Browsing by Author "Fisher, Emily Claire"

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    The multimodality of creaminess perception : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Manawatū Campus, Palmerston North, New Zealand. EMBARGOED until 21 August 2026.
    (Massey University, 2024-02-28) Fisher, Emily Claire
    Creaminess is a complex sensory sensation that drives consumer acceptability of milk. To date, creaminess research has focused on instrumental and compositional measures overlooking the critical consumer perspective. This research took a consumer-led approach to unlock new insights into the underlying sensory attributes driving consumer creaminess perception using perceptual modelling. Robust sensory data, from a trained panel, was combined with consumer approaches for accurate modelling. Initially, attributes and modalities perceived to drive milk creaminess were identified through discussion with consumers representative of two key dairy markets, China and New Zealand (NZ). Subsequently, a milk sample set (n=32) was developed, and an expert panel trained to profile the samples based on attributes identified by consumers. A novel methodological investigation, on the impact of panel training with Polarised Sensory Positioning (PSP) of the sample set, was also explored. Focusing on NZ consumers, participants (n=117) evaluated creaminess and liking perception of the milk samples. Critically, regression modelling was employed to identify key attributes driving creaminess perception based on expert panel data. Several novel findings were discovered. Drivers of creaminess differed to some degree between NZ and Chinese consumers indicating cultural differences across markets. Trained panel sensory data revealed multicollinearity between attributes measured to describe the sample set. Modelling approaches were able to identify key attributes required to predict creaminess. New findings that training has little impact on PSP outcomes was also ascertained. Pertinently combining four attributes, across different modalities, in an Elastic net regression model (‘yellow’, ‘watery’ flavour, ‘in-mouth thickness’ and ‘astringency’) successfully predicted creaminess (R2=0.9514), however these attributes were highly correlated with others retained in a PLS model. Each model had its relative merits. Of further note, consumer creaminess response was highly variable and cluster analysis revealed two different consumer segments with perception impacted by sensitivity to certain attributes: ‘green tinge’, ‘cardboard’, ‘salty’, ‘cooked’, ‘fat separation’, ‘grassy’, ‘buttery’, ‘melting’, ‘cream’ aroma, ‘smoothness’, and ‘astringent’. This research revealed new understanding concerning perceptual attributes contributing to consumer creaminess perception and provided clearer targets for the dairy industry to ensure milk creaminess levels align to consumer expectations and related commercial gain.