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A model was developed to predict spatial glass transition temperature (T(g)) distributions in glassy maltodextrin particles during transient moisture sorption. The simulation employed a numerical mass transfer model with a concentration dependent apparent diffusion coefficient (D(app)) measured using Dynamic Vapor Sorption. The mass average moisture content increase and the associated decrease in T(g) were successfully modeled over time. Large spatial T(g) variations were predicted in the particle, resulting in a temporary broadening of the T(g) region. Temperature modulated differential scanning calorimetry confirmed that the variation in T(g) in nonequilibrated samples was larger than in equilibrated samples. This experimental broadening was characterized by an almost doubling of the T(g) breadth compared to the start of the experiment. Upon reaching equilibrium, both the experimental and predicted T(g) breadth contracted back to their initial value.

Glassy carbohydrate microcapsules are widely used for the encapsulation of flavors in food applications, and are made using various thermal processes (for example, extrusion). During manufacturing, these carbohydrate melts are held at elevated temperatures and color can form due to nonenzymatic browning reactions. These reactions can negatively or positively affect the color and flavor of microcapsules. The rate of color formation of maltodextrin and maltodextrin/sucrose melts at elevated temperatures was determined spectrophotometrically and was found to follow pseudo zero-order kinetics. The effect of temperature was adequately modeled by an Arrhenius relationship. Reaction rate constants and Arrhenius parameters were determined for individual wavelengths in the visible range (360 to 700 nm at 1 nm intervals). Transient processes (temperature changes with time) were modeled as a sequence of small isothermal events, and the equivalent thermal history at a reference temperature calculated using the Arrhenius relationship. Therefore, spectral transmittance curves could be predicted with knowledge of the time/temperature relationship. Validation was conducted by subjecting both melts to a transient thermal history. Experimental transmittance spectrum compared favorably against predicted values. These spectra were optionally converted to any desirable color space (for example, CIELAB, XYZ, RGB) or derived parameter (for example, Browning Index). The tool could be used to better control nonenzymatic browning reactions in industrial food processes.

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