Predictive studies: modeling the development of structure and colour in food

printing

Better control and optimisation of baking outcomes is possible using mathematical models that can predict the effects of formulation and processing changes on the physical and chemical characteristics of the product. Not only is predictive capability useful for product development

per se, but necessary for the 3D colour food printer which is being designed as an on-demand,

customised food production system. From a coloration point of view, the formulation and therefore the physical characteristics of the printing substrate – ‘the blank canvas’ - will vary according to user-defined specifications for sensory and nutritional properties. If substrate characteristics likely to affect colour rendition, such as the degree of browning and light scattering (texture) can be predicted, then the dye recipes needed to reproduce desired colours in the substrate can accordingly be computed and adjusted.

This section focusses on examples of studies in which the development of browning and structure under various conditions has been modelled, in different food systems, and is

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organised by the different modelling methodologies used. These studies have been selected for inclusion here because of their direct relevance to baking (De Cindio and Correra, 1995; Kocer

et al., 2007; Mundt and Wedzicha, 2007), or because they cover ingredients or processes that

feature in baking, such as browning (Han and Floros, 1998) and carbohydrates and caramelisation (Sleeuwen et al., 2013). Browning has been modelled for reasons of it being a desirable quality in some food systems, but not in others.

3.5.5.1.Using response surface methodology

Response surface methodology (RSM) is used to measure then model the simultaneous effects of multiple independent variables, or factors, on the output(s) of a given system or process, usually with the goal of determining the combination(s) of factors that are needed give optimal outputs (Bezerra et al., 2008). In developing the model, data are collected according to an experimental design that defines the levels of the different factors, and tests different combinations across multiple experimental runs. Polynomial functions are fitted to the experimental data; a good fit is indicated by low residual values, or small differences between computed and experimental data. The term ‘response surface’ refers to the graphical representation of the modelled data. The predictive ability of RSM models is limited to the range of factor levels that are used in their development.

Of direct relevance to baking, Kocer et al. (2007) modelled the interactive effects of simultaneous sugar- and fat- replacement by polydextrose in a high-ratio cake formulation, on batter and cake properties (including relative cake height, average bubble size, and Hunter L and b values) using response surfaces. This was done to determine whether further reduction in energy content (relative to sugar-only and fat-only replacement) was possible for ‘tolerable’ changes in expansion characteristics and crumb colour (see also Table 3.3).

Response surfaces were used by Han and Floros (1998) to model the change in colour of potassium sorbate powder as a function of heating time and temperature, to investigate the possible use of the powder as an indicator for high temperature processes such as baking. As

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indicated by the respective decrease and increase in measured HunterLab L and b values, heating caused the powder to turn a dark yellow, which was likely due to oxidative degradation. The implication for the addition of potassium sorbate as a preservative to foods is that the foods could discolour during heating, reducing their quality, by either oxidation of the preservative in the presence of other food components, or the involvement of its carboxyl group in Maillard browning.

3.5.5.2.Using kinetic models

Whereas RSM relies on producing replicate outputs from known factor and factor level combinations and using statistical techniques to fit an appropriate empirical function (Berns, 2000, Bezerra et al., 2008), other models aim to describe a more direct relationship between outputs and changes in raw materials or processing variables (de Cindio and Correra, 1995), by investigating the rates at which these processes occur, and the factors influencing the rates of reaction. Factors affecting reaction rates include the type, physical state and concentration of the reactants, temperature and pressure, and the presence of catalysts.

3.5.5.2.1. Texture

The kinetics of the various processes contributing to baking were combined to develop a comprehensive model for the baking process as a whole (De Cindio and Correra, 1995), with the model intended for use in the optimisation of textural characteristics. The model takes into account the processes occurring at the level of bubble and the paste directly surrounding and interacting with the bubble (incorporating leavening kinetics, bubble expansion, paste rheology and diffusion of CO2 and water), and at the dough level (heat transfer and mass transfer of CO2

and water). Parameter values for the model were determined experimentally or taken from literature data (De Cindio and Correra, 1995). In a simulation of the three main phases of baking – mixing, leavening and heating - using a yeast-leavened formulation, the model was able to predict changes in total mean specific volume (representing softness), moisture content (firmness) and pH, with time.

116 3.5.5.2.2. Browning

The Maillard reaction is a non-enzymatic reaction between reducing carbohydrates and amino acids or proteins that contributes much of the desirable roasted flavours and brown colour on the surface of baked goods (Figoni, 2008). It can also cause undesirable browning in processes such as during the preparation of condensed milk (Hofmann, 2001), and during the storage of milk powders because that the reaction can also occur at room temperature given enough time. Nutrients such as the amino acid lysine are lost through their involvement in the reaction (Koksel and Gokmen, 2008). Usually temperatures of at least 50°C are needed for the Maillard reaction to occur (Zhou and Therdthai, 2008). Caramelisation, which is the degradation of sugars in the presence of temperatures of at least 160°C to 170°C, also contributes browning, and gives cooked sugar flavours.

Other than temperature and heating duration, the Maillard and caramelisation reactions are affected by such factors as pH, moisture content (as indicated by the level of water activity, aw, or available water), and the types of amino acid and sugar that are involved. In both reactions, the rate of browning is increased by increasing the pH or in the presence of reducing monosaccharides, while lowering the pH slows browning (Figoni, 2008). The presence of salt lowers batter caramelisation temperatures (Sahin, 2008). For the Maillard reaction, colour development increases at intermediate moisture levels (aw 0.3 to 0.7), with the implication that the reaction will be slow in high moisture foods, and when temperatures are low, or in dry systems. Aroma (flavour) development also depends on the total amount and relative proportions of amino acid and sugar on the product surface; unique aromas are produced with each different combination of temperature and time conditions (Sahin, 2008).

Kinetic models that have been developed to predict the extent of browning in baked goods, and in other food systems, with time and under different experimental conditions, are used to predict changes in either CIELAB values (Zanoni et al., 1995; Broyart et al., 1998), or in reflectance or absorption spectra (Mundt and Wedzicha, 2007; Sleeuwen et al., 2013). Although following a kinetic process, the direct fitting of a model to CIELAB values as a function of time is an

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empirical approach (Mundt and Wedzicha, 2007). Modelling of spectral changes with time effectively describes chemical kinetics, following the formation of reaction intermediates and products (Mundt and Wedzicha, 2007), since the concentration of a coloured species in solution has direct relationship to the measured absorption of the solution. Where available, predicted changes in full absorption/transmission spectra as a function of time and temperature (rather than changes at a single wavelength or a few wavelengths) can then be expressed as a perceived change under specified illuminant and observer conditions, by subsequent derivation of CIELAB, followed by ΔE*ab or Browning Index, values (Sleeuwen et al., 2013).

Mundt and Wedzicha (2007) developed their kinetic model to predict the change in measured reflectance (RGB values) with time during the browning of biscuit dough under controlled temperature and water activity conditions, with reflectance in the model expressed in terms of the Kubelka-Munk function. The model fitted well to experimental RGB data collected for ‘standard’ dough samples at three levels each of temperature and water activity, aw (and thereby revealing that aw had no effect on the rate of browning across the range observed). The model also fitted well to data obtained from test doughs containing three levels of added sugar, where again aw was found not to have an effect on the browning kinetics. The focus of the kinetic model developed by Sleeuwen et al. (2013) was the control of browning in a carbohydrate system in which excessive browning during heating might cause colour and flavour to deviate from specification. Glassy carbohydrate microcapsules which are used for the encapsulation of food flavours, and which should be either un-coloured, or contain added colorants for providing visual appeal to foods, are exposed to elevated temperatures during their production. The model was based on wavelength-dependent reaction rate constants and Arrhenius parameters (indicating respectively the kinetics and temperature dependence of colour formation) obtained from measured absorption/transmission spectra of maltodextrin (MD) and maltodextrin/sucrose (MD/S) melts which were used as model systems. For both types of melt, when subjected to a simulated industrial thermal process, there was found to be little difference between the spectra predicted by the model and the experimental spectra.

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In neither of the studies discussed (Mundt and Wedzicha, 2007; Sleeuwen et al., 2013) was the concentration of the browning intermediates and products themselves actually measured; the Maillard reaction for example leads to the formation of a multitude of low and high molecular weight compounds (Hofmann, 2001), through a complex series of reactions (Koksel and Gokmen, 2008). However in future, there might be scope to relate the kinetics of browning, and perceived changes in browning directly to the formation of key Maillard browning reaction products. Hofmann (2001) characterised the key chromophores in a Maillard reaction mixture of D-xylose and L-alanine by a process of HPLC analysis, screening by Color Dilution Analysis for the most intensely coloured HPLC fractions, and then, following identification of the compounds their relative colour impact was defined by a novel Color Activity Value (CAV), which is the ratio of concentration to visual detection threshold. Furthermore, the percent contribution of each compound to the colour of the mixture was determined from the ratio of the CAV of the compound to the Color Dilution factor for the mixture.

The spectral approach to the kinetic modelling of browning is compatible with the spectrally- based Kubelka-Munk predictive colour blending models discussed earlier in Part 1 of this review. This raises the possibility of combining principles from the two methods to model the effects of the ‘native’ colour of the 3D colour printing substrate on the dye quantities that are needed to render colours in the substrate. In addition to investigating colour changes in melts that were initially un-coloured Sleeuwen et al. (2013) modelled the effect of browning reactions on a hypothetical MD/S melt initially light blue in colour (as referenced by a solution of Brilliant Blue FCF), which was predicted to change to green.

Of course, the type of browning that 3D colour food printing is concerned with is internal (crumb) coloration rather than external crust browning, before any dye is added. Kinetic studies of surface browning (such as the ones described above) do however provide useful modelling principles that could be applied to the printer. Examples of variables whose effects might need to be modelled include the colours of egg yolk and of various and alternative fat, sugar and flour ingredients that get carried through from the batter stage to the final product. The effects of

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these ingredients would be set against a background of a structure developing over time, which in the end, depending on its density and moistness should make its own contribution to perceived (un-dyed) crumb colour.