Consumer acceptance and sensory drivers of liking of Minas Frescal Minas cheese manufactured using milk subjected to ohmic heating: Performance of machine learning methods

The consumer acceptance (n = 100) and the sensory drivers of liking of Minas frescal cheese manufactured with milk subjected to ohmic heating (0, 4, 8, and 12 V/cm⁻¹, CONV, OH₄, OH₈, and OH₁₂, 72–75 °C/15 s) were investigated. Machine learning techniques (random forest, gradient boosted trees, and extreme learning machine; RF, GBT, and ELM) were used to determine the sensory drivers of liking. No significant differences were observed among the cheeses for most of the sensory attributes, for all treatments, suggesting that ohmic heating may be an adequate technology for Minas Frescal cheese processing with the advantage of improving its overall liking. Machine learning methods presented a good agreement with the experimental data, allowing the identification of the attribute's juiciness, white color, homogenous mass, Minas Frescal cheese flavor as the sensory drivers of liking, while the attribute bitter taste was identified as a driver of disliking. These results should be taken into consideration when adopting emerging technologies, such as ohmic heating for the manufacture of Minas frescal cheese.