Mass transfer dynamics of simultaneous water gain and solid loss during soaking of pigeon pea grains

Pigeon pea is the most prominently used protein source for human consumption. This is one of the leading pulse utilized in various applications in food processing. Soaking is one of the mandatory unit operation required to convert it from raw to a consumable form. Insights into its mass transfer dynamics enable the design and optimization of processing conditions with due consideration of retention of its quality and nutrition. In the present investigation, the mass transfer dynamics of simultaneous water gain and solid loss taking place during soaking was studied . The experimental samples were soaked at 35, 50, 75, 85, and 100 °C to represent the lower and upper gelatinization temperatures. The water diffusivities varied from 5.492 × 10^-10 to 13.84 × 10^-10 m²/s when soaking temperature was increased from 35 to 100 °C. Similarly, solid diffusivities vary between 6.27 × 10^-10 at 35 °C and 9.48 × 10^-10 m²/s at 100 °C. The activation energies of both the phenomenon were estimated using the Arrhenius equation. The mass transfer process has been identified with three distinct stages. The first stage was characterized by void filling, expansion, and solid hydration. The second stage was marked at 5% of solid loss and characterized by solid loss, while the third stage contributed to the starch gelatinization process. Simultaneous water gain and solid loss in the grain was observed throughout the process at varying rates. The insights into mass transfers from this study can help in further modeling of pigeon pea soaking behavior. PRACTICAL APPLICATION: Soaking is one of the mandatory step for further utilization of legume grains. Various desirable and undesirable changes take place during the soaking process. Pigeon pea being a leading legume, understanding of the dynamics of various chemical, physical, and compositional changes during soaking is very much required for a process design. Also the representation of these behaviors in mathematical models is essential to further understand and optimize the operations for maximum retention of quality attributes, minimum wastage, and low-energy consumption.