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Food Analytical Methods

, Volume 11, Issue 5, pp 1291–1302 | Cite as

Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder

  • Saumita Kar
  • Bipan Tudu
  • Anil K. Bag
  • Rajib Bandyopadhyay
Article
  • 159 Downloads

Abstract

Turmeric (Curcumina Longa) is a globally traded commodity which is subjected to economically motivated chemically unsafe adulteration, namely metanil yellow. In this work, we report a simplistic and convenient approach to find the adulteration of turmeric with metanil yellow by near-infrared (NIR) spectroscopy coupled with chemometrics. Pure turmeric sample was prepared in the laboratory and spiked with different concentrations of metanil yellow. The reflectance spectra of 248 pure turmeric, metanil yellow, and adulterated samples (1–25%) (w/w) were collected using NIR spectroscopy. The calibration models based on NIR spectra of 144 samples were built for two different regression models, principal component analysis (PCR), and partial least square (PLSR) methods. Another 72 samples were used for external validation. The coefficient of determination (R 2) and root mean square error of calibration for validation and prediction were found to be 0.96–0.99, 0.44–0.91, respectively, for most of the results depending upon different pre-processing techniques and mathematical models used. The original reflectance spectra, the 1st derivative plot, the plot of PLSR regression coefficient (β), and the first three principal component loadings revealed metanil-related absorption regions. To verify the robustness of the models, the figures of merit (FOM) of the models were calculated with the help of net analyte signal (NAS) theory. Overall, it was found that PLSR yielded superior results as compared to the PCR technique. These methods can be applied to other spices also to detect the adulteration rapidly and without any prior sample preparations and with low cost.

Keywords

NIR spectroscopy Turmeric powder Metanil yellow powder Regression analysis Figures of merit Net analyte signal 

Notes

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with Ethical Standards

Conflict of Interest

Saumita Kar declares that she has no conflict of interest. Bipan Tudu declares that he has no conflict of interest. Anil K. Bag declares that he has no conflict of interest. Rajib Bandyopadhyay declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

References

  1. Alishahi A, Farahmand H, Prieto N, Cozzolino D (2010) Identification of transgenic foods using NIR spectroscopy: a review. Spectrochim Acta A Mol Biomol Spectrosc 75(1):1–7.  https://doi.org/10.1016/j.saa.2009.10.001 CrossRefGoogle Scholar
  2. ASTM (2005) E1655: standard practice for multivariate quantitative analysis. West ConsohokenGoogle Scholar
  3. Ayza A, Belete E (2015) Food adulteration: its challenges and impacts, detection of starch adulteration in onion powder by FT-NIR and FTIR spectroscopy. J Food Sci Qual Manage 41:50–57Google Scholar
  4. Aznar M, López R, Cacho J, Ferreira V (2003) Prediction of aged red wine aroma properties from aroma chemical composition partial least squares regression models. J Agric Food Chem 51(9):2700–2707.  https://doi.org/10.1021/jf026115z CrossRefGoogle Scholar
  5. Bao Y, Liu F, Kong W, Sun HY, Qiu Z (2014) Measurement of soluble solid contents and pH of white vinegars using VIS/NIR spectroscopy and least squares support vector machine. J Food Bioprocess Technol 7(1):54–61.  https://doi.org/10.1007/s11947-013-1065-0 CrossRefGoogle Scholar
  6. Basri KN, Hussain MN, Bakar J, Sharif Z, Khir MFA, Zoolfakar AS (2017) Classification of palm oil adulteration via portable NIR spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 173:335–342.  https://doi.org/10.1016/j.saa.2016.09.028 CrossRefGoogle Scholar
  7. Bonan S, Fedrizzi G, Menotta S, Elisabetta C (2013) Simultaneous determination of synthetic dyes in foodstuffs and beverages by high-performance liquid chromatography coupled with diode-array detector. J Dyes Pigments 99(1):36–40.  https://doi.org/10.1016/j.dyepig.2013.03.029 CrossRefGoogle Scholar
  8. Brown CD, Montoto LV, Wentzel PD (2000) Derivative preprocessing and optimal corrections for baseline drift in multivariate calibration. Appl Spectrosc 54(7):1055–1068.  https://doi.org/10.1366/0003702001950571 CrossRefGoogle Scholar
  9. Cen H, He Y (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. J. Trends food Sci Tech 18(2):72–83.  https://doi.org/10.1016/j.tifs.2006.09.003 CrossRefGoogle Scholar
  10. Chainani N (2003) Safety and anti-inflammatory activity of curcumin: a component of turmeric (Curcuma longa). J Altern Complement Med 9(1):161–168.  https://doi.org/10.1089/107555303321223035 CrossRefGoogle Scholar
  11. Chen H, Ta C, Lin Z, Wu T (2017) Detection of melamine adulteration in milk by near-infrared spectroscopy and one-class partial least squares. Spectrochim. Acta Mol Biomol Spectrosc 173:832–836.  https://doi.org/10.1016/j.saa.2016.10.051 CrossRefGoogle Scholar
  12. David IE, Victoria LB, Warwick BD et al (2012) Fingerprinting food: current technologies for the detection of food adulteration and contamination. Chem Soc Rev 41:5706–5727CrossRefGoogle Scholar
  13. Dejun L, Chigang X (2016) Rapid detection of cotton content based on near infrared spectroscopy technology. Int J Signal Process Image Process Pattern Recogn 9:25–34Google Scholar
  14. Depczynski U, Frost VJ, Molt K (2000) Genetic algorithms applied to the selection of factors in principal component regression. Anal Chim Acta 420(2):217–227.  https://doi.org/10.1016/S0003-2670(00)00893-X CrossRefGoogle Scholar
  15. Dhakal S, Chao K, Schmidt W, Qin J, Kim M, Chan D (2016) Evaluation of turmeric powder adulterated with metanil yellow using FT-Raman and FT-IR spectroscopy. J Foods 5(2):36.  https://doi.org/10.3390/foods5020036 CrossRefGoogle Scholar
  16. Diez E, Saiz JMG, Pizzaro C (2004) Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy. Anal Chim Acta 525(2):171–182.  https://doi.org/10.1016/j.aca.2004.08.057 CrossRefGoogle Scholar
  17. Ding HB, Xu RJ (2000) Near-infrared spectroscopic technique for detection of beef hamburger adulteration. J Agric Food Chem 48(6):2193–2198.  https://doi.org/10.1021/jf9907182 CrossRefGoogle Scholar
  18. Dixit S, Pandey RC, Das M (1995) Food quality surveillance on colours in eatables. J Food Sci Technol 32:373–376Google Scholar
  19. Dixit S, Khanna KS, Das M (2008) A simple 2-directional high-performance thin-layer chromatographic method for the simultaneous determination of curcumin, metanil yellow and sudan dyes in turmeric, chili, and curry powders. J AOAC Int 91(6):1387–1396Google Scholar
  20. Dixit S, Purshottam SK, Khanna SK, Das M (2009) Surveillance of the quality of turmeric powders from city markets of India on the basis of curcumin content and the presence of extraneous colours. Food Addit Contam 26(9):1227–1231.  https://doi.org/10.1080/02652030903016586 CrossRefGoogle Scholar
  21. Fu H, Yin Q, Xu L, Wang W, Chen F, Yang T (2017) A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum. Spectrochim Acta A Mol Biomol Spectrosc 182:17–25.  https://doi.org/10.1016/j.saa.2017.03.074 CrossRefGoogle Scholar
  22. Gayo J, Hale SA, Blanchard SM (2006) Quantitative analysis and detection of adulteration in crab meat using visible and near-infrared spectroscopy. J Agric Food Chem 54(4):1130–1136.  https://doi.org/10.1021/jf051636i CrossRefGoogle Scholar
  23. Hernandez JM, Villanova RJG, Martin IG (2008) Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market. Anal Chim Acta 622(1-2):189–194.  https://doi.org/10.1016/j.aca.2008.05.049 CrossRefGoogle Scholar
  24. Inácio MRC, Moura MFV, Lima KMG (2011) Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection. Vib Spectrosc 57(2):342–345.  https://doi.org/10.1016/j.vibspec.2011.07.002 CrossRefGoogle Scholar
  25. Jha S (2016) Rapid detection of food adulterants and contaminants. Academia Press, LondonGoogle Scholar
  26. Kubose D, Chai J, Greene J (2004) Charged synthetic nonwoven filtration media and method for producing same US patent 20040116026Google Scholar
  27. Kumar SG, Nayaka H, Dharmesh SM, Salimath PV (2006) Free and bound phenolic antioxidants in amla (Emblica officinalis) and turmeric (Curcuma longa). J Food Compos Anal 19(5):446–452.  https://doi.org/10.1016/j.jfca.2005.12.015 CrossRefGoogle Scholar
  28. Long GL, Winefordner JD (1983) Limit of detection: a closer look at the IUPAC definition. Anal Chem 55:712–724CrossRefGoogle Scholar
  29. Lorber A (1986) Error propagation and figures of merit for quantification by solving matrix equations. Anal Chem 58(6):1167–1172.  https://doi.org/10.1021/ac00297a042 CrossRefGoogle Scholar
  30. Mauer LJ, Chernyshova AA, Hiatt A, Deering A, Davis R (2009) Melamine detection in infant formula powder using near- and mid-infrared spectroscopy. J Agric Food Chem 57(10):3974–3980.  https://doi.org/10.1021/jf900587m CrossRefGoogle Scholar
  31. Nagraja TN, Desiraju T (1993) Effects of chronic consumption of metanil yellow by developing and adult rats on brain regional levels of noradrenaline, dopamine and serotonin, on acetylcholine esterase activity and on operant conditioning. Food Chem Toxicol 31(1):41–44.Google Scholar
  32. Nath PP, Sarkar K, Trader P, Mondal M, Das K, Paul G (2015) Practice of using metanil yellow as food colour to process food in unorganized sector of West Bengal—a case study. J Int Food Res 22:1424–1428Google Scholar
  33. Norris KH, Hart JR (1963) NIR spectroscopy in handbook of organic compounds. Academic Press, San DiegoGoogle Scholar
  34. Olivieri AC, Faber NKM, Ferre J, Boque R, Kalivas JH, Mark H (2006) Uncertainty estimation and figures of merit for multivariate calibration: (IUPAC technical report). Pure Appl Chem 78:633CrossRefGoogle Scholar
  35. Peng GJ, Chang MH, Fang M, Tsai CF, Tseng S-H, Kao YM, Chou HK, Cheng HF (2017) Incidents of major food adulteration in Taiwan between 2011 and 2015. Food Control 72:142–152CrossRefGoogle Scholar
  36. Purba MK, Agarwal N, Shukla SK (2015) Detection of non-permitted food colors in edibles. J Forensic Res S4:S4–003Google Scholar
  37. Ranzan C, Strohm A, Ranjan et al (2014) Wheat flour characterization using NIR and spectral filter based on ant colony optimization. Chemom Intell Lab 132:133–140.  https://doi.org/10.1016/j.chemolab.2014.01.012 CrossRefGoogle Scholar
  38. Ravindran PN, Babu KN, Sivaraman K (2007) Turmeric the genus curcuma. CRC Press, Boca RatonGoogle Scholar
  39. Rezzi S, Axelson DE, Héberger K, Reniero F, Mariani C, Guillou C (2005) Classification of olive oils using high throughput flow H NMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks. Anal Chim Acta 552(1-2):13–24.  https://doi.org/10.1016/j.aca.2005.07.057 CrossRefGoogle Scholar
  40. Sarraguc MC, Lopes JA (2009) The use of net analyte signal (NAS) in near infrared spectroscopy pharmaceutical applications: interpretability and figures of merit. Anal Chim Acta 642(1-2):179–185.  https://doi.org/10.1016/j.aca.2008.10.006 CrossRefGoogle Scholar
  41. Schmutzler M, Huck CW (2014) Automatic sample rotation for simultaneous determination of geographical origin and quality characteristics of apples based on near infrared spectroscopy (NIRS). Vib Spectrosc 72:97–104.  https://doi.org/10.1016/j.vibspec.2014.02.010 CrossRefGoogle Scholar
  42. Souza LM, Mitsutake H, Gontij LC, Borges WN (2014) Quantification of residual automotive lubricant oil as an adulterant in Brazilian S-10 diesel using MIR spectroscopy and PLS. J Fuel 130:257–262.  https://doi.org/10.1016/j.fuel.2014.03.051 CrossRefGoogle Scholar
  43. Srivastava LP, Khanna SK, Singh GB, KrishnaMurti CR (1982) In vitro studies on the biotransformation of metanil yellow. Environ Res 27(1):185–189.  https://doi.org/10.1016/0013-9351(82)90069-X CrossRefGoogle Scholar
  44. Tanaka K, Kuba Y, Sasakai T, Hiwatashi F, Komatsu K (2008) Quantization of curcuminoids in curcuma rhizome by near-infrared spectroscopic analysis. J Agric Food Chem 56(19):8787–8792.  https://doi.org/10.1021/jf801338e CrossRefGoogle Scholar
  45. Thompson M, Ellison SLR, Wood R (2002) Harmonized guidelines for single-laboratory validation of methods of analysis. Pure Appl Chem 74:835–855CrossRefGoogle Scholar
  46. Workman J, Weyer L (2007) Practical guide to interpret near-infrared spectroscopy. CRC Press, Boca RatonGoogle Scholar
  47. Xie L, Ye X, Liu D, Ying Y (2009) Quantification of glucose, fructose and sucrose in barberry juices by NIR and PLS. J Near Infrared Spectrosc 114:1135–1140Google Scholar
  48. Yeow ST, Shahar A, Aziz NA, Ansur MS, Yusof YA, Taip FS (2011) The influence of operational parameter sand feed preparation in a convective batch ribbon powder mixer. Drug Des Devel Ther 5:465–469.  https://doi.org/10.2147/DDDT.S25047 Google Scholar
  49. Zeaiter M, Roger JM (2005) Robustness of models developed by multivariate calibration. Part II: the influence of pre-processing methods. J Trends Anal Chem 24(5):437–445.  https://doi.org/10.1016/j.trac.2004.11.023 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Instrumentation and Electronics EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Applied Electronics and Instrumentation EngineeringHeritage Institute of TechnologyKolkataIndia
  3. 3.Laboratory of Artificial Sensory SystemsITMO UniversitySaint PetersburgRussia

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