Exploratory Assessment of Spray‐Drying Parameters and Preliminary Machine‐Learning Modelling of Plukenetia volubilis Oil‐Herbal Extract Emulsions

Co‐formulating polyunsaturated oils with phytochemical‐rich plant extracts presents practical challenges during spray drying, including emulsion instability and wall deposition. This exploratory study examined the effects of oil‐to‐water ratio, carrier type, carrier loading, and inlet temperature on a Plukenetia volubilis (Sacha Inchi) oil emulsion containing Andrographis paniculata and Moringa oleifera extracts using a sequential one‐factor‐at‐a‐time (OFAT) screening approach. Among the conditions investigated, a 1:9 oil‐to‐water ratio, 25% Arabic gum, and an inlet temperature of 140°C produced the highest powder recovery (93.8%). Scanning electron microscopy showed predominantly spherical particles with generally intact external surfaces, while phytochemical assays of the selected powder gave antioxidant activity of 97.58 mg AAE/g, total phenolic content of 16.15 mg GAE/g, and total flavonoid content of 10.87 mg QE/g. Exploratory Support Vector Machine (SVM) models for these three phytochemical responses produced higher in‐domain performance metrics than baseline Decision Tree Regression models (SVM R 2 = 0.734–0.858; DTR R 2 = 0.344–0.562). Powder recovery and moisture content were treated only as measured powder characteristics and were not included as machine‐learning targets. The small dataset ( n = 30), restricted OFAT design, absence of external validation, and lack of measured emulsion and encapsulation descriptors prevent claims of generalisable prediction or confirmed mechanism. The findings identify preliminary formulation trends and define priorities for subsequent multifactorial experimentation and physicochemical validation.