Prediction of bread wheat baking quality using an optimized GlutoPeak ® -Test method

Bouachra, Safia; Begemann, Jens; Aarab, Lotfi; Hüsken, Alexandra

doi:10.1016/j.jcs.2017.05.006

Cited by 33 publications

(32 citation statements)

References 20 publications

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“…Independent variable MT was a positive predictor of V sp which had a medium level effect. In the research of Bouachra et al (), the model which included the torque measured 15 s before MT and protein content explained the variation in loaf volume by 63%. Therefore, in order to achieve successful prediction of loaf volume it is necessary to combine at least two rapid tests.…”

Section: Resultsmentioning

confidence: 98%

“…When NaCl is added to flour during mixing, it shields the charges on the gluten molecules, lowers the repulsion between proteins and allows protein chains to strongly connect to each other which improves gluten‐network formation (Miller & Hoseney, ). Among the solvents used to run the GlutoPeak instrument, Bouachra et al () found that 1% sodium chloride solution was the most convenient one in predicting loaf volume.…”

Section: Resultsmentioning

confidence: 99%

“…Baking test is considered to be one of the best methods for predicting suitability of tested flour for the production of high‐quality bread. However, it is difficult to use baking test as a flour quality indicator for commercial purpose because it is time‐consuming and requires trained stuff (Bouachra, Begemann, Aarab, & Hüsken, ). Furthermore, the breeders are seeking for the approach that would enable quality testing of wheat varieties at early stages of plant development using a limited amount of sample.…”

Section: Introductionmentioning

confidence: 99%

“…The GlutoPeak test was found to be useful for predicting some conventional rheological properties of common wheat, for example, flour water absorption, indicators of dough mixing stability, tenacity, and extensibility (Fu, Wang, & Dupuis, ; Marti, Ulrici, Foca, Quaglia, & Pagani, ). Bouachra et al () used GlutoPeak tester to predict the bread volume, and reported that the best prediction of loaf volume can be achieved with GlutoPeak torque measured 15 s before the maximum torque and protein content. Therefore, literature data on the possibility to successfully predict bread quality by using a GlutoPeak instrument are still scarce and needs to be investigated.…”

Section: Introductionmentioning

confidence: 99%

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Predicting rheological behavior and baking quality of wheat flour using a GlutoPeak test

Rakita

Dokić

Hadnađev

et al. 2017

Journal of Texture Studies

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Baking test is considered as the most reliable method for assessing wheat-baking quality. However, baking test requires trained stuff, time, and large sample amount. These disadvantages have led to a growing demand to develop new rapid tests which would enable prediction of baked product quality with a limited flour size. Therefore, we tested the possibility of using a GlutoPeak tester to predict loaf volume and breadcrumb textural properties. Discrimination of wheat varieties according to quality with a restricted flour amount was also examined. Furthermore, we proposed the limit values of GlutoPeak parameters which would be highly beneficial for millers and bakers when determine suitability of flour for end-use.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting rheological behavior and baking quality of wheat flour using a GlutoPeak test

Rakita

Dokić

Hadnađev

et al. 2017

Journal of Texture Studies

View full text Add to dashboard Cite

show abstract

“…All other parameters were similar among the clusters. Previous studies showed promising correlations for wheat flours between the parameters determined by the gluten aggregation test and the baking performance ( Bouachra, Begemann, Aarab, & Hüsken 2017 ; Marti, Augst, Cox and Koehler, 2015 ). Weak flours were characterized by a rapid formation of the gluten network, followed by a fast degradation, as opposed to strong flours that took more time to build up the gluten network, but remained more stable ( Goldstein et al., 2010 ).…”

Section: Resultsmentioning

confidence: 99%

Predicting vital wheat gluten quality using the gluten aggregation test and the microscale extension test

Schopf

Scherf

2020

Current Research in Food Science

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Vital gluten is a by-product of wheat starch production and commonly used in bread making, but its quality is difficult to predict. The most accurate method to determine vital gluten quality is the baking experiment, but this approach is time- and labor-intensive. Therefore, the aim was to identify faster and easier ways to predict vital gluten quality. Three different approaches, the gliadin/glutenin ratio, the gluten aggregation test and the microscale extension test, were assessed for their predictive value regarding the baking performance of 46 vital gluten samples using two recipes. Hierarchical clustering classified the vital gluten samples into 23 samples with good, 15 with medium and eight with poor quality. Protein-related parameters, such as the gliadin/glutenin ratio, were not reliable to predict gluten quality, because the correlations to the bread volumes were weak. The gluten aggregation test and the microscale extension test were reliable methods to predict vital gluten quality for use in baking based on a scoring system. Both methods need less material, time and labor compared to baking experiments. Especially, maximum torque, peak maximum time, the ratio between peak30 and peak180 as well as the corresponding distance at maximum resistance to extension seem to be suitable alternatives to predict vital gluten quality.

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Predictive ability of four small‐scale quality tests for dough rheological properties and baking quality in hard red spring wheat

2021

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Background and Objectives Predictive models for use in early generation hard spring wheat breeding were evaluated for Farinograph and baking quality. Flour samples were prepared on the Quadrumat Jr. and Buhler mill from 48 genotypes grown at four locations in North Dakota. Findings Models developed from 13 predictor variables from GlutoPeak, Glutomatic, Near Infrared Reflectance (NIR), and Solvent Retention Capacity (SRC) predicted Farinograph water absorption (ABS) and baking absorption (BA) with cross‐validated prediction accuracies of 0.71 and 0.58, respectively. For Farinograph peak time (PT), Farinograph Stability (STAB), mixing time (MT), and loaf volume (LV), the prediction accuracies were high in the model development dataset (r2 ranging from 0.60 to 0.76). However, in the cross‐validation set, the prediction accuracies only ranged from 0.30 to 0.48. Conclusions GlutoPeak parameters can be used to predict ABS and BA in early breeding stages. Prediction of stability and peak time showed potential, and models should be improved to better predict mixing time and loaf volume. Significance and novelty Results from a multi‐location trial involving a large number of genotypes are needed to validate the prediction accuracy for end‐use traits in hard red spring wheat. Comparing results from different mill types and diverse genotypes ensures broad applicability.

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Prediction of bread wheat baking quality using an optimized GlutoPeak ® -Test method

Cited by 33 publications

References 20 publications

Predicting rheological behavior and baking quality of wheat flour using a GlutoPeak test

Predicting rheological behavior and baking quality of wheat flour using a GlutoPeak test

Predicting vital wheat gluten quality using the gluten aggregation test and the microscale extension test

Predictive ability of four small‐scale quality tests for dough rheological properties and baking quality in hard red spring wheat

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