Correlation between sensory and instrumental characterization of developed sunscreens containing grape seed extract and a commercial product / Liudmila Yarovaya in INTERNATIONAL JOURNAL OF COSMETIC SCIENCE, Vol. 44, N° 5 (10/2022)  

[article]  inINTERNATIONAL JOURNAL OF COSMETIC SCIENCE > Vol. 44, N° 5 (10/2022) . - p. 569-587 Titre : Correlation between sensory and instrumental characterization of developed sunscreens containing grape seed extract and a commercial product Type de document : document électronique Auteurs : Liudmila Yarovaya, Auteur ; Neti Waranuch, Auteur ; Wudtichai Wisuitiprot, Auteur ; Watcharee Khunkitti, Auteur Année de publication : 2022 Article en page(s) : p. 569-587 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Analyse sensorielle Caractérisation Extraits de plantes:Extraits (pharmacie) Formulation (Génie chimique) Ingrédients cosmétiques Peau -- Soins et hygiène Pépins de raisins Produits antisolaires Raisin et constituants Rhéologie Statistiques Texture -- Analyse Index. décimale : 668.5 Parfums et cosmétiques Résumé : - Objective : In the development of cosmetic products, sensory evaluation is an important step in determining consumer acceptance before it is released on a market but is often time-consuming and costly. However, correlating sensory characteristics with instrumental parameters using multivariate techniques is a potential way to facilitate the development of cosmetic products. - Methods : Sunscreen formulations varied in the content of grape seed extract (GSE) and ultraviolet (UV) filters, and benchmark products were characterized using sensory descriptive analysis and instrumental analysis. Principal Component Analysis (PCA) was applied to the panel's performance data to study how well the panelists performed compared to each other and to find an association between rheological and textural instrumental parameters of cream samples. Further, applying Partial Least Squares (PLS) regression analysis, the association between sensory attributes and instrumental parameters was analyzed. In addition, a preference for the sensory properties of the studied sunscreen products that are important for consumers living in Southeast Asia was assessed by PLS. - Results : In this study, both the sensory and instrumental properties of all tested formulations were described well by PCA. The practicality of PLS was confirmed by an established correlation between sensory attributes from the categories of appearance (glossiness), pick-up (integrity of shape, firmness, glossiness, stringiness), and after-feel (glossiness, spreadability, stickiness) with both rheological and textural parameters. Although the instrumental analysis could not completely replace sensory evaluation, a described method applying PLS can be used as an additional cost-effective and time-saving method during the development of cosmetic products. Moreover, PLS revealed that sunscreens with a light texture and glossy appearance providing smooth skin after-feel are likely to be preferred over the thicker formulations having a residual color appearance in Southeast Asia. - Conclusion : Until a universal model is created, the cosmetics developers and companies can apply a described method of determining sensory properties from the instrumental parameters of their own products. Future studies will be worth exploiting the applicability of the PLS regression model on instrumental datasets predicting sensory characteristics of other sunscreen products. Note de contenu : - MATERIALS AND METHODS : Chemicals - Preparation of grape seed extract - Preparation of sunscreen formulations - Sensory evaluation - Instrumental determinations - Textural analysis - Statistical analysis - RESULTS : Sensory analysis - Instrumental profiling - Partial least squares (PLS) regression analysis - Table 1 : Sunscreen formulations - Table 2 : Definitions of attributes used for sensory evaluation - Table 3 : Rheological parameters of cream samples derived through controlled oscillatory strain sweep and shear flow analysis - Table 4 : Textural parameters of cream samples - Table 5 : Partial Least Square regression model calibration and cross-validation for predicted and reference responses DOI : https://doi.org/10.1111/ics.12807 En ligne : https://drive.google.com/file/d/1unxmlYIYtQF3kJhZJge7WgK8qDm6fJIM/view?usp=shari [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=38154 [article]

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