During ohmic cooking food is heated quickly, efficiently, and uniformly. The heating capacity depends, among other factors, on the food's electrical conductivity (s, Siemens/m). This study aimed to estimate s for gluten-free batters. The samples were formulated using a gluten-free premix, milk, and egg. An ohmic cell (9x9x10 cm) with two stainless steel electrodes was built. For baking the normal household electrical supply was used (50 Hz), setting the voltage to 135, 180, or 220 V. During baking, 110 g of batter was baked in each test, and the following data was recorded for the three voltages: the current, voltage, sample height, and internal temperature. The tests were performed at temperatures below 60°C, minimizing water evaporation, starch gelatinization, and energy loss to the environment. The values ​​of s at different temperatures were obtained using two methods. First, we used a traditional estimation equation: s=I*L/(U*A), where I is the current (A), L is the electrode gap (m), U is the voltage (V), and A is the electrode area (m²). The experimental data for s and T were fitted to polynomials. The predicted s was used to calculate the macroscopic energy balance to estimate the batter temperature's evolution. Comparing the predicted and experimental batter temperature profiles, the errors were 3.31, 9.79, and 28.17% for the three voltages, respectively. Second, the relationship between s and the temperature was estimated by solving an inverse problem; the energy balance was calculated using a fourth-order Runge–Kutta method coupled with a nonlinear fitting method to minimize the difference between the predicted and experimental temperature profiles. In this case, the temperature prediction errors were 0.32, 0.38, and 0.97%, respectively. Using both methods, it was found that s increased as the temperature increased. The inverse method, which involved the use of the temperature data, significantly outperformed the traditional method in terms of the prediction accuracy. Although it is more complex, it provides better estimations.