Detection of Adulteration in Edible Oil Using FT-IR Spectroscopy and Machine Learning
International Journal of Biochemistry Research & Review,
Aims: To detect the adulterant in edible oil rapidly.
Study Design: Authenticity and adulteration detection in edible oils are the increasing challenges for researchers, consumers, industries and regulatory agencies. Traditional approaches may not be the most effective option to combat against adulteration in edible oils as that’s are complex, laborious, expensive, require a high degree of technical knowledge when interpreting data and produce hazardous chemical. Consequently, a cost effective, rapid and reliable method is required.
Place and Duration of the Study: The experiment was conducted jointly in the laboratory of the Department of Food Technology and Rural Industries, Bangladesh Agricultural University, Mymensingh and the Institute of Food Science and Technology, BCSIR, Dhaka.
Methods: In this study, Fourier Transform Infrared spectroscopy coupled with multivariate analysis was used for adulteration detection in sunflower and rice bran oil. Sunflower oil was adulterated with soybean oil in the range of 10-50% (v/v) and rice bran oil was adulterated with palm oil in the range of 4-40% (v/v) at approximately 10% and 5% increments respectively. FTIR spectra were recorded in the wavenumber range of 4000-650cm-1.
Results: FTIR spectra data in the whole spectral range and reduced spectral range were used to develop a partial least square regression (PLSR) model to predict the level of adulteration in sunflower and palm oils. Good prediction model was obtained for all PLSR models with a coefficient of determination (R2) of >= 0.985 and root mean square errors of calibration (RMSEC) in the range of 0-1.7325%.
Conclusion: The result suggested that FTIR spectroscopy associated with multivariate analysis has the great potential for a rapid and non-destructive detection of adulteration in edible oils laborious conventional analytical techniques.
- edible oil
How to Cite
Alamprese C, Casale M, Sinelli N, Lanteri S, Casiraghi E. Detection of minced beef adulteration with turkey meat by UV–vis, NIR and MIR spectroscopy. LWT - Food Science and Technology. 2013;53(1):225-232.
Ellis DI, Brewster VL, Dunn WB, Allwood JW, Golovanov AP, Goodacre R. Fingerprinting food: Current technologies for the detection of food adulteration and contamination. Chem Soc Rev. 2012;41(17):5706-5727.
Kamruzzaman M, Makino Y, Oshita S, Liu S. Assessment of visible near-infrared hyperspectral imaging as a tool for detection of horsemeat adulteration in minced beef. Food and Bioprocess Technology. 2015;8(5):1054-1062.
Zhao M, Downey G, O'Donnell CP. Detection of adulteration in fresh and frozen beefburger products by beef offal using mid-infrared ATR spectroscopy and multivariate data analysis. Meat Sci. 2014;96(2 Pt A):1003-1011.
Urickova V, Sadecka J. Determination of geographical origin of alcoholic beverages using ultraviolet, visible and infrared spectroscopy: A review. Spectrochim Acta A Mol Biomol Spectrosc. 2015;148:131-137.
Beaten V, Fernández Pierna JA, Dardenne P, Meurens M, García-González DL, Aparicio-Ruiz R. Detection of the presence of hazelnut oil in olive oil by FT-Raman and FT-MIR spectroscopy. Journal of Agriculture and Food Chemistry. 2005;53: 6201–6206.
García-González DL, Aparicio-Ruiz R, Aparicio R. Virgin olive oil - Chemical implications on quality and health. European Journal of Lipid Science and Technology. 2008;110(7):602-607.
Espiñeira M, Vieites JM, Santaclara FJ. Species authentication of octopus, cuttlefish, bobtail and bottle squids (families Octopodidae, Sepiidae and Sepiolidae) by FINS methodology in seafoods. Food Chemistry. 2010;121(2): 527-532.
Kamruzzaman M, Barbin D, ElMasry G, Sun DW, Allen P. Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat. Innovative Food Science and Emerging Technologies. 2012a;16:316-325.
Manley M, De Bruyn N, Downey G. Classification of three-year old, unblended South African brandy with near-infrared spectroscopy. NIR News. 2003;14:8-9.
Ruoff K, Luginbuhl W, Kunzli R. Authentication of the botanical and geographical origin of honey by mid-infrared spectroscopy. Journal of Food Agricultural Chemistry. 2006;54:6873-6880.
Li B, Wang H, Zhao Q, Ouyang J, Wu Y. Rapid detection of authenticity and adulteration of walnut oil by FTIR and fluorescence spectroscopy: A comparative study. Food Chem. 2015;181:25-30.
Mamani-Linares LW, Gallo C, Alomar D. Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy. Meat Sci. 2012;90(2):378-385.
Sun X, Zhang L, Li P, Xu B, Ma F, Zhang Q, Zhang W. Fatty acid profiles based adulteration detection for flaxseed oil by gas chromatography mass spectrometry. LWT - Food Science and Technology. 2015;63(1):430-436.
Mohammed Kamruzzaman YM, Seiichi Oshita. An appraisal of hyperspectral imaging for non-invasive authentication of geographical origin of beef and pork. Proceedings International Conference of Agricultural Engineering, Zurich; 2014.
Longobardi F, Casiello G, Cortese M. Discrimination of geographical origin of lentils (Lens culinaris Medik.) using isotope ratio mass spectroscopy combined with chemometrics. Food Chemistry. 2015;05: 020.
Picque D, Cattenoz T, Corrieu G. Discrimination of red wines according to their geographical origin and vintage year by the use of mid-infrared spectroscopy. Science Aliments. 2005;25:207-220.
Ballin NZ. Authentication of meat and meat products. Meat Sci. 2010;86(3):577-587.
Rasmus Bro, Van Den Berg F, Anette Thybo, Charlotte M. Anderseny, Bo M. Jørgensenx, Henrik Andersen. Multivariate data analysis as a tool in advanced quality monitoring in the food production chain. Trends in Food Science & Technology. 2002;13:235-244.
Russell LM, Bahadur R, Hawkins LN, Allan J, Baumgardner D, Quinn PK, Bates TS. Organic aerosol characterization by complementary measurements of chemical bonds and molecular fragments. Atmospheric Environment. 2009;43(38): 6100-6105.
Takahama S, Johnson A, Russell LM. Quantification of carboxylic and carbonyl functional groups in organic aerosol infrared absorbance spectra. Aerosol Science and Technology. 2013;47(3):310-325.
Svante Wold MS a. LE. PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems. 2001;58:109-130.
Safwan M, Obeidat MSK. a. WMO. Classification of edible oils and uncovering adulteration of virgin olive oil using FTIR with the aid of chemometrics. Australian Journal of Basic and Applied Sciences. 2009;3(3):2048-2053.
Kamruzzaman M, Makino Y, Oshita S. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. Journal of Food Engineering. 2016;170:8-15.
Maggio RM, Cerretani L, Chiavaro E, Kaufman TS, Bendini A. A novel chemometric strategy for the estimation of extra virgin olive oil adulteration with edible oils. Food Control. 2010;21(6):890-895.
Oussama A, Elabadi F, Platikanov S, Kzaiber F, Tauler R. Detection of olive oil adulteration using FT-IR spectroscopy and PLS with variable importance of projection (VIP) scores. Journal of the American Oil Chemists' Society. 2012;89(10):1807-1812.
Abdel-Rahman EM, Mutanga O, Adam E, Ismail R. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. ISPRS Journal of Photo-grammetry and Remote Sensing. 2014;88: 48-59.
Kamruzzaman M, ElMasry G, Sun DW, Allen P. Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta. 2012;714:57-67.
Perez-Rodriguez M, Horak-Terra I, Rodriguez-Lado L, Martinez Cortizas A. Modelling mercury accumulation in minerogenic peat combining FTIR-ATR spectroscopy and partial least squares (PLS). Spectrochim Acta A Mol Biomol Spectrosc. 2016;168:65-72.
Ruthenburg TC, Perlin PC, Liu V, McDade CE, Dillner AM. Determination of organic matter and organic matter to organic carbon ratios by infrared spectroscopy with application to selected sites in the IMPROVE network. Atmospheric Environment. 2014;86:47-57.
Gurdeniz G, Ozen B. Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data. Food Chemistry. 2009;116(2):519-525.
Hirri A, Gammouh M, Gorfti A, Kzaiber F, Bassbasi M, Souhassou S, Balouki A, Oussama A. The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and estimation of extra virgin olive oil adulteration with old olive oil. Sky Journal of Food Science. 2015;4:060-066.
Kowalski PGBR. Partial least-squares regression: A tutorial. Analytica Chimica Acta. 1986;186:1-17.
Rinnan A, Berg FVD, Engelsen SB. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends in Analytical Chemistry. 2009;28(10):1201-1222.
Urbano Cuadrado M, Luque de Castro MD, Perez Juan PM, Gomez-Nieto MA. Comparison and joint use of near infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination of wine parameters. Talanta. 2005;66(1): 218-224.
Zhang Q, Liu C, Sun Z, Hu X, Shen Q, Wu J. Authentication of edible vegetable oils adulterated with used frying oil by Fourier Transform Infrared Spectroscopy. Food Chem. 2012;132(3):1607-1613.
Christy AA, KS, Du Y, Ozaki K. The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics. Analytical Sciences. 2004;20:935-940.
Yap KYL, Chan SY, Lim CS. Infrared-based protocol for the identification and categorization of ginseng and its products. Food Research International. 2007;40(5): 643-652.
Quiñones-Islas N, Meza-Márquez OG, Osorio-Revilla G, Gallardo-Velazquez T. Detection of adulterants in avocado oil by Mid-FTIR spectroscopy and multivariate analysis. Food Research International. 2013;51(1):148-154.
Mauer BFOALJ. Detection of hazelnut oil adulteration using FT-IR spectroscopy. Agricultural and Food Chemistry. 2002;50: 3898-3901.
Tay A, Singh RK, Krishnan SS, Gore JP. Authentication of olive oil adulterated with vegetable oils using Fourier transform infrared spectroscopy. LWT-Food Science and Technology. 2002;35:99-103.
Elzey B, Pollard D, Fakayode SO. Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis. Food Control. 2016;68:303- 309.
Rohman A, Man YBC. The use of Fourier transform mid infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil. Food Chemistry. 2011;129(2): 583-588.
Rohman A, Man YC. Fourier transform infrared (FTIR) spectroscopy for analysis of extra virgin olive oil adulterated with palm oil. Food Research International. 2010;43(3):886-892.
Abstract View: 3057 times
PDF Download: 898 times