Can Machine Learning be used to evaluate creams and body lotions characteristics?
The chemical nature of a cream or a body lotion contributes to the classification of these products. Its type of emulsion is also an element of distinction among products. So are the emulsifier used and the ionicity. These components are very familiar to the savvy R&D cosmetologist who has been formulating cream long before any simulation tool was available. Yet, it implied so far a laboratory trial to get an idea of the impact of changing any one of the ingredients. Performing a lab trial consumes time and money. And sometimes it is a necessary step to undergo. However, in some cases, Machine Learning can alleviate the burden of systematic trial by predicting the outcome of modifying the formulation. In other words, anticipating the future characteristics of a given cream formulation based on the sole knowledge of its components before it makes it to lab testing is now possible.
Cosmetology experts deal with numerous components to achieve optimal formulations: solvents, preservatives, antimicrobials, antioxidants, and dyes are some of the various raw materials used in cream and lotions formulation. These raw materials must meet several criteria for application in cosmetology. Among them, there is microbial cleanliness, i.e., avoiding the contamination of the product by microbes. But of importance are also the absence of chemical impurities and the resistance against light. The stability in the presence of heat or air oxidation is of premium importance. Physicochemical characteristics have to be reproducible and avoid the development of harmful substances. All these features are the core of a successful cosmetic product.
Cosmetologists have strong knowledge of cosmetics formulations. They know how their previous trials have behaved in time concerning homogeneity. They have strong know-how on reaching excellent rheological properties, i.e., viscosity, consistency. They are aware of the effect of their formulation on sensory properties (smell, taste, color). They have accurate measures on the pH of former formulations. In a nutshell, they have these various data available on the previous products formulated. These are typically Small Data since they pertain to a few dozen or hundred of products.
TADA makes it possible for cosmetologists to anticipate the characteristics of the future product and gain precious time in the product design cycle. Indeed, TADA is a machine learning tool that learns from Small Data sets (instead of millions of Data) and can predict the specificities of future products. It shortens the product design cycle by anticipating most of its critical criteria, hence eliminating the need for yet a few of the typical prototyping cycles. No need to be a Data Scientist to use TADA; it is accessible to anybody in a few clicks.
MyDataModels brings a self-service solution to cosmetology professionals who want to save time on product formulation by using their Small Data.
Using TADA, the Small Data Machine Learning tool, cosmetologists can design a new product in a fraction of the time (and cost). In a few minutes, TADA gives an estimate of the major product features even before being actually tested. A major share of the cost resides in the lab testing. Saving on lab time saves both time and money on product formulation.
“TADA shortens the product design cycle by speeding up the try and fail phases of product formulation”
Depending on the result of the estimations, the formulation can be modified several times, consequently without touching a beaker. Once the formula is good in simulation, it can be prepared and tested in a lab environment for a fraction of the usual cost.