Estimation of the Best Classification Algorithm and Fraud Detection of Olive Oil by Olfaction Machine

Document Type : Research Article

Authors

• M. R. Zarezadeh ¹
• M. Aboonajmi ¹
• M. Ghasemi-Varnamkhasti ²
• F. Azarikia ³

¹ Department of Agro-technology, College of Abouraihan, University of Tehran, Tehran, Iran

² Department of Mechanical Engineering of Biosystem, Shahrekord University, Shahrekord, Iran

³ Department of Food Technology, College of Abouraihan, University of Tehran, Tehran, Iran

https://doi.org/10.22067/jam.v11i2.84105

Abstract

Introduction
Extra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases due to these benefits, EVOO is expensive so unfortunately adulteration in EVOO by mixing it with other cheap and low cost and low value oils such as canola, sunflower, palm and etc. is very common. Adulteration leads to health and financial losses and sometimes cause serious illness. Olive oil has various quality levels which depend on different factors such as olive cultivar, storage, oil extracting process etc.
Materials and Methods
There are numerous food quality evaluation and adulteration detection approaches which include destructive and non-destructive methods. Control sample (EVOO) was applied from "DANZEH food industry", Lowshan, Gilan Province. For ensuring that control sample is extra virgin, a sample was tested in "Rahpooyan e danesh koolak Lab." Tehran, Iran; according to "Institute of standards and industrial research of Iran" ISIRI number: 4091 and INSO 13126-2. Eight semi-conductor gas sensors "FIS, MQ3, MQ3, MQ4, MQ8, MQ135, MQ136, TGS136, TGS813 AND TGS822" applied in used olfaction machine. In this study there were 6 treatments: 1- Pure EVOO, 2- EVOO with 5% adulteration, 3- EVOO with 10% adulteration, 4- EVOO with 20% adulteration, 5- EVOO with 35% adulteration and 6- EVOO with 50% adulteration. Adulteration created with ordinary frying oil (including sunflower, canola, and maize oils). Each treatment prepared in seven samples and each sample test was repeated seven times. In this study, olfaction machine, a non-destructive, simple and user friendly System applied. As mentioned, the olfaction machine includes eight different sensors, so each test has eight graphs. Four features (1- Sensor output (mV) in start of odor pulse (refer to fig. 3) 2- Sensor output at the end of odor pulse 3- Average of sensor output during odor pulse and 4- Difference of sensor output at the end and start of start of odor pulse); So 32 features extracted and analyzed and finally effective sensors reported.
Results and Discussion
Histogram and box plot of raw data showed that the data are not normal and need some preprocessing operations. Preprocessing facilitates data analyzing and classifying extracted features. After preprocessing, the standard data, divided into two classes: train data (70%) and test data (30%). Data classified with 4 different classifier models which include: K-nearest neighbors, support vector machine, artificial neural network and Ada-boost. Results showed that KNN method, with 89.89% and SVM with 86.52% classified with higher accuracy. Similarly, the confusion matrix showed the reasonable results of classifying operation. Also, three effective sensors in classifying determined TGS2620, MQ5 and MQ4 respectively, and on the other side, sensors such as MQ3 and MQ8 have the minimum effect on classifying so it is possible to remove these sensors from the sensor array without effective impress on results. This may cause decrease in the olfaction machine price and reduce analyzing time.
Conclusion
Due to increasing adulteration in foods, especially in olive oil and its significant effects on people's health and financial losses, a simple, cheap and non-destructive quality evaluation extended. Results showed that the olfaction machine with metal oxide semiconductor (especially including TGS 2620, MQ5 and MQ4 sensors) can use for classification and adulteration detection of extra virgin olive oil. Evaluation of this system's output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration).

Keywords

• Classification
• Fraud detection
• Olive oil
• Olfaction machine
• Quality

20.1001.1.22286829.1400.11.2.18.3

Open Access

©2021 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Alavi Rafiee, S., R. Farhoosh, and M. Haddad Khodaparast. 2012. Physicochemical properties of Iranian commercial olive oils. Iranian Journal of Nutrition Sciences & Food Technology 7: 85-94. (In Farsi)

2. Aued-Pimentel, S., E. Takemoto, R. Antoniassi, and E. S. G. Badolato. 2006. Composition of tocopherols in sesame seed oil: an indicative of adulteration. Grasas y Aceites 57: 205-210.

3. Baeten, V., and R. Aparicio. 2000. Edible oils and fats authentication by Fourier transform Raman spectrometry. BASE.

4. Borràs, E., J. Ferre, R. Boque, M. Mestres, L. Aceña, and O. Busto. 2015. Data fusion methodologies for food and beverage authentication and quality assessment–A review. Analytica Chimica Acta 891: 1-14.

5. Dankowska, A., and W. Kowalewski. 2019. Comparison of different classification methods for analyzing fluorescence spectra to characterize type and freshness of olive oils. European Food Research and Technology 245: 745-752.

6. Estakhroueiyeh, H. R., and E. Rashedi. 2015. Detecting moldy bread using an e-nose and the KNN classifier. Pages 251-255. 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE): IEEE.

7. Fahim Danesh, M., and M. E. Bahrami. 2015. Evaluation of adulteration in sesame oil using Differential Scanning Calorimetry. Food Science and Technology 13: 81-89. (In Farsi)

8. Fayyaz, P., S. S. Mohtasebi, A. Jafari, and A. Masoudi. 2019. Development and Evaluation of an Electronic Nose System Based on MOS Sensors to Detect and to Distinguish Lemon Essential Oils. Journal of Agricultural Machinery 9. (In Farsi)

9. Garrido-Delgado, R., M. del Mar Dobao-Prieto, L. Arce, and M. Valcarcel. 2015. Determination of volatile compounds by GC–IMS to assign the quality of virgin olive oil. Food Chemistry 187: 572-579.

10. Ghasemi-Varnamkhasti, M., S. S. Mohtasebi, M. Siadat, H. Ahmadi, and S. H. Razavi. 2015. From simple classification methods to machine learning for the binary discrimination of beers using electronic nose data. Engineering in Agriculture, Environment and Food 8: 44-51.

11. Haddi, Z., H. Alami, N. El Bari, M. Tounsi, H. Barhoumi, A. Maaref, N. Jaffrezic-Renault, and B. Bouchikhi. 2013. Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International 54: 1488-1498.

12. Hajinezhad, M., S. S. Mohtasebi, M. Ghasemi-varnamkhasti, and M. Aghbashlo. 2016. Detecting Adulteration in Lotus Honey Using a Machine Olfactory System. Journal of Agricultural Machinery 7: 439-450. (In Farsi)

13. Homapour, M., M. Ghavami, Z. Piravivanak, and E. Hosseini. 2016. Evaluation of Chemical Characteristics of Extra Virgin Olive Oils Extracted from Three Monovarieties of Mari, Arbequina and Koroneiki in Fadak and Gilvan Regions. Journal of Food Biosciences and Technology 6: 77-85.

14. Huo, Q.-G., X.-B. Jin, and H.-M. Zhang. 2012. Multi-label classification for Oil Authentication. Pages 711-714. 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery: IEEE.

15. Jafari, A., A. Fazayeli, and M. R. Zarezadeh. 2014. Estimation of orange skin thickness based on visual texture coarseness. Biosystems Engineering 117: 73-82.

16. Korotčenkov, G. S. 2013. Handbook of Gas Sensor Materials: Properties, Advantages and Shortcomings for Applications. Conventional Approaches. Springer.

17. Lee, D.-S., B.-S. Noh, S.-Y. Bae, and K. Kim. 1998. Characterization of fatty acids composition in vegetable oils by gas chromatography and chemometrics. Analytica Chimica Acta 358: 163-175.

18. Lee, D.-S., E.-S. Lee, H.-J. Kim, S.-O. Kim, and K. Kim. 2001. Reversed phase liquid chromatographic determination of triacylglycerol composition in sesame oils and the chemometric detection of adulteration. Analytica Chimica Acta 429: 321-330.

19. Maghsudi, S. 1999. Technology of olive and it's products Tehran: Iran Agriculture Science Press. (In Farsi)

20. Martı́n, Y. G., J. L. P. Pavon, B. M. Cordero, and C. G. A. Pinto. 1999. Classification of vegetable oils by linear discriminant analysis of electronic nose data. Analytica Chimica Acta 384: 83-94.

21. Mildner‐Szkudlarz, S., and H. H. Jeleń. 2010. Detection of olive oil adulteration with rapeseed and sunflower oils using mos electronic nose and SMPE‐MS. Journal of Food Quality 33: 21-41.

22. Naderi-Boldaji, M., M. Mokhtari, M. Ghasemi-Varnamkhasti, and M. Tohidi. 2019. Feasibility of using a cylindrical resonator sensor for adulteration detection in sesame oil. Innovative Food Technologies (JIFT).

23. Ok, S. 2017. Detection of olive oil adulteration by low-field NMR relaxometry and UV-Vis spectroscopy upon mixing olive oil with various edible oils. Grasas y Aceites 68: 173.

24. Oliveros, M. C. C., J. L. P. Pavon, C. G. A. Pinto, M. E. F. Laespada, B. M. Cordero, and M. Forina. 2002. Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils. Analytica Chimica Acta 459: 219-228.

25. Ozen, B. F., and L. J. Mauer. 2002. Detection of hazelnut oil adulteration using FT-IR spectroscopy. Journal of Agricultural and Food Chemistry 50: 3898-3901.

26. Pashaei, A., and F. Shavakhi. 2017. Application of calorimeter for detection of adulteration of extra virgin olive oil mixed with aspersed oil as a substitute for gas chromatography. Journal of Food Research (Agricultural Science) 27: 139-149. (In Farsi)

27. Pearce, T. C., S. S. Schiffman, H. T. Nagle, and J. W. Gardner. 2006. Handbook of machine olfaction: electronic nose technology. John Wiley & Sons.

28. Pyle, D. 1999. Data preparation for data mining. morgan kaufmann.

29. Rizki, H., W. Terouzi, F. Kzaiber, H. Hanine, and A. Oussama. 2016. Quantification of Adulterations in Sesame Oil with Inferior Edible Oils by using ATR-FTIR Coupled to Chemometrics. IOSR Journal of Environmental Science, Toxicology and Food Technology, e-ISSN: 2319-2402.

30. Samadi, S., and M. Hosseyni nezhad. 2015. Smart sensors in the food industry: Technology and applications. (In Farsi)

31. Sanaeifar, A., S. Mohtasebi, M. Ghasemi-Varnamkhasti, and H. Ahmadi. 2014. Design, Construction and Performance Evaluation of a Metal Oxide Semiconductor (MOS) Based Machine Olfaction (Electronic Nose) for Monitoring of Banana Ripeness. Journal of Agricultural Machinery 5: 111-121.

32. Sega, A., I. Zanardi, L. Chiasserini, A. Gabbrielli, V. Bocci, and V. Travagli. 2010. Properties of sesame oil by detailed 1H and 13C NMR assignments before and after ozonation and their correlation with iodine value, peroxide value, and viscosity measurements. Chemistry and Physics of Lipids 163: 148-156.

33. Seo, H. Y., J. Ha, D. B. Shin, S. L. Shim, K. M. No, K. S. Kim, K. B. Lee, and S. B. Han. 2010. Detection of corn oil in adulterated sesame oil by chromatography and carbon isotope analysis. Journal of the American Oil Chemists' Society 87: 621-626.

34. Šmejkalova, D., and A. Piccolo. 2010. High-power gradient diffusion NMR spectroscopy for the rapid assessment of extra-virgin olive oil adulteration. Food Chemistry 118: 153-158.

35. Tay, A., R. Singh, S. Krishnan, and J. Gore. 2002. Authentication of olive oil adulterated with vegetable oils using Fourier transform infrared spectroscopy. LWT-Food Science and Technology 35: 99-103.

36. Torrecilla, J. S., E. Rojo, J. C. Dominguez, and F. Rodriguez. 2010. A novel method to quantify the adulteration of extra virgin olive oil with low-grade olive oils by UV− vis. Journal of Agricultural and Food Chemistry 58: 1679-1684.

37. Varidi, M. J., M. Varidi, M. Vajdi, and A. Sharifpour. 2018. Design, development and application of electronic nose instrument to rapidly detect spoilage of air, vacuum and modified atmosphere packaged camel minced meat. Journal of Food Science and Technology 15.

38. Wojnowski, W., T. Majchrzak, T. Dymerski, J. Gębicki, and J. Namieśnik. 2017. Portable electronic nose based on electrochemical sensors for food quality assessment. Sensors 17: 2715.

39. Xu, L., X. Yu, L. Liu, and R. Zhang. 2016. A novel method for qualitative analysis of edible oil oxidation using an electronic nose. Food Chemistry 202: 229-235.

40. Xu, Y., M. Hassan, F. Kutsanedzie, H. Li, and Q. Chen. 2018. Evaluation of extra-virgin olive oil adulteration using FTIR spectroscopy combined with multivariate algorithms. Quality Assurance and Safety of Crops & Foods 10: 411-421.

41. Zhao, X., D. Dong, W. Zheng, L. Jiao, and Y. Lang. 2015. Discrimination of adulterated sesame oil using mid-infrared spectroscopy and chemometrics. Food Analytical Methods 8: 2308-2314.