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Testing edible oil authenticity by using smartphone based spectrometer

Hanh Hong Mai  1, Tran Thinh Le  1

Faculty of Physics, VNU University of Science, Vietnam National University,

334 Nguyen Trai, Hanoi, Vietnam

 PDF, 922 kB

DOI: 10.18287/2412-6179-CO-604

Pages: 189-194.

Full text of article: English language.

In recent years, there has been an increasing interest in the classification of edible vegetable oils, examining authenticity and in detecting possible adulteration of high quality, expensive extra virgin olive oils with low-cost edible oils. Classical methods such as gas chromatography, liquid chromatography, Fourier transform infrared and nuclear magnetic resonance, mass spectrometry, and Raman spectroscopy have been widely applied to examine the authenticity of edible oils. De-spite of their high sensitivity and accuracy, these methods are significantly expensive for daily life testing, especially in resource-poor regions. Furthermore, they are time-consuming as samples have to be analyzed in dedicated laboratories. In this paper, we propose a compact, low-cost, port-able smartphone-based spectrometer for testing edible oil authenticity. Using simple laboratory op-tical components and a smartphone, we developed a compact spectrometer which can function in the wavelength range of 400–700 nm with the spectrum/pixel resolution of 0.334 nm / pixel. The images captured by the smartphone were converted into intensity distribution plots versus wave-length. As a proof of concept, the smartphone based spectrometer was utilized to measure the variations in fluorescent intensity of the mixed oils of expensive extra virgin olive oil and low-cost rice oil with different percentages. The results obtained the spectrometer were in good agreement with that from a laboratory spectrometer, thus, confirmed its adequate sensitivity and accuracy. Due to the cost effectiveness, the adequate sensitivity, and the portability, the smartphone based spectrometer can be applied in numerous applications such as in-field testing, lifestyle monitoring, and home diagnostics.

spectroscopy, fluorescence and luminescence, image processing, sensors, smartphone.

Mai HH, Le TTh. Testing edible oil authenticity by using smartphone based spectrometer. Computer Optics 2020; 44(2): 189-194. DOI: 10.18287/2412-6179-CO-604.

This research was supported by the International Foundation for Science (IFS), Stockholm, Sweden, and by the Organization for the Prohibition of Chemical Weapons (OPCW), through a grant to Dr. Hanh Hong Mai. Grant NO. I-2-W-6258-1.


  1. Rohman A, Man YBC. Authentication of extra virgin olive oil from sesame oil using FTIR spectroscopy and gas chromatography. Int J Food Prop 2012; 15: 1309-1318. DOI: 10.1080/10942912.2010.521607.
  2. Blanch GP, Caja M del M, del Castillo ML, Herraiz M. Comparison of different methods for the evaluation of the authenticity of olive oil and hazelnut oil. J Agric Food Chem 1998; 46: 3153-3157. DOI: 10.1021/jf9800209.
  3. Lerma-García MJ, Ramis-Ramos G, Herrero-Martínez JM, Simó-Alfonso EF. Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy. Food Chem 2010; 118: 78–-83. DOI: 10.1016/j.foodchem.2009.04.092.
  4. Zamora R, Alba V, Hidalgo FJ. Use of high-resolution 13C nuclear magnetic resonance spectroscopy for the screening of virgin olive oils. J Am Oil Chem Soc 2001; 78: 89-94. DOI: 10.1007/s11746-001-0225-z.
  5. Peña F, Cárdenas S, Gallego M, Valcárcel M. Direct olive oil authentication: Detection of adulteration of olive oil with hazelnut oil by direct coupling of headspace and mass spectrometry, and multivariate regression techniques. J Chromatogr A 2005; 1074: 215-221. DOI: 10.1016/j.chroma.2005.03.081.
  6. Zou M-Q, Zhang X-F, Qi X-H, Ma H-L, Dong Y, Liu C-W, et al. Rapid authentication of olive oil adulteration by Raman Spectrometry. J Agric Food Chem 2009; 57: 6001-6006. DOI: 10.1021/jf900217s.
  7. Sayago A, Morales MT, Aparicio R. Detection of hazelnut oil in virgin olive oil by a spectrofluorimetric method. Eur Food Res Technol 2004; 218: 480-483. DOI: 10.1007/s00217-004-0874-9.
  8. Kyriakidis NB, Skarkalis P. Fluorescence spectra measurement of olive oil and other vegetable oils. J AOAC Int 2000; 83: 1435-1439.
  9. Mu T, Chen S, Zhang Y, Guo P, Chen H, Liu X, et al. Classification of edible oils using 532 nm laser-induced fluorescence combined with support vector machine. Anal Methods 2013; 5: 6960. DOI: 10.1039/c3ay40987b.
  10. Kongbonga YGM, Ghalila H, Onana MB, Majdi Y, Lakhdar Z Ben, Mezlini H, et al. Characterization of vegetable oils by fluorescence spectroscopy. Food Nutr Sci 2011; 02: 692-699. DOI: 10.4236/fns.2011.27095.
  11. Nikolova K, Eftimov T, Perifanova M, Brabant D. Quick fluorescence method for the distinguishing of vegetable oils. J Food Sci Eng 2012; 2: 674-684.
  12. Navruz I, Coskun AF, Wong J, Mohammad S, Tseng D, Nagi R, et al. Smart-phone based computational microscopy using multi-frame contact imaging on a fiber-optic array. Lab Chip 2013; 13: 4015-4023. DOI: 10.1039/C3LC50589H.
  13. Yu H, Tan Y, Cunningham BT. Smartphone fluorescence spectroscopy. Anal Chem 2014; 86: 8805-8813. DOI: 10.1021/ac502080t.
  14. Hossain MA, Canning J, Cook K, Jamalipour A. Smartphone laser beam spatial profiler. Opt Lett 2015; 40: 5156. DOI: 10.1364/OL.40.005156.
  15. Wang Y, Liu X, Chen P, Tran NT, Zhang J, Chia WS, et al. Smartphone spectrometer for colorimetric biosensing. Analyst 2016; 141: 3233-3238. DOI: 10.1039/C5AN02508G.
  16. Liu Y, Liu Q, Chen S, Cheng F, Wang H, Peng W. Surface plasmon resonance biosensor based on smart phone platforms. Sci Rep 2015; 5: 12864. DOI: 10.1038/srep12864.
  17. Bremer K, Roth B. Fibre optic surface plasmon resonance sensor system designed for smartphones. Opt Express 2015; 23: 17179. DOI: 10.1364/OE.23.017179.
  18. Zhuo Y, Cunningham BT. Label-free biosensor imaging on photonic crystal surfaces. Sensors (Switzerland) 2015; 15: 21613-21635. DOI: 10.3390/s150921613.
  19. Gallegos D, Long KD, Yu H, Clark PP, Lin Y, George S, et al. Label-free biodetection using a smartphone. Lab Chip 2013; 13: 2124. DOI: 10.1039/c3lc40991k.
  20. Dantu V, Vempati J, Srivilliputhur S. Non-invasive blood glucose monitor based on spectroscopy using a smartphone. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 3695-3698. DOI: 10.1109/embc.2014.6944425.
  21. Hossain MA, Canning J, Ast S, Cook K, Rutledge PJ, Jamalipour A. Combined "dual" absorption and fluorescence smartphone spectrometers. Opt Lett 2015; 40: 1737-1740. DOI: 10.1364/OL.40.001737.
  22. Dutta S, Sarma D, Nath P. Ground and river water quality monitoring using a smartphone-based pH sensor. AIP Adv 2015; 5: 1-10. DOI: 10.1063/1.4921835.
  23. Kazanskiy NL; Kharitonov SI; Khonina SN; Volotovskiy SG. Simulation of spectral filters used in hyperspectrometer by decomposition on vector Bessel modes. Proc SPIE 2015; 9533: 95330L. DOI: 10.1117/12.2183429.
  24. Kazanskiy NL. Modeling diffractive optics elements and devices. Proc SPIE 2018; 10774: 107740O. DOI: 10.1117/12.2319264.
  25. Blank VA, Strelkov YS, Skidanov RV. Axicon for imaging spectrometer. J Phys Conf Ser 2019; 1368(2): 022003. DOI: 10.1088/1742-6596/1368/2/022003.
  26. Ivliev NA, Podlipnov VV, Skidanov RV. A compact imaging hyperspectrometer. J Phys Conf Ser 2019; 1368(2): 022053. DOI: 10.1088/1742-6596/1368/2/022053.
  27. Kazanskiy NL, Skidanov RV. Technological line for creation and research of diffractive optical elements. Proc SPIE 2019; 11146: 111460W. DOI: 10.1117/12.2527274.
  28. Soifer VA. Diffractive nanophotonics and advanced information technologies. Her Russ Acad Sci 2014; 84(1): 9-20. DOI: 10.1134/S1019331614010067.
  29. Zimichev EA, Kazanskiy NL. Serafimovich PG. Spectral-spatial classification with k-means++ particional clustering. Computer Optics 2014; 38(2): 281-286.
  30. Kuznetsov AV, Myasnikov VV. A comparison of algorithms for supervised classification using hyperspectral data. Computer Optics 2014; 38(3): 494-502.
  31. Fursov VA, Bibikov SA, Bajda OA. Thematic classification of hyperspectral images using conjugacy indicator. Computer Optics 2014; 38(1): 154-158.
  32. Denisova AYu, Myasnikov VV. Anomaly detection for hyperspectral imaginary. Computer Optics 2014; 38(2): 287-296.
  33. Myasnikov EV. Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. Computer Optics 2017; 41(4): 564-572. DOI: 10.18287/2412-6179-2017-41-4-564-572.
  34. Nikonorov AV, Petrov MV, Bibikov SA, Yakimov PY, Kutikova VV, Morozov AA, Skidanov RV, Kazanskiy NL. Deep learning-based enhancement of hyperspectral images using simulated ground truth. 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) 2018; 1-9. DOI: 10.1109/PRRS.2018.8486408.
  35. Long KD, Yu H, Cunningham BT. Smartphone instrument for portable enzyme- linked immunosorbent assays. Biomed Opt Express 2014; 5: 3792. DOI: 10.1364/BOE.5.003792.
  36. Yu H, Le HM, Kaale E, Long KD, Layloff T, Lumetta SS, et al. Characterization of drug authenticity using thin-layer chromatography imaging with a mobile phone. J Pharm Biomed Anal 2016; 125: 85-93. DOI: 10.1016/j.jpba.2016.03.018.
  37. Priye A, Bird SW, Light YK, Ball CS, Negrete OA, Meagher RJ. A smartphone-based diagnostic platform for rapid detection of Zika, chikungunya, and dengue viruses. Sci Rep 2017; 7: 44778. DOI: 10.1038/srep44778.
  38. Matasaru C. Mobile phone camera possibilities for spectral imaging. Master Thesis Report. University of Eastern Finland; 2014.


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