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

Leheng Jiang, Yuhao Cheng, Qiao Sun, Xiaoming Guo, Xiuping Dong, Yizhen Huang, Xiaojing Leng

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Leheng JiangCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.ORCID 0009-0007-7386-9793

Yuhao ChengCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.

Qiao SunShenzhou Space Biotechnology Group, Beijing 100086, China.

Xiaoming GuoShenzhen Key Laboratory of Food Nutrition and Health, Institute for Innovative Development of Food Industry, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China.

Xiuping DongState Key Laboratory of Marine Food Processing and Safety Control, National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

Yizhen HuangCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.ORCID 0000-0002-5610-3055

Xiaojing LengCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.

Funding

National Key R&D Program of China 2024YFD2101504National Key R&D Program of China 2024YFF1106601

6 · The paper itself

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.

Indexed as

oat milkphysicochemical propertiesproduct developmentsensory attributes

Identifiers

PMID41829209

DOI10.3390/foods15050936

PMCPMC12984210

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.