Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder
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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.
KeywordsNIR spectroscopy Turmeric powder Metanil yellow powder Regression analysis Figures of merit Net analyte signal
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- ASTM (2005) E1655: standard practice for multivariate quantitative analysis. West ConsohokenGoogle Scholar
- 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
- 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
- Dixit S, Pandey RC, Das M (1995) Food quality surveillance on colours in eatables. J Food Sci Technol 32:373–376Google Scholar
- 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
- 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
- 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
- Jha S (2016) Rapid detection of food adulterants and contaminants. Academia Press, LondonGoogle Scholar
- Kubose D, Chai J, Greene J (2004) Charged synthetic nonwoven filtration media and method for producing same US patent 20040116026Google Scholar
- 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
- 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
- Norris KH, Hart JR (1963) NIR spectroscopy in handbook of organic compounds. Academic Press, San DiegoGoogle Scholar
- Purba MK, Agarwal N, Shukla SK (2015) Detection of non-permitted food colors in edibles. J Forensic Res S4:S4–003Google Scholar
- Ravindran PN, Babu KN, Sivaraman K (2007) Turmeric the genus curcuma. CRC Press, Boca RatonGoogle Scholar
- 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
- Workman J, Weyer L (2007) Practical guide to interpret near-infrared spectroscopy. CRC Press, Boca RatonGoogle Scholar
- 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