Water mobility and microstructure of gluten network during dough mixing using TD NMR.

This work aimed to establish the relationships between flour components, dough behaviour and changes in water distribution at mixing. TD NMR was used to track water distribution in dough during mixing for different mixing times and hydration levels. Four commercial wheat flours with distinct characteristics were expressly selected to exhibit various dough behaviours at mixing. TD NMR measurements of mixed dough samples revealed four to five water mobility domains depending on the flour type and the mixing modality. A classification tree procedure was used to identify characteristic patterns of water mobility in dough, called hydration states (HS). The HS changes with experimental conditions are highly dependent on flour characteristics, and HS were assigned to physical/chemical changes in the gluten network during dough formation. This study proposes an interpretation of the water distribution in dough based on gluten network development. This will help to adapt the mixing process to the flour characteristics.

Can instrumental characterization help predicting sour taste perception of wheat sourdough bread?

Sourdough bread is known to have a characteristic sour taste. To guarantee consumer acceptability, sour taste should be monitored to assure constant bread quality. However, little is known about bread sour taste perception, especially how it evolves during tasting, neither if some simple measurements could help predict it. The aims of this study were to characterize the evolution of sour taste perception during bread tasting and to determine which bread instrumental variables can be correlated to it. For that purpose, eight types of bread were made with different sourdoughs and baking processes to obtain wide ranges of acidity, density and Fermentation Quotient. Bread were characterized by instrumental methods (i.e. pH, Total Titratable Acidity, organic acid content and density measurements) and their sour taste was determined by Quantitative Descriptive Analysis and a dynamic method called Progressive Profiling. As a result, it appeared that breads were perceived as significantly different throughout tasting. The "sour taste profile" was globally similar among breads with the highest intensity reached at the swallowing point. Progressive Profiling seemed then an efficient and simple method to evaluate the intensity of food organoleptic properties as well as the persistence after swallowing. Surprisingly, bread acetic acid content and Fermentation Quotient showed no effect on sour taste perception. Conversely, from all the physicochemical characteristics monitored, bread pH correlated with sour taste the most, explaining up to 97% of sour taste variations. Measuring bread pH could therefore constitute a quick and easy way to predict bread sour taste perception in research and industry.

The effect of organic wheat flour by-products on sourdough performances assessed by a multi-criteria approach.

In this study, we determined the effect of organic (i) flour ash content (1%-1.4%) and (ii) flour by-product addition (bran, shorts and germ) on sourdough performances. After five consecutive back-sloppings, sourdough was used for bread-making and its bread-related properties were assessed. No effect of flour composition factors (i & ii) on sourdough lactic acid bacteria and yeasts were highlighted. Nonetheless, they greatly altered lactic acid and acetic acid sourdough contents from 6.9 to 17.4 g/kg and from 0.9 to 2.2 g/kg, respectively. The flour ash content (i) had a significant and positive effect on sourdough acidity and CO2 production. Bread made with sourdough with a high ash content had a significantly higher acidity and specific volume. These physicochemical differences between breads were perceived by sensory evaluation in a significant way. Sourdough supplemented (ii) with germ had higher lactic acid and carbon dioxide contents than sourdough supplemented with bran and shorts. Hence, flour composition, combining ash content and flour by-products, appears to be an effective factor to obtain a better control of sourdough performances.

A polyphasic approach to study the dynamics of microbial population of an organic wheat sourdough during its conversion to gluten-free sourdough.

To develop a method for organic gluten-free (GF) sourdough bread production, a long-term and original wheat sourdough was refreshed with GF flours. The dynamics of the sourdough microbiota during five months of back-slopping were analyzed by classical enumeration and molecular methods, including PCR-temporal temperature gel electrophoresis (PCR-TTGE), multiplex PCR, and pulsed field gel electrophoresis (PFGE). The results showed that the yeast counts remained constant, although Saccharomyces cerevisiae, present in the initial wheat sourdough, was no longer detected in the GF sourdough, while lactic acid bacteria (LAB) counts increased consistently. In the first phase, which was aimed at obtaining a GF sourdough from wheat sourdough, Lactobacillus sanfranciscensis, L. plantarum, and L. spicheri were the main LAB species detected. During the second phase, aimed at maintaining the GF sourdough, the L. plantarum and L. spicheri populations decreased whereas L. sanfranciscensis persisted and L. sakei became the predominant species. Multiplex PCRs also revealed the presence of several L. sakei strains in the GF sourdough. In a search for the origin of the LAB species, PCR-TTGE was performed on the flour samples but only L. sanfranciscensis was detected, suggesting a flour origin for this typical sourdough species. Thus, while replacement of the wheat flour by GF flour influenced the sourdough microbiota, some of the original sourdough LAB and yeast species remained in the GF sourdough.

A polyphasic approach to study the dynamics of microbial population of an organic wheat sourdough during its conversion to gluten-free sourdough

To develop a method for organic gluten-free (GF) sourdough bread production, a long-term and original wheat sourdough was refreshed with GF flours. The dynamics of the sourdough microbiota during five months of back-slopping were analyzed by classical enumeration and molecular methods, including PCR-temporal temperature gel electrophoresis (PCR-TTGE), multiplex PCR, and pulsed field gel electrophoresis (PFGE). The results showed that the yeast counts remained constant, although Saccharomyces cerevisiae, present in the initial wheat sourdough, was no longer detected in the GF sourdough, while lactic acid bacteria (LAB) counts increased consistently. In the first phase, which was aimed at obtaining a GF sourdough from wheat sourdough, Lactobacillus sanfranciscensis, L. plantarum, and L. spicheri were the main LAB species detected. During the second phase, aimed at maintaining the GF sourdough, the L. plantarum and L. spicheri populations decreased whereas L. sanfranciscensis persisted and L. sakei became the predominant species. Multiplex PCRs also revealed the presence of several L. sakei strains in the GF sourdough. In a search for the origin of the LAB species, PCR-TTGE was performed on the flour samples but only L. sanfranciscensis was detected, suggesting a flour origin for this typical sourdough species. Thus, while replacement of the wheat flour by GF flour influenced the sourdough microbiota, some of the original sourdough LAB and yeast species remained in the GF sourdough (AU)

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Predicting the quality of wheat flour dough at mixing using an expert system.

Modelling the links between the mixing process conditions of wheat flour dough and the properties of the dough is a challenge. This paper presents a systematic modelling approach based on qualitative algebra to represent human expertise in this domain. Qualitative models of wheat dough mixing have been implemented as an expert system, called Ascopain. The relations between the process conditions - flour specifications and kneading conditions - and the dough sensory properties, have been formalised by means of qualitative functions. An extensive evaluation of Ascopain is provided by comparing the simulation results, first to experts' predictions, and second, to experimental results of sensory evaluation of mixed dough properties. The good matching level proves the accuracy and the robustness of the expert-system and, overall, its ability to implement a reasoning on the influences of process conditions to predict actual dough properties, starting from ingredient characteristics.