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.

消费者的接受程度（n = 100）和使用牛奶进行欧姆加热（0、4、8和12 V / cm ^-1，CONV，OH ₄，OH ₈和OH制成）制成的Minas奶酪的感觉驱动力₁₂，72–75°C / 15 s）进行了研究。机器学习技术（随机森林，梯度增强树和极限学习机； RF，GBT和ELM）用于确定喜欢的感觉驱动器。在所有处理中，对于大多数感官特性，奶酪之间均未观察到显着差异，这表明欧姆加热可能是米纳斯弗雷斯卡尔奶酪加工的适当技术，其优点是改善了奶酪的整体风味。机器学习方法与实验数据吻合良好，可以将属性的多汁性，白色，均质，米纳斯·弗雷斯卡奶酪的味道识别为喜欢的感觉驱动器，而将苦味属性识别为不喜欢的驱动器。