Development and Evaluation of an Electronic Nose System Based on MOS Sensors to Detect and to Distinguish Lemon Essential Oils

Document Type : Research Article

Authors

• P. Fayyaz ¹
• S. S. Mohtasebi ¹
• A. Jafari ¹
• A. Masoudi ²

¹ Department of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

² Managing director of Avishan Khane Tabiat Sabz.co

https://doi.org/10.22067/jam.v9i2.69568

Abstract

Introduction
Essences or essential oils are aromatic compounds that are found in different organs of the plants. Essences can be classified into three groups of natural, synthetic and natural like. Most of the methods that are used to detect and to distinguish essential oils are based on chromatographic methods. However, these analytical methods are time consuming and require expert operators to work with required devices. Moreover, it is necessary to prepare the samples. An electronic nose is known as a tool for mimicking the sense of smell. This tool usually consists of an array of sensors which are used to identify and to isolate a variety of complex odors at a low cost. Since there has been no research on the usage of an electronic nose system for detection and separation of essential oils, the purpose of this study is to develop and to evaluate an electronic nose system for identification and classification of various types of commercial lemon essential oils (synthetic types).
Materials and Methods
The proposed system consists of a sensor chamber, a sample chamber, an array of MOS sensors, electro valves, a pump, a data acquisition cart and, a processor. Essential oils used in this study includes  eight types of synthetic commercial lemon essential oils that were prepared by ((Avishan Khane Tabiat Sabz)) Company located in chemistry and chemical engineering research center of Iran. One gram sample of each essential oil was prepared to be placed in the sample chamber. Each experiment was carried out in 9 replicates and in three stages of 1- Baseline correction (250 s) 2- Sample smell injection (400 s) and 3- Sensors chamber cleaning (200 s). Data received from the sensors signals were initially preprocessed and normalized and then three methods of principal component analyses (PCA), linear discriminant analyses (LDA) and artificial neural network (ANN) were used to process the data. Both PCA and LDA methods were run using the Unscramble x10.4 software and the artificial neural network was used with the help of NeuroSolution 5 software. In ANN, the classification was carried out using a multilayer perceptron (MLP) and Leave-one-out technique. Leave-one-out is an acceptable method for evaluating the performance of the classification algorithms when the number of samples is low.
Results and Discussion
In order to analyze the data obtained from the sensor array, first, the principal components analysis (PCA) method was used. In this method, the first two principal components of PC 1 and PC 2 totally covered 99% of the data variance. Another plot called as loading plot was used to determine the effects of each sensor responses in pattern recognition analyzes. According to this plot, all sensors had high loading coefficients. In case of distinguishing the lemon essential oils, the results of the linear discriminant analysis (LDA) method showed that this method can distinguish eight types of lemon essential oils with an accuracy of %98. The artificial neural network (ANN) also separated the essential oils with the accuracy of the above %91.
Conclusion
An Electronic nose system based on semiconductor metal oxide sensors is a powerful tool that can be used as a substitute for traditional methods. In general, this study showed that the electronic nose system based on MOS sensors has the ability to detect and to distinguish commercial lemon essential oils. Considering the wide ranges and economical nature of the essential oils, it is suggested that applications of the electronic nose can be more expanded in the subject of the essential oils of different products.

Keywords

• Electronic nose
• Essential oil
• PCA
• LDA
• ANN

20.1001.1.22286829.1398.9.2.1.2

References

1. Amy, L., C. Silvia, K. Ganesh, S. Prabakaran, and B. John. 2015. Electronic Noses for Food Quality: A Review. Journal of Food Engineering 144: 103-111.

2. Anonymous. 2004. Minimum criteria for the establishment and operation of essential oils, distillate and plant extracts for food, Beverage, cosmetic and sanitary purposes. Department of Food, Beverage, Cosmetics and Food Supervision. Food and Drug Administration, Ministry of Health.

3. Arshak, K., E. Moore, G. M. Lyons, J. Harris, and S. Clifford. 2004. A review of gas sensors employed in electronic nose applications. Sensor Review 24: 181-198.

4. Balakrishnama, S., and A. Ganapathiraju, 1998. Linear discriminant analyses - A brief tutorial. Institute for Signal and Information Processing, Department of Electrical and Computer Engineering. Mississippi State University. USA

5. Başer, K., and G. Buchbauer. 2016. Handbook of essential oils: science, technology, and applications (second edition). Taylor & Francis Group.

6. Bhattacharyya, N., and R. Bandhopadhyay. 2010. Electronic Nose and Electronic Tongue Nondestructive Evaluation of Food Quality. pp. 73-100.

7. Brudzewskia, K., S. Osowskib, and T. Markiewiczb. 2004. Classification of milk by means of an electronic nose and SVM neural network. Sensors and Actuators B: Chemical 98: 291-298.

8. Dai, Y., R. Zhi, L. Zhao, H. Gao, B. Shi, and H. Wang. 2015. Longjing tea quality classification by fusion of features collected from E-nose. Chemometrics and Intelligent Laboratory Systems 144: 63-70.

9. Gareth, J., W. Daniela, H. Trevor, and T. Robert. 2013. An Introduction to Statistical Learning. Springer Texts in Statistics Volume 103.

10. Ghasemi-Varnamkhasti, M. 2017. Fabrication and development of a machine olfaction system combined with pattern recognition techniques for detecting formalin adulteration in raw milk. Iranian Journal of Biosystems Engineering 47: 761-770.

11. Gorji-Chakespari, A., A. M. Nikbakht, F. Sefidkon, M. Ghasemi-Varnamkhasti, and E. L. Valero. 2016. Classification of essential oil composition in Rosa damascena Mill. genotypes using an electronic nose. Journal of Applied Research on Medicinal and Aromatic Plants 4: 27-34.

12. Haddia, Z., A. Amaria, H. Alamia, N. El Barib, E. Llobetc, and B. Bouchikhi. 2011. A portable electronic nose system for the identification of cannabis-based drugs. Sensors and Actuators 155: 456-463.

13. Hajinejad, M., S. S. Mohtasebi, M. Ghasemi-varnamkhasti, and M. Aghbashlou. 2015. Classification of different floral origin and adulterated honeys using an olfaction machine. Iranian Journal of Biosystems Engineering 47: 415-423. (In Farsi).

14. Heidarbeigi, K., S. S. Mohtasebi, A. Foroughirad, M. Ghasemi-Varnamkhasti, Sh. Rafiee, and K. Rezaei. 2015. Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties 18: 1391-1401.

15. Hyung-Ki, H., W. Hyun, H. Dong, K. Seung-Ryeol, H. Chul, L. Kyuchung, and M. Toyosaka. 1996. E-nose system with micro-gas sensor array Sensors and Actuators B: Chemical 36: 338-341.

16. Marcone, M. F. 2004. Composition and properties of Indonesian palm civet coffee. Food Research International 37: 901-912.

17. Momeni, T. and N. Shahrokhi. 1991. Essential oils and their therapeutic actions. Tehran, Iran: University of Tehran. (In Farsi).

18. Otles, S. 2008. Handbook of Food Analysis Instruments. CRC Press.

19. Peris, M., and L. Escuder-Gilabert. 2009. A 21st century technique for food control: Electronic noses. Analytica Chimica Acta 638: 1-15.

20. Rushing, C., A. Bulusu, H. I. Hurwitz, A. B. Nixon, and H. Pang. 2015. A leave-one-outcross-validation SAS macro for the identification of markers associated with survival. Computers in Biology and Medicine 57: 123-129.

21. Russo, M., D. Serra, F. Suraci, and S. Postorino. 2012. Effectiveness of electronic nose systems to detect bergamot (Citrus bergamia Rissoet Poiteau) essential oil quality and genuineness. The Journal of Essential Oil Research 24: 137-151.

22. Sanaeifar, A., S. S. Mohtasebi, M. Ghasemi-Varnamkhasti, and H. Ahmadi. 2015. 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 (1): 111-121.

23. Sokolova, M., and G. Lapalme. 2009. A systematic analysis of performance measures for classification tasks. Information Processing and Management 45: 427-437.

24. Tiên Do, T. K., F. Hadji-Minaglou, S. Antoniotti, and X. Sylvain. 2015. Authenticity of essential oils. Trends in Analytical Chemistry 66: 146-157.