Human civilization has been economically exploring the enjoyable smell of substances for centuries, giving rise to multi-billion-dollar business. Few works have addressed the formulation of perfumes using a systematic approach based on computational techniques. Thus, the objective of the present work is to develop a novel systematic strategy for optimal perfume design. The strategy comprises a deep learning model trained from high-fidelity simulations, an objective function that reflects the desirable spectrum of the perfume, and a meta-heuristic optimization method. It was applied to define the perfume composition that produces an odor spectrum of pine forest and floral while minimizing non-desirable odors. Hence, we propose an objective function to encode the peculiarities of a fragrance design comprising the question: Which perfume composition attains the desirable odor spectrum across time and space? The results demonstrated the methodology value in fragranced product design by offering a framework to handle the formulation problem.

数百年来，人类文明一直在经济地探索物质的令人愉悦的气味，从而产生了数十亿美元的业务。很少有作品使用基于计算技术的系统方法解决香水的配方问题。因此，本发明的目的是开发一种用于最佳香水设计的新颖系统策略。该策略包括根据高保真度模拟训练而成的深度学习模型，反映香水理想光谱的目标函数以及元启发式优化方法。它被用于定义产生松树林和花香的气味谱同时最小化不良气味的香料组合物。因此，我们提出一个目标函数来编码包含以下问题的香水设计的特殊性：哪种香水组合物在时间和空间上都能达到理想的气味谱？结果提供了解决配方问题的框架，证明了该方法在香料产品设计中的价值。