Detection of Different Percentages of Palm in Corn Oil with the Help of an Electric Nose

Document Type : Research Article

Authors

• Z. Zangene Wandi ¹
• H. Javadikia ²
• N. Aghili Nategh ³
• L. Naderloo ²

¹ M.Sc. of Mechanical Engineering of Biosystems Department, Razi University, Kermanshah, Iran

² Mechanical Engineering of Biosystems Department, Razi University, Kermanshah, Iran

³ Mechanical Engineering of Biosystems Department, Sonqor Agriculture Faculty, Razi University, Kermanshah, Iran

https://doi.org/10.22067/jam.2021.70930.1046

Abstract

Introduction
The use of corn oil in diets is due to its positive effects on cardiovascular and immune systems. Corn oil is composed of 99% triacylglycerol, with 59% unsaturated fatty acids and 13% saturated fatty acids. Of the unsaturated fatty acids, 24% contain a double bond. Because of this composition, corn oil can be a good alternative to other oils high in saturated fatty acids, as it reduces blood cholesterol levels.
This study employed an electrical nasal system to detect the amount of palm oil present in corn oil. The properties extracted from the signals obtained by the device were processed using principal component analysis, artificial neural networks, infusion, and response surface methods. The results were then compared to find the best method for detecting palm oil levels in corn oil.
Materials and Methods
The required palm oil was obtained from the Nazgol Oil Agro-industrial Plant, while the corn oil was obtained from natural lubrication centers. To prepare samples with different percentages of palm oil, 75 grams of palm oil and corn oil with the specified percentages were mixed and stored in special containers.
In the electrical nose system, ten metal oxide semiconductor sensors (MOS) were used to collect output data. Pre-processing operations were performed on this data using RSM, ANFIS, PCA, and ANN methods to estimate the percentage of palm oil in corn oil. The Unscrambler V.9 software, Design Expert 8.07.1, and MATLAB R2013a were used to analyze the results.
Results and Discussion
Based on the Score plot, PC-1 and PC-2 explain 53% and 25%, respectively, describing the variance between samples for a total of 78 data points. The analysis indicates that sensors 7 and 8 have minimal impact on the detection process and can be removed from the sensor array. When reducing the cost of the olfactory system's sensor array, sensor 6 plays a more significant role than other sensors in detecting corn oil with palm composition.
According to the loading diagram of palm percentage in corn oil, the MQ6 sensor had the least effect in classifying different percentages of palm in corn oil and pattern identification. Out of all functional parameters (accuracy, sensitivity, and specificity), the RSM method is deemed more appropriate for determining the percentage of palm in corn oil.
Regarding the separation of corn oil and palm oil by ANFIS, RSM, and ANN, the results in Table 3-1 indicate that the RSM method is better suited for classifying corn and palm oil.
Conclusion
In this study, we used an electronic multi-sensor system based on metal oxide sensors to analyze various aromatic compounds in different oil and palm samples and to detect the presence of palm. The system provided comparable information for classifying different samples of palm oils. Using PCA, ANN, ANFIS, and RSM methods, we evaluated the system's performance in differentiating and classifying various oil and palm samples.
The results obtained from the loading diagrams for the detection of palm in corn oil indicated that the MQ6 sensor had the least impact on the detection process. Therefore, this sensor can be removed from the sensor array.
Additionally, our analysis showed that using the RSM method is more effective in detecting different percentages of palm in corn oil. Overall, our study demonstrates the efficacy of the electronic multi-sensor system in analyzing different oil and palm samples and detecting the presence of palm.

Keywords

• Fatty acid
• Fraud
• Sensory evaluation

20.1001.1.22286829.1402.13.2.5.4

Main Subjects

• Post-harvest technologies

©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. Adibzadeh, A., Zaki Dizaji, H., & Nategh, N. A. (2019). Feasibility of Detecting Sugarcane Varieties by Electronic Nose Technique in Sugarcane Syrup. Iranian Journal of Biosystem Engineering, 51, 1-10. https://doi.org/10.22059/IJBSE.2019.287027.665209
2. Ayari, F., Mirzaee-Ghaleh, E., Rabbani, H., & Heidarbeigi, K. (2020). Implementation of a Machine Olfaction for the Detection of Adulteration in Cow Ghee. Journal of Agricultural Machinery, 10(2), 129-139. (in Persian with English abstract). https://doi.org/10.22067/jam.v10i2.67524
3. Berger, K. G. (1981). Food uses of palm oil. Porim occasional paper, 2, 1-27.
4. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(6), 1-13. https://doi.org/10.1186/s12864-019-6413-7
5. Doleman, B. J., & Lewis, N. S. (2001). Comparison of odor detection thresholds and odor discriminablities of a conducting polymer composite electronic nose versus mammalian olfaction. Sensors and Actuators B: Chemical, 72, 41-50. Available at: https://people.ee.duke.edu/~lcarin/DeminingMURI/Doleman_Sensor_Actuators_2001.pdf
6. Feizy, J., & Jahani, M. (2020). A chromatographic method for detection of palm oil in butter. Journal of Food and Bioprocess Engineering, 3, 47-52. https://doi.org/10.22059/JFABE.2020.76393
7. Foroughi-Rad, A., Mohtasebi, S. S., Ghasemi, M., & Omid, M. (2014). Nondestructive quality evaluation of Abbot Kiwifruit using electronic nose. Iranian Journal of Biosystems Engineering, 45, 1-9. https://doi.org/10.22059/IJBSE.2014.51285
8. Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat, M., Ahmadi, H., & Razavi, S. H. (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. https://doi.org/10.1016/j.eaef.2014.07.002
9. Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., Siadat, M., Lozano, J., Ahmadi, H., Razavi, S. H., & Dicko, A. (2011). Aging fingerprint characterization of beer using electronic nose. Sensors and Actuators B: Chemical, 159, 51-59.
10. Golchin, A., Zaki Dizaji, H., Surestani, M. M., & Fardevani, M. E. K. (2019). The Electronic Nose Technique for Nondestructive clustering of Basil as a Medicinal Plant. Non-destructive Testing Technology, 2(4), 54-60. https://doi.org/10.30494/JNDT.1398.95385
11. Gutierrez-Osuna, R. (2000). Pattern analysis for machine olfaction: a review. IEEE Sensors Journal, 2(3), 189-202. https://doi.org/10.1109/JSEN.2002.800688
12. Hai, Z., & Wang, J. (2006). Detection of adulteration in camellia seed oil and sesame oil using an electronic nose. European Journal of Lipid Science and Technology, 108, 116-124. https://doi.org/10.1002/ejlt.200501224
13. Jang, J. S. R. (1991). Fuzz Modeling Using Generalized Neural Networks and Kalman Filter Algorithm in Proceedings of the 9^th National Conference on Artificial Intelligence. Anaheim, CA, USA.
14. Karami, H. R., Keyhani, M., & Mowla, D. (2016). Experimental analysis of drag reduction in the pipelines with response surface methodology. Journal of Petroleum Science and Engineering, 138, 104-112. https://doi.org/10.1016/j.petrol.2015.11.041
15. Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2018). Real-time aroma monitoring of mint (Mentha spicata) leaves during the drying process using electronic nose system. Measurement, 124, 447-452. https://doi.org/10.1016/j.measurement.2018.03.033
16. Lupton, J. R. (2005). For Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, DC: US Government Printing Office: Institute of Medicine. Report no.
17. Marina, A. M., Che Man, Y. B., & Amin, I. (2010). Use of the SAW Sensor Electronic Nose for Detecting the Adulteration of Virgin Coconut Oil with RBD Palm Kernel Olein. Journal of the American Oil Chemists' Society, 87, 263-270. https://doi.org/10.1007/s11746-009-1492-2
18. Mildner-Szkudlarz, S., & Jeleń, H. H. (2008). The potential of different techniques for volatile compounds analysis coupled with PCA for the detection of the adulteration of olive oil with hazelnut oil. Food Chemistry, 110(3), 751-761. https://doi.org/10.1016/j.foodchem.2008.02.053
19. Mirmiran, P., Shideh, F., Aminpour, A., & Raei, F. (2001). The effect of corn oil on the metabolism of laboratory mice. Research in Medicine, 25, 43-46.
20. Neapolitan, R. E. (2012). Contemporary artificial intelligence. Boca Raton. Fla: CRC.
21. Nik-Mehr, S., Abdshahi, A., & Mirzaei, A. (2015). Evaluation of welfare effects of changes in the market inventory of edible oils in Iran. Agricultural Economics Research, 8(1), 71-83. Available at: https://ensani.ir/file/download/article/20160903152009-10006-210.pdf
22. Rashidi, H., & Birmie, M. (2014). Palm oil: benefits and harms. Third National Conference on Food Science and Industry, Quchan.
23. Sanaeifar, A., Zaki Dizaji, H., Jafari, A., & Guardia, M. D. L. (2017). Early detection of contamination and defect in foodstuffs by electronic nose: A review. TrAC Trends in Analytical Chemistry, 97, 257-271. https://doi.org/10.1016/j.trac.2017.09.014
24. Scott, S. M., James, D., & Zulfiqur, A. (2007). Data analysis for electronic nose systems. Microchimica Acta, 156 (3), 183-207. https://doi.org/10.1007/s00604-006-0623-9
25. Seif Elahi, F. (2011). Investigation of olfactory properties of palm olein and cottonseed oils and their various mixtures. Iranian Chemical Engineering Journal, 10, 16-22.
26. Siew, W. L., & Chong, C. L. (1998). Phase transition of crystals in palm olein. PORIM Report PO 283: 1-71.
27. Tawhidi, M., Ghasemi-Vernamkhasti, M., GhaffariNia, V., Mohtasbi, S. S., & Bonyadian, M. (2016). Fabrication and development of a machine olfaction system combined with pattern recognition techniques for detecting formalin adulteration in raw milk. Iranian Journal of Biosystem Engineering, 47, 761-770.
28. Zaki Dizaji, H., Adibzadeh, A., & Aghili Nategh, N. (2021). Application of E-nose technique to predict sugarcane syrup quality based on purity and refined sugar percentage. Journal of Food Science and Technology, 58, 4149-4156. https://doi.org/10.1007/s13197-020-04879-4