For citation:
Sharapova E. K., Emelyanov O. E., Amelin V. G., Tretyakov A. V. Detection of adulteration of fish caviar by inductively coupled plasma mass spectrometry. Izvestiya of Saratov University. Chemistry. Biology. Ecology, 2025, vol. 25, iss. 4, pp. 393-405. DOI: 10.18500/1816-9775-2025-25-4-393-405, EDN: NYDEZK
Detection of adulteration of fish caviar by inductively coupled plasma mass spectrometry
The use of inductively coupled plasma mass spectrometry for differentiation of natural and imitated caviar of various fish species: salmon, sturgeon, and particulate species has been proposed. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) have been used as tools for multivariate statistical analysis. The use of PCA and HCA have allowed effective visualisation of differences between caviar samples in terms of authenticity. It has been found that samples from different groups have been located in separate quadrants on PCA plots and formed separate clusters on dendrograms. The analysis of load plots has showed that magnesium, phosphorus, zinc and iron are the key elements responsible for the separation of samples by authenticity. For salmon fishcaviar, the main difference between natural and imitated caviar is due to the concentrations of these elements, with natural caviar having higher values. In the case of sturgeon caviar, the influence of copper is additionally noticeable, which also contributes to the differentiation between natural and imitated caviar. PCA plots and dendrograms for sturgeon fish show a clear differentiation between the different types of caviar: natural, imitation and halibut caviar. For particulate fish, differentiation of natural and imitated pike caviar is observed based on the same elements as for other caviar types. The high effi ciency of using mass spectrometry in combination with chemometric methods for detection of adulteration of fi sh caviar of diff erent species by their elemental composition has been proved.
- Воронцова Е. В., Воронцов А. Л. Обеспечение качества и безопасности пищевой продукции как основа обеспечения продовольственной безопасности Российской Федерации в условиях глобализации пищевого рынка // Юридический вестник ДГУ. 2021. Т. 40, вып. 4. С. 75–80. https://doi. org/10.21779/2224-0241-2021-40-4-75-80
- Tavakoli S., Luo Y., Regenstein J. M., Daneshvar E., Bhatnagar A., Tan Y., Hong H. Sturgeon, caviar, and caviar substitutes: From production, gastronomy, nutrition, and quality change to trade and commercial mimicry // Rev. Fish. Sci. Aquacult. 2021. Vol. 29, № 39. P. 753–768. https://doi. org/10.1080/23308249.2021.1873244
- Machado T. M., Tabata Y. A., Takahashi N. S., Casarini L. M., Neiva C. R. P., Henriques M. B. Caviar substitute produced from roes of rainbow trout (Oncorhynchus mykiss) // Acta Sci. Technol. 2016. Vol. 38, № 2. P. 233–240. https://doi.org/ 10.4025/actascitechnol.v28i2.27944
- Farag M. A., Abib B., Tawfi k S., Shafi k N., Khattab A. R. Caviar and fi sh roe substitutes: Current status of their nutritive value, bio-chemical diversity, authenticity and quality control methods with future perspectives // Trends Food Sci. Technol. 2021. Vol. 110. P. 405–417. https://doi. org/ 10.1016/j.tifs.2021.02.015.
- Ситникова Н. В. Идентификация и фальсификация икры в России // Ученые записки Санкт-Петербургского имени В. Б. Бобкова филиала Российской таможенной академии. 2007. № 2 (28). С. 84–101.
- Калюжная Т. В., Орлова Д. А., Родак Г. Н. Идентификация икры лососевых пород рыб с помощью полимеразной цепной реакции с наблюдением в реальном времени // Международный вестник ветеринарии. 2021. № 4. С. 88–92. https://doi. org/10.52419/issn2072-2419.2021.4.88
- Santiago-Felipe S., Tortajada-Genaro L. A., Puchades R., Maquieira A. Recombinase polymerase and enzyme-linked immunosorbent assay as a DNA amplifi cation-detection strategy for food analysis // Anal. Chim. Acta. 2014. Vol. 811. P. 81–87. https://doi.org/10.1016/j.aca.2013.12.017
- Taboada L., Sanchez A., Sotelo C. G. A new real-time PCR method for rapid and specifi c detection of ling (Molva molva) // Food Chem. 2017. Vol. 228. P. 469–476. https://doi. org/10.1016/j.foodchem.2017.01.117.
- Pappalardo A. M., Petraccioli A., Capriglione T., Ferrito V. From fi sh eggs to fi sh name: Caviar species discrimination by coibar-rfl p, an e cient molecular approach to detect fraud in the caviar trade // Molecules. 2019. Vol. 24, № 13. Article 2468. https://doi. org/10.3390/molecules24132468
- Абрамова Л. С., Козин А. В., Гусева Е. С. Проблема фальсификации зернистой икры лососевых рыб и пути решения // Пищевые системы. 2022. Т. 5, № 4. С. 319–326. https://doi. org/10.21323/2618-9771-2022-5-4-319-326
- Mazarakioti E. C., Zotos A., Thomatou A. A., Kontogeorgos A., Patakas A., Ladavos A. Inductively coupled plasma-mass spectrometry (ICP-MS), a useful tool in authenticity of agricultural poducts’ and foods’ origin // Foods. 2022. Vol. 11, № 22. Article 3705. https://doi. org/10.3390/foods11223705
- Третьяков А. В., Абраменкова О. И., Подколзин И. В., Соловьев А. И. Идентификация географической принадлежности мяса и икры методом химического фингерпринтинга // Ветеринария сегодня. 2012. № 2 (2). С. 39–46.
- Amelin V. G., Emel’yanov O. E., Tret’yakov A. V., Gergel’ M. A., Zaitseva E. V. Identifi cation and detection of adulterations of salmon caviar by PCR, IR Spectrometry, and digital coloriometry // J. Anal. Chem. 2025. Vol. 80, № 4. P. 766–777. https://doi. org/10.1134/S1061934825700194
- Vasconi M., Tirloni E., Stella S., Coppola C., Lopez A., Bellagamba F., Bernardi C., Moretti V. M. Comparison of chemical composition and safety issues in fi sh roe products: Application of chemometrics to chemical data // Foods. 2020. Vol. 9, № 5. Р. 540–545. https://doi. org/10.3390/foods9050540
- Родионова О. Е. Хемометрический подход к исследованию больших массивов химических данных // Российский химический журнал. 2006. Т. 50, № 2. С. 128–144.
- Родионова О. Е., Померанцев А. Л. Хемометрика: достижения и перспективы // Успехи химии. 2006. Т. 75, № 4. С. 302–321.
- Oliveri P., Malegori C., Casale M. Chemometrics: Multivariate analysis of chemical data // Chemical Analysis of Food (Second Edition). Academic Press, 2020. P. 33–76. https://doi. org/10.1016/B978-0-12-813266-1.00002-4
- Houhou R., Bocklitz T. Trends in artifi cial intelligence, machine learning, and chemometrics applied to chemical data // Analytical Science Advances. Wiley, 2021. P. 128–141. https://doi. org/10.1002/ansa.202000162