Researcher assesses alternative methods to sensory profiling for cosmetics

By Andrew MCDOUGALL

- Last updated on GMT

Researcher assesses alternative methods to sensory profiling for cosmetics

Related tags Marketing Cosmetics

The sensory qualities of a cosmetic product are essential nowadays and consequently sensory analysis methods are being used more to support projects more efficiently, according to a new paper.

For over 30 year cosmetic companies have implemented the sensory assessment to validate the formulation of cosmetic products, and now Dr Anne-Marie Pensé-Lhéritier has reviewed some of the latest alternative methods available, publishing her paper in the International Journal of Cosmetic Science​.

Sensory assessment is important in a product’s development as it allows marketing, R&D, and quality control teams to adopt a common method dedicated to creativity, development and production of cosmetic products.

In the past, this meant descriptive sensory profiling allowing an experienced panel to assess the qualitative and quantitative characteristics of a product in order to get a more accurate image of the product.

However, given the new demands of the industrial world and the new innovation paths – which mean shorter development times, complex products, or willingness to include the consumers in the process – a need to access new methods has arisen.

And so, Dr Pensé-Lhéritier reviewed some of the alternative methods that are being developed to allow faster sensory positioning and earlier screening of the prototype products – namely the flash profile, the pivot profile, and the check all that apply (CATA) methods – all of which are being developed to allow faster sensory positioning and earlier screening of the prototype products.

“These ‘alternative’ methods that offer product positioning, ratings and attribute citation frequency, can be implemented with either a panel of experts or with consumers,”​ she says.

“The flash profile, the pivot profile or the check all that apply (CATA) are just a few of the methods that the cosmetic professionals have started to test.”

Dr Pensé-Lhéritier says that with these methods, the consumers’ perception can also be integrated in the upstream process; with each method having its advantages and inconveniences.

Flash profile

Flash profile is a quick sensory profiling technique based on the combination of free choice profiling and a comparative evaluation of the whole product set.

Pensé-Lhéritier says the advantage of flash profile is to provide a product map in a very short time, but the main inconvenience is that it not suitable for large numbers of products because of saturation effect.

Pivot profile

The pivot profile method is based on a free expression of the difference between two products, with one being regarded as pivot, and will be always present for all comparisons.

The comparisons will then see products assessed as ‘more’ or ‘less’ than the pivot product and grouped based on this.

Calculations are applied on these counts in order to then identify the characteristics of each product by considering the terms of the whole judges per product, it is also possible to identify the characteristics of each of the judges considering the terms of the whole products per judges.

In her paper, Dr Pensé-Lhéritier states that pivot profile obtains a sensory map very quickly, but the critical point is the choice of the reference product which has to be very central in terms of sensory characteristics.

CATA method

Finally, the check-all-that-apply (CATA) method was designed for consumers rather than panellists, and sees each person receive a questionnaire with attributes to choose from, which can then be linked to the buying intent, or even to an acceptability concept.

As the cosmetic industry wants the consumers to actively take part in the descriptive assessment, and sometimes in the conception, of cosmetic products, this method would make sense.

It is a very simple method to understand for the assessors, but the main limitation is that the analysis is performed on nominal data less powerful than quantitative data, says Pensé-Lhéritier.

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