The aim of this study was to formulate and optimize a gel-based tartlet by replacing egg protein with plant-based ingredients. Additionally, a mathematical model was proposed that correlates consumer acceptance data with measurable texture profiles and physicochemical properties.
The study established sensory acceptance as the target function for optimization, created from statistical design (DOE), using experimental data on mixtures and responses in terms of viscosity, density, colour delta, and pH, as well as texture profile in terms of hardness. Once the data was obtained, optimization was applied to find the firmest and most appealing texture. A Box–Behnken design was used to optimize the oil and protein content of each formulation. To replicate the egg-tartlet's gel texture, 13 mixtures (such as corn, oats, rice, amaranth, chia seeds, chickpeas, soybeans, and almonds) were prepared, then heat-treated at 170 °C for 30 minutes. The optimal formula was a mixture of coconut cream (40%), almond-amaranth protein (25%), and corn starch (35%) on a wet basis.
The resulting texture was measured at 0.18 N ± 0.01 and the viscosity was measured at 35.7 cP ± 12, and the texture profile analysis included hardness, elasticity, chewiness, and masticability. The consumer preference data was used to study the control tartlet made with eggs and two plant-based versions. The consumer preference data was analysed using a discriminative test to identify whether the substitution approximated the desired texture. A semi-trained panel of 35 people who had received texture identification training evaluated texture, taste, and acceptability. Each panel member signed an informed consent form authorised by the institution's ethics committee.
The statistically optimized formula was presented to the panel on a 9-point scale and a descriptive scale of intensity texture attributes. The panel accepted the plant-based gel-tartlet, but described its elasticity and chewiness as low-intensity attributes. The proposed correlation among textural attributes and sensory perception can be further utilized as a tool for formula development.
