Part 2: Cereals, pulses and milled cereal products. Hungarian Standard Institution. Mujico, J. R., Lombardía, M., Mena, M. C., Méndez, E., & Albar, J. P. (2011). A highly sensitive real-time PCR system for quantification of wheat contamination in gluten-free food

for

celiac

patients.

Food

Chemistry,

128,

795–801.

http://dx.doi.org/10.1016/j.foodchem.2011.03.061. Navarro, V., del Pilar Fernández-Gil, M., Simón, E., & Bustamante, M. Á. (2017). Gluten: general aspects and international regulations for products for celiac people. In: Nutritional and analytical approaches of gluten-free diet in celiac disease. SpringerBriefs in Food, Health, and Nutrition. Springer, Cham. http://dx.doi.org/10.1007/978-3-31953342-1_2. Ozuna, C. V., & Barro, F. (2018). Characterization of gluten proteins and celiac diseaserelated immunogenic epitopes in the Triticeae: cereal domestication and breeding contributed to decrease the content of gliadins and gluten. Molecular Breeding, 38, 1-16. http://dx.doi.org/10.1007/s11032-018-0779-0. Pahlavan, A., Sharma, G. M., Pereira, M., & Williams, K. M. (2016). Effects of grain species and cultivar, thermal processing, and enzymatic hydrolysis on gluten quantitation. Food Chemistry, 208, 264-271. http://dx.doi.org/10.1016/j.foodchem.2016.03.092. Prabhasankar, P., & Rao, P. H. (2001). Effect of different milling methods on chemical composition of whole wheat flour. European Food Research and Technology, 2013, 465469. http://dx.doi.org/10.1007/s002170100407.

30

Rallabhandi, P. (2012). Gluten and celiac disease - an immunological perspective. Journal of AOAC International, 95, 5–24. http://dx.doi.org/10.5740/jaoacint.SGE_Rallabhandi. Schalk, K., Lang, C., Wieser, H., Koehler, P., & Scherf, K.A. (2017a) Quantitation of the immunodominant 33-mer peptide from α-gliadin in wheat flours by liquid chromatography

tandem

mass

spectrometry.

Scientific

Reports,

7,

45092.

http://dx.doi.org/10.1038/srep45092. Schalk, K., Lexhaller, B., Koehler, P., & Scherf, K.A. (2017b). Isolation and characterization of gluten protein types from wheat, rye, barley and oats for use as reference materials. PLoS ONE, 12, e0172819. http://dx.doi.org/10.1371/journal.pone.0172819. Schall, E., Bugyi, Z., Hajas, L., Török, K., & Tömösközi, S. (2019). Applicability of ELISA methods for high gluten-containing samples. Acta Alimentaria, in press Scherf, K. A., & Poms, R. E. (2016). Recent developments in analytical methods for tracing gluten.

Journal

of

Cereal

Science,

67,

112-122.

http://dx.doi.org/10.1016/j.jcs.2015.08.006. Shan, L., Molberg, Ø., Parrot, I., Hausch, F., Filiz, F., Gray, G.M., Sollid, L.M., Khosla, C. (2002). Structural basis for gluten intolerance in celiac sprue. Science, 297, 2275-2279. http://dx.doi.org/10.1126/science.1074129. Sharma, G. M., Rallabhandi, P., Williams, K. M., & Pahlavan, A. (2016). Characterization of antibodies for grain-specific gluten detection. Journal of Food Science, 81, 810-816. http://dx.doi.org/10.1111/1750-3841.13241. Uhlen, A. K., Dieseth, J. A., Koga, S., Ulrike Böcker, U., Hoel, B., Anderson, J. A., & Moldestad, A. (2015). Variation in gluten quality parameters of spring wheat varieties of different origin grown in contrasting environments. Journal of Cereal Science, 62, 110116. http://dx.doi.org/10.1016/j.jcs.2015.01.004.

31

Valdes, I., García, E., Llorente, M., & Méndez, E. (2003). Innovative approach to low-level gluten determination in foods using a novel enzyme-linked immunosorbent assay protocol. European Journal of Gastroenterology & Hepatology. 15, 465-474. http://dx.doi.org/10.1097/01.meg.0000059119.41030.df. van Eckert, R., Berghofer, E., Ciclitira, P. J., Chirdo, F., Denery-Papini, S., Ellis, H. J., Ferranti, P., Goodwin, P., Immer, U., Mamone, G., Méndez, E., Mothes, T., Novalin, S., Osman, A., Rumbo, M., Stern, M., Thorell, L., Whim, A., & Wieser, H. (2006). Towards a new gliadin reference material-isolation and characterisation. Journal of Cereal Science, 43, 331–341. http://dx.doi.org/10.1016/j.jcs.2005.12.009. van Eckert, R., Bond, J., Rawson, P., Klein, Ch. L., Stern, M., & Jordan, T. W. (2010). Reactivity of gluten detecting monoclonal antibodies to a gliadin reference material. Journal of Cereal Science, 51, 198-204. http://dx.doi.org/10.1016/j.jcs.2009.11.012. Wieser, H. (2000). Comparative investigations of gluten proteins from different wheat species I. Qualitative and quantitative composition of gluten protein types. European Food Research and Technology, 211, 262-268. Wieser, H., Antes, S., & Seilmeier, W. (1998). Quantitative determination of gluten protein types in wheat flour by reversed-phase high-performance liquid chromatography. Cereal Chemistry, 75, 644–650. http://dx.doi.org/10.1094/CCHEM.1998.75.5.644. Wieser, H., & Koehler, P. (2009). Is the calculation of the gluten content by multiplying the prolamin content by a factor of 2 valid? European Food Research and Technology, 229, 9–13. http://dx.doi.org/10.1007/s00217-009-1020-5.

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Table 1 Range of quantitative selection criteria based on our previous study (Hajas et al., 2018) Selection criteria Parameter Crude protein content (%)

Range 12.1 - 15.7

Gliadin/glutenin ratio

2.1 - 3.1

α-gliadin/γ-gliadin ratio

1.1 - 1.6

Gliadin recovery (ELISA Kit A) (%)

85 - 158

Gliadin recovery (ELISA Kit B) (%)

79 - 135

33

Table 2 Crude protein content, ratio of monomeric and polymeric protein fractions and ash content of laboratory and pilot scale flour samples (all values are expressed on dry matter basis; monomeric and polymeric protein contents are expressed as percentage of total protein extract) Scale of production (harvest year)

Laboratory (2014)

Laboratory (2016)

Pilot (2016)

a

Flour sample

Parameter Crude protein (%)ab

Monomeric (%)abc

Polymeric (%)abc

Monomeric/ Polymeric ratioc

Akteur

15.6 A

46.0 AB

48.0 A

0,96

Carberry

15.4 B

46.1 AB

48.0 A

0,96

Mv Magvas

12.6

C

47.5

A

44.7

BCD

1,06

Yitpi

12.6

C

46.7

AB

46.1

ABC

1,01

Yumai-34

13.3

D

47.5

A

45.2

ABCD

1,05

Akteur

13.9 E1

42.0 C1

47.5 AC1

0,88

0.42 1

Carberry

18.7 F2

47.5 A23

44.7 BCD2

1,06

0.55 2

Mv Magvas

12.1 G3

42.7 CD14

44.1 BD2

0,97

0.51 23

Yitpi

16.6 H4

43.6 BCD14

43.7 BD2

1,00

0.50 234

Yumai-34

16.7 H4

45.7 ABD234

42.6 D234

1,07

0.56 2

Blend

15.4 A5

44.1 BCD14

43.3 BD24

1,02

0.50 234

Akteur

13.6 6

51.7 56

40.5 345

1,28

0.47 134

Carberry

17.7 7

56.9 7

35.9 6

1,59

0.46 134

Mv Magvas

12.0 3

51.3 5

38.1 56

1,35

0.42 14

Yitpi

15.7 8

50.7 258

40.3 35

1,26

0.55 2

Yumai-34

15.2 9

54.6 67

36.5 6

1,49

0.45 134

Blend

14.7 10

52.0 58

39.5 5

1,32

0.40 1

Ash content (%)b

Within each column, mean values marked with different capital letters are significantly different (p <

0.05; one-way ANOVA, Fisher's LSD) b

Within each column, mean values marked with different numbers are significantly different (p <

0.05; one-way ANOVA, Fisher's LSD) c

Monomeric and polymeric protein contents are expressed as percentage of total protein extract

34

Table 3 Comparison between measured and calculated concentrations of crude protein, protein fractions, protein types and protein subunits of laboratory and pilot scale flour blends and the calculated mean values of the 23 wheat cultivars from our previous study (Hajas et al., 2018) (all values are expressed on dry matter basis) Method

Dumas SE-HPLC

Parameter

Crude protein (%) Monomeric Polymeric

RP-HPLC

(%)b

Calculated valuea

Measured value

Calculated valuea

15.4

15.7

14.7

14.9

13.9

44.1

44.3

53.4

52.3

47.7

39.1

38.8

45.6

1.0

1.0

1.4

1.3

1.1

(%)c

58.8

57.5 *

62.1

61.9

63.5

30.1

31.8 *

28.3

27.0 *

24.4

2.0

1.8

2.2

2.4

2.7

5.7

5.3 *

5.5

5.3 *

4.3

(%)c

ω5-gliadins

(%)c

ω1,2-gliadins

5.7

5.5 *

3.5

4.6 *

6.2

α-gliadins

(%)c

27.0

26.7

31.7

31.4

30.2

γ-gliadins

(%)c

20.5

20.0 *

21.4

20.6 *

22.8

ωb-gliadins

(%)c

(%)c

0.7

0.9 *

0.8

1.0 *

0.3

HMW-GS

(%)c

9.2

9.5 *

8.9

8.3 *

7.7

LMW-GS

(%)c

20.2

21.3 *

18.7

17.6 *

16.4

11.2

12.4

11.9

11.6

10.1

150

167

143

154

121

13.4

13.3

15.8

14.6

8.9

180

177

191

196

107

Gliadin content (g/100g flour) Gliadin recovery

(%)d

Gliadin content (g/100g flour) Gliadin recovery

a

Measured value

44.5

Gliadin/Gluteninc

ELISA kit B

Mean value of 23 cultivars (Hajas et al. 2018)

43.3

Glutenins

ELISA kit A

Pilot scale blend sample (2016)

Monomeric/polymericb Gliadins

(%)b

Laboratory scale blend sample (2016)

(%)d

Within each row, calculated values marked with asterisks are significantly different from measured

values (p < 0.05; one-way ANOVA, Fisher's LSD) b

c

Monomeric and polymeric protein contents are expressed as percentage of total protein extract RP-HPLC results are expressed as percentage of total extractable protein (= sum of Osborne

fractions) d

Gliadin recoveries are calculated based on gliadin content measured by RP-HPLC

35

Table 4 Proportion of gliadin, glutenin and different gluten protein types in wheat flours determined by RP-HPLC (all values are expressed on dry matter basis) Scale of production (harvest year)

Laboratory (2014)

Laboratory (2016)

Pilot (2016)

a

Flour sample

Parameter Gliadins Glutenins (%)abc (%)abc

Gliadin/ ω5 Glutenin ratioc (%)abc

ω1,2 (%)abc

α (%)abc

γ (%)abc

ωb (%)abc

Akteur

61.7 A

27.9 A

2.2

5.5 A

6.5 A

31.3 A

18.4 A

0.6 A

Carberry

63.6 B

23.1 B

2.8

7.3 B

4.3 B

30.0 B

22.0 B

0.3 B

Mv Magvas

62.7 C

24.3 C

2.6

3.0 C

4.6 C

29.1 C

26.0 C

0.3 B

Yitpi

59.6 D

28.4 D

2.1

5.1 D

5.4 D

27.2 DE

21.9 B

0.2 C

Yumai-34

61.7 A

26.6 E

2.3

3.7 E

5.3 E

31.3 A

21.4 D

0.2 C

Akteur

55.2 E1

34.3 F1

1.6

5.2 F1

5.8 F1

27.3 D1

16.8 E1

1.3 D1

Carberry

57.4 F2

32.7 G2

1.8

7.7 G2

5.3 E2

23.5 F2

20.9 F2

1.0 E2

Mv Magvas

57.6 F2

29.8 H3

1.9

2.7 H3

4.6 C3

27.0 E3

23.3 G3

0.6 F3

Yitpi

58.0 F2

30.8 I4

1.9

5.9 I4

5.5 G4

25.9 G4

20.6 H4

0.6 A4

Yumai-34

59.4 DG3

31.2 J5

1.9

4.9 J5

6.2 H5

29.8 B5

18.4 A5

0.8 G5

Blend

58.8 G3

30.1 H3

2.0

5.7 K6

5.7 I6

27.0 E6

20.5 H4

0.7 H6

Akteur

58.0 2

30.8 4

1.9

5.3 1

4.9 7

31.0 7

16.9 1

1.7 7

Carberry

69.2 4

19.2 6

3.6

8.0 7

3.9 8

32.8 8

24.6 6

0.4 8

Mv Magvas

59.1 3

28.7 7

2.1

2.6 8

3.8 9

29.8 59

23.0 7

1.1 9

Yitpi

60.6 5

28.3 8

2.1

5.9 4

5.2 10

29.4 9

20.1 8

0.9 10

Yumai-34

62.7 6

28.1 8

2.2

4.8 9

5.2 10

34.0 10

18.8 9

1.1 11

Blend

62.1 6

28.3 8

2.2

5.5 10

3.5 11

31.7 11

21.4 10

0.8 6

Within each column, mean values marked with different capital letters are significantly different (p <

0.05; one-way ANOVA, Fisher's LSD) b

Within each column, mean values marked with different numbers are significantly different (p <

0.05; one-way ANOVA, Fisher's LSD) c

RP-HPLC results are expressed as percentage of total extractable protein (= sum of Osborne

fractions)

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Table 5 Gliadin content and gliadin recovery in laboratory and pilot scale wheat flours using two different ELISA test kits (all values are expressed on dry matter basis; gliadin recoveries are calculated based on gliadin content measured by RP-HPLC) Scale of production (harvest year)

Laboratory (2014)

Laboratory (2016)

Pilot (2016)

a

Flour sample

Gliadin content (g/100g flour)ab

Gliadin recovery (%)abc

ELISA kit A

ELISA kit B

ELISA kit A

ELISA kit B

Akteur

10.6 AB

8.5 AB

116 A

92 AB

Carberry

8.9 AB

8.6 A

99 AB

96 AB

Mv Magvas

6.1 C

5.9 B

84 B

81 A

Yitpi

8.6 AC

8.4 AB

116 A

113 AB

Yumai-34

8.7 AC

9.3 A

115 AB

122 B

Akteur

10.3 AB12

9.9 A12

163 CD12

156 D1

Carberry

16.1 D3

15.8 C345

183 D1

179 CD12

Mv Magvas

8.7 A1

8.9 A2

152 C23

157 D1

Yitpi

13.6 E45

15.3 C34

169 CD12

190 C23

Yumai-34

13.5 E45

15.1 CD36

165 CD12

185 CD2

Blend

11.2 B26

13.4 D67

150 C23

180 CD12

Akteur

8.2 1

11.7 17

122 4

173 12

Carberry

14.9 34

17.8 5

135 34

161 12

Mv Magvas

8.4 1

10.9 12

142 234

185 12

Yitpi

13.7 45

15.1 346

170 12

188 23

Yumai-34

12.6 456

17.5 45

159 123

220 3

Blend

11.9 256

15.8 345

143 234

191 23

Within each column, mean values marked with different capital letters are significantly different (p <

0.05; one-way ANOVA, Fisher's LSD) b

Within each column, mean values marked with different numbers are significantly different (p <

0.05; one-way ANOVA, Fisher's LSD) c

Gliadin recoveries are calculated based on gliadin content measured by RP-HPLC

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Eszter Schall: Methodology, Validation, Formal analysis, Investigation, Writing - Original Draft, Visualization Katharina A. Scherf: Conceptualization, Methodology, Investigation, Writing - Review & Editing Zsuzsanna Bugyi: Writing - Original Draft, Writing - Review & Editing Lívia Hajas: Methodology, Validation Kitti Török: Methodology Peter Koehler: Conceptualization, Writing - Review & Editing Roland E. Poms: Conceptualization Stefano D’Amico: Methodology, Investigation Regine Schoenlechner: Conceptualization, Methodology, Writing - Review & Editing Sándor Tömösközi: Conceptualization, Methodology, Validation, Writing - Original Draft, Writing - Review & Editing, Supervision

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Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Budapest, May 20, 2019

Eszter Schall

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Highlights of the work: „Characterisation and comparison of selected wheat (Triticum aestivum L.) cultivars and their blends to develop a gluten reference material”

1. Using of blend flour may reduce the analytical error due to genetic variability 2. Using of blend flour may reduce the analytical error due to effect of harvest year 3. The blend flour represents well the wheat cultivars which it is made from 4. The production of large amount of reference material candidate has been solved

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