Literature cited

1. Zhang L, Babi DK, Gani R. New vistas in chemical product and process design. Annual Review of Chemical and Biomolecular Engineering . 2016;7(1):557-582.
2. Gani R, Ng KM. Product design – Molecules, devices, functional products, and formulated products. Computers & Chemical Engineering . 2015;81:70-79.
3. Conte E, Gani R, Ng KM. Design of formulated products: A systematic methodology. AIChE Journal . 2011;57(9):2431-2449.
4. Global Cosmetics Products Market . 360 research reports. 2018. https://www.360researchreports.com/global-cosmetics-products-market-13100793 (accessed March 2020)
5. Kontogeorgis GM, Mattei M, Ng KM, Gani R. An integrated approach for the design of emulsified products. AIChE Journal . 2019;65(1):75-86.
6. Pensé‐Lhéritier A-M. Recent developments in the sensorial assessment of cosmetic products: a review. International Journal of Cosmetic Science . 2015;37(5):465-473.
7. Benson HAE, Roberts MS, Leite-Silva VR, Walters KA. Cosmetic Formulation Principles and Practice . 1st ed. Florida, USA: CRC Press; 2019.
8. Taifouris M, Martín M, Martínez A, Esquejo N. Challenges in the design of formulated products: multiscale process and product design.Current Opinion in Chemical Engineering . 2020;27:1-9.
9. Karunanithi AT, Achenie LEK, Gani R. A new decomposition-based computer-aided molecular/mixture design methodology for the design of optimal solvents and solvent mixtures. Ind Eng Chem Res . 2005;44(13):4785-4797.
10. Austin ND, Sahinidis NV, Konstantinov IA, Trahan DW. COSMO-based computer-aided molecular/mixture design: A focus on reaction solvents.AIChE Journal . 2018;64(1):104-122.
11. Jonuzaj S, Adjiman CS. Designing optimal mixtures using generalized disjunctive programming: Hull relaxations. Chemical Engineering Science . 2017;159:106-130.
12. Jonuzaj S, Akula PT, Kleniati P-M, Adjiman CS. The formulation of optimal mixtures with generalized disjunctive programming: A solvent design case study. AIChE Journal . 2016;62(5):1616-1633.
13. Zhang L, Kalakul S, Liu L, Elbashir NO, Du J, Gani R. A computer-aided methodology for mixture-blend design. applications to tailor-made design of surrogate fuels. Ind Eng Chem Res . 2018;57(20):7008-7020.
14. Kalakul S, Zhang L, Fang Z, et al. Computer aided chemical product design – ProCAPD and tailor-made blended products. Computers & Chemical Engineering . 2018;116:37-55.
15. Yunus NA, Gernaey KV, Woodley JM, Gani R. A systematic methodology for design of tailor-made blended products. Computers & Chemical Engineering . 2014;66:201-213.
16. Marvin WA, Rangarajan S, Daoutidis P. Automated generation and optimal selection of biofuel-gasoline blends and their synthesis routes.Energy Fuels . 2013;27(6):3585-3594.
17. Liu Q, Zhang L, Liu L, et al. OptCAMD: An optimization-based framework and tool for molecular and mixture product design.Computers & Chemical Engineering . 2019;124:285-301.
18. Jones O, Ben Selinger A. The chemistry of cosmetics. https://www.science.org.au/curious/people-medicine/chemistry-cosmetics (access March 2020).
19. Omidbakhsh N, Duever TA, Elkamel A, Reilly PM. A systematic computer-aided product design and development procedure: Case of disinfectant formulations. Ind Eng Chem Res . 2012;51(45):14925-14934.
20. Smith BV, Ierapepritou M. Framework for consumer-integrated optimal product design. Ind Eng Chem Res . 2009;48(18):8566-8574.
21. Bagajewicz M, Hill S, Robben A, et al. Product design in price-competitive markets: A case study of a skin moisturizing lotion.AIChE Journal . 2011;57(1):160-177.
22. Conte E, Gani R, Cheng YS, Ng KM. Design of formulated products: Experimental component. AIChE Journal . 2012;58(1):173-189.
23. Zhang L, Fung KY, Zhang X, Fung HK, Ng KM. An integrated framework for designing formulated products. Computers & Chemical Engineering . 2017;107:61-76.
24. Arrieta-Escobar JA, Bernardo FP, Orjuela A, Camargo M, Morel L. Incorporation of heuristic knowledge in the optimal design of formulated products: Application to a cosmetic emulsion. Computers & Chemical Engineering . 2019;122:265-274.
25. Zhang X, Zhou T, Zhang L, Fung KY, Ng KM. Food product design: A hybrid machine learning and mechanistic modeling approach. Ind Eng Chem Res . 2019;58(36):16743-16752.
26. Zhang L, Mao H, Liu L, Du J, Gani R. A machine learning based computer-aided molecular design/screening methodology for fragrance molecules. Computers & Chemical Engineering . 2018;115:295-308.
27. Goyal S, Goyal EK. Cascade and feedforward backpropagation artificial neural networks models for prediction of sensory quality of instant coffee flavoured sterilized drink. Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition . 2011;2(6):7882.
28. Baki G, Alexander KS. Introduction to Cosmetic Formulation and Technology . John Wiley & Sons; 2015.
29. Personal Care Products Council. Cosmetic ingredient dictionary. https://cosmeticsinfo.org/Ingredient-dictionary (accessed March 2020).
30. European Commission. CosIng database. https://ec.europa.eu/growth/sectors/cosmetics/cosing_en (accessed March 2020).
31. Gani R, Hytoft G, Jaksland C, Jensen AK. An integrated computer aided system for integrated design of chemical processes.Computers & Chemical Engineering . 1997;21(10):1135-1146.
32. Cardona Jaramillo JEC, Achenie LE, Álvarez OA, Carrillo Bautista MP, González Barrios AF. The multiscale approach to the design of bio-based emulsions. Current Opinion in Chemical Engineering . 2020;27:65-71.
33. Bernardo FP, Saraiva PM. A conceptual model for chemical product design. AIChE Journal . 2015;61(3):802-815.
34. Wibowo C, Ng KM. Product-oriented process synthesis and development: Creams and pastes. AIChE Journal . 2001;47(12):2746-2767.
35. Wibowo C, Ng KM. Product-centered processing: Manufacture of chemical-based consumer products. AIChE Journal . 2002;48(6):1212-1230.
36. Kim SH, Boukouvala F. Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques. Optim Lett . May 2019.
37. Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: A review. Computers & Chemical Engineering . 2018;108:250-267.
38. Teixeira MA, Rodríguez O, Mata VG, Rodrigues AE. The diffusion of perfume mixtures and the odor performance. Chemical Engineering Science . 2009;64(11):2570-2589.
39. Bronaugh RL, Maibach HI. Percutaneous Absorption: Drugs–Cosmetics–Mechanisms–Methodology . 3rd ed. New York, USA: Marcel Dekker, Inc.; 1999.
40. Hada S, Herring RH, Eden MR. Mixture formulation through multivariate statistical analysis of process data in property cluster space. Computers & Chemical Engineering . 2017;107:26-36.
41. Hill M. Product and process design for structured products.AIChE Journal . 2004;50(8):1656-1661.
42. Schweidtmann AM, Mitsos A. Deterministic global optimization with artificial neural networks embedded. J Optim Theory Appl . 2019;180(3):925-948.
43. Schweidtmann AM, Huster WR, Lüthje JT, Mitsos A. Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks. Computers & Chemical Engineering . 2019;121:67-74.
44. Grossmann IE, Trespalacios F. Systematic modeling of discrete-continuous optimization models through generalized disjunctive programming. AIChE Journal . 2013;59(9):3276-3295.
45. Beykal B, Boukouvala F, Floudas CA, Pistikopoulos EN. Optimal design of energy systems using constrained grey-box multi-objective optimization. Computers & Chemical Engineering . 2018;116:488-502.
46. Boukouvala F, Floudas CA. ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems. Optim Lett . 2017;11(5):895-913.
47. Eason JP, Biegler LT. A trust region filter method for glass box/black box optimization. AIChE Journal . 2016;62(9):3124-3136.
48. Craig S. How to review fragrance? https://bespokeunit.com/fragrance/formula/ (assessed March 2020).
49. Mata VG, Gomes PB, Rodrigues AE. Engineering perfumes. AIChE Journal . 2005;51(10):2834-2852.
50. Shcherbakov D, Massebeuf S, Normand V. Flash-point prediction of fragrances or flavours accounting for non-ideality of the liquid phase.Flavour and Fragrance Journal . 2019;34(1):63-69.
51. Poucher WA. Perfumes, Cosmetics and Soaps: Vol. II, the Production, Manufacture and Application of Perfumes . 9th ed. Dordrecht: Springer Science; 1993.
52. Teixeira MA, Rodríguez O, Rodrigues AE. The perception of fragrance mixtures: A comparison of odor intensity models. AIChE Journal . 2010;56(4):1090-1106.
53. Liaw H-J, Gerbaud V, Li Y-H. Prediction of miscible mixtures flash-point from UNIFAC group contribution methods. Fluid Phase Equilibria . 2011;300(1):70-82.
54. Jouyban A. Review of the cosolvency models for predicting drug solubility in solvent mixtures: An update. Journal of Pharmacy & Pharmaceutical Sciences . 2019;22:466-485.
55. Teixeira MA, Rodríguez O, Mota FL, Macedo EA, Rodrigues AE. Evaluation of group-contribution methods to predict VLE and odor intensity of fragrances. Ind Eng Chem Res . 2011;50(15):9390-9402.
56. Ng KM, Gani R. Chemical product design: Advances in and proposed directions for research and teaching. Computers & Chemical Engineering . 2019;126:147-156.
57. Fung KY, Ng KM, Zhang L, Gani R. A grand model for chemical product design. Computers & Chemical Engineering . 2016;91:15-27.
Figure 1. The General Methodology of Optimization-based Cosmetic Formulation