The Nose Knows
The perfume and scents industry is not one that makes headlines often. Perfume ads with celebrities tend to come and go but over the last 25 years there have been some scents for both men and women that have sustained themselves on the best-seller list and generated millions of dollars in revenue. As an example in 2022 Dior (CDI.FR) Sauvage was selling at the rate of $4.6m/day for much of the year and last year the perfume market was estimated to be between $50.5b and $55.5b US, with an expected CAGR of between 4.7% and 5.9%[1].
[1] Sources: Estee Lauder, VMR.com, CB Insights
[1] Sources: Estee Lauder, VMR.com, CB Insights
Scent developers start their process with an idea. It could come from examining current popular scents to capitalize on a trend, or it could come from a creative point of view, maybe recalling a travel destination or personal experience. The process then moves to the selection stage where the perfumer, based on expertise, selects fragrances that they believe will represent their concept. What follows is an extended trial and error process where the scents are blended to form a ‘top note’, the basis for the overall scent, ‘accords’ that push the scent in a particular direction (rose, marine, etc.), while making sure that the materials have a consumer-oriented longevity and projection (how far away the scent can be noticed), all of which are developed by trial and error.
There is software that can help perfumers, even AI based software like Philyra, developed by Symrise (SY1.XE) and IBM (IBM) and released in 2018. The software contains a database of 3.5m legacy formulas and 2,000 raw materials, and, according to the company “…is able to guide perfumers towards exciting and surprising solutions, explore new combinations and materials without human bias, and help perfumers update and improve upon iconic fragrances.” In particular, Philyra helps perfumers to work toward using sustainable materials in their development.
While software platforms like this help the scent development process, it is a long and arduous process that takes many months or years until the right combination of scent and materials is reached. Even with software providing assistance to perfumers and the expertise of a professional and experienced ‘nose’ (1st tier perfumers can make over $400K/year) commercial success is certainly not guaranteed and the cost of development, materials, and advertising can be quite financially burdensome, even for a large company.
But fear not perfumers, as a group of Japanese scientists have taken the idea of AI scent development further and created a Generative Diffusion Network for creating scents. This new model uses mass spectrometry data from 166 essential oils to isolate 9 ‘odor descriptors’ that can be used to form scent combinations which are then tested for accuracy in a double-blind (human) process where participants had to match the AI aroma with the appropriate descriptors.
To illustrate: “As an illustration of the procedure, for the first sample of the sensory test two odor descriptors, Wood and Spicy, were selected. A random 201-dimensional vector of Gaussian noise was chosen as the seed for the OGDiffusion network. The network was then run in inference mode, generating a mass spectrum as the output. This mass spectrum was subsequently analyzed using non-negative matrix factorization to identify the essential oils required for the mixture. The analysis determined the following essential oils and proportions: Cypress (0.10), Angelica root (0.07), Cuminum cyminum (0.05), and Trachyspermum ammi (0.78). The specified amounts of each essential oil were pipetted into 5 mL vials and diluted with alcohol at a 2:1 ratio. The resulting mixtures were prepared for sensory evaluation in odor vials. Table S1 shows the essential oil recipes used in all sensory experiments.”
There is software that can help perfumers, even AI based software like Philyra, developed by Symrise (SY1.XE) and IBM (IBM) and released in 2018. The software contains a database of 3.5m legacy formulas and 2,000 raw materials, and, according to the company “…is able to guide perfumers towards exciting and surprising solutions, explore new combinations and materials without human bias, and help perfumers update and improve upon iconic fragrances.” In particular, Philyra helps perfumers to work toward using sustainable materials in their development.
While software platforms like this help the scent development process, it is a long and arduous process that takes many months or years until the right combination of scent and materials is reached. Even with software providing assistance to perfumers and the expertise of a professional and experienced ‘nose’ (1st tier perfumers can make over $400K/year) commercial success is certainly not guaranteed and the cost of development, materials, and advertising can be quite financially burdensome, even for a large company.
But fear not perfumers, as a group of Japanese scientists have taken the idea of AI scent development further and created a Generative Diffusion Network for creating scents. This new model uses mass spectrometry data from 166 essential oils to isolate 9 ‘odor descriptors’ that can be used to form scent combinations which are then tested for accuracy in a double-blind (human) process where participants had to match the AI aroma with the appropriate descriptors.
To illustrate: “As an illustration of the procedure, for the first sample of the sensory test two odor descriptors, Wood and Spicy, were selected. A random 201-dimensional vector of Gaussian noise was chosen as the seed for the OGDiffusion network. The network was then run in inference mode, generating a mass spectrum as the output. This mass spectrum was subsequently analyzed using non-negative matrix factorization to identify the essential oils required for the mixture. The analysis determined the following essential oils and proportions: Cypress (0.10), Angelica root (0.07), Cuminum cyminum (0.05), and Trachyspermum ammi (0.78). The specified amounts of each essential oil were pipetted into 5 mL vials and diluted with alcohol at a 2:1 ratio. The resulting mixtures were prepared for sensory evaluation in odor vials. Table S1 shows the essential oil recipes used in all sensory experiments.”
The idea behind this model is to eliminate problems that exist with current AI scent development systems. Such systems are based on proprietary data and require expert human intervention, along with results being hard to reproduce. While they are considered helpful to those in the scent profession, they are not automated and that is where this new model goes. The system learns without needing prior chemical composition knowledge and is able to generate precise results that can be reproduced exactly, and mass spectrometry data can be easily represented as weighted sums, a function commonly used in LLMs.
So, will those wishing to become perfumers or scent specialists be out of a job? In some ways the answer is yes, as there will be less need for the trial and error development system used today and that means less learning situations for those coming up in the industry, but again humans are essential, even in this automated scenario, as there must be someone who can test the combinations created by the AI, even if they were created without human assistance. Without a ‘nose’ to smell the combinations there is no subjective point to attach to the scent. So, in this case, such an AI system will reduce the amount of work associated with the development of scents but will still require a high-quality professional to make sure that the scent is a pleasant or exciting as expected. The nose knows.
So, will those wishing to become perfumers or scent specialists be out of a job? In some ways the answer is yes, as there will be less need for the trial and error development system used today and that means less learning situations for those coming up in the industry, but again humans are essential, even in this automated scenario, as there must be someone who can test the combinations created by the AI, even if they were created without human assistance. Without a ‘nose’ to smell the combinations there is no subjective point to attach to the scent. So, in this case, such an AI system will reduce the amount of work associated with the development of scents but will still require a high-quality professional to make sure that the scent is a pleasant or exciting as expected. The nose knows.
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