Comparison of different ways of handling L-shaped data for integrating sensory and consumer information

•The interpretation of L-shaped data of sensory attributes, liking ratings, consumer attributes provided useful information.•Two approaches (two-step PLS regression vs one-step simultaneous L-PLS regression) are compared.•Yoghurt data (sensory profiling, liking ratings, consumer attributes) was used as an illustration.•Two-step PLS and L-PLS regression approaches provided similar results when integrating sensory, and consumer information.•Two-step PLS regression approach provided more direct interpretation of individual differences in liking. 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.