Optimization of butter fruit incorporated fat spread using instrumental textural analysis: a response surface methodology

Insights into Drivers of Liking for Avocado Pulp (Persea americana): Integration of Descriptive Variables and Predictive Modeling

Trends in new food products focus on low-carbohydrate ingredients rich in healthy fats, proteins, and micronutrients; thus, avocado has gained worldwide attention. This study aimed to use predictive modeling to identify the potential sensory drivers of liking for avocado pulp by evaluating acceptability scores and sensory descriptive profiles of two commercial and five non-commercial cultivars. Macronutrient composition, instrumental texture, and color were also characterized. Trained panelists performed a descriptive profile of nineteen sensory attributes. Affective data from frequent avocado adult consumers (n = 116) were collected for predictive modeling of an external preference map (R² = 0.98), which provided insight into sensory descriptors that drove preference for particular avocado pulps. The descriptive map explained 67.6% of the variance in sensory profiles. Most accepted pulps were from Hass and Colin V-33; the latter had sweet and green flavor notes. Descriptive flavor attributes related to liking were global impact, oily, and creamy. Sensory drivers of texture liking included creamy/oily, lipid residue, firmness, and cohesiveness. Instrumental stickiness was disliked and inversely correlated to dry-matter and lipids (r = -0.87 and -0.79, respectively). Color differences (∆E_ab*) also contributed to dislike. Sensory-guided selection of avocado fruits and ingredients can develop products with high acceptability in breeding and industrialization strategies.