Consumer acceptability and purchase intent of probiotic yoghurt with added glucose oxidase using sensometrics,artificial neural networks and logistic regression

This research aimed to identify the drivers of acceptance and purchase intent of a probiotic (Bifidobacterium longum BL05) nonflavoured yoghurt supplemented with glucose oxidase, and to model the consumers’ acceptability using sensometrics and artificial neural networks (ANN). Consumers (n = 100) evaluated the degree of liking of yoghurt assays in respect of appearance, aroma, taste, texture and overall linking. Sensometric techniques – multiple linear regression (MLR), partial least squares regression (PLS), principal component regression (PCR) – and ANN were used to model the overall liking. Sensory drivers of global acceptance and purchase intent were also determined using logistic regression (LR). Hierarchical cluster analysis (HCA) identified three consumer segments that presented differences in all sensory attributes evaluated (P < 0.05). The ANN model showed the best performance to predict overall liking, followed by the MLR, PLS and PCR, indicating that taste and texture were the most significant attributes impacting the yoghurts overall liking. In accordance with the logistic models, overall acceptance and purchase intent could be predicted with 81.94 and 85.49% accuracy, respectively. The logistic regression indicated that taste was the attribute that contributed significantly (P < 0.0001) to higher scores for purchase intent and was considered the driver of acceptance.