Turmeric identification and adulteration detection by digital colorometry and near-IR spectroscopy methods

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The possibility to identify and establish the fact of turmeric adulteration by simple and affordable methods using methods of infrared spectroscopy, digital colorometry and chemometric processing of spectral data was shown. Near-infrared spectroscopy was used to differentiate between samples of turmeric powder purchased in India, made by grinding the roots, and commercial samples, and to separate them from samples with impurities of flour, starch, breadcrumb and chalk by analyzing diffuse reflectance spectra using principal component methods, hierarchical cluster analysis and formal independent class analogy modeling. The same approaches were applied to the simpler and less costly colorometric method. Chemometric processing of the obtained data confirmed the lack of similarity of the analyzed turmeric samples with samples containing additives and allowed the determination of impurities using multivariate regression analysis algorithms. Comparison of the results obtained by IR spectroscopy and digital colorometry showed their equivalent efficiency, which allowed us to recommend the more affordable colorometric method for routine quality control and detection of turmeric adulteration.

About the authors

O. E. Emelyanov

Alexander Grigorievich and Nikolai Grigorievich Stoletov Vladimir State University

Vladimir, Russia

V. G. Amelin

Alexander Grigorievich and Nikolai Grigorievich Stoletov Vladimir State University; All-Russian State Center for Quality and Standardization of Animal Drugs and Feeds

Email: amelinvg@mail.ru
Vladimir, Russia; Vladimir, Russia

A. V. Tretyakov

All-Russian State Center for Quality and Standardization of Animal Drugs and Feeds

Vladimir, Russia

References

  1. Запорожченко А.А., Суботялов М.А. Биологическая активность и терапевтический потенциал Curcuma longa (обзор литературы) // Сибирский научный медицинский журнал. 2023. Т. 43. № 3. С. 15. https://doi.org/10.18699/SSMJ20230302
  2. Wojcik M., Krawczyk М., Wojcik Р., Cypryk К., Wozniak L.A. Molecular mechanisms underlying curcumin-mediated therapeutic effects in type 2 diabetes and cancer // Oxid. Med. Cell. Longevity. 2018. Article ID 9698258. https://doi.org/10.1155/2018/9698258
  3. Venigalla M., Gyengesi E., Munch G. Curcumin and Apigenin – novel and promising therapeutics against chronic neuroinflammation in Alzheimer’s disease // Neural Regen Res. 2015. V. 10. № 8. Р. 1182. https://doi.org/0.4103/1673-5374.162686
  4. Sasikumar B. Turmeric / Handbook of Herbs and Spices (Second edition). 2012. V. 1. Р. 526. https://doi.org/10.1533/9780857095671.526
  5. ГОСТ ISO 5562-2017. Пряности. Куркума целая и молотая (порошкообразная). Технические условия. М.: Стандартинформ, 2017. 7 с.
  6. Абдуллаева Л.С., Лучкин М.А., Лунева Т.А., Сла­щинин Д.Г. Оценка качества пряностей / ­Молодые ученые в решении актуальных проблем науки: Сборник материалов Всероссийской научно-практической конференции студентов, аспирантов и молодых ученых. 2023. С. 461.
  7. Sahu P.K., Panda J., Jogendra Kumar Y.V. V., Ranjitha S.K. A robust RP-HPLC method for determination of turmeric adulteration // J. Liq. Chromatogr. Relat. Technol. 2020. V. 43. № 7-8. P. 247. https://doi.org/10.1080/10826076.2020.1722162
  8. Вострикова Н.Л., Минаев М.Ю., Чиковани К.Г. Определение подлинности куркумы // Пищевые системы. 2021. Т. 4. № 1. С. 62. https://doi.org/10.21323/2618-9771-2021-4-1-62-70
  9. Nallappan K., Dash J., Ray S., Pesala B. Identification of adulterants in turmeric powder using terahertz spectroscopy / 2013 38th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz). 2013. P. 1. https://doi.org/10.1109/IRMMW-THz.2013.6665688
  10. Tamiji Z., Habibi Z., Pourjabbar Z., Khoshayand M.R., Sadeghi N., Hajimahmoodi M. Detection and quantification of adulteration in turmeric by spectroscopy coupled with chemometrics // J. Consum. Prot. Food Saf. 2022. V. 17. P. 221. https://doi.org/10.1007/s00003-022-01380-2
  11. Kar S., Tudu B., Jana A., Bandyopadhyay R. FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder // Food Addit. Contam. Part A. 2019. V. 36. № 6. P. 863. https://doi.org/10.1080/19440049.2019.1600746
  12. Chaminda Bandara W.G., Kasun Prabhath G.W., Sahan Chinthana Bandara Dissanayake D.W., Herath V.R., Roshan Indika Godaliyadda G.M., Bandara Ekanayake M.P., Demini D., Madhujith T. Validation of multispectral imaging for the detection of selected adulterants in turmeric samples // J. Food Eng. 2020. V. 266. Article 109700. https://doi.org/10.1016/j.jfoodeng.2019.109700
  13. Шаока З.А.Ч., Большаков Д.С., Амелин В.Г. Использование смартфона в химическом анализе // Журн. аналит. химии. 2023. Т. 78. № 4. С. 317. (Shogah Z.A.Ch., Bolshakov D.S., Amelin V.G. Using a smartphone in chemical analysis // J. Anal. Chem. 2023. V. 78. № 4. P. 317. https://doi.org/10.31857/S0044450223030131)
  14. Amelin V.G., Emel’yanov O.E., Shogaha Z.A. Ch., Tret’yakov A.V. Determination of the mass fraction of milk fat in bottled milk using a contactless colorimetric method // J. Anal. Chem. 2024. V. 79. № 11. P. 1509. https://doi.org/10.1134/S1061934824700904
  15. Amelin V.G., Emel’yanov O.E., Shogaha Z.A. Ch., Tret’yakov A.V. Detection and identification of starch and flour adulteration by digital colorimetry and Fourier-tansform near-IR spectroscopy // J. Anal. Chem. 2024. V. 79. № 11. P. 1515. https://doi.org/10.1134/S1061934824700916
  16. Johnson J.B., Walsh K.B., Naiker M., Ameer K. The use of infrared spectroscopy for the quantification of bioactive compounds in food: A Review // Molecules. 2023. V. 28. № 7. Article 3215. https://doi.org/10.3390/molecules28073215

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2025 Russian Academy of Sciences

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).