SENSORY CHARACTERIZATION OF COSMETIC EMULSIONS 99 panelists used for DSA. We agree with these suggestions and believe that the number of consumers should be at least 50 per survey. In our study, we did not have a proposed application for our creams (e.g., daily facial cream) we primarily wanted to see how effectively consumers can differentiate between the emulsions based on their sensory characteristics. If there is a target application for the products to be evaluated, term selection should take this application into account, and the CATA survey should have a question about potential applications. In addition, as mentioned previously, most of our participants were regular users of hand and body lotions. We did not exclude people who used products less than two to three times a week. However, we believe that when CATA surveys are used to screen prototypes for a certain target application, participants should be recruited from the target group and should be regular users to provide meaningful results. Synthesizing previous theories, it was shown that CATA surveys are a reliable and power- ful tool to measure consumers’ sensory perception and to evaluate cosmetic and personal care products. Our untrained consumers could perceive differences and similarities be- tween products. CATA surveys may serve as a viable complimentary to DSA performed by trained panelists. This technique can be of particular interest to companies that do not have a trained panel or do not have time and/or resources to train a panel for a specifi c application. In addition, it was also proven that skin feel of the tested cosmetic emulsions was primarily determined by the emulsifi ers. ACKNOWLEDGMENTS The authors would like to thank the raw ingredient suppliers, including Inolex, Phoenix Chemical, ShinEtsu, DuPont Tate & Lyle, Ashland, Lonza, and Croda, for donating the ingredients, and our study participants for their time and participation. REFERENCES (1) V. A . L. Wortel and J. W. Wiechers, Skin sensory performance of individual personal care ingredients and marketed personal care products, Food Qual. Prefer., 11, 121–127 (2000). (2) L. R igano, Sensory in cosmetics, Cosmet. Toilet., 127(9), 628–634 (2012). (3) G. B aki and M. Chandler, What’s new in sensory focused formulation? C&B. Cosmet. Househ. Chem. Market, 4, 32–33 (2014). (4) M. C handler and G. Baki, Formulating the carrier phase for clinical success, EuroCosmetics, 22, 26–28, 2014. (5) M. C . Meilgaard, G. V. Civille, and B. T. Carr. Sensory Evaluation Techniques. (CRC Press, Boca Raton, FL, 2006), pp. 15–20. (6) ASTM E1490-11, Standard Guide for Two Sensory Descriptive Analysis Approached for Skin Creams and Lotions. (ASTM International, West Conshohocken, PA, 2011). (7) P. V arela and G. Ares, Novel Techniques in Sensory Characterization and Consumer Profi ling. (CRC Press, Boca Raton, FL, 2014), pp. 1–8. (8) A. M . Pense-Lheritier, Recent developments in the sensorial assessment of cosmetic products: A review, Int. J. Cosmet. Sci., 37, 465–473 (2015). (9) F. T . Kleij and P. A. D. Musters, Text analysis of open-ended survey responses: A complementary method to preference mapping, Food Qual. Prefer., 14(1), 43–52 (2003). (10) H. T. Lawless, N. Sheng, and S. S. C. P. Knoops, Multidimensional scaling of sorting data applied to cheese perception, Food Qual. Prefer., 6, 91–98 (1995). (11) J. Delarue and J. M. Sieffermann, Sensory mapping using fl ash profi le—Comparison with a conven- tional descriptive method for the evaluation of the fl avour of fruit dairy products, Food Qual. Prefer., 15, 383–392 (2004).

JOURNAL OF COSMETIC SCIENCE 100 (12) J. Pages, M. Cadoret, and S. Le, The sorted napping: A new holistic approach in sensory evaluation, J. Sens. Stud., 25, 637–658 (2010). (13) B. Thuillier, D. Valentin, R. Marchal, and C. Dacremont, Pivot© profi le: A new descriptive method based on free description, Food Qual. Prefer., 42, 66–77 (2015). (14) L. Dooley, Y. S. Lee, and J. F. Meullenet, The application of check-all-that-apply (CATA) consumer profi ling to preference mapping of vanilla ice cream and its comparison to classical external preference mapping, Food Qual. Prefer., 21, 394–401 (2010). (15) G. Ares and S. R. Jaeger, Check-all-that-apply questions: Infl uence of attribute order on sensory product characterization, Food Qual. Prefer., 28, 141–153 (2013). (16) J. Adams, A. Williams, B. Lancaster, and M. Foley, Advantages and uses of check-all-that-apply re- sponse compared to traditional scaling of attributes for salty snacks, 7th Pangborn Sensory Science Symposium. Minneapolis, MN, 2007. (17) S. R. Jaeger and G. Ares, Lack of evidence that concurrent sensory product characterization using CATA questions bias hedonic scores, Food Qual. Prefer., 35, 1–5 (2014). (18) T. Worch. L. Sebastien, and P. Punter, How reliable are the consumers? Comparison of sensory profi les from consumers and experts, Food Qual. Prefer, 21, 309–318 (2010). (19) G. Ares, C. Barreiro, R. Deliza, A. Giménez, and A. Gámbaro, Application of a check-all-that-apply question to the development of chocolate milk desserts, J. Sens. Stud., 25, 67–86 (2010). (2 0) F. Bruzzone, G. Ares, and A. Giménez, Consumers’ texture perception of milk desserts. II—Comparison with trained assessors’ data, J. Texture Stud., 43(3), 214–226 (2012). (21 ) C. Dederen, J. Donahue, C. Verboom, Visualizing the impact of emulsifi ers on emulsion perception, Cosmet. Toilet., 128(12), 884–891 (2013). (22 ) J. Wiechers, M. C. Taelman, V. Wortel, C. Verboom, and J. Dederen, Emollients and emulsifi ers exert their sensory impact in different phases of the sensory evaluation process but how does one demonstrate the absence of such an infl uence? IFSCC Mag., 5(2), 99–105 (2002). (23 ) M. Chatfi eld and A. Mander, The Skillings–Mack test (Friedman test when there are missing data), Stata J., 9(2), 299–305 (2009). (24 ) E. B. Manoukian, Mathematical Nonparametric Statistics. (Gordon & Breach, New York, NY, 1986), p. 45. (25 ) T. Jacobsen and R. W. Gunderson, “Applied cluster analysis,” in Statistical Procedures in Food Re- search, J. R. Piggott. Ed. (Elsevier Science Publishing, New York, NY, 1986), pp. 361–408. (26 ) J. Pages, MFA: Multiple Factor Analysis by Example Using R. (CRC Press, Boca Raton, FL, 2015), pp. 192–208. (27 ) Arlacel TM, LC data sheet, accessed April 4, 2017, www.croda.com. (28 ) G, Zocchi, “Skin feel agents,” in Handbook of Cosmetic Science and Technology, 3rd Ed. A. O. Barel, M. Paye, and H. I. Mainbach. Eds. (Informa Healthcare, New York, NY, 2009), pp. 357–370. (29 ) S. R. Jaeger, M. K. Beresford, A. G. Paisley, L. Antunez, L. Vidal, R. S. Cadena, A. Gimenez, and G. Ares, Check-all-that-apply (CATA) questions for sensory product characterization by consumers: Inves- tigations into the number of terms used in CATA questions, Food Qual. Prefer., 42, 154–164 (2015). (30 ) E. T. Fossel, Topical and oral delivery of arginine to cause benefi cial effects, US Patent 6458841 B2, 2000. (31 ) T. Aktar, J. Chen, R. Ettelaie, and M. Holmes, Evaluation of the sensory correlation between touch sensitivity and the capacity to discriminate viscosity, J. Sens. Stud., 30(2), 98–107 (2015). (32 ) H. R. Moskowitz, Base size in product testing: A psychophysical viewpoint and analysis, Food Qual. Prefer., 8, 247–255 (1997).