Investigation into the use of the amylase SD assay of milled wheat extracts as a predictor of baked bread quality

Mangan, David; Szafrańska, Anna; McKie, Vincent A.; McCleary, Barry V.

doi:10.1016/j.jcs.2016.06.015

Cited by 12 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, SPC showed a statistically significant (P≤0.01) positive correlation between WA and parameters determined by Mixolab C3-C4 and C5-C4. The result of correlation coefficient of C5-C4 was in accordance with Mangan et al (2016) who examined existence of correlation among amylase activity, FN, mixolab parameters and baking quality. On the other Table 4.…”

supporting

confidence: 82%

“…On the other Table 4. Correlation matrix (Pearson) of breadmaking quality parameters of wheat kernel and flour starch component ** significant at 0.01 probability level; ** significant at 0.05 probability level hand, the result of correlation coefficient of C3-C4 was not in accordance with Mangan et al (2016). The similar situation was with correlation coefficients of these parameters with FN.…”

mentioning

confidence: 87%

See 1 more Smart Citation

Impact of genetic and climatic factors on parameters of breadmaking quality of wheat kernel and flour starch component

Živančev¹,

Torbica²,

Momčilović³

et al. 2017

Ratar i povrt

View full text Add to dashboard Cite

Summary: This study investigates how genetic and climatic factors affect parameters of breadmaking quality of wheat kernel and flour starch component. Nine wheat cultivars with different combinatons of HMW-GS were grown in three production years. Various rheological devices such as Falling Number (FN), Farinograph, Amylograph, Mixolab and SDmatic were used for characterization of milled wheat samples. The most results showed that climatic factors affected parameters of breadmaking quality of wheat kernel and flour starch component more than HMW-GS composition. However, some results of the bread making quality parameters that are considered to be very reliable indicators of changes in starch component of wheat in wet years, such as FN and maximum peak of viscosity by Amylograph, were dependent of HMW-GS composition.

show abstract

supporting

confidence: 82%

mentioning

confidence: 87%

Impact of genetic and climatic factors on parameters of breadmaking quality of wheat kernel and flour starch component

Živančev¹,

Torbica²,

Momčilović³

et al. 2017

Ratar i povrt

View full text Add to dashboard Cite

show abstract

“…Therefore, the importance of adjusting the level of α-amylase activity in wheat flours is essential for bread making. For this purpose, the most common practice used in bread making is the addition of fungal α-amylase during the baking process [7]. Wheat flour with an insufficient amount of α-amylase activity will lead to increased staling rates, thus reducing the shelf life of the finished products and determining a reduced loaf volume and crumb firmness, poor flavor, and pale crust color [1].…”

Section: Introductionmentioning

confidence: 99%

“…This explains why the measurement of α-amylase activity in wheat flour is highly recommended. This measurement can be made through both colorimetric and viscometric methods [7]. The colorimetric methods are not usually used by bakery processors due to practical reasons such as price (more expensive per analysis because they involve the use of reagents and require long assay time), low sensitivity, difficulty in continuously monitoring the α-amylase activity in wheat flours in bakery factories, and the fact that they require, personnel with experience in chemical analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The most commonly used viscometric methods in practice, which are also the standardized ones, are the Falling number [3,5,9,10,11,12], Brabender amylograph-viscograph [13,14], Rapid Visco Analyser [3,10,11], and the Chopin Mixolab [7,10,12,15]. The Falling number (FN) method is usually used by bakery processors to estimate the α-amylase activity in wheat flour, as the FN is considered one of the most important quality characteristic of wheat flour [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Pasting Properties of Dough from Mixolab Measurements Using Artificial Neuronal Networks

Codină

Dabija

Oroian

2019

Foods

View full text Add to dashboard Cite

An artificial neuronal network (ANN) system was conducted to predict the Mixolab parameters which described the wheat flour starch-amylase part (torques C3, C4, C5, and the difference between C3-C4and C5-C4, respectively) from physicochemical properties (wet gluten, gluten deformation index, Falling number, moisture content, water absorption) of 10 different refined wheat flours supplemented bydifferent levels of fungal α-amylase addition. All Mixolab parameters analyzed and the Falling number values were reduced with the increased level of α-amylase addition. The ANN results accurately predicted the Mixolab parameters based on wheat flours physicochemical properties and α-amylase addition. ANN analyses showed that moisture content was the most sensitive parameter in influencing Mixolab maximum torque C3 and the difference between torques C3 and C4, while wet gluten was the most sensitive parameter in influencing minimum torque C4 and the difference between torques C5 and C4, and α-amylase level was the most sensitive parameter in predicting maximum torque C5. It is obvious that the Falling number of all the Mixolab characteristics best predicted the difference between torques C3 and C4.

show abstract

Streamlined alpha‐amylase assays for wheat preharvest sprouting and late maturity alpha‐amylase detection

Hauvermale

Parveen

Harris³

et al. 2023

Agrosystems Geosci & Env

View full text Add to dashboard Cite

Late maturity alpha-amylase (LMA) and preharvest sprouting (PHS) lead to elevated alpha-amylase in wheat (Triticum aestivum L.) grain. Risk of poor end-product quality due to elevated alpha-amylase is detected in the wheat industry using the Hagberg-Perten falling number (FN) method. In breeding programs, selection for PHS and LMA tolerance requires higher throughput methods requiring a smaller sample size than the 7 g required for the FN method. Specifically, LMA can only be screened only using detection of alpha-amylase activity or protein after cold treatment of individual wheat spikes at a specific stage of grain development resulting in very small samples (≤1 g). This study developed and evaluated a high throughput 96-well method for the Phadebas alpha-amylase enzyme assay for small wheat grain samples and compared this method to FN and the Megazyme Alpha-Amylase SD (Sprout Damage) Assay Kit performed on the automated Awareness Technology ChemWell-T Analyzer. In parallel, the efficacy of low-cost small-scale milling methods was evaluated relative to traditional larger scale mills. The Phadebas enzyme activity was highly reproducible and showed a strong correlation to the SD enzyme assay and FN method regardless of which mill was used to process the grain. The SD assay offers simpler standardization and calculation of enzyme activity, whereas the Phadebas assay offers higher sensitivity and lower expense. Both the 96-well Phadebas and automated Megazyme SD assays are suitable for alpha-amylase detection from small samples, and the use of low-cost coffee grinders to process small samples did not significantly impact assay performance.

show abstract

Investigation into the use of the amylase SD assay of milled wheat extracts as a predictor of baked bread quality

Cited by 12 publications

References 18 publications

Impact of genetic and climatic factors on parameters of breadmaking quality of wheat kernel and flour starch component

Impact of genetic and climatic factors on parameters of breadmaking quality of wheat kernel and flour starch component

Prediction of Pasting Properties of Dough from Mixolab Measurements Using Artificial Neuronal Networks

Streamlined alpha‐amylase assays for wheat preharvest sprouting and late maturity alpha‐amylase detection

Contact Info

Product

Resources

About