Full Paper IFSCC 2017

Authors: Petra HUBER1, Annette BONGARTZ1, Marie-Louise CEZANNE1, Nina JULIUS1

1Department of Food and Beverage Innovation ILGI, Zurich University of Applied Science ZHAW, Switzerland

How far can we predict sensorial feelings by instrumental modelling?

Abstract

The extent to which the sensorial attributes of facial and sun protection products can be predicted by instrumental modelling representing tribological data

The sensorial benefits of cosmetic products are known to have a considerable influence on consumer product choice. Furthermore, descriptors of sensorial impressions or claims are acknowledged as the new “consumer exciter”. The scientific discipline of sensory analysis, which describes the relationship between products and their perception and evaluation by the human senses, and sensory testing methods are powerful tools that can be used to assist in the development of cosmetic product s and enhance the effectiveness of marketing and sales campaigns.

The objective of this study is to assess whether there is any correlation between sensorial approaches to product evaluation and predictive models derived from instrumental physicochemical measurements and to assess whether sensory perceptions can be predicted by the models. Having confirmed that rheology and texture analysis are excellent tools to evaluate sensory texture attributes during the “pick up”, and some attributes during the “rub out” phase, data from complementary tribological trials are presented and discussed. The objective is to promote a better understanding of how the current limitations in physicochemical techniques corresponding to sensory methods might be overcome, especially in the “rub out” and “afterfeel” phases.

It was concluded that there is no acceptable substitute for the human fingertip. Sensory panel testing provides valuable and reliable data that is both accurate and reproducible. This remains the “gold standard”. Nevertheless, sensory testing capabilities need to be enhanced in an effort to improve the effectiveness of product formulation development by the cosmetics industry. At an early stage of development, predictive models can provide valuable support as prescreening tools. Combined with classical sensorial methods, predictive data modelling has the potential to create value for both the cosmetics industry and the consumer.

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Introduction

Improvements in digital technology continue to provide opportunities for enhanced measurement, evaluation and modelling through the means of appropriate algorithms. It would undoubtedly be advantageous to capitalise on such an opportunity to increase efficiency in the cosmetic industry and the question has arisen as to whether these processes are also suitable for the prediction of the sensorial properties of a product. Consistent and reproducible test conditions are a prerequisite for all sensory measurements, which are often designed to evaluate the effects of a cosmetic (Huber P., 2017). (The classical methodology of sensory science employing objective profiling by trained experts in addition to testing for product acceptance by users (consumer science) is still considered to be the gold standard.

Correlation studies between the values obtained from sensory analysis (attributes) and the measurement of physical product characteristics (rheology, viscosity, texture) have suggested some interesting relationships. Various physical parameters correlate very well with some sensorial properties and are suitable as predictors, when certain basic conditions are met. Reproducible results are obtained, especially in the early product application phase “pick up” and “rub out”, in appropriately selected product samples (Guest. et al., 2013, Greenaway R. E. 2010, Gilbert L. et al., 2013). Sensory product characteristics, such as spreadability or stringiness, exhibit strong to moderate correlation in these studies. However, there is little evidence to suggest a correlation in the “afterfeel” phase for the attributes absorbency, product residence and skin feel, i.e. softness.

The challenge is to represent sensory perceptions in the “afterfeel” phase, such as absorbency and stickiness, product residue and the final skin feeling, with the measurement of physical characteristics. The former can be represented by means of measurement of rheology and texture. If the product (bulk) is better distributed on the skin after application, a thin film is formed which can be determined by tribological measurements before it splits into its constituent water, oil and possibly polymer phases (corresponding to the different phases of hydrodynamic or aqueous lubrication (Adams et al., 2007)). Depending on their chemical properties, these components interact with the skin and are adsorbed, resorbed or evaporate. The condition of the stratum corneum changes and hence its resistance to friction also changes. If the product leaves a greasy-oily lubricating film, the resistance decreases (Guest S. et al., 2013). This complex process should be broken down into the smallest possible “work units” from a technical point of view. Tribology, which is the scientific study of interactions between contacting surfaces in relative motion (Gohar R. et Rahnejat H. (2008), Greenaway R. E. (2010)), enables the interactions that are of particular interest, friction and lubrication, to be assessed. Various tribological measuring methods, incorporating either rotating or linearly sliding movements across surfaces, in particular the skin, have been described in the literature (Sivamani J. et al., 2003 ).

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Material and Methods

Products

Eight male facial care and six sun protection products were selected on the basis of their market information to include various textures and both emulsions and gels of differing viscosities. The products were representative of the products available on the Swiss cosmetics market. Products were ultimately selected from international as well as internationally-active, Swiss companies.

Sensorial assessment

A trained cosmetics sensory panel (n = 9), familiar with the requirements of the test, evaluated the attributes in accordance with a modified ZHAW standard method (adapted version according to ASTM (1997/2003)) (Huber P., 2015). The attributes were evaluated against a reference sample on a continuous line scale, where 100 was the highest, most intense and 0 the lowest, least intense value. All tests were conducted in the sensory testing booths of the ZHAW sensory facility. Sample coding, the questionnaire and the panellists’ electronic data sampling were conducted using FIZZ sensory software 2.47B (Biosystemes). The statistical analysis was performed with the software XLSTAT 2016.

The 17 clearly defined sensorial attributes tested (as seen in Fig. 1) were divided into sensory monitoring phases such as “pick up” phase (B), “rub out” phase during application (C), “afterfeel” phase after application (D).

Fig. 1: Sensory Assessment (descriptive profile) on a scale of 0 -100 (0=low, 100=intense value) with reference sample (0;100),average rating of 9 panellists for each attribute and 6 samples of cluster sun protection.

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Fig. 2: Sensory Assessment (descriptive profile) on a scale of 0 -100 (0=few, 100=intense value) with reference sample (0;100),average rating of 9 panellists for each attribute and 8 samples of cluster face cream for men .

Rheological data

The rheological parameters were measured using a Modular Compact Rheometer MCR 302, Anton Paar (plate 25mm). Key data were recorded, including the viscosity ɳ at different shear rates [Pas] between 1 and 100 rpm, the elastic or storage modulus Gʹ [Pa], the viscous or loss modulus Gʹʹ [Pa], the loss factor tan δ [G’’/G’], the yield point γ [Pa], shear strain deformation 𝛾𝛾L [%] and shear stress τ [Pa] for the characterisation of the linear-viscoelastic range LVE.

Frictiometric data

A Frictiometer FR700 (Courage & Khazaka, Germany) was used with a circular rotating Teflon head (disc 16 mm). At constant speed (rpm) and under normal force (0.7N), the friction head was applied to the skin and the resulting torque was measured, and the result displayed in arbitrary Frictiometer units (FU). The measurement results were denoted as Frictio 1-4. The measurements immediately after application of the product were indicated by Frictio 1 and 2 whereas measurements after the product had been removed were indicated by Frictio 3 and 4 (mean values from triplicate determinations). Measurement conditions included measurement on the same test person in predefined test zones on the anterior forearm, in zones similar to those used by panellists in the sensory profiling.

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Fig. 3: Frictiometer FR 700 with rotational Teflon disc (picture credit Courage+Khazaka electronic GmbH, Germany)

Although various factors influence the friction properties of the skin (humidity, moisture content of the skin, application pressure of the measuring device), these parameters are controllable. In this study, the frictiometric assessment was performed on the skin taking care to specifically minimise variation during the test. Ongoing internal studies at the ZHAW are examining the extent to which alternative skin models or materials are suitable for completing these measurements.

Statistical analysis

Statistical analysis of selected data was performed using the software RStudio, Version 0.99.896 (Pearson correlation and linear modelling) to assess the potential correlation between sensory assessment and physical data.

The extent of the linear relationship was represented by the correlation analysis measured by the Pearson correlation coefficient. The t-test was used to check for statistical significance of the underlying samples (sensory and physical). A linear regression analysis was carried out in order to evaluate a possible functional relationship. The closer the determined measure R2 was to 1, the more precisely a prediction of the predicted properties (sensory attributes) could be made from the measured physical data.

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Results

In the Pearson correlation matrix over all the product categories (n=14), approx. 2.9 % of the combinations showed a strong correlation (0.8-1) and 12% a moderate correlation (0.6-0.8). For the product cluster face care 16% showed a strong and 14.3% a moderate correlation resp. 17.6% and even 21.4% for the cluster sun protection. Sensory properties such as peaking, tackiness, density, spreadability and waxy residue demonstrated strong correlation in this study. In the face care cluster, a strong correlation could even be established for nearby the whole pick up and rub out phase. Furthermore for the cluster sun protection more correlations could be found in the afterfeel phase. These correlations will now be investigated to determine both their functional and causal relationship. Table 1 shows the basic correlation coefficients according to Pearson between measured physical data and sensory-tested properties using the entire product category face care and sun protection (n = 14) as an example. Values above 0.75 were assessed as sufficiently strong correlations and taken into account for the subsequent modeling. To ensure the statistical reliability of the correlation, a student t-test was performed with a value for p = 0.05. Tab. 1: Section from the Pearson correlation matrix showing correlation between the measured physical data and the sensory-tested properties, from top to bottom, for both product categories face care and sun protection (n=14), for face care only (n=8), and for sun protection only (n=6) (dark indicates a strong (0.8-1), mid and pale a moderate correlation (0.6-0.8), bold print means a statistical reliability of the correlation with a value for p = 0.05 ).

It is noteworthy that when the total product number (n = 14) is divided into the individual product categories (sun products n = 6; face care products n=8), some different physical measurement data and methods contributed to the correlation. For example, in the case of measurement of the

Sensory attribute/phase 1B 2B 3B 4B 5B 1C 2C 3C 4C 1D 2D 3D 4D 5D 6D 7D 8D

Physical dataFrictio 1 0.587 0.335 0.143 0.553 0.678 -0.675 -0.530 0.686 0.643 -0.247 -0.242 -0.149 0.269 0.226 -0.276 0.493 -0.145Frictio 2 0.821 0.178 0.041 0.914 0.847 -0.622 -0.574 0.032 0.004 -0.131 0.348 -0.118 0.197 0.451 -0.236 0.124 0.033Frictio 3 -0.044 0.042 0.158 0.116 0.070 -0.096 -0.217 0.037 0.079 0.277 0.483 -0.575 0.052 0.610 -0.125 0.100 -0.606Frictio 4 0.211 0.063 0.283 0.428 0.290 -0.192 -0.338 -0.061 -0.028 0.390 0.521 -0.678 0.087 0.878 -0.052 0.220 -0.669Viscosity 1 0.692 -0.271 -0.335 0.688 0.817 -0.771 -0.661 0.141 0.152 -0.013 -0.029 -0.279 -0.017 0.362 -0.111 0.204 -0.200Viscosity 2 0.746 -0.203 -0.216 0.780 0.860 -0.736 -0.681 0.019 0.011 0.035 0.163 -0.308 -0.027 0.428 -0.119 0.154 -0.226Viscosity 3 0.710 -0.161 -0.166 0.791 0.826 -0.655 -0.645 -0.083 -0.076 0.121 0.238 -0.352 -0.038 0.463 -0.030 0.186 -0.235Viscosity 4 0.674 -0.107 -0.159 0.789 0.797 -0.622 -0.654 -0.129 -0.111 0.149 0.308 -0.372 -0.053 0.473 -0.017 0.167 -0.226Storage modulus G' 0.220 -0.106 0.050 0.159 0.337 -0.414 -0.258 0.376 0.516 -0.118 -0.111 -0.140 0.398 0.123 -0.314 0.198 -0.060Loss modulus G'' 0.251 -0.137 0.035 0.185 0.377 -0.461 -0.310 0.420 0.551 -0.134 -0.145 -0.159 0.429 0.146 -0.356 0.253 -0.089Loss factor -0.389 0.013 0.225 -0.211 -0.334 0.397 0.273 -0.325 -0.295 0.399 0.434 -0.420 -0.123 0.436 0.000 0.210 -0.530Yield point 0.774 -0.110 -0.184 0.787 0.776 -0.607 -0.503 -0.046 -0.067 -0.203 0.201 -0.075 0.251 0.481 -0.243 0.182 -0.092LVE shear strain def. -0.066 0.188 0.025 0.120 0.184 -0.250 -0.493 0.089 0.094 0.327 0.135 -0.502 -0.435 0.216 0.058 0.265 -0.483LVE shear stress 0.557 -0.074 0.024 0.576 0.563 -0.316 -0.264 -0.183 -0.234 -0.079 0.190 -0.077 0.186 0.359 -0.203 0.376 -0.177

Sensory attribute/phase 1B 2B 3B 4B 5B 1C 2C 3C 4C 1D 2D 3D 4D 5D 6D 7D 8D

Physical dataFrictio 1 0.475 0.273 -0.330 0.553 0.568 -0.419 -0.255 0.664 0.744 -0.426 -0.343 0.429 0.083 0.121 0.115 0.695 0.176Frictio 2 0.927 0.138 -0.814 0.938 0.961 -0.929 -0.708 0.391 0.283 -0.753 0.151 0.578 0.217 0.171 -0.182 -0.129 0.517Frictio 3 -0.240 0.112 -0.265 -0.155 -0.300 0.194 0.090 -0.469 -0.581 -0.090 0.502 -0.355 0.207 0.474 0.008 -0.222 -0.448Frictio 4 0.072 0.036 -0.515 0.191 -0.010 -0.122 -0.220 -0.432 -0.589 -0.099 0.367 -0.421 0.178 0.779 0.133 -0.231 -0.462Viscosity 1 0.971 -0.190 -0.802 0.892 0.955 -0.930 -0.646 0.703 0.605 -0.675 -0.059 0.357 0.440 0.390 -0.088 0.024 0.163Viscosity 2 0.988 -0.169 -0.800 0.904 0.968 -0.961 -0.722 0.656 0.538 -0.698 0.054 0.339 0.482 0.360 -0.115 -0.109 0.191Viscosity 3 0.983 -0.095 -0.788 0.930 0.972 -0.957 -0.767 0.583 0.474 -0.661 0.079 0.321 0.442 0.352 -0.036 -0.130 0.220Viscosity 4 0.971 0.007 -0.797 0.955 0.968 -0.965 -0.833 0.480 0.357 -0.672 0.167 0.313 0.412 0.357 -0.037 -0.190 0.256Storage modulus G' 0.832 -0.198 -0.612 0.707 0.825 -0.812 -0.552 0.920 0.822 -0.742 -0.046 0.357 0.575 0.288 -0.315 0.150 0.055Loss modulus G'' 0.725 -0.170 -0.493 0.601 0.730 -0.708 -0.464 0.965 0.889 -0.706 -0.092 0.351 0.559 0.229 -0.346 0.257 0.019Loss factor -0.530 0.052 0.051 -0.464 -0.578 0.487 0.403 -0.459 -0.512 0.125 0.268 -0.366 0.022 0.299 -0.065 -0.005 -0.483Yield point 0.949 -0.142 -0.812 0.870 0.935 -0.946 -0.683 0.753 0.610 -0.804 0.043 0.377 0.518 0.420 -0.297 0.027 0.126LVE shear strain def. -0.351 0.691 0.229 -0.073 -0.290 0.263 -0.258 -0.306 -0.298 0.126 0.302 -0.444 0.018 0.304 0.301 0.241 -0.387LVE shear stress 0.707 -0.150 -0.457 0.589 0.715 -0.691 -0.470 0.972 0.902 -0.686 -0.103 0.336 0.553 0.221 -0.334 0.280 0.009

Sensory attribute/phase 1B 2B 3B 4B 5B 1C 2C 3C 4C 1D 2D 3D 4D 5D 6D 7D 8D

Physical dataFrictio 1 0.881 0.483 0.801 0.971 0.963 -0.930 -0.834 0.977 0.927 0.157 0.667 -0.681 0.350 0.858 -0.949 0.607 -0.775Frictio 2 0.668 0.225 0.687 0.786 0.695 -0.719 -0.720 0.880 0.893 0.085 0.380 -0.548 0.632 0.956 -0.785 0.448 -0.538Frictio 3 0.428 -0.159 0.410 0.668 0.826 -0.906 -0.955 0.691 0.797 0.655 0.188 -0.894 0.011 0.882 -0.644 0.735 -0.854Frictio 4 0.512 -0.004 0.578 0.705 0.783 -0.836 -0.854 0.804 0.904 0.516 0.338 -0.855 0.333 0.984 -0.738 0.755 -0.810Viscosity 1 0.052 -0.421 -0.083 0.269 0.562 -0.652 -0.726 0.204 0.318 0.901 -0.061 -0.846 -0.551 0.421 -0.182 0.701 -0.807Viscosity 2 0.145 -0.356 -0.039 0.365 0.641 -0.727 -0.794 0.264 0.346 0.820 -0.043 -0.807 -0.577 0.426 -0.248 0.634 -0.795Viscosity 3 0.031 -0.434 -0.071 0.251 0.550 -0.641 -0.714 0.205 0.333 0.923 -0.053 -0.864 -0.522 0.439 -0.184 0.730 -0.815Viscosity 4 -0.042 -0.468 -0.095 0.170 0.476 -0.567 -0.641 0.156 0.307 0.960 -0.059 -0.870 -0.487 0.424 -0.134 0.765 -0.803Storage modulus G' -0.140 -0.032 0.514 -0.028 0.168 -0.161 -0.081 0.241 0.499 0.439 0.380 -0.442 0.280 0.379 -0.386 0.626 -0.333Loss modulus G'' -0.110 -0.086 0.519 0.029 0.225 -0.236 -0.179 0.299 0.567 0.496 0.347 -0.520 0.303 0.482 -0.420 0.672 -0.397Loss factor -0.148 -0.558 -0.162 0.040 0.267 -0.372 -0.489 0.100 0.271 0.906 -0.159 -0.807 -0.221 0.483 -0.012 0.721 -0.682Yield point -0.003 -0.342 0.356 0.227 0.446 -0.528 -0.573 0.422 0.676 0.775 0.139 -0.827 0.159 0.755 -0.431 0.819 -0.682LVE shear strain def. 0.225 -0.198 -0.077 0.392 0.629 -0.686 -0.720 0.203 0.200 0.591 -0.028 -0.589 -0.708 0.211 -0.215 0.404 -0.646LVE shear stress -0.157 -0.565 -0.404 -0.024 0.142 -0.245 -0.388 -0.058 0.022 0.768 -0.311 -0.631 -0.356 0.280 0.197 0.497 -0.543

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viscosity, this is probably due to non-Newtonian products (emulsions, hydro dispersion gels), which can behave like a Newtonian fluid at low shear rates (pseudoplastic behaviour with linear behaviour). The titanium dioxide (TiO2) content in most sun protection products is more indicative of viscoplastic behaviour. (Moravkova T. et Stern P., 2011)

Furthermore, certain measurement methods are not suitable for measuring all products, for example, some liquids have too low a viscosity for certain viscosity measuring techniques, as the characteristic lies outside the method’s accepted measurement range.

Linear models were created for the combination pairs from the Pearson matrix with a factor of at least 0.75 to represent an easy to understand model with just one physical measurement method (Tables 2 and 3). The linear models were described by the straight line y = a + bx. The adjusted R2 denotes the corrected coefficient of determination and the p value ensures the statistical reliability of the correlation.

Tab. 2: Predictive model equations for product category face care for men (n=8) with indication of the goodness of fit data for the prediction between scores (R2) from rheological and frictiometric analysis and sensorial assessed parameters.

Tab. 3: Predictive model equations for product category sun protection (n=6) with indication of the goodness of fit data for the prediction between scores (R2) from frictiometric analysis and sensorial assessed parameters.

By way of example, the model equations are visually represented in the following figures. For the prediction of peaking, for example, two different rheological measured data were significant,viscosity and yield point.

Model a (intercept) b (slope) Adjusted R2 p valuePeaking (Viscosity 1) 0.846 0.227 0.934 < 0.0001Peaking (Yield point) 4.638 0.049 0.885 0.000Spreadability(Frictio 2) 117.278 -0.471 0.839 0.001Waxy product residue (Frictio 4) 4.264 0.022 0.54 0.023

Model a (intercept) b (slope) Adjusted R2 p valuePeaking(Frictio 1) -10.505 0.152 0.720 0.02Spreadability(Frictio 1) 103.213 -0.224 0.831 0.007Waxy product residue (Frictio 4) 2.128 0.025 0.960 0.000

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Fig. 4: Representation of the linear model for the prediction of peaking, based on measured viscosity at 1 rpm (left) and based on measured yield point (right) over all measured face care products (n=8), with the model equation y = 0.846 + 0.227 x (adjusted R2=0.934) and y = 4.638 + 0.049 x (adjusted R2=0.885) respectively and a 95% confidence interval (dotted line).

For the sun protection products, the tribological measurement of Frictio 1 also corresponded to a 72% prediction of the data (Fig. 5).

Fig. 5: Representation of the linear model for the prediction of peaking based on measured frictiometric units, such as Frictio 1 over all measured sun protection products (n=6) with the model equation y = -10.505 + 0.152 x (adjusted R2=0.720) and a 95% confidence interval (dotted line).

With the focus on tribological observations and how they contribute to a prediction model, spreadability as assessed by a sensory panel demonstrated a strong negative correlation with low frictio 1 or 2 units, as shown for sun protection (Fig. 6) and for face care (Fig. 7).

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Fig. 6: Representation of the linear model for the prediction of spreadability based on measured frictiometric units, such as Frictio 1 over all measured sun protection products (n=6) with the model equation y = 103.213 - 0.224 x (adjusted R2=0.831) and a 95% confidence interval (dotted line).

Fig. 7: Representation of the linear model for the prediction of spreadability based on measured frictiometric units, such as Frictio 2 over all measured face care products (n=8) with the model equation y=- 117.278 - 0.471 x (adjusted R2= 0.839) and a 95% confidence interval (dotted line).

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The contribution of Frictio 4 values exhibited strong correlation for the prediction of waxy residue from sun protection (adjusted R2 =0.920), Fig. 8 and a moderate correlation for the prediction from face care products for men (adjusted R2= 0.540), Fig. 9

Fig. 8: Representation of the linear model for the prediction of waxy product residue based on measured frictiometric units, such as Frictio 4, over all measured sun protection products (n=6) with the model equation y = 2.13 + 0.025 x (adjusted R2 =0.960) and a 95% confidence interval (dotted line).

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Fig. 9: Representation of the linear model for the prediction of waxy residue based on measured frictiometric units, such as Frictio 4 over all face care measured products (n=8) with the model equation y=- 4.264 + 0.022 x (adjusted R2= 0.540) and a 95% confidence interval (dotted line).

Discussion

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 R)2 the models discussed here represented between 72 and 96% for sun protection and 54-84% for face care products. In the absence of alternative physical measurement, especially for the “afterfeel” phase, this is considered positive. However, a general model covering all product categories could not be derived. Sensory profiling by a trained panel remains the only acceptable standard.

The range of linearity in each correlation pair (physical characteristic versus sensory attribute) can be attributed to different areas of the measurement data cloud.

The sensory attributes which may be redundant and which may be omitted in further studies can be identified based on an analysis of the main components. Furthermore , experience gained in unpublished studies has shown that product categories can be correlated differently by means of the choice of measurement method.

A linear modelling technique was adopted as the simplest possible modelling process; however, multidimensional and multivariate modelling might improve prediction.

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Conclusion and Outlooks

The challenge to represent the individual sensory perceptions in the “afterfeel” phase by means of a physical measurement has not yet been satisfactorily addressed. The understanding of the tribological interactions between the finger and the skin of a product user has to be improved through comparison with further tribological measuring techniques. Significant sensory characteristics can be individually represented by instrumental measurements, particularly in the “pick up” and partly in the “rub out” phase. If the appropriate product category is considered, these instrumental techniques can represent cost-effective techniques for use in product pre-screening tests. However, no predictive models have been developed, which would be broadly applicable and hence would be suitable for product development, since the measuring methods used, the sensory vocabulary and the samples selected for assessment are too varied. Until modelling techniques are further improved, the sensitivity of the human being will continue to provide the only reliable means of assessing the sensory profile of a cosmetic product.

Acknowledgment: The authors would like to thank Stella Cook-Gummery, Institute of Food and Beverage Innovation ILGI, and Dr. Ivo Kälin, Institute of Applied Simulation IAS, Zurich University of Applied Science ZHAW, Wädenswil, Switzerland, for their assistance.

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