SORPTION ISOTHERMS AND THERMODYNAMIC PROPERTIES OF POWDERED MILK

A new method to determine the water activity and the net isosteric heats of sorption for low moisture foods at elevated temperatures

In recent years, research studies have shown that the thermal resistance of foodborne pathogens in the low moisture foods is greatly influenced by the water activity (a_w) at temperatures relevant to thermal treatments for pathogen control. Yet, there has been a lack of an effective method for accurate measurement of a_w at those temperatures. Thus, the main aim of this study was to evaluate a new method for measuring a_w of food samples at elevated temperatures. An improved thermal cell with a relative humidity and temperature sensor was used to measure the a_w of the three different food samples, namely, organic wheat flour, almond flour, and non-fat milk powder, over the temperature range between 20 and 80°C. For a constant moisture content, the a_w data was used to estimate the net isosteric heat of sorption (q_st). The q_st values were then used in the Clausius Clapeyron equation (CCE) equation to estimate the moisture sorption isotherm for all test food samples at different temperatures. For all the tested samples of any fixed moisture content, a_w value generally increased with the temperature. The energy for sorption decreased with increasing moisture content. With the experimentally determined q_st value, CCE describes well about the changes in a_w of the food samples between 20 and 80°C. This study presents a method to obtain a_w of a food sample for a specific moisture content at different temperatures which could be extended to obtain q_st values for different moisture contents and hence, the moisture sorption isotherm of a food sample at different temperatures.

Soft computing modelling of moisture sorption isotherms of milk-foxtail millet powder and determination of thermodynamic properties

Moisture sorption isotherms of spray-dried milk-foxtail millet powder were determined at 10, 25 and 40 °C. Sorption data was fitted using classical and soft-computing approaches. The isotherms were of type II, and equilibrium moisture content (EMC) was temperature dependent. The BET monolayer moisture content decreased from 3.30 to 2.67 % as temperature increased from 10 to 40 °C. Amongst the classical models, Ferro-Fontan gave the best fit of EMC-aw data. However, the Sugeno-type adaptive neuro-fuzzy inference system (ANFIS) with generalized bell-shaped membership function performed better than artificial neural network and classical models with RMSE as low as 0.0099. The isosteric heat of sorption decreased from 150.32 kJ mol(-1) at 1 % moisture content to 44.11 kJ mol(-1) at 15 % moisture. The enthalpy-entropy compensation theory was validated, and the isokinetic and harmonic mean temperatures were determined as 333.1 and 297.5 K, respectively.