Different approaches for handling L-shaped data are compared for the first time in a study conducted with Norwegian consumers. Consumers (n = 101) valuated eight different yoghurt profiles varying in three intrinsic attributes such as viscosity, particle size, and flavour intensity following a full factorial design. Sensory attributes, consumers’ liking ratings, and consumer attributes were collected. Data were analysed using two different approaches of handling L-shaped data: approach one used two-step Partial Least Square (PLS) Regression using L-shaped data including the three blocks such as sensory attributes, consumers’ liking ratings, and consumer attributes, while approach two was based on one-step simultaneous L-Partial Least Square (L-PLS) Regression model of the same three blocks of data. The different approaches are compared in terms of centering, step procedures, interpretations, flexibility, and outcomes. Methodological implications and recommendations for academia and future research avenues are outlined.