There has been an increase in the consumption of plant‐based milk alternatives (PBMA), though these often differ in sensory properties compared to cow milk. Some consumers specifically seek PBMA with sensory qualities that resemble cow milk; however, such products are not yet available on the market. This study compared the chemical composition, particle size, color, viscosity, physical stability, and volatile compounds of seven commercial milk types: almond, coconut, oat, rice, soy, plant‐hybrid (a blend of plant‐based ingredients), and cow milk. Sensory descriptive analysis was used to evaluate appearance, aroma, flavor, and mouthfeel across the samples. All PBMA, except soy milk, had lower protein levels ( 75) and creamy texture, attributed to their higher fat content and lactones. Plant‐hybrid PBMA, despite lacking cereal ingredients, shared sensory similarities with oat and rice milk. Volatile compound analysis identified benzaldehyde in almond milk, contributing to its characteristic almond‐like aroma. Coconut milk contained lactones, which are associated with sweet and creamy notes. Blending complementary PBMA ingredients, such as coconut and soy milk, would effectively mimic cow milk by introducing dairy‐like aroma, creaminess, and improved stability. Practical Application : There is a need to reformulate PBMA to improve their sensory quality and meet consumer expectations. By understanding how chemical and physical properties influence the overall sensory experience, manufacturers can make better ingredient choices and optimize production processes to produce more desirable products.

Plant-based milk alternatives (PBMA) have emerged as popular substitutes for cow milk, driven by health, environmental, and ethical considerations. However, their ability to replicate the sensory and physicochemical properties of dairy remains a critical challenge for industry. This review critically examines the extent to which almond, soy, and oat PBMA replicate key sensory attributes of ultra-high temperature (UHT) full cream cow milk, focusing on appearance, texture, and flavour. Furthermore, it explores the relationship between these sensory attributes and the physicochemical properties of PBMA to elucidate the underlying reasons for the observed differences. A comparative analysis of compositional differences reveals fundamental limitations linked to plant protein functionality, carbohydrate structure, fat composition, and mineral fortification, all of which contribute to disparities in creaminess, mouthfeel, colour, and flavour. Technological strategies such as particle size reduction, enzymatic hydrolysis, and flavour masking have improved specific attributes, yet no PBMA fully replicates the holistic sensory experience of dairy. Emerging approaches, including blended formulations, precision fermentation, and artificial intelligence (AI)-driven optimisation, show promise in narrowing these gaps. Nonetheless, a complete replication of UHT cow milk remains elusive, highlighting the need for continued research and innovation to either approximate dairy properties more closely or enhance PBMA’s unique qualities to drive consumer acceptance.

Plant‐based milk alternatives (PBMAs) are now increasingly used by some consumers in similar ways to cow milk. However, they differ in their physicochemical and sensory properties. Improving PBMAs to better mimic dairy is limited by the time and cost of development trials. Machine learning offers a promising approach to predict optimal formulations without the need for extensive trial and error. In this study, Random Forest regression (RFR) and gradient boosting regression (GBR) models were used to formulate PBMAs (Formulation 1 and 2, respectively) that mimic cow milk. Sensory analysis, viscosity, stability, color, particle size, and headspace volatiles were compared to cow milk to assess model accuracy. Both formulations were sensorially distinct from coconut, oat, soy, almond, pasteurized, and ultra‐high temperature (UHT) cow milk, associated with vanilla, caramel, astringent, bitter, metallic, and nutty notes, compared to the sweet, dairy, and coconut notes of dairy. The formulations contained aldehydes, including hexanal and pentanal from soy, although compounds including vanillin, octanal, and 1‐pentanol were shared with cow milk. UHT cow (0.68 µm) had the smallest average particle size ( D ₅₀ ), followed by Formulation 2 and pasteurized cow (1.04 µm), with Formulation 1 (1.62 µm) as the largest. The UHT and pasteurized cow milk had higher whiteness indices compared with Formulation 1 and Formulation 2. Formulations exhibited faster sedimentation compared to cow milk. The models correctly predicted viscosity and some volatiles. However, predictive performance was limited by the small dataset and restricted access to commercial formulations. A larger dataset and access to formulations would improve model accuracy. Practical Applications: This research demonstrates the potential of predictive modelling for the development of plant‐based alternatives to animal‐derived ingredients. Such an approach would reduce reliance on trial and error in product development, saving time and development costs while meeting consumer needs.