SRC-CHOPIN

Automated measurement of the solvent retention capacity (SRC) profile

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1
Understand flour functionality

• By simultaneously and individually analyzing the main functional flour components (damaged starch, glutenins, pentosans) that directly influence final product quality.

2
Universal test

• From breeding to baking, the entire wheat and flour industry and its supply chain benefit from SRC analysis, using a common language.

3
Improved precision and reproducibility

• The SRC test method is recognized worldwide.*
The SRC-CHOPIN machine identifies the solvent tubes, stores the flour weighing data, injects the solvents, shakes, centrifuges and drains the tubes, and then calculates all the results.

PRINCIPLE

The principle of the SRC method (in accord with AACC Approved Method 56-11) is based on the preferential solvation and swelling of the three polymeric, network-forming flour components by selected, material-specific solvents.

The greater the swelling, and the greater the resistence of the swollen network to compression by centrifugation, the higher is the solvent retention.

The method enables the measurement of four key quality parameters of flour in one single test: Water absorption with the water SRC, Glutenin functionality with the lactic acid SRC, Pentosan functionality with the sucrose SRC, Damaged starch functionality with the sodium carbonate SRC.

These four functional properties are key parameters for the quality control of wheat flour. The SRC-CHOPIN finds its best uses in the breeding, milling, and baking industries, but can also add value throughout the rest of the wheat and flour supply chain.

APPLICATIONS

WHEAT BREEDERS

Whole-meal measurements:

Whole-meal measurements:
Studies have shown that SRC values obtained on a whole-grain wheat flour or meal can be used to predict SRC values for the corresponding straight-grade flour. This allows testing of small-sized breeding samples of wheat.

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SRC testing can also be extended and usefully adapted to whole-grain flours or meals of grains and seeds other than wheat, such as oat flours and cocoa powders.

• Predictive correlations with existing methods:
SRC values show good predictive correlations with other existing characterization methods (e.g. Mixograph, Zeleny test, Farinograph, etc.), and serve as valuable and insight-filled guides in developmental wheat breeding programs.

WHEAT/FLOUR MILLING INDUSTRY

• Wheat tempering: 
Wheat tempering is an important stage of the milling process. Monitoring SRC values as a function of tempering conditions can help optimize flour functionality for a given extraction rate.

• Wheat or flour blending:  
It is possible to calculate the results for each of the four SRC parameters for a wheat or flour blend, based on proportions by weight, in order to obtain a required flour functionality.

• « Chlorination » :
With increasing extent of flour chlorination, SRC analysis showed characterizing increases in water, sodium carbonate and sucrose SRC values, but a decrease in lactic acid SRC value.

SOFT WHEAT FLOUR BAKING INDUSTRY

• Cookies and crackers: : 
Typical SRC values for a good-quality cookie/cracker flour are water SRC <51%, lactic acid SRC > 87%, sodium carbonate SRC < 64%, and sucrose SRC < 89%.

• Sponge-and-dough products: : 
Typical SRC values for a good-quality sponge-and-dough flour are water SRC < 57%, lactic acid SRC > 100%, sodium carbonate SRC < 72%, and sucrose SRC < 96%.

Japanese sponge cake:
Water SRC values have been shown to be positively correlated with Japanese sponge cake volume.

HARD WHEAT FLOUR BAKING INDUSTRY

• Bread volume: : 
The higher the lactic acid SRC value of a bread flour, the higher is the baked loaf volume.

• Bread specific volume: : 
Higher sucrose SRC and/or sodium carbonate SRC values are detrimental to high specific volume.

• Crumb-grain score: 
Increased SRC values for three solvents (lactic acid, sucrose, and sodium carbonate) result in a stiffer crumb structure in bread.

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ACCURACY
One drawback of the manual SRC method is that different laboratories can use different equipment (shaking, centrifuge), thus potentially leading to significant variations in test results. Because of its fully automated process, the SRC-CHOPIN offers practitioners a precision generally unachievable using the manual SRC method. Internal repeatability is greatly enhanced, but the biggest benefit is realized when comparing results from one laboratory to another.
Thanks to this new opportunity for increased accuracy, SRC-CHOPIN results can be used to improve flour quality, and as quality targets for development of functional, end-use application-specific flour specifications.

SIMPLICITY
The manual SRC method calls for a series of operations, some of which are extremely operatordependent and -variable (e.g. shaking). The SRC-CHOPIN greatly simplifies all these operations by automating each and every step of the method, from flour weighing to calculation of results.

TIME-SAVING
The manual SRC method includes many manual operations, some of which (e.g. shaking) have a very large influence on the results. But thanks to its fully automated procedure, the SRC-CHOPIN not only provides more accurate data, it also saves time, thus enabling users to test more flour samples per day.

 

ADAPTABILITY
The SRC-CHOPIN is primarily designed to test simultaneously two flours, each with all four SRC solvents (water, sucrose, sodium carbonate, and lactic acid). But the system is also perfectly suited to analyze just a single flour, or eight different flour samples with the same single solvent.
The built-in software enables flexible testing of any possible combination of flour and solvent of interest to the user.

ALL-INCLUSIVE
The SRC-CHOPIN is a complete system; it includes a tube-shaking system, a centrifuge, and a tubedraining system. It is equipped with an external balance for weighing both the initial flour sample and the final wet pellet, in order to calculate the four SRC values and display them as an ordered profile on the integrated screen. All SRC data can also be transferred to a PC for further manipulations, such as calculation of a flour’s Gluten Performance Index.

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