Ashwini Kumar Rawat
Fragrance compounding is traditionally considered to be an art, unsullied by science, and often at best used in conjunction with a compounding pyramid for a guideline to accord generation. The current state of scientific understanding of olfaction has not allowed for the predictive modeling of scents. Existing quantitative estimators used in the industry, like the heuristic methodologies based on empirical correlations like the odor value (OV) have to typically account for variations in sociological conditions such as geography, gender and require a large number of trained human specialists. Equation of state methods is not currently scalable or theoretically valid for the complex multi-component mixtures, which are used as perfumes. The intractable complexity of multi-component mixture analysis precludes the ability of the equation of state (EOS) methods to aid the industry. We have found that ultrafast optical probing of the precursor compound components of fragrances provides insights into the industrial fragrance process. From the signal generated by the refractive index changes in the solutions of accord primitives due to the heat dissipation processes, we have been able to infer the optimal accord concentration. This signal is generated by changes in the refractive index due to the various modes of heat dissipation, is known as the thermal lens effect, one of which (convection) has not considered previously. The signal in alcohols is notable as it establishes a strong correlation between the TL signal and physical properties, like mobility, steric effects, and hydrogen bonding. We leverage these thermal lens effects as a control parameter for predictive accord generation and to gain insights into the light-matter interactions for industrial use.
No datasets are available for this submission.