Predictive modelling of the farinographic parameters

Baking qualities of flours are determined by certain factors (gluten content, gluten quality, quality of other proteins, native enzymatic activity, added additives, structure and texture of starch granules, etc.), which are relatively known, but hardly predictable, because their interaction determines complex nonliniar rheological behavior. There are also technological factors, such as: type and intensity of kneading and modelling, conditions of fermentation and baking etc. The parameters describing these changes often show inhomogeneous variations, disordered apparently, without periodic components. However, these apparently nonhomogenous variations of the quality parameters of flours can be studied and improved by the methods of mathematical statistics, so that it makes possible to find some rules to stand at the basis of predictive modelling. Estimation of complex technological qualities of flours (hydration capacity, development time, stability, softening), based on the physical and chemical parameters (moisture, ash content, protein content, wet gluten content, falling number etc.) can be significant for the baking science and technology; this allows a rapid adaptation of the technological processes to the work conditions required by the quality of the raw material we use.

Over time, there have been many researches that have investigated the potential of using the farinographic analysis in the evaluation of the technological qualities of wheat flours. Latest research, such as those of Hruskova et al. (2000) and Miralbes (2004) showed that farinographic parameters can be estimated using infrared spectroscopy (NIR). In China, Deng et al. (2005) have shown the correlation of farinographic parameters with mixographic parameters, extensographic parameters, and some quality parameters of bread (bread volume and bread firmness). Similar research, conducted in Europe on 17 wheat cultivars by van Bockstaele et al (2008) showed that the best predictive model for the volume of bread was based on the combination of the parameters: protein content, farinographic hydration capacity and the alveografic P/L ratio.

In this research we have developed the best predictive model for the farinographic parameters of doughs obtained from Romanian wheat flours, starting our research from simple physico-chemical parameters of flours, such as: protein content, moisture, ash, wet gluten content, gluten index etc.

We analyzed 100 wheat flour samples from Romanian wheat, the harvests of the years 2006 to 2009, in order to elaborate predictive models for farinographic parameters, namely: Hydration Capacity, Development time, Stability, Softening, starting from the physical and chemical parameters (Protein Content, Moisture, Ash Content, Wet Gluten Content, Gluten Index, Falling Number). Our results showed that all the farinographic parameters were correlated with specific physical and chemical parameters in different degrees of significance. The most significant correlations that have been described refer to the pairs Moisture – Hydration Capacity (-0.43***), Protein content – Hydration Capacity (0.41***), Protein Content – Development time (0.41***), Falling number – Development time (0.42***), Gluten index – Hydration Capacity (0.50***) and Ash Content – Hydration Capacity (0.47***). Regarding the obtained predictive models, we found that the best predictors for the Hydration Capacity of doughs are Moisture, Ash Content and Falling number. For the parameter Development time, the best predictors were Protein Content and Falling number and for the Stability, the best predictors were Protein Content and Ash Content. For the Softening parameter of doughs, the significant predictors were Moisture and Falling number.

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