Formulation and Optimization of Nanoemulsions Using the Natural Surfactant Saponin from Quillaja Bark

Abstract

Replacing synthetic surfactants by natural alternatives when formulating nanoemulsions has gained attention as a sustainable approach. In this context, nanoemulsions based on sweet almond oil and stabilized by saponin from Quillaja bark with glycerol as cosurfactant were prepared by the high-pressure homogenization method. The effects of oil/water (O/W) ratio, total surfactant amount, and saponin/glycerol ratio on their stability were analyzed. The formation and stabilization of the oil-in-water nanoemulsions were analyzed through the evaluation of stability over time, pH, zeta potential, and particle size distribution analysis. Moreover, a design of experiments was performed to assess the most suitable composition based on particle size and stability parameters. The prepared nanoemulsions are, in general, highly stable over time, showing zeta potential values lower than −40 mV, a slight acid behavior due to the character of the components, and particle size (in volume) in the range of 1.1 to 4.3 µm. Response surface methodology revealed that formulations using an O/W ratio of 10/90 and 1.5 wt% surfactant resulted in lower particle sizes and zeta potential, presenting higher stability. The use of glycerol did not positively affect the formulations, which reinforces the suitability of preparing highly stable nanoemulsions based on natural surfactants such as saponins.

Comparative Study of Physicochemical Properties of Nanoemulsions Fabricated with Natural and Synthetic Surfactants

“…Particularly, high-energy (pressure) homogenization is the technique more effective and more used in practice, which employs mechanical tools to induce disruptive forces to break up and blend the oily phase and oil droplets. In fact, recent studies continue to demonstrate the effectiveness of this technique for (nano)emulsion fabrication [13][14][15][16][17][18][19][20][21][22], even when using essential oils [23][24][25][26]. On the other hand, the aqueous phase, which typically consists of water, can contain other polar components (e.g., co-solvents, minerals, acids, and bases) where the type and concentration of these components determines the polarity, interfacial tension, rheology, pH, and ionic strength, to name just a few prominent examples that influence the formation, stability, and physicochemical properties of the nanoemulsion [10].…”

Sanse Powder Essential Oil Nanoemulsion Negatively Regulates TRPA1 by AMPK/mTOR Signaling in Synovitis: Knee Osteoarthritis Rat Model and Fibroblast-Like Synoviocyte Isolates

“…In the present study, we first extracted the EOs from Sanse Powder, a Chinese herbal compound prescription for external use which is effective in clinical treatment of KOA and studied the preparation process of transforming them into nanoemulsion. Consistent with the other scholars [ 28 , 29 ], we also screened the matrix materials and proportions needed for SP-NES through high-pressure homogenization method. The obtained SP-NEs observed as uniformly dispersed small particles under transmission electron microscopy.…”

Formulation of Environmentally Safe Graffiti Remover Containing Esterified Plant Oils and Sugar Surfactant

“…The design of experiments (DoE) and quality by design (QbD) approaches based on statistical calculations are usually employed in formulation optimization. Analysis of variance (ANOVA) of the response surfaces estimated by d -optimal design for dependent variables Y 1 –Y 4 indicated that the quadratic model exhibited the best fit in all cases, which is a typical solution in mixture designs in the literature [ 36 , 39 , 40 , 61 ]. The minor modifications for quadratic fitting were included for the viscosity (Y 1 ) and the runoff speed (Y 3 ) responses by implementing inverse linear regression in order to increase the robustness of the optimization model.…”

“…d -optimal design experimental matrices are typically not orthogonal, and the response factor (dependent variables) estimates are correlated. Through the usage of probability functions and value prediction, d -optimal models surpass conventional designs because they can be used even when the design space is constrained (where there are not perfectly convenient interactions between independent variables) and where too many experimental runs are not acceptable due to low resources and limited time for experimentation [ 38 , 39 , 40 ].…”