How far can we predict sensorial feelings by instrumental modelling?

Huber, Petra; Bongartz, Annette; Cezanne, Marie-Louise; Julius, Nina

Publication type:	Conference other
Type of review:	Peer review (abstract)
Title:	How far can we predict sensorial feelings by instrumental modelling?
Authors:	Huber, Petra Bongartz, Annette Cezanne, Marie-Louise Julius, Nina
et. al:	No
Published in:	2nd IPCE Conference
Conference details:	43rd National SICC Congress and 2nd IPCE Conference, Sestri Levante, Italy, 3-5 June 2018
Issue Date:	5-Jun-2018
Language:	English
Subjects:	Sensory assessment; Cosmetic; Modelling; Frictiometry; Rheology; Correlation
Subject (DDC):	660: Chemical engineering
Abstract:	How far can we predict sensorial feelings by instrumental modelling? According to experts, sensory benefits are closely linked to consumer product choice. Furthermore, descriptions of sensorial impressions or claims for cosmetic products are the new “consumer exciter”. Sensory testing methods that describe the relationship between products and their perception and evaluation by the human senses can therefore be powerful tools in developing, marketing and selling cosmetic products effectively. This presentation evaluates predictive models using instrumental data modeling and examines potential correlations between sensorial approaches and instrumental physical-chemical measurements, and hence determines whether sensory perceptions can be predicted by physical-chemical measurements. Current practice is summarized confirming that rheology and texture analysis are excellent tools for evaluating sensory texture attributes during the “pick up” phase, and some attributes during the “rub out” phase [1]. Subsequently, the “afterfeel” phase is considered, focusing on whether some complementary tribological trials can provide further insights. Tribology, which is the scientific study of interactions between contacting surfaces in relative motion [2, 3] enables friction and lubrication, the interactions that are of particular interest, to be assessed. If the product (bulk) is better distributed on the skin after application, a thin film is formed which can be detected by tribological measurements before it splits into its constituent water, oil and possibly polymer phases. Recent research on facial and sun protection products at the ZHAW has identified the extent to which the sensorial attributes can be predicted by instrumental modelling representing tribological data. Some statistical interventions, such as Pearson correlation and linear modelling, were used to assess the potential correlation between sensory assessment and physical data. A strong correlation between the sensory properties such as peaking, tackiness, density, spreadability and waxy residue was demonstrated in this study. In the facial care cluster, strong correlations could even be established for almost the whole “pick up” and “rub out” phases. Additionally, further correlations were found in the “afterfeel “phase for the sun protection cluster. Satisfactory correlations were found in the Pearson matrix with the primary focus being the frictiometric measurements. According to the values for the coefficient of determination, R2 (adjusted R2) the previously mentioned models represented between 72 and 96% for sun protection and 54-84% for facial care products. In the absence of alternative physical measurement, especially for the “afterfeel” phase, this is considered positive. However, a generic model which could be applied to all product categories could not be derived. Whereas in this study, a linear modelling technique was adopted as the simplest possible modelling process, it is felt that multidimensional and multivariate modelling might improve prediction. There is no perfect substitute for the human fingertip! Sensory panel testing delivers valuable and reliable data that are both accurate and reproducible, and this remains the “gold standard” [4]. At an early stage of development, predictive models can serve as meaningful prescreening tools and can create value for both the consumer and the cosmetics industry when combined with classical sensorial methods.
Further description:	[1] Gilbert L., Savary G., Grisel M., Picard C., Predicting sensory texture properties of cosmetic emulsions by physical measurements. Chemometr Intell Lab. 124 (2013) 21-31 [2] Greenaway R. E., “Psychorheology of Skin Cream,” University of Nottingham, Nottingham, 2010. [3] Gohar, R., Rahnejat H., Fundamentals of Tribology. London, Imperial College Press 2008 [4] Huber P., Chapter: Sensory Measurement—Evaluation and Testing of Cosmetic Products In: Sakamoto E., Cosmetic Science and Technology: Theoretical Principles and Applications, Ed. Elsevier, 2017
URI:	https://digitalcollection.zhaw.ch/handle/11475/27582
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Food and Beverage Innovation (ILGI)
Appears in collections:	Publikationen Life Sciences und Facility Management

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Huber, P., Bongartz, A., Cezanne, M.-L., & Julius, N. (2018, June 5). How far can we predict sensorial feelings by instrumental modelling? 2nd IPCE Conference.

Huber, P. et al. (2018) ‘How far can we predict sensorial feelings by instrumental modelling?’, in 2nd IPCE Conference.

P. Huber, A. Bongartz, M.-L. Cezanne, and N. Julius, “How far can we predict sensorial feelings by instrumental modelling?,” in 2nd IPCE Conference, Jun. 2018.

HUBER, Petra, Annette BONGARTZ, Marie-Louise CEZANNE und Nina JULIUS, 2018. How far can we predict sensorial feelings by instrumental modelling? In: 2nd IPCE Conference. Conference presentation. 5 Juni 2018

Huber, Petra, Annette Bongartz, Marie-Louise Cezanne, and Nina Julius. 2018. “How Far Can We Predict Sensorial Feelings by Instrumental Modelling?” Conference presentation. In 2nd IPCE Conference.

Huber, Petra, et al. “How Far Can We Predict Sensorial Feelings by Instrumental Modelling?” 2nd IPCE Conference, 2018.