Multi-Modal Data Fusion for Quality Discrimination and Flavor Analysis of Commercial Oat Milk

TL;DR

This study analyzes commercial oat milk samples to understand sensory quality and flavor differences using multiple analytical methods.

Contribution

A classification framework for oat milk quality based on sensory and physicochemical data is introduced.

Findings

Ideal-like oat milk samples have balanced flavor profiles with moderate sweetness and burntness.

Ideal-exceeding samples surpass expectations in sweetness or graininess intensity.

Sensory differences are linked to lipid oxidation and physical stability factors like fiber and protein content.

Abstract

In this study, 10 popular commercial oat milk samples were analyzed for sensory quality and flavor chemistry using the Ideal Profile Method (IPM), electronic nose (E-nose), and gas chromatography-mass spectrometry (GC-MS). Based on consumer cognitive mapping of ideal products, samples were classified into “Ideal-like” and “Ideal-exceeding” categories. Ideal-like products exhibited light white appearance, pronounced oatiness, moderate sweetness and burntness, and low graininess, presenting a balanced flavor profile, whereas Ideal-exceeding samples surpassed consumer expectations in sweetness or graininess intensity, delivering stronger sensory stimulation. Furthermore, sensory differentiation among categories primarily stemmed from synergistic effects of lipid oxidation levels (e.g., 3,5-octadien-2-one) and physical stability (fiber and protein content affecting particle size distribution). This classification framework reveals that ideal sensory quality can be achieved through diverse physicochemical pathways in commercial oat milk, providing theoretical guidance for product formulation optimization and quality standardization.

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