ISSN 2074-9414 (Print),
ISSN 2313-1748 (Online)

Rating System for Quality Control of Functional Instant Drinks

Abstract
Excessive information about a functional product and its qualitative characteristics increases the competitiveness of the product and requires constant monitoring. The present research objective was to develop a rating system of qualitative characteristics for functional instant drinks. The system relied on the method of multidimensional statistical analysis and involved the main descriptors of consumer criteria and sensory properties of the product.
The study featured functional instant fruit and berry drinks, e.g. raspberry, cranberry, cranberry, black currant, blueberry, sea buckthorn, blackberry, apricot, peach, apple, etc. The methods included a focus group approach, a sensory analysis, a new ordinal scale, and a modified method of multivariate statistical evaluation (PCA).
The authors introduced a unified score scale for sensory evaluation of functional instant drinks, which reflected both standard quality indicators and consumer selection criteria. A modified method of multivariate statistical evaluation (PCA) was used to identify the characteristics that consumers see as the most important. The resulting rating system of the main descriptors reflects the information about consumer preferences and reveals significant sensory characteristics and indicators of quality of functional instant drinks. Clustering revealed two groups of consumers, young people aged 18–29 and those aged 30–59, with their own defining quality indicators.
The article contains some useful labeling recommendations for producers of functional instant beverages. The recommendations can increase the competitiveness of the product and meet consumer demand for information. The new scale can be used for various types of functional fruit and berry drinks.
Keywords
Drinks, raw material, consumer criteria, descriptors, PCA analysis
REFERENCES
  1. Morais RMSC, Morais AMMB, Dammak I, Bonilla J, Sobral PJA, Laguerre J-C, et al. Functional dehydrated foods for health preservation. Journal of Food Quality. 2018;2018. https://doi.org/10.1155/2018/1739636
  2. Sandrakova IV, Reznichenko IYu. Health food consumers research. Practical Marketing. 2019;274(12):22–27. (In Russ.).
  3. Gurʹyanov YuG, Lobach EYu. Assessment of consumer preferences for new functional products. Polzunovskiy Vestnik. 2012;(2–2):187–190. (In Russ.).
  4. Early R. Making life look and taste better. Food Science and Technology. 2020;34(3):52–55. https://doi.org/10.1002/fsat.3403_13.x
  5. Abuajah CI, Ogbonna AC, Osuji CM. Functional components and medicinal properties of food: a review. Journal of Food Science and Technology. 2015;52(5):2522–2529. https://doi.org/10.1007/s13197-014-1396-5
  6. Erchova TA, Bojko SD, Tchernichova AN. Development of dry mixes of drinks for athletes during the competition. Food Industry. 2018;(2):64–68. (In Russ.).
  7. Logvinchuk TM. Formulations of functional drinks based on soluble chicory. Polzunovskiy Vestnik. 2019;(4):58–62. (In Russ.).
  8. Sosjura EA, Romanenko ES, Esaulko NA, Selivanova MV, Aysanov TS, Mil'tjusov VE, et al. Development of technology for the production of functional beverages based on beer wort. Beer and Beverages. 2019;(1):38–42. (In Russ.).
  9. Myakinnikova EI, Kasyanov GI. Technology of dry quickly restored drinks on the base of subtropical fruits. Scientific Works of the Kuban State Technological University. 2015;(4):288–300. (In Russ.).
  10. Bakin IA, Reznichenko IYu, Mustafina AS, Aleksenko LA. Design of soft drinks compositions based on blackcurrant berries bhytoextracts. Storage and Processing of Farm Products. 2019;(2):37–50. (In Russ.). https://doi.org/10.36107/spfp.2019.71
  11. Kelenkova ES, Egorova EYu. Use of dry extracts of fruit and berry raw materials to increase the nutritional value of kvasses of fermentation. News of Institutes of Higher Education. Food Technology. 2021;379(1):35–39. https://doi.org/10.26297/0579-3009.2021.1.8
  12. Kravchenko SN, Miller ES, Plotnikova IO, Popov AM. Improvement of the extraction process in the production of instant drinks. Storage and Processing of Farm Products. 2018;(1):5–10. (In Russ.).
  13. Shaw EF, Charters S. Functional drinks containing herbal extracts. In: Ashurst PR, editor. Chemistry and technology of soft drinks and fruit juices. John Wiley and Sons; 2016. pp. 310–355. https://doi.org/10.1002/9781118634943.ch12
  14. Kaur J, Kumar V, Kumar S, Aggarwal P, Sharma K, Bhadariya V. Process optimization for the preparation of tea and fruit-oriented energy drink: A nutritional approach. Journal of Food Processing and Preservation. 2021;45(4). https://doi.org/10.1111/jfpp.15363
  15. Podkorytov AG, Kadnikova IA, Podkorytova EA, Kovalev VV. Development of technology of a dry concentrate beverage based on modified pectin with the addition of Far East wild plants. Bulletin of the Far Eastern Federal University. Economics and Management. 2019;91(3):165–177. (In Russ.). https://doi.org/10.24866/2311-2271/2019-3/165-177
  16. Malcolmson LJ, Winkler-Moser JK. Flavor and sensory aspects. In: Shahidi F, editor. Bailey's industrial oil and fat products. John Wiley and Sons; 2020. https://doi.org/10.1002/047167849X.bio032.pub2
  17. Muche S, Pietzner V. Sensory evaluation: making it possible to experience basic chemical concepts with nose and tongue. Chemkon. 2020;27. https://doi.org/10.1002/ckon.202000016
  18. Lisitsyn AB, Chernukha IM, Nikitina MA. Russian methodology for designing multicomponent foods in retrospect. Foods and Raw Materials. 2020;8(1):2–11. https://doi.org/10.21603/2308-4057-2020-1-2-11
  19. Fadhil R, Agustina R. A multi-criteria sensory assessment of Cucumis melo (L.) using fuzzy-Eckenrode and fuzzy-TOPSIS methods. Foods and Raw Materials. 2019;7(2):339–347. https://doi.org/10.21603/2308-4057-2019-2-339-347
  20. de Schipper NC, Van Deun K. Model selection techniques for sparse weight-based principal component analysis. Journal of Chemometrics. 2021;35(2). https://doi.org/10.1002/cem.3289
  21. Meng C, Zeleznik OA, Thallinger GG, Kuster B, Gholami AM, Culhane AC. Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in Bioinformatics. 2016;17(4):628–641. https://doi.org/10.1093/bib/bbv108
  22. Guo J, Wang X, Li Y, Wang G. Fault detection based on weighted difference principal component analysis. Journal of Chemometrics. 2017;31(11). https://doi.org/10.1002/cem.2926
  23. Beddo V, Kreuter F. A handbook of statistical analyses using SPSS. Journal of Statistical Software. 2004;11(2). https://doi.org/10.18637/jss.v011.b02
  24. Jolliffe IT. Principal component analysis, 2nd ed. New York: Springer; 2002. 518 p.
  25. Filzmoser P, Nordhausen K. Robust linear regression for high-dimensional data: An overview. Wiley Interdisciplinary Reviews: Computational Statistics. 2020;12(4). https://doi.org/10.1002/wics.1524
  26. Billard L, Kim J. Hierarchical clustering for histogram data. Wiley Interdisciplinary Reviews: Computational Statistics. 2017;9(5). https://doi.org/10.1002/wics.1405
How to quote?
Mustafina AS, Reznichenko IYu, Bakin IA, Shilov SV. Rating System for Quality Control of Functional Instant Drinks. Food Processing: Techniques and Technology. 2022;52(1):144–155. (In Russ.). https://doi.org/10.21603/2074-9414- 2022-1-144-155
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