To reveal the correlation of sensory attributes in different types of cosmetics
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Presented by: Xue-dong(Dario) Liu
Background：
Sensory evaluation has been widely used in skin care products development. Bunches of sensory attributes are set to help researchers not only to link consumer real needs, but also to deeply understand products behaviours. However, more attributes are set, more difficulties for sensorial panelists to define, and more confusion for consumer to fill the questionnaire designed by dedicated researchers. Thus, a considerable proportion of product failure can be led to a mismatch between sensory properties and consumer needs. In this case, to deeply analyze and highlight principle attributes which might correlate several corresponding attributes, would be more practical significance. For example, from product conception to post-launch monitoring, this kind of analysis could help researches to deeply understand products behaviours from consumer feedback both on products sensory and effects. Additionally, the analysis could also provide reference for researches to design the right questionnaire when sensory attributes are chosen.
Materials＆Methods：
Four kinds of skin care cosmetics (toner、serum、emulsion and Cream) were evaluated by 8~10 panelists，who were professionally trained with high acuity and accuracy. The experimental design was as following: there were 9 characteristic attributes for toner products, and a total of 20 toners were tested. Similarly, 15 attributes for serum with 16 serum products. 14 attributes for emulsion with 17 emulsion products ;15 attributes for cream with total 28 test cream products. These panelists evaluated the same kind of cosmetic weekly, taking six samples at each time. Origin 2021 was used to export correlation heat maps, and SPSS2 2.0 analysis software was used for factor analysis. The factor analysis in principal component analysis was used to evaluate the importance of sensory attributes, to synthesize several variables with a certain correlation into a small number of factors, and to study how a group of measured indicators with a complex relationship was dominated by a few internal independent factors.
Result:
(1)According to correlation heat maps of attributes, among with four kinds of skin care cosmetics, there were a negative correlation between transparency and product thickness, product thickness and absorption rate, skin freshness. On the contrary, product thickness with skin stickiness, skin moisture and skin smoothness showed positive correlations.
(2) In the interpretation of total variance from toner、serum、emulsion、cream, there were respective three、four、two、three components greater than 1, and the variances of these important components respectively accounted for 68.787%、72.497%、63.604%、64.219% of all these principal components. Of all the extracted principal components, the first principal component of these four kinds of cosmetics all included the sensory attributes which were skin moisture and skin softness and so on.
Conclusion:
In this study, we could get the conclusion that the same type of cosmetic products had obvious three key factors, correlating several sensory attributes. Further more, similar correlation could be found by principal component analysis in different type of skin care products such as toner, serum, emulsion and cream. Additionally, in the process of product development, more satisfying products could be designed and possibility of product success could be improved by fully understanding the needs of consumers, grasping the relationship between these sensory attributes and reasonably designing the sensory questionnaires.
Sensory evaluation has been widely used in skin care products development. Bunches of sensory attributes are set to help researchers not only to link consumer real needs, but also to deeply understand products behaviours. However, more attributes are set, more difficulties for sensorial panelists to define, and more confusion for consumer to fill the questionnaire designed by dedicated researchers. Thus, a considerable proportion of product failure can be led to a mismatch between sensory properties and consumer needs. In this case, to deeply analyze and highlight principle attributes which might correlate several corresponding attributes, would be more practical significance. For example, from product conception to post-launch monitoring, this kind of analysis could help researches to deeply understand products behaviours from consumer feedback both on products sensory and effects. Additionally, the analysis could also provide reference for researches to design the right questionnaire when sensory attributes are chosen.
Materials＆Methods：
Four kinds of skin care cosmetics (toner、serum、emulsion and Cream) were evaluated by 8~10 panelists，who were professionally trained with high acuity and accuracy. The experimental design was as following: there were 9 characteristic attributes for toner products, and a total of 20 toners were tested. Similarly, 15 attributes for serum with 16 serum products. 14 attributes for emulsion with 17 emulsion products ;15 attributes for cream with total 28 test cream products. These panelists evaluated the same kind of cosmetic weekly, taking six samples at each time. Origin 2021 was used to export correlation heat maps, and SPSS2 2.0 analysis software was used for factor analysis. The factor analysis in principal component analysis was used to evaluate the importance of sensory attributes, to synthesize several variables with a certain correlation into a small number of factors, and to study how a group of measured indicators with a complex relationship was dominated by a few internal independent factors.
Result:
(1)According to correlation heat maps of attributes, among with four kinds of skin care cosmetics, there were a negative correlation between transparency and product thickness, product thickness and absorption rate, skin freshness. On the contrary, product thickness with skin stickiness, skin moisture and skin smoothness showed positive correlations.
(2) In the interpretation of total variance from toner、serum、emulsion、cream, there were respective three、four、two、three components greater than 1, and the variances of these important components respectively accounted for 68.787%、72.497%、63.604%、64.219% of all these principal components. Of all the extracted principal components, the first principal component of these four kinds of cosmetics all included the sensory attributes which were skin moisture and skin softness and so on.
Conclusion:
In this study, we could get the conclusion that the same type of cosmetic products had obvious three key factors, correlating several sensory attributes. Further more, similar correlation could be found by principal component analysis in different type of skin care products such as toner, serum, emulsion and cream. Additionally, in the process of product development, more satisfying products could be designed and possibility of product success could be improved by fully understanding the needs of consumers, grasping the relationship between these sensory attributes and reasonably designing the sensory questionnaires.