Non-destructive Detection of Sesame Oil Adulteration by Portable FT-NIR, FT-MIR, and Raman Spectrometers Combined with Chemometrics

Abstract

Edible oils are often adulterated with fixed oils because of their high quality and price. Sesame oil is prone to adulteration due to its high commodity value and popularity. Therefore, a rapid, simple, and non-invasive method to detect adulteration in sesame oil is necessary for quality control purposes. Handheld and portable FT-NIR, FT-MIR, and Raman spectrometers are easy to operate, non-destructive, rapid, and easy to transport for in-situ assessments as well as being cheaper alternatives to traditional instruments. This study aimed to evaluate three different vibrational spectroscopic techniques in detecting sesame oil adulteration with sunflower and canola oil. Sesame oils were adulterated with fixed oils at different concentrations (0 – 25%) (w/w). Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to authenticate pure sesame oil and Partial Least Squares Regression (PLSR) to predict the levels of the adulterant. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). In all three instruments, SIMCA provided distinct clusters for pure sesame oils and adulterated samples with interclass distance (ICD) over 3. Furthermore, FT-NIR and FT-MIR showed excellent performance in predicting adulterant levels with rval>0.96. Specifically, the FT-MIR unit provided more precise classification and PLSR prediction models over FT-NIR and Raman units. Still, all the units can be used as an alternative method to traditional methods such as GC, GC-MS, etc. These units showed great potential for in-situ surveillance to detect sesame oil adulterations.

Keywords

• sesame oil
• adulteration
• portable devices
• NIR
• MIR
• Raman

References

1. 1. Ozulku G, Yildirim RM, Toker OS, Karasu S, Durak MZ (2017) Rapid detection of adulteration of cold pressed sesame oil adultered with hazelnut, canola, and sunflower oils using ATR-FTIR spectroscopy combined with chemometric. Food Control, 82:212–6. DOI: https://doi.org/10.1016/j.foodcont.2017.06.034.
2. 2. Wang R, Liu K, Wang X, Tan M (2019) Detection of Sesame Oil Adulteration Using Low-Field Nuclear Mag-netic Resonance and Chemometrics. Int J Food Engineering, 20180349, DOI: https://doi.org/10.1515/ijfe-2018-0349.
3. 3. Gharby S, Harhar H, Bouzoubaa Z, Asdadi A, El Yadini A, Charrouf Z (2017) Chemical characterization and oxidative stability of seeds and oil of sesame grown in Morocco. Journal of the Saudi Soc of Agri Sci, 16(2):105-11. DOI: https://doi.org/10.1016/j.jssas.2015.03.004.
4. 4. Zhang L, Shuai QQ, Li P, Zhang Q, Ma F, Zhang W, et al. (2016) Ion mobility spectrometry fingerprints: A rapid detection technology for adulteration of sesame oil. Food Chemistry, 192:60-6. DOI: https://doi.org/10.1016/j.foodchem.2015.06.096.
5. 5. Warra AA (2011) Sesame (Sesamum indicum L.) seed oil methods of extraction and its prospects in cosmetic industry: A review. Bayero Journal of Pure and Applied Sciences, 4(2):164-8. DOI: https://doi.org/10.4314/bajopas.v4i2.33.
6. 6. FAO. (2018). FAOSTAT online statistical service. URL: http://faostat.fao.org/.
7. 7. Seo HY, Ha J, Shin DB, Shim SL, No KM, Kim KS, et al. (2010) Detection of corn oil in adulterated sesame oil by chromatography and carbon isotope analysis. Journal of the American Oil Chemists' Society, 87(6):621–6. DOI: https://doi.org/10.1007/s11746-010-1545-6. 8. Aykas DP, Karaman AD, Keser B, Rodriguez-Saona LE (2020) Non-Targeted Authentication Approach for Extra Virgin Olive Oil. Foods, 9(2):221. DOI: https://doi.org/10.3390/foods9020221.
8. 9. Subramanian A, Alvarez VB, Harper WJ, Rodriquez-Saona LE (2011) Monitoring amino acids, organic acids, and ripening changes in Cheddar cheese using Fourier-transform infrared spectroscopy. International Dairy Jour-nal, 21(6):434–40. DOI: https://doi.org/10.1016/j.idairyj.2010.12.012.

9. 10. Tengstrand E, Rosen J, Hellenas KE, Aberg KM (2013) A concept study on non-targeted screening for chemical contaminants in food using liquid chromatography–mass spectrometry in combination with a metabolomics ap-proach. Anal Bioanal Chem, 405, 1237–43. DOI: https://doi.org/10.1007/s00216-012-6506-5.
10. 11. Quinones-Islas N, Meza-Marquez OG, Osorio-Revilla G, Gallardo-Velazquez T (2013) Detection of adulter-ants in avocado oil by Mid-FTIR spectroscopy and multivariate analysis. Food Research International, 51(1):148-54. DOI: https://doi.org/10.1016/j.foodres.2012.11.037.
11. 12. Chen H, Lin Z, Tan C (2018) Fast quantitative detection of sesame oil adulteration by near-infrared spectros-copy and chemometric models. Vibrational Spectroscopy, 99:178-83. DOI: https://doi.org/10.1016/j.vibspec.2018.10.003.
12. 13. Rodriguez-Saona LE, Aykas DP, Borba KR, Urtubia A (2020) Miniaturization of optical sensors and their po-tential for high-throughput screening of foods. Current Opinion in Food Science, 31: 136–50. DOI: https://doi.org/10.1016/j.cofs.2020.04.008.
13. 14. Miaw CSW, Sena MM, Souza SVC, et al (2018) Variable selection for multivariate classification aiming to de-tect individual adulterants and their blends in grape nectars. Talanta 190:55–61. DOI: https://doi.org/10.1016/j.talanta.2018.07.078.
14. 15. Ichihara K, Shibahara A, Yamamoto K, Nakayama T (1996) An improved method for rapid analysis of the fatty acids of glycerolipids. Lipids 31:535–9. DOI: https://doi.org/10.1007/BF02522648.
15. 16. De Maesschalck R, Candolfi A, Massart DL, Heuerding S (1999) Decision criteria for soft independent model-ling of class analogy applied to near infrared data. Chemom Intell Lab Syst 47:65–77. DOI: https://doi.org/10.1016/S0169-7439(98)00159-2.
16. 17. Wold S (1976) Pattern recognition by means of disjoint principal components models. Pattern Recognit 8:127–39. DOI: https://doi.org/10.1016/0031-3203(76)90014-5
17. 18. Lavine BK (2000) Clustering and Classification of Analytical Data. Encycl Anal Chem 1–21. DOI: https://doi.org/10.1002/9780470027318.a5204.
18. 19. Ballabio D, Todeschini R (2009) Infrared Spectroscopy for Food Quality Analysis and Control Multivariate Classification for Qualitative Analysis. In: Sun D-W (ed) Infrared Spectroscopy for Food Quality Analysis and Control, 1st edn. Elsevier, Burlington, MA, pp 83–104.
19. 20. Haaland DM, Thomas EV (1988) Partial Least-Squares Methods for Spectral Analyses. 1. Relation to Other Quantitative Calibration Methods and the Extraction of Qualitative Information. Anal Chem 60:1193–202. DOI: https://doi.org/10.1021/ac00162a020.
20. 21. Jong S (1993) PLS Fits Closer Than PCR. J Chemom 7:551–7. DOI: https://doi.org/10.1515/jpme.1998.26.4.325.
21. 22. Brereton RG (2000) Introduction to multivariate calibration in analytical chemistry. Analyst 125:2125–54. DOI: https://doi.org/10.1039/b003805i.
22. 23. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: A basic tool of chemometrics. Chemom Intell Lab Syst 58:109–30. DOI: https://doi.org/10.1016/S0169-7439(01)00155-1.
23. 24. Hourant P, Baeten V, Morales MT, Meurens M, Aparicio R (2000) Oil and fat classification by selected bands of near-infrared spectroscopy. Appl. Spectrosc. 54:1168–74.
24. 25. Yang H, Irudayaraj J, Paradkar MM (2005) Discriminant analysis of edible oils and fats by FTIR, FT-NIR and FT-Raman spectroscopy. Food Chemistry, 93:25–32.
25. 26. Aykas DP, Rodriguez-Saona LE (2016). Analytical Methods Assessing potato chip oil quality using a portable infrared spectrometer combined with pattern recognition analysis, Analytical Methods, 1–11. DOI: https://doi.org/10.1039/C5AY02387D.
26. 27. Rodriguez-Saona LE, Giusti MM, Shotts M (2016) Advances in infrared spectroscopy for food authenticity testing. In Advances in food authenticity testing. DOI: https://doi.org/10.1016/B978-0-08-100220-9.00004-7.
27. 28. Covaciu FD, Grosan-Berghian C, Feher I, Magdas DA (2020) Edible Oils Differentiation Based on the Deter-mination of Fatty Acids Profile and Raman Spectroscopy—A Case Study. Applied Sciences, 10(23), 8347. DOI: https://doi.org/10.3390/app10238347.
28. 29. Abdi H (2010). Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat 2:97–106. DOI: https://doi.org/10.1002/wics.51.
29. 30. Urbano-Cuadrado M, Luque De Castro MD, Perez Juan P M, Gomez-Nieto MA (2005) Comparison and joint use of near infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination of wine parameters. Talanta, 66(1):218–24. DOI: https://doi.org/10.1016/j.talanta.2004.11.011.