Clustering of oleogel production methods reveals pitfalls and advantages for sustainable, upscalable, and oxidative stable oleogels

Oleogels are expected to replace unhealthy solid and semi-solid fats in the food industry due to their promising health and environmental impacts. However, to bring the benefits of oleogels to society, oleogel production should be moved from laboratory to industrial scale, which occurs when the classification of oleogel preparation methods is first contextualized within an industrial perspective. However, the traditional classifications of oleogels do not reveal relevant information to facilitate the fat-to-oleogel transition.This study proposes a quantitative and novel classification of oleogelation approaches based on (i) the overall heat experienced by the oil during oleogelation (which strongly influences the oxidative/storage stability of the oleogel); (ii) the overall electrical energy consumption by all devices during oleogelation (which correlates to sustainability, upscaling ability, and cost of oleogelation); and (iii) overall oleogelation time (which correlates with costs and warehouse storage). We calculated these parameters for 216 oleogelation cases retrieved from the literature and classified them using a K-means clustering algorithm coupled with scree-plot analysis.Oleogelation approaches are classified into low-, medium-, and high-input methods, with low-input ap-proaches being the most promising for oleogel nutritional aspects, their sustainability, and industrial relevance. The generally accepted assumptions that direct methods are superior to indirect methods (due to their fewer inputs) are not correct and oleogelation methods should be carefully examined case-by-case. This novel classi-fication of oleogels lays the foundations for a better understanding on the scalability of oleogel preparation methods with the final aim of materializing the fat-to-oleogel transition.