Application of check-all-that-apply and non-metric partial least squares regression to evaluate attribute's perception and consumer liking of apples

Attribute's perception and impact on liking are crucial in quality assessments. An approach with analytical and sensory methods is often necessary to ensure a reliable description of quality perception without neglecting product's characterization. Moreover, the presence of nonlinear patterns demands appropriate models. A methodology is proposed to assess quality and attributes of apples cultivated at different orchard's elevation. Physicochemical, sensory and consumer tests with check-all-that-apply and penalty analysis were performed. A non-metric partial least square model (NM-PLS) was applied with a new coding system. The methodology highlighted similar results at all assessments for taste, with no significant differences among samples. Texture was evaluated different among assessments. Differences were found for hardness and astringency only at the panel level, while there was an agreement considering crispness, better described by the analytical index “average drop.” The NM-PLS model confirmed sweet, intense odor and the latent attribute “notes” as the most related to liking. The methodology confirmed that differences found among trained panelists my not be relevant for consumers, therefore, an integrated approach is needed. It shows a new system to create a regression model that provides information at a specific and a global attribute level to deal with multicomponent parameters. Practical Applications This work describes a new consumer test methodology to investigate attributes perception and product penalties, therefore, it can be used by industries and researchers to have a deeper understanding of quality and liking of new products. It also highlights advantages and drawbacks of different quality assessment types and suggests an integrated approach is key to assess quality, consumer liking and attribute's perception. Finally, it proposes a new index to investigate crispiness.