Evaluation of GlutoPeak test for prediction of bread wheat flour quality, rheological properties and baking performance

Evaluation of GlutoPeak test for prediction of bread wheat flour quality, rheological properties and baking performance

Journal of Cereal Science 90 (2019) 102827 Contents lists available at ScienceDirect Journal of Cereal Science journal homepage: www.elsevier.com/lo...

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Journal of Cereal Science 90 (2019) 102827

Contents lists available at ScienceDirect

Journal of Cereal Science journal homepage: www.elsevier.com/locate/jcs

Evaluation of GlutoPeak test for prediction of bread wheat flour quality, rheological properties and baking performance

T

Çiğdem Mecitoğlu Güçbilmez∗, Mehmet Şahin, Aysun Göçmen Akçacık, Seydi Aydoğan, Berat Demir, Sümeyra Hamzaoğlu, Sadi Gür, Enes Yakışır Bahri Dağdaş International Agricultural Research Institute, Ministry of Agriculture and Forestry, 42020, Karatay, Konya, Turkey

A R T I C LE I N FO

A B S T R A C T

Keywords: GlutoPeak Gluten Wheat quality Bread properties

The aim of this study was to determine the capability of GlutoPeak test for discrimination of wheat varieties according to their strength, as well as to assess the possibility in predicting rheological parameters and baking quality. For this purpose, 12 winter bread wheat genotypes with different quality were analyzed for protein content, wet gluten, Zeleny sedimentation (ZLN), hardness, gluten aggregation and dough rheological properties. The correlations of GlutoPeak indices with other parameters were evaluated. Moreover, Principle Component Analysis (PCA) of all data and the detailed analysis of GlutoPeak aggregation profiles of wheat varieties were performed. Obtained results exhibited that except for peak maximum time (PMT), GlutoPeak parameters had positive significant correlations with Single-Kernel Characterization System-Hardness (SKCS–H), ZLN, Farinograph water absorption capacity (FWAC), Alveograph energy (AW), bread volume (B. Volume) at 1% level and specific volume (S. Volume) mostly at 1% level. PMT had negative correlations with them at 1% level. The relation of GlutoPeak data with conventional quality indices supports the parameters used for the separation of flour according to their quality characteristics. Also, this study showed the usability of GlutoPeak test for prediction of baking properties of flours.

1. Introduction Wheat (Triticum aestivum L.) is one of the most used raw materials, which forms the primary ingredients of bread, biscuits, pasta and other baked goods. The grain gluten content is the basic criterion in the selection of new varieties in breeding plans (Kaur Chandi and Seetharaman, 2012) and in evaluating baking quality (Bouachra et al., 2017). To determine gluten and baking performance, various quick chemical analyses have been used, such as sodium dodecyl sulfate sedimentation test, gluten index, solvent retention capacity and Zeleny sedimentation test (Kweon et al., 2011; Bouachra et al., 2017). Agronomics, genetics, milling and baking conditions are the major factors affecting flour and final product quality (Kweon et al., 2011). The flour's technological behavior depends not only on protein and gluten content but also on interactions of macromolecules effective on dough properties (Marti et al., 2015). For analyzing the gluten strength and flour properties, dough rheology methods such as Alveograph, Farinograph, Mixograph, Extensograph have conventionally been used as credible techniques in quality testing. Farinograph has been the most

widely used instrument to measure water absorbance (WA) of wheat flour to get a standard dough consistency (i.e., 500 BU) and the rheological properties of dough (Tsilo et al., 2013; Fu et al., 2017). The grade of gluten development or breakdown is a main factor in forming dough consistency (Fu et al., 2017). Farinograph records the resistance that dough sample displays against blades during mixing. The curve obtained from torque versus time is used to estimate water absorption of flour, development time, stability, degree of softening of dough. Alveograph has been used to measure the resistance of dough to expansion. The principle of the method depends on blowing of a bubble from dough until rupture (Dobraszczyk, 2004). Good bread-making performance is usually indicated by high values of total energy used to blow the bubble (W) and maximum pressure during inflation (P). However, use of these techniques is labor intensive, time-consuming, requires large samples and not suitable for wheat lines at early stages due to limited material availability (Kaur Chandi and Seetharaman, 2012; Marti et al., 2015). The GlutoPeak has been recently suggested for the quick evaluation of wheat flour quality with a small amount of material. It is a rapid shear-based device for measuring the aggregation



Corresponding author. E-mail addresses: [email protected] (Ç. Mecitoğlu Güçbilmez), [email protected] (M. Şahin), [email protected] (A. Göçmen Akçacık), [email protected] (S. Aydoğan), [email protected] (B. Demir), [email protected] (S. Hamzaoğlu), [email protected] (S. Gür), [email protected] (E. Yakışır). https://doi.org/10.1016/j.jcs.2019.102827 Received 29 April 2019; Received in revised form 24 August 2019; Accepted 2 September 2019 Available online 04 September 2019 0733-5210/ © 2019 Published by Elsevier Ltd.

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Abbreviations TKW TW PC ZLN SKCS-H PCA AM BEM PM PMT GPC GGLUT

GW GlutoPeak energy GWAC GlutoPeak water absorption capacity CGLUT Glutomatic wet gluten content AW Alveograph energy WAC water absorption capacity FWAC Farinograph water absorption capacity WA water absorbance B. Weight bread weight B. Volume bread volume S. Volume specific volume P maximum pressure applied in Alveograph during inflation W the area under the Alveograph curve which represents the total energy used to blow the bubble

thousand kernel weight test weight protein content obtained from Leco Zeleny sedimentation value single-kernel characterization system-hardness principle component analysis torque 15 s before maximum torque maximum torque torque 15 s after maximum torque peak maximum time GlutoPeak protein content GlutoPeak wet gluten content

established with AOAC 992.23 (AOAC, 2000), AACC 38-12A (AACC, 2000) and ICC-116 (ICC, 2008), respectively. Leco FP 528 analyzer (Leco Inc, St Joseph, MI) and Glutomatic (Bastak, Ankara, Turkey) were used to measure protein content (PC) and wet gluten (CGLUT), respectively.

behavior of gluten. In this test, the sample is mixed with solvent at a certain speed. The development of gluten network exerts a resistance against the mixing paddle and then the developed gluten network is broken down by the applied mechanical stress. These result in the formation of a torque-time curve, which provides the peak maximum time, torque maximum, torque before maximum, torque after maximum. Marti et al. (2015) reported the significant correlation of GlutoPeak indices with most of the conventional parameters determining the characteristics of flour. In this study, the bread wheat varieties were analyzed by conventional quality analyses. The same samples were also analyzed with the recently used test, GlutoPeak. The obtained parameters were examined for correlations between each other. Unlike other studies, this is the first study detecting the GlutoPeak protein, GlutoPeak wet gluten, GlutoPeak W-value, GlutoPeak water absorption by using Rapid Flour Check Method. The correlations between these data and the data obtained from conventional methods were evaluated. For assessment of the usability of GlutoPeak test as an alternative method beside the other commonly used methods, multivariate correlations with PCA were also used. GlutoPeak profiles and wheat varieties graphics according to GlutoPeak maximum torque versus Farinograph WAC index and Alveograph W index were interpreted for the prediction of bread wheat quality and discrimination of wheat varieties according to their strength. Additionally, the potential of using rapid GlutoPeak test for prediction of baking properties of bread wheat was investigated by determining the correlations between GlutoPeak indices and bread volume and specific volume. Assessment of bread-making quality of wheat flour is very important for millers and bakers.

2.3. Farinograph test Water absorption capacity (WAC) of flour samples were determined according to the AACC method 54–21 (AACC, 2000) using FarinographAT equipped with 50 g mixing bowl (model 810151.001, Brabender, Duisburg, Germany). 2.4. Alveograph test Alveograph analyses were performed with the use of Chopin Alveograph (Model Alveo PC, Chopin, France) according to the AACC method 54–30A (AACC, 2000). Alveograph characteristics of wheat flour were automatically recorded by the Alveo-PC computer software program. 2.5. GlutoPeak test The GlutoPeak test was performed using the Brabender GlutoPeak device (model 803400, Brabender GmbH&Co KG, Duisburg, Germany) as described by Wiertz (2018). For analysis, 9 g of sample flour was dispersed in 9 g of distilled water in stainless steel cup of device. Analysis was carried out for 300 s at 36 °C and 2750 rpm spindle speed by Rapid Flour Check method. The measured parameters were automatically recorded by the device. These were: Peak maximum time (PMT) corresponds to the time needed to reach maximum torque; expressed in seconds. Torque maximum (BEM) corresponds to the maximum torque of gluten; expressed in GlutoPeak Unit (GPU). Torque before maximum (AM) corresponds to the torque value 15 s before maximum torque; expressed in GlutoPeak Unit (GPU). Torque after maximum (PM) corresponds to the torque value 15 s after maximum torque; expressed in GlutoPeak Unit (GPU). GlutoPeak protein content (GPC), GlutoPeak wet gluten (GGLUT), GlutoPeak W-value (GW), GlutoPeak water absorption capacity (GWAC) parameters were also determined by the software automatically.

2. Materials and methods 2.1. Materials This study included 12 registered winter bread wheat varieties, cultivated at Bahri Dağdaş International Agricultural Research Institute during 2016–2017 growing period in rainfed conditions. Samples conditioned for 12 h to 14.5% moisture content (AACC 26-95, 2000) were milled with Brabender Quadrumat Junnior (model 880101, Brabender Ohg Duisburg, Germany) according to the AACC 26–50 method (AACC, 2000). The refined flours from 12 samples were used for analyses. All measurements were carried out in duplicate. 2.2. Kernel and refined flour analyses

2.6. Baking test and bread quality evaluation Thousand-kernel weight (TKW) of grain samples were determined according to Williams et al. (1988). Test weight (TW) and SKCS-H of wheat samples were measured using the standard AACC International official methods 55–10 and 55–31 (AACC, 2000). Protein content, wet gluten, Zeleny sedimentation test of milled refined flour were

Baking test was carried out according to AACC method 10-10B (AACC, 2000) with slight modifications. For 100 g flour basis, 1.5% salt, 3% yeast and water based on values obtained from Farinograph were used. The bulk fermentation of obtained dough was conducted in a 2

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between the GlutoPeak BEM value is probably based on the hydration capacity of wheat flour that can influence the consistency of the gluten slurry caused by high speed shearing applied during GlutoPeak test (Fu et al., 2017). WAC of hard wheat flour has been considered to be affected by protein content and damaged starch of flour (Tipples et al., 1978; Rakszegi et al., 2014). In our study, GWAC and FWAC were independent of PC, while a positive strong relationship between GPC and the FWAC (r = 0.8664, p < 0.01) and GWAC (r = 0.9736, p < 0.01) was found. Moreover, strong correlation of GWAC with FWAC (r = 0.9158, p < 0.01) is a very important finding in terms of reliable and rapid prediction of FWAC by GlutoPeak device. To the best of our knowledge, this is the first study that report correlations between energy, water absorption, wet gluten, protein content determined by GlutoPeak Rapid Flour Check method with the same parameters obtained by conventional methods. Our data highlight the suitability of this method for reliable and rapid prediction of FWAC (r = 0.9158, p < 0.01). Additionally, a moderate correlation (r = 0.6273, p < 0.01) was found between wet gluten content value obtained from GlutoPeak and Glutomatic. Protein content indices were correlated weakly (r = 0.4439, p < 0.05) in this research. The correlations showed that GlutoPeak can be used to differentiate among qualities at raw material reception in a short time. Evaluation of the GlutoPeak protein, GlutoPeak wet gluten, GlutoPeak W-value, GlutoPeak water absorption by using Rapid Flour Check Method offers considerable time savings. In addition, to evaluate the flour samples’ quality and predict the quality of end product, a traditional dough testing instrument, Alveograph is mostly used. For this purpose, the W index from Alveograph parameters curve was considered. In this study, it was determined that the AW value was positively correlated with GlutoPeak values AM (r = 0.6292, p < 0.01), PM (r = 0.7596, p < 0.01), BEM (r = 0.7658, p < 0.01), GW (r = 0.7484, p < 0.01), GPC (r = 0.7650, p < 0.01), and negatively correlated with PMT (r = −0.5318, p < 0.01). The obtained data imply that samples with higher Alveograph energy formed a strong cohesive matrix in a short time. The significant high correlation between GW and AW (r = 0.7484, p < 0.01) is again an important finding to use the GlutoPeak values for assessment of prediction of wheat flour, bread and other end products quality. However, there is an inconsistency between

fermentation cabinet at 30 °C and 70–80% relative humidity for 30 and 30 min. After these periods, doughs were punched, molded and fermentation was continued for 55 min at 30 °C. Baking was performed in an air-convection oven (Ekomak, Konya, Turkey) for 15–20 min at 230 °C. Bread volume was determined by rapeseed displacement in a bread volume measurer, according to AACC method 10–05 (AACC, 2000). Weights of breads (B. Weight) were measured and their specific volumes were calculated. 2.7. Statistical analysis Statistical analyses were conducted using Student's test in JMP11 (2014) program (SAS Institute, ISBN:978-1-62959-560-3) at a significance level of p < 0.05. Pearson's correlation coefficients (r) were determined. PCA was performed to analyze the relationships existing between conventional quality indices, bread properties and GlutoPeak parameters by using PCA module in the same program. Furthermore, it was used as a technique to describe and compare the samples. 3. Results and discussion 3.1. Correlation of GlutoPeak indices with other quality parameters The mean values of quality characteristics (TKW, TW, SKCS-H) of kernels from 12 bread wheat varieties are presented along with CV% and LSD values for every measurement (Table 1). Chemical, rheological and bread-making indices of flour from these varieties were also evaluated (Table 2). It was found that variety had a significant effect on conventional and GlutoPeak parameters (p < 0.01). The examination of Tables 1 and 2 indicates that samples having high levels of SKCS-H values also mainly have high ZLN, PC and GPC. Flours made from generally hard wheat varieties that have high gluten and protein content are called strong flour. For the production of bread, strong wheat varieties of bread wheat flours are preferred since this allows the provision of a strong gluten network (Marti et al., 2015). The data of correlations between conventional analyses and GlutoPeak test are given in Table 3. A negative moderate correlation between SKCS-H and PMT (r = −0.6659, p < 0.01) and a positive strong correlation between SKCS-H and BEM (r = 0.7776, p < 0.01) were observed. This is an expected result because weak gluten exhibits low peak/no peak with longer aggregation time while strong gluten exhibits high peaks with short peak time (Anonymous, 2017). PMT is an indicator of gluten aggregation kinetics and had a negative strong correlation with GPC (r = −0.8992, p < 0.01) and also with gluten (CGLUT, GGLUT) (r = −0.7608, p < 0.01; r = −0.8645, p < 0.01) and moderate correlation with ZLN (r = −0.5071, p < 0.01). Zeleny sedimentation test is used for determining the gluten content and gluten strength (Hruškova and Famera, 2003; Jirsa et al., 2008). In this study, moderate positive correlation of ZLN with GlutoPeak values BEM (r = 0.6719, p < 0.01), AM (r = 0.5521, p < 0.01), PM (r = 0.6432, p < 0.01) and negative correlation with PMT explain the usability of GlutoPeak values for the determination of strong bread wheats. Also, it was determined that the strong wheat varieties usually exhibit high PM and AM values like BEM values in this research. AM value representing the value before gluten aggregation completion, may reflect the glutenin content of wheat samples (Bouachra et al., 2017). The high AM value corresponds to a higher degree of cross linking and increased gluten network elasticity (Bouachra et al., 2017). Among the Farinograph parameters, water absorption capacity, which is the amount of water required to achieve the consistency corresponding to a curve that centers on the 500 BU line, was strongly and positively correlated with GlutoPeak indices AM (r = 0.9115, p < 0.01), BEM (r = 0.8447, p < 0.01), PM (r = 0.9075, p < 0.01), GPC (r = 0.8664, p < 0.01), GGLUT (r = 0.8563, p < 0.01), GW (r = 0.8372, p < 0.01), GWAC (r = 0.9158, p < 0.01), and negatively correlated with PMT (r = −0.8049, p < 0.01). The high relation

Table 1 Quality characteristics of 12 winter bread wheat varieties. Variety

Ahmetağa Aliağa Bayraktar-2000 Bozkır Dağdaş-94 Eraybey Esperia Göksu-99 Konya-2002 Sönmez-2001 Taner Tosunbey Mean value CV (%) LSD Significance*

TKW

TW

(g)

(kg/100 lt)

33.9fg 41.8a 34.5fg 39.5abc 40.6ab 37.2cde 36.3def 32.6g 42.0a 38.8bcd 38.2bcd 35.3ef 37.6 3.1 2.5 **

78.9abc 79.3a 78.1bcd 79.0abc 78.0cd 78.3abcd 77.5de 76.8e 79.2ab 78.7abc 77.5de 78.8abc 78.3 0.6 1.1 **

SKCS-H

75.7a 49.3c 42.0c 55.9bc 84.8a 55.9bc 75.2a 52.6c 72.7ab 73.9a 81.2a 83.2a 66.9 11.6 17.0 **

a-g : Values in the same column with different superscripts indicate a statistically significant difference. *: Significance between varieties at 1% level indicted by two asterisks (**). TKW: thousand kernel weight, TW: test weight, SKCS-H: single-kernel characterization system-hardness, CV: coefficient of variation, LSD: least significant differences.

3

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Table 2 Chemical, rheological and bread-making indices of flour from 12 winter bread wheat varieties. GlutoPeak Values Variety

Ahmetağa Aliağa Bayraktar-2000 Bozkır Dağdaş-94 Eraybey Esperia Göksu-99 Konya-2002 Sönmez-2001 Taner Tosunbey Mean value CV (%) LSD Significance*

AM

BEM

Conventional Quality Indices PMT

PM

GPC

GGLUT

GW

GWAC −4

(GPU)

(GPU)

(s)

(GPU)

(%)

(%)

(J*10

26d 19f 14g 24e 32b 24e 29c 20f 36a 33b 29c 26d 26 3.5 2.0 **

53f 58e 31ı 52f 69ab 47h 68bc 49g 64d 70a 64d 67c 57.7 0.9 1.2 **

86d 75de 201a 100c 38g 118b 82d 120b 56f 39g 65ef 99c 89.9 5.62 11.08 **

41e 40e 28g 41e 52b 40e 52bc 37f 55a 56a 50d 51cd 45.3 1.5 1.5 **

11.0cd 11.5c 9.2f 11.0de 12.6b 10.6e 12.6b 10.7e 12.6b 13.1a 12.3b 12,5b 11.6 1.8 0.5 **

24.9c 25.9c 18.3f 24.7cd 29.2ab 23.6de 30.0a 23.0e 28.2b 30.1a 28.2b 29.5a 26.3 2.1 1.2 **

214e 288d 56h 219e 420a 158g 399b 181f 367c 427a 367c 400b 291.3 2.5 16.3 **

)

PC

CGLUT

ZLN

Bread Characteristics

AW

FWAC −4

(%)

(%)

(%)

(ml)

(J*10

60.3e 59.7ef 52.8h 59.7ef 66.1b 59.1f 65.1c 58g 66.2b 67.1a 64.4cd 64.2d 61.9 0.6 0.8 **

12.0de 12.4de 12.1de 11.8e 13.8b 10.6f 13.4bc 12.8cd 12.2de 12.3de 12.1de 14.8a 12.5 3.3 0.9 **

23.5e 31.3a 12.1g 25.5cd 32.5a 21.3f 25.9c 28.6b 28b 24de 25cde 25.7cd 25.3 3.2 1.8 **

40.5ab 32.5cd 20.0f 25.0ef 29.5de 30.5de 45.0a 28.0de 38.0bc 34.0cd 40.5ab 43.0ab 33.9 8.6 6.1 **

206d 168f 121g 168f 180e 180e 256b 170f 214c 216a 254c 269b 200.2 2.0 8.6 **

)

B. Weight

B. Volume 3

S. Volume

(%)

(g)

(cm )

(cm3/g)

61.8d 57.4f 55.2g 60.2e 65.1a 60.3e 62cd 57.2f 63.3bc 64.2ab 62.9bcd 63.5b 61.1 1.1 1.4 **

138.2e 131.9ı 133.7h 139.8d 140.9c 136.9f 141.5bc 135.7g 133.8h 138.8e 142.9a 142.2ab 139.3 0.3 0.9 **

463a 363e 318g 348f 418d 420cd 428c 423cd 418d 438b 438b 438b 410.7 0.9 7.8 **

3.35a 2.75h 2.37j 2.49ı 2.96g 3.07de 3.02f 3.11c 3.12c 3.15b 3.06e 3.08d 3 0.3 0.02 **

a-j : Values in the same column with different superscripts indicate a statistically significant difference. *: Significance between varieties at 1% level indicted by two asterisks (**). AM: torque 15 s before maximum torque, BEM: maximum torque, PM: torque 15 s after maximum torque, PMT: peak maximum time, GPC: GlutoPeak protein content, GGLUT: GlutoPeak wet gluten content, GW: GlutoPeak energy, GWAC: GlutoPeak water absorption capacity, PC: protein content obtained from Leco, CGLUT: Glutomatic wet gluten content, ZLN: Zeleny sedimentation value, AW: Alveograph energy, FWAC: Farinograph water absorption capacity, B. Weight: bread weight, B. Volume: bread volume, S. Volume: specific volume, GPU: GlutoPeak Unit, CV: coefficient of variation LSD: least significant differences.

Table 3 Coefficient of correlations (r) between conventional quality indices, bread properties and GlutoPeak parameters. Parameter

TKW

TW

PC

ZLN

SKCS-H

AM

BEM

PMT

PM

TW PC ZLN SKCS-H AM BEM PMT PM GPC GGLUT CGLUT GW AW GWAC FWAC B. Weight B. Volume S. Volume

0.4322 −0.0768 0.0254 0.1840 0.4632* 0.4481* −0.5828** 0.4581* 0.4639* 0.4049* 0.4727* 0.4662* 0.0123 0.4624* 0.3413 −0.1310 −0.1775 −0.1558

−0.0512 0.1150 −0.0113 0.1550 0.0949 −0.1821 0.1336 0.1438 0.1488 0.0673 0.0928 −0.0297 0.1073 0.1289 −0.2536 −0.1563 −0.1021

0.4128* 0.4771* 0.1618 0.4776* −0.2101 0.3608 0.4439* 0.4929** 0.3850 0.5234** 0.4126* 0.3627 0.3311 0.3843 0.2132 0.1343

0.6930** 0.5521** 0.6719** −0.5071** 0.6432** 0.7011** 0.7180** 0.3279 0.6371** 0.8952** 0.6372** 0.5854** 0.4599* 0.7390** 0.6871**

0.7607** 0.7776** −0.6659** 0.7909** 0.7996** 0.7693** 0.3592 0.7767** 0.7611** 0.8007** 0.8493** 0.7123** 0.7106** 0.6097**

0.8243** −0.8453** 0.9390** 0.8721** 0.8285** 0.4651* 0.8169** 0.6292** 0.9376** 0.9115** 0.4451* 0.6365** 0.5910**

−0.8956** 0.9471** 0.9754** 0.9796** 0.6844** 0.9857** 0.7658** 0.9634** 0.8447** 0.5522** 0.6133** 0.5320**

−0.8552** −0.8992** −0.8645** −0.7608** −0.8534** −0.5318** −0.8956** −0.8049** −0.3551 −0.5877** −0.5636**

0.9633** 0.9489** 0.5267** 0.9511** 0.7596** 0.9929** 0.9075** 0.5259** 0.6278** 0.5570**

Parameter

GPC

GGLUT

CGLUT

GW

AW

GWAC

FWAC

B. Weight

B. Volume

GGLUT CGLUT GW AW GWAC FWAC B. Weight B. Volume S. Volume

0.9733** 0.6163** 0.9699** 0.7650** 0.9736** 0.8664** 0.5011** 0.6332** 0.5707**

0.6273** 0.9673** 0.7939** 0.9532** 0.8563** 0.5679** 0.6216** 0.5390**

0.6169** 0.2860 0.5775** 0.4292* 0.1257 0.3571 0.3668

0.7484** 0.9607** 0.8372** 0.5386** 0.5455** 0.4594*

0.7434** 0.6985** 0.6879** 0.7334** 0.6290**

0.9158** 0.5326** 0.6441** 0.5726**

0.6601** 0.6862** 0.5816**

0.4995** 0.2881

0.9692**

*: Correlation is significant at 5% level. **: Correlation is significant at 1% level. TKW: thousand kernel weight, TW: test weight, PC: protein content obtained from Leco, ZLN: Zeleny sedimentation value, SKCS-H: single-kernel characterization system-hardness, AM: torque 15 s before maximum torque, BEM: maximum torque, PM: torque 15 s after maximum torque, PMT: peak maximum time, GPC: GlutoPeak protein content, GGLUT: GlutoPeak wet gluten content, GW: GlutoPeak energy, GWAC: GlutoPeak water absorption capacity, CGLUT: Glutomatic wet gluten content, AW: Alveograph energy, FWAC: Farinograph water absorption capacity, B. Weight: bread weight, B. Volume: bread volume, S. Volume: specific volume. 4

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et al. (2017) in terms of the correlations between GlutoPeak maximum torque and Alveograph energy (r = 0.56, p < 0.01; r = 0.778, p < 0.01) and with Rakita et al. (2017) and Sissons (2016)) in terms of the correlations between PMT and CGLUT (r = −0.841, p < 0.01 and r = −0,78, p < 0.01).

the values of Alveograph energy obtained from GlutoPeak and Alveograph. While the AW values of Dağdaş-94 and Eraybey were obtained as 180*10−4 J, their GW were found as 420*10−4 J and 158*10−4 J respectively. Except for Bayraktar and Eraybey, GW values of varieties were higher than AW values. The results indicate that differences between the energy values increased especially between stronger varieties. These findings are compatible with Fu et al. (2017) in terms of the correlations between GlutoPeak maximum torque and Farinograph water absorption capacity (r2 = 0.97), with Marti et al. (2014), Rakita

3.2. GlutoPeak indices and bread quality B. Weight, B. Volume and S. Volume varied from 131.9 to 142.9 g, 318–463 cm3 and 2.38–3.35 cm3/g respectively (Table 2). All the

Fig. 1. Principal Component Analysis for (a) quality traits (b) 12 winter bread wheat varieties. 5

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p < 0.01), FWAC (r = 0.6601, p < 0.01). B. Volume had a positive correlation with ZLN (r = 0.7390, p < 0.01), SKCS-H (r = 0.7106, p < 0.01), AM (r = 0.6365, p < 0.01), BEM (r = 0.6133, p < 0.01), PM (r = 0.6278, p < 0.01), GPC (r = 0.6332, p < 0.01), GGLUT (r = 0.6216, p < 0.01), GW (r = 0.5455, p < 0.01), AW (r = 0.7334, p < 0.01), GWAC (r = 0.6441, p < 0.01), FWAC (r = 0.6862, p < 0.01), and negative moderate correlation with PMT

tested properties were significantly influenced by genotype. Table 3 presents the correlations between the measured properties. B. Weight was positively correlated with ZLN (r = 0.4599, p < 0.05), AM (r = 0.4451, p < 0.05), SKCS-H (r = 0.7123, p < 0.01), BEM (r = 0.5522, p < 0.01), PM (r = 0.5259, p < 0.01), GPC (r = 0.5011, p < 0.01), GGLUT (r = 0.5679, p < 0.01), GW (r = 0.5386, p < 0.01), AW (r = 0.6879, p < 0.01), GWAC (r = 0.5326,

Fig. 2. GlutoPeak profiles of (a) bread wheat refined flour belonged to Sönmez-2001, Dağdaş-94, Konya-2002, Esperia, Taner, Tosunbey varieties (b) bread wheat refined flour belonged to Aliağa, Ahmetağa, Bozkır, Göksu-99, Eraybey, Bayraktar -2000 varieties. 6

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(r = −0.5877, p < 0.01). Şahin et al. (2013) also reported positive and significant correlation between B. Volume and ZLN, FWAC, SKCSH. While the criteria such as protein and gluten amount are mostly affected by the environment, Zeleny sedimentation value is under the influence of heredity and is mostly affected by variety (Şahin et al., 2013). A similar correlation is valid for specific volume except for the significance of correlation for GW (p < 0.05). This is an expected result since the flours characterized by high gluten content, ZLN and SKCS-H value create cohesive gluten network in a short time with a high energy, providing high bread volume and specific volume. Issarny et al. (2017) found similar results about the relationships between hard wheat flours and GlutoPeak maximum torque, peak time, energy values and also bread loaf volume. Rakita et al. (2017) is also in agreement with this study in terms of the correlations between specific volume and BEM, PMT. The strong varieties such as Sönmez-2001, Taner, Tosunbey also showed high bread volume. Bayraktar, the weakest genotype, gave loaf

of low volume. However, unlike the quality characteristics, Ahmetağa exhibited the highest bread volume and specific volume.

3.3. Interpretation of PCA, GlutoPeak profiles and graphical properties of wheat varieties To consider all data simultaneously, the data belonging to wheat varieties were examined with PCA. By this way, a 2 principal components model that explains 75.92% of all data variance was obtained. In Fig. 1a, the PC1 vs PC2 score is presented. The vectors for PC, ZLN, SKCS-H, AM, BEM, PM, GPC, GGLUT, CGLUT, AW, GWAC, FWAC form a cluster whereas PMT is positioned in the left top of the plot, showing a different behavior from others. The fact that PMT's vector points into the opposite direction, indicates that it strongly negatively correlated with PC1, whereas the other parameters correlated positively with it. It also means that samples with shorter PMT were characterized by higher FWAC, GWAC, GW, CGLUT, GGLUT, GPC, PM, AM. PMT's position is in

Fig. 3. Discrimination of wheat varieties according to (a) GlutoPeak maximum torque and Farinograph WAC index (b) GlutoPeak maximum torque and Alveograf W index. 7

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Bozkır, Eraybey, Göksu-99 and the third clustered group is Dağdaş-94, Esperia, Konya-2002, Sönmez-2001, Taner, Tosunbey. The first group, including only Bayraktar-2000, had a BEM value of 31 GPU, AW value of 121*10-4 J and FWAC of 55.2%. The second group clustered across the range of 47–58 GPU BEM values, 168–206*10-4 J AW values and 57.2–61.8% FWAC values. The third clustered group that generally demonstrates higher quality flour properties among the varieties exhibited higher BEM values in the range 64–70 GPU, higher AW values in the range of 180–269 *10-4 J, and higher FWAC values in the range of 62–65.1%. The distinction of varieties according to GlutoPeak indices were almost parallel to Alveograph energy and Farinograph water absorption values. Also, the distribution of varieties along the two principal components relates to wheat discrimination in Fig. 3b. Therefore, it can be concluded that GlutoPeak test could be used for the distinction of wheat flours according to their quality. The similar conclusions were obtained in the studies made by Marti et al. (2015) and Rakita et al. (2017).

agreement with Malegori et al. (2018) who found that stronger samples with higher farinographic stability placed in the right-top corner, whereas the samples with low farinographic stability are arranged in the left bottom quarter. The results were similar with this study such that samples having better GlutoPeak values placed in the right-top corner and right bottom quarter (Fig. 1b). However, the weaker samples with low GlutoPeak parameters were positioned in the left-bottom quarter. The inference of a positive relationship between GlutoPeak values (AM, BEM, PM) and GPC, ZLN, SKCS-H, GGLUT, AW, FWAC, GWAC, B.Weight, B. Volume, S.Volume can be made from Fig. 1a. CGLUT vector was separated a little from the cluster that shows the decreasing level of correlation. TKW and TW are positioned in the right bottom quarter, far away from the cluster, having positive PC1 and negative PC2 values in the bi-plot. This indicates that, in contrast to FWAC, SKCS-H, PC, ZLN, AW, B. Weight, B. Volume, S.Volume, they also correlated with PC2. The distribution of varieties along the two principal components is shown in Fig. 1b. A clear discrimination between the genotypes was observed. Tosunbey, Taner, Esperia, samples with the highest AW, are in the right top quarter. Sönmez-2001, Konya2002, Dağdaş-94, having positive PC1 values and negative PC2 values, possess higher AW values than most of the others. These six genotypes have higher GlutoPeak values (except for PMT) and FWAC than the others. However, the three varieties positioned in the right-bottom quarter, have the lowest PMT values. Ahmetağa, Eraybey, Bozkır, Göksu-99, Aliağa, Bayraktar-2000 have all negative PC1 values. Bayraktar-2000 is positioned at the lower value of PC1 and is separated from the others. This is an expected result because its quality values are also lower. Aggregation behavior of gluten can be measured by GlutoPeak device. For this purpose, gluten is washed out and separated by mixing from flour-water slurry. After a characteristic time, gluten aggregates and this aggregation behavior is defined by a peak in the torque curve. After reaching the maximum torque peak, the gluten network is destroyed by ongoing mixing and the curve is decreased. This analysis is presented as a curve by the software as shown in Fig. 2. The varieties with strong gluten showed high peak (BEM) with short time (PMT) (Fig. 2a). The other flour and rheological properties of these varieties are compatible with their corresponding profiles as explained with correlation tables. However, these strong wheat genotypes had two peaks instead of one. This might be related to the strength of gluten. The gluten aggregates cannot be destroyed at the first time of the mixing completely because of its strength and the second peak occurs. This situation is encountered in the study of Bouachra et al. (2017) in which they suggest that distilled water as a solvent does not classify the samples significantly. But Marti et al. (2015) used both double distilled water and NaCl solution to determine the differences between obtained results. It was found that there was no need for NaCl solution, water is sufficient in order to have satisfactory predictions about the conventional parameters. The genotypes with weaker gluten showed late and low peaks. The correlation data of the weaker varieties in this study are in agreement with their GlutoPeak profiles (Fig. 2b). Their maximum peak time were longer (75–201 s) and maximum peak torque were lower (31–58 GPU). The possibility of using rapid GlutoPeak test for discrimination of wheat varieties according to their quality is a very important finding for breeders, millers and bakers. The high values of W are related with the wheat flour dough rheological properties and therefore bread-making performance. Also, strong gluten flour has a higher water absorption. So, the relationship between FWAC and GlutoPeak index BEM (Fig. 3a), AW and BEM (Fig. 3b) can be evaluated for the discrimination of wheat flour according to their strength. From both figures, it could be observed that although there are some deviations, varieties showed generally similar clustering when analyzed according to BEM with Alveograph W index and Farinograph WAC index. Three groups can be seen from Fig. 3a and b. The first one is Bayraktar-2000, the second clustered group is Ahmetağa, Aliağa,

4. Conclusion This study demonstrates significant correlations of GlutoPeak indices with other conventional quality parameters and also with themselves. These relations indicated the great potential of using GlutoPeak test to characterize wheat flours according to their quality. Also, the obtained correlation between GlutoPeak indices and bread volume and specific volume showed the possibility of using GlutoPeak test for prediction of baking properties of bread wheat. The results of this study imply the potential of using rapid GlutoPeak test as an alternative method instead of time-consuming and labor-intensive analyses. Additionally, small sample size requirement is another important advantage of this test. Therefore, these obtained results approved the usability of GlutoPeak device for breeders in early generation breeding lines, for millers to control their raw materials, for bakers to rapidly check raw materials for baking properties. However, in this study, particularly strong wheat varieties had two peaks instead of one in GlutoPeak test. Moreover, the inconsistency between the values of energy obtained from GlutoPeak and Alveograph is a remarkable issue that should be focused on. In order to use the GlutoPeak test especially in wheat breeding programs, additional studies need to be done to confirm these findings on more samples. Conflicts of interest The authors declare that they have no conflict of interest. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jcs.2019.102827. References AACC, 2000. Approved Methods of American Association of Cereal Chemists, tenth ed. Methods 26-95, 26-50, 38-12A, 55-10, 55-31, 54-21, 54–30A, 10-10B, 10-05. Minnesota, USA. Anonymous, 2017. Brabender® GlutoPeak® Rapid Method for the Measurement of the Gluten Quality. Duisburg, Germany. AOAC, 2000. Official Methods of Analysis of Association of Official Analytical Chemists, seventeenth ed. Method 992.23. Gaithersburg, MD. Bouachra, S., Begemann, J., Aarab, L., Husken, A., 2017. Prediction of bread wheat baking quality using an optimized GlutoPeak®-Test method. J. Cereal Sci. 76, 8–16. Dobraszczyk, B.J., 2004. Wheat/dough rheology. In: Wrigly, C.W., Corke, H., Walker, C.E. (Eds.), Encyclopedia of Grain Science, pp. 400–416 Wisconsin. Fu, B.X., Wang, K., Dupuis, B., 2017. Predicting water absorption of wheat flour using high shear-based GlutoPeak test. J. Cereal Sci. 76, 116–121. Hruškova, M., Faměra, O., 2003. Prediction of wheat and flour Zeleny sedimentation value using NIR technique. Czech J. Food Sci. 21, 91–96. ICC, 2008. International Association for Cereal Science and Technology. Standard No. 116/1. Issarny, C., Cao, W., Falk, D., Seetharaman, K., Bock, J.E., 2017. Exploring functionality

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