Keywords
acidulants
simplex-lattice mixture design
hedonic scale
R programming
How to Cite
Abstract
Acidulants are crucial for enhancing and balancing the flavor profiles of confectionery products, such as gummy candies, ensuring an optimal sensory experience. This study aimed to develop an R script using the simplex-lattice mixture design to optimize the sourness and flavor levels and the combined response of these attributes for orangeflavored gummy candies, demonstrating its application in improving the sensory qualities of confectionery products. The gummy candies were prepared according to previous research, incorporating citric, malic, and fumaric acids based on the experimental design. The R scripts were provided and uploaded to the GitLab platform for download and analysis (https://gitlab.com/FoodChem-DataSci-Lab/orange-flavored-gummy-candies). The effects of these acids on sourness and flavor were assessed using a 5-point hedonic scale by 30 trained judges. The data were analyzed with R, resulting in mathematical models for the acids’ individual effects, interactions, and combined responses. Effect (Piepel direction) and contour plots were generated as well. The optimal mixture was determined to be 4.95 g of citric acid, 4.65 g of malic acid, and 5.40 g of fumaric acid, achieving an optimal combined response value of 107.14. In conclusion, balancing these three acids is critical to optimizing sourness and flavor levels in orange-flavored gummy candies. This study provided a valuable methodology for formulating confectionery products with enhanced sensory profiles. It demonstrated the capability of R to address complex problems in both the confectionery industry and academia, emphasizing its importance as an analytical tool for developing products with improved sensory characteristics.
References
González-Otamendi, M. D. J., Pérez-Flores, J. G., Contreras-López, E., Soto-Vega, K., García-Curiel, L., Pérez-Escalante, E., Islas-Martínez, D., Jijón, C. Á., & Portillo-Torres, L. A. (2024). Uso de polioles en la industria de la confitería. Ciencia Latina Revista Científica Multidisciplinar, 8(3), 499-528. https://doi.org/10.37811/cl_rcm.v8i3.11259
Gómez, M. M. R., & Sánchez, N. E. O. (2011). Productos gelificados y aireados. En Confitería: De lo artesanal a la tecnología (pp. 179-209). Universidad Autónoma de Aguascalientes.
Hartel, R. W., Von Elbe, J. H., & Hofberger, R. (2018). Jellies, gummies and licorices. En R. W. Hartel, J. H. Von Elbe, & R. Hofberger, Confectionery science and technology (pp. 329-359). Springer International Publishing. https://doi.org/10.1007/978-3-319-61742-8
Chen, X., Zhang, W., Quek, S. Y., & Zhao, L. (2023). Flavor–food ingredient interactions in fortified or reformulated novel food: Binding behaviors, manipulation strategies, sensory impacts, and future trends in delicious and healthy food design. Comprehensive Reviews in Food Science and Food Safety, 22(5), 4004-4029. https://doi.org/10.1111/1541-4337.13195
Hou, L., Zhang, Y., Li, C., Wang, X., & Wang, S. C. (2021). Determination of main bitter compounds in soaked and germinated sesame pastes. Journal of Oleo Science, 70(1), 31-38. https://doi.org/10.5650/jos.ess20169
Qian, R., Sun, C., Bai, T., Yan, J., Cheng, J., & Zhang, J. (2024). Recent advances and challenges in the interaction between myofibrillar proteins and flavor substances. Frontiers in Nutrition, 11, 1378884. https://doi.org/10.3389/fnut.2024.1378884
Goldenberg, L., Yaniv, Y., Kaplunov, T., Doron‐Faigenboim, A., Carmi, N., & Porat, R. (2015). Diversity in sensory quality and determining factors influencing mandarin flavor liking. Journal of Food Science, 80(2), S418-S425. https://doi.org/10.1111/1750-3841.12742
Hülber-Beyer, É., Bélafi-Bakó, K., & Nemestóthy, N. (2021). Low-waste fermentation-derived organic acid production by bipolar membrane electrodialysis—An overview. Chemical Papers, 75(10), 5223-5234. https://doi.org/10.1007/s11696-021-01720-w
Mao, Y., Tian, S., Qin, Y., & Cheng, S. (2021). An optimized organic acid human sensory sourness analysis method. Journal of the Science of Food and Agriculture, 101(14), 5880-5887. https://doi.org/10.1002/jsfa.11240
Rodríguez-Sánchez, F. (2020). Quince consejos para mejorar nuestro código y flujo de trabajo con R. Ecosistemas, 29(3). https://doi.org/10.7818/ECOS.2129
Santos, F. G., Fratelli, C., Muniz, D. G., & Capriles, V. D. (2018). Mixture design applied to the development of chickpea‐based gluten‐free bread with attractive technological, sensory, and nutritional quality. Journal of Food Science, 83(1), 188-197. https://doi.org/10.1111/1750-3841.14009
Squeo, G., De Angelis, D., Leardi, R., Summo, C., & Caponio, F. (2021). Background, applications and issues of the experimental designs for mixture in the food sector. Foods, 10(5), 1128. https://doi.org/10.3390/foods10051128
K K, S., C, S. C., K, S., M C, J., Raj, A., & Kappally, S. (2023). Statistical design of experiments using R program for the optimization of an extended-release neem oil matrix tablet. Journal of Pharmaceutical Innovation, 18(1), 205-219. https://doi.org/10.1007/s12247-022-09640-2
Lawson, J., & Willden, C. (2016). Mixture experiments in R using mixexp. Journal of Statistical Software, 72(Code Snippet 2). https://doi.org/10.18637/jss.v072.c02
Lawson, J., & Willden, C. (2011). mixexp: Design and analysis of mixture experiments (p. 1.2.7) [Dataset]. https://doi.org/10.32614/CRAN.package.mixexp
Boyd, A., & Sun, D. L. (2024). salmon: A symbolic linear regression package for Python. Journal of Statistical Software, 108(8). https://doi.org/10.18637/jss.v108.i08
Liu, X., Yue, R.-X., Xu, J., & Chatterjee, K. (2016). Algorithmic construction of R-optimal designs for second-order response surface models. Journal of Statistical Planning and Inference, 178, 61-69. https://doi.org/10.1016/j.jspi.2016.05.003
Sánchez Villena, A. (2019). Uso de programas estadísticos libres para el análisis de datos: Jamovi, Jasp y R. Revista Perspectiva, 20(1), 112-114. https://doi.org/10.33198/rp.v20i1.00026
Ke, Y., Yang, R., & Liu, N. (2024). Comparing open-access database and traditional intensive care studies using machine learning: Bibliometric analysis study. Journal of Medical Internet Research, 26, e48330. https://doi.org/10.2196/48330
Mittal, D., Mease, R., Kuner, T., Flor, H., Kuner, R., & Andoh, J. (2023). Data management strategy for a collaborative research center. GigaScience, 12, giad049. https://doi.org/10.1093/gigascience/giad049
Freire, M. A. M., Lema, L. D. C. Z., & Rivera, S. M. H. (2021). Estadística descriptiva con R. Gráficos avanzados y aplicaciones. Editorial Universidad Nacional de Chimborazo. https://doi.org/10.37135/u.editorial.05.35
Salas-Molina, F., Pla-Santamaria, D., García-Bernabeu, A., & Utrero-González, N. (2023, July 13). Una revisión de experiencias y recursos educativos para aprender economía y finanzas con Python. IN-RED 2023: IX Congreso de Innovación Educativa y Docencia en Red. https://doi.org/10.4995/INRED2023.2023.16511
Canett Romero, R., Ledesma Osuna, A. I., Robles, S., Morales Castro, R., León Martínez, L., & León-Gálvez, R. (2004). Caracterización de galletas elaboradas con cascarilla de orujo de uva. Archivos Latinoamericanos de Nutrición, 54(1), 93-99. https://ve.scielo.org/scielo.php?script=sci_arttext&pid=S0004-06222004000100014
Kayacier, A., Yüksel, F., & Karaman, S. (2014). Simplex lattice mixture design approach on physicochemical and sensory properties of wheat chips enriched with different legume flours: An optimization study based on sensory properties. LWT - Food Science and Technology, 58(2), 639-648. https://doi.org/10.1016/j.lwt.2014.03.032
Plustea, L., Dossa, S., Dragomir, C., Cocan, I., Negrea, M., Obistioiu, D., Poiana, M.-A., Voica, D., Berbecea, A., & Alexa, E. (2024). Comparative study of the nutritional, phytochemical, sensory characteristics and glycemic response of cookies enriched with lupin sprout flour and lupin green sprout. Foods, 13(5), 656. https://doi.org/10.3390/foods13050656
Duarte, B. P. M., Atkinson, A. C., Granjo, J. F. O., & Oliveira, N. M. C. (2021). Optimal design of mixture experiments for general blending models. Chemometrics and Intelligent Laboratory Systems, 217, 104400. https://doi.org/10.1016/j.chemolab.2021.104400
Galvagnini, F., Fredi, G., Dorigato, A., Fambri, L., & Pegoretti, A. (2021). Mechanical behaviour of multifunctional epoxy/hollow glass microspheres/paraffin microcapsules syntactic foams for thermal management. Polymers, 13(17), 2896. https://doi.org/10.3390/polym13172896
Homayouni Rad, A., Pirouzian, H. R., Toker, O. S., & Konar, N. (2019). Application of simplex lattice mixture design for optimization of sucrose-free milk chocolate produced in a ball mill. LWT, 115, 108435. https://doi.org/10.1016/j.lwt.2019.108435
Buruk Sahin, Y., Aktar Demirtaş, E., & Burnak, N. (2016). Mixture design: A review of recent applications in the food industry. Pamukkale University Journal of Engineering Sciences, 22(4), 297-304. http://doi.org/10.5505/pajes.2015.98598
Grosso, G. S., Méndez, L. M. R., Tangarife, M. P. O., & Arias, N. R. (2015). Diseño experimental de mezclas como herramienta para la optimización de cremolácteos de mango. Revista Colombiana de Investigaciones Agroindustriales, 2(1), 16-24. https://doi.org/10.23850/24220582.166
Mendoza-Combatt, J. C., Fuentes-Medina, L., Mendoza-Combatt, M., & National Open and Distance University, Cartagena. (2021). Design of lattice simplex mixtures as a statistical tool for the inclusion of cowpea bean flour (Vigna unguiculata) in a cheese stick formulation. Revista Mexicana de Ingeniería Química, 20(3), 1-14. https://doi.org/10.24275/rmiq/Alim2433
Ospina‐Casas, K., Laguado‐Escobar, D., & Narváez‐Cuenca, C. (2022). Using a mixture of hydrocolloids to mimic texture and rheological properties of a massive consumption food product. Journal of Food Processing and Preservation, 46(4). https://doi.org/10.1111/jfpp.16440
Kumle, L., Võ, M. L.-H., & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53(6), 2528-2543. https://doi.org/10.3758/s13428-021-01546-0
Miles, J. (2014). R squared, adjusted R squared. En R. S. Kenett, N. T. Longford, W. W. Piegorsch, & F. Ruggeri (Eds.), Wiley StatsRef: Statistics reference online (1st ed.). Wiley. https://doi.org/10.1002/9781118445112.stat06627
Jankowski, J. (2017). Mixture seeding for sustainable information spreading in complex networks. En N. T. Nguyen, S. Tojo, L. M. Nguyen, & B. Trawiński (Eds.), Intelligent information and database systems (vol. 10191, pp. 191-201). Springer International Publishing. https://doi.org/10.1007/978-3-319-54472-4_19
Piepel, G. F. (1982). Measuring component effects in constrained mixture experiments. Technometrics, 24(1), 29-39. https://doi.org/10.1080/00401706.1982.10487706
Talens, C., Llorente, R., Simó-Boyle, L., Odriozola-Serrano, I., Tueros, I., & Ibargüen, M. (2022). Hybrid sausages: Modelling the effect of partial meat replacement with broccoli, upcycled brewer’s spent grain and insect flours. Foods, 11(21), 3396. https://doi.org/10.3390/foods11213396
Olubi, O., Felix-Minnaar, J. V., & Jideani, V. A. (2021). Physicochemical, mineral and sensory characteristics of instant Citrullus lanatus mucosospermus (Egusi) soup. Foods, 10(8), 1817. https://doi.org/10.3390/foods10081817
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Copyright (c) 2025 Lizbeth Chávez-Reyes, Laura García-Curiel, Jesús Guadalupe Pérez Flores, Emmanuel Pérez-Escalante, Elizabeth Contreras-López, Lizbeth Anahí Portillo-Torres, Luis Guillermo González-Olivares, Carlos Ángel-Jijón