In the field of encapsulation, microcapsules containing perfume have emerged as effective vehicles for delivering active ingredients across various applications. The present study employed a multivariate analysis framework to examine polyacrylate microcapsules for household products synthesized using different acrylate monomers. The advanced multivariate approach allowed us to quantify critical properties such as the Molecular Weight between Cross-links (MW_c), mechanical attributes, Encapsulation Efficiency (EE), and On-Fabric delivery. It is worth noting that the mechanical properties were gauged using a novel nanoindentation technique, which measures the Rupture Force per unit diameter (RFD). Both Encapsulation Efficiency and On-Fabric delivery were assessed using GC-MS. Our findings identified the optimal microcapsule system as one synthesized with 100% aromatic hexafunctional urethane acrylate, showcasing a 94.3% Encapsulation Efficiency and an optimal RFD of 85 N/mm. This system achieved an exemplary On-Fabric delivery rate of 307.5 nmol/L. In summary, this research provides crucial insights for customizing microcapsule design to achieve peak delivery efficiency. Furthermore, by designing acrylic monomers appropriately, there is potential to reduce the amount of active ingredients used, owing to enhanced delivery efficiency and the optimization of other microcapsule properties. Such advancements pave the way for more environmentally friendly and sustainable production processes in the fast-moving consumer goods industry.

Despite the growing economic importance of the fragrance industry, perfume formulation remains largely guided by empirical knowledge and iterative trial-and-error approaches. The structured design of fragrances, typically organised into top, middle, and base notes through the blending of perfume raw materials, is therefore time-consuming, costly, and difficult to generalise. Existing computational approaches have begun to address this challenge, but are commonly limited to small ingredient sets or require extensive sensory data that are not always available. This work proposes a computer-aided optimisation framework for Eau de Parfum formulation that simultaneously maximises perceived olfactory intensity and minimises formulation cost. The resulting optimisation problem is formulated to preserve the structural balance of top, middle, and base notes inherent to the perfume pyramid. Application of the framework to a fruity–floral Eau de Parfum formulation demonstrates a substantial increase in perceived intensity alongside a reduction in total formulation cost of approximately 64%. The proposed approach is intended as a decision-support tool for perfumers, enabling rapid exploration of cost–intensity trade-offs once a candidate ingredient set has been defined. The framework offers a step towards more systematic, data-driven perfume engineering and provides a foundation for future extensions incorporating ingredient selection and sensory constraints.