Application of Electronic Nose to Detect Pomegranate Paste Adulteration Using Pattern Recognition Methods and Artificial Neural Network

Document Type : Research Article


1 Institute of Agricultural Education and Extension, Agricultural Research, Education and Extension Organization (AREEO),Tehran, Iran

2 Lecturer, Institute of Agricultural Education and Extension, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.


Adulteration in food products is regarded as a main challenge in food industry, which adversely affects food quality and health. Owing to pleasant taste and antioxidant properties, pomegranate paste is one of the most valuable and desirable foods in the people diets in some countries. As a luxury and expensive food, it is likely to be adulterated by some producers or distributors for the more profits. In this study development and application of machine olfaction system using array of gas sensors to detect adulteration in pomegranate paste was aimed. Principal Component Analysis (PCA), Linear Discrimination analysis (LDA) and Artificial Neural Network (ANN) methods were used to analyze response of the sensor arrays. Based on the results, PCA with two components PC1 and PC2 described 94% of total data variance. In LDA method, the classification accuracy of pomegranate paste samples was 97.65% which higher than PCA method. The values of correlation coefficient (R2) and root mean squared error (RSME) of neural network in ANN method using the structure of 6-9-7 were 0.984 and 0.0018 respectively. This study reveals that the electronic nose device can be used as a non-destructive tool to classify and detect adulteration of different classes of pomegranate paste.

Graphical Abstract

Application of Electronic Nose to Detect Pomegranate Paste Adulteration Using Pattern Recognition Methods and Artificial Neural Network


  • Machine Olfaction system development to detection of pomegranate paste adulteration  
  • Sensor response analysis to determine pattern recognition by using Chemometric method
  • Classification and detection adulteration in pomegranate paste by artificial neural network with the high accuracy


Main Subjects

1] Akbarpour, V., Milani, J., & Hemmati, K. (2009). Mechanical properties of pomegranate seeds affected by moisture content. American-Eurasian Journal of Agricultural and Environmental Science, 6(4), 447-453.
[2] Holland, D., Hatib, K., & Bar-Ya'akov, I. (2009). 2 Pomegranate: Botany, horticulture, breeding. Horticultural reviews, 35(2), 127-191.
[3] Olmo-Vega, A., García-Sánchez, F., Simón-Grao, S., Simón, I., Lidón, V., Nieves, M., & Martínez-Nicolás, J. J. (2017). Physiological responses of three pomegranate cultivars under flooded conditions. Scientia Horticulturae, 224, 171-179.
[4] Talaei, A., Askari, M., Bahadoran, F., & Sherafatyan, D. (2004). Study the effect of hot water and polyethylene bags on postharvest life and fruit quality of pomegranate cv. Malas-e-Saveh. J Agri Sci, 35, 369-77.
[5] Ahmadi, K., Ebadzadeh, H.R., Hatami, F., Abdeshah, H. & A. Kazemian. 2019. Agricultural Statistics 1399 (Volume 3: Horticultural Products), Information and Communication Technology Center, Ministry of Jihad Agriculture (MAJ), 157p. [In Persian]
[6] Zarei, M., Azizi, M. (2011). Evaluation of Some Physicochemical Characteristics of Six Iranian Pomegranate (Punica granatum L.) Cultivars Fruit at Ripening Stage. Journal of Horticultural Science, 24(2),-. doi: 10.22067/jhorts4.v1389i2.7995. [In Persian]
[7] Prakash, C. V. S., & Prakash, I. (2011). Bioactive chemical constituents from pomegranate (Punica granatum) juice, seed and peel-a review. Int J Res Chem Environ, 1(1), 1-18.
[8] Johanningsmeier, S. D., & Harris, G. K. (2011). Pomegranate as a functional food and nutraceutical source. Annual review of food science and technology, 2, 181-201.
[9] Heber, D., Schulman, R. N., & Seeram, N. P. (Eds.). (2006). Pomegranates: ancient roots to modern medicine. CRC press.
[10] Shishebor, F., Mohammadshahi, M., Zakerkish, M., Saki, A., Shirani, F., Zarei, M., & Zare, M. (2015). Effect of Concentrated Pomegranate Juice on Cardiovascular Factors in Patients with Type 2 Diabetes. Journal of Isfahan Medical School, 32(309), 1944-1953. [In Persian]
[11] El Darra, N., Rajha, H. N., Saleh, F., Al-Oweini, R., Maroun, R. G., & Louka, N. (2017). Food fraud detection in commercial pomegranate molasses syrups by UV–VIS spectroscopy, ATR-FTIR spectroscopy and HPLC methods. Food Control, 78, 132-137.
[12] Boggia, R., Casolino, M. C., Hysenaj, V., Oliveri, P., & Zunin, P. (2013). A screening method based on UV–Visible spectroscopy and multivariate analysis to assess addition of filler juices and water to pomegranate juices. Food chemistry, 140(4), 735-741.
[13] Vardin, H., Tay, A., Ozen, B., & Mauer, L. (2008). Authentication of pomegranate juice concentrate using FTIR spectroscopy and chemometrics. Food Chemistry, 108(2), 742-748.
[14] Ehling, S., & Cole, S. (2011). Analysis of organic acids in fruit juices by liquid chromatography− mass spectrometry: an enhanced tool for authenticity testing. Journal of agricultural and food chemistry, 59(6), 2229-2234.
[15] Naderi-Boldaji, M., Mokhtari, M., Ghasemi-Varnamkhasti, M., & Tohidi, M. (2019). Feasibility of using a cylindrical resonator sensor for adulteration detection in sesame oil. Innovative Food Technologies, 6(3), 409-420. [In Persian].
[16] Ghasemi-Varnamkhasti, M., Mishra, P., Ahmadpour-Samani, M., Naderi-Boldaji, M., Ghanbarian, D., Tohidi, M., & Izadi, Z. (2019). Rapid detection of grape syrup adulteration with an array of metal oxide sensors and chemometrics. Engineering in Agriculture, Environment and Food, 12(3), 351-359. [In Persian]
[17] Gliszczyńska-Świgło, A., & Chmielewski, J. (2017). Electronic nose as a tool for monitoring the authenticity of food. A review. Food Analytical Methods, 10(6), 1800-1816.
[18] Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S., & Rezaei, K. (2015). Detection of adulteration in saffron samples using electronic nose. International Journal of Food Properties, 18(7), 1391-1401.
[19] Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2016). A portable electronic nose as an expert system for aroma-based classification of saffron. Chemometrics and Intelligent Laboratory Systems, 156, 148-156.
[20] Haddi, Z., Alami, H., El Bari, N., Tounsi, M., Barhoumi, H., Maaref, A. & Bouchikhi, B. E. N. A. C. H. I. R. (2013). Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles. Food Research International, 54(2), 1488-1498.
[21] Ordukaya, E., & Karlik, B. (2017). Quality control of olive oils using machine learning and electronic nose. Journal of Food Quality, 2017.
[22] Hosseini, H., Minaei, S., Beheshti, B. (2022). Evaluation of pattern recognition for detecting adulteration in sesame oil using machine olfaction system based on multivariate analysis. Agricultural Mechanization and Systems Research, (), -. doi: 10.22092/amsr.2022.356371.1400. [In Persian]
[23] Shabani, P., Izadi, Z., Ghasemi-Varnamkhasti, M., Tohidi, M., Reezi, S. (2018). Olfactory machine system an effective solution for detection of adulteration in rosewater. Innovative Food Technologies, 6(1), 75-89. doi: 10.22104/jift.2018.2940.1712. [In Persian]
[24] Chen, Q., Zhao, J., Chen, Z., Lin, H., & Zhao, D. A. (2011). Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools. Sensors and Actuators B: Chemical, 159(1), 294-300.
[25] Zou, H. Q., Li, S., Huang, Y. H., Liu, Y., Bauer, R., Peng, L. & Yan, Y. H. (2014). Rapid identification of Asteraceae plants with improved RBF-ANN classification models based on MOS sensor E-nose. Evidence-Based Complementary and Alternative Medicine, 2014.
[26] Mohammad-Razdari, A., Ghasemi-Varnamkhasti, M., Yoosefian, S., Siadat, M., Izadi, Z., Rostami, S. (2018). Detection of pumpkin puree adulteration in tomato paste using a gas sensor array. Innovative Food Technologies, 6(1), 137-148. doi: 10.22104/jift.2018.2982.1726. [In Persian]
[27] Sanaeifar, A., Mohtasebi, S., Ghasemi-Varnamkhasti, M., Ahmadi, H. (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. doi: 10.22067/jam.v5i1.27159. [In Persian]
[28] Solimany, M.H., Rabban, H. & Mirzaee- Ghaleh, E. (2020). Detection in pure grenadine using gas sensing array. In: Proceeding of the 12th Int. Cong. of biosystems engineering and mechanization (pp.–), Ahwaz, IRAN. COI Code: NCAMEM12_158 [In Persian]
[29] Zhang, Y., Krueger, D., Durst, R., Lee, R., Wang, D., Seeram, N., & Heber, D. (2009). International multidimensional authenticity specification (IMAS) algorithm for detection of commercial pomegranate juice adulteration. Journal of Agricultural and Food Chemistry, 57(6), 2550-2557.
[30] Kamal, Y. T., Alam, P., Alqasoumi, S. I., Foudah, A. I., Alqarni, M. H., & Yusufoglu, H. S. (2018). Investigation of antioxidant compounds in commercial pomegranate molasses products using matrix-solid phase dispersion extraction coupled with HPLC. Saudi Pharmaceutical Journal, 26(6), 839-844.
[31] Arshak, K., Moore, E., Lyons, G.M., Harris, J. and Clifford, S. (2004), "A review of gas sensors employed in electronic nose applications", Sensor Review, 24(2), 181-198.
[32] Pearce, T. C., Schiffman, S. S., Nagle, H. T., & Gardner, J. W. (Eds.). (2006). Handbook of machine olfaction: electronic nose technology. John Wiley & Sons.
[33] Men, H., Chen, D., Zhang, X., Liu, J., & Ning, K. (2014). Data fusion of electronic nose and electronic tongue for detection of mixed edible-oil. Journal of Sensors, 7 pages.
[34] Hajinezhad, M., Mohtasebi, S., Ghasemi-Varnamkhasti, M., Aghbashlo, M. (2017). Detecting Adulteration in Lotus Honey Using a Machine Olfactory System. Journal of Agricultural Machinery, 7(2), 439-450. doi: 10.22067/jam.v7i2.52910. [In Persian]
[35] Tohidi, M., Ghasemi-Varnamkhasti, M., Ghasemi-Nafchi, M., Naderi boldaji, M., Jamalizadeh, F., Safieddin Ardebili, S., Khani, M. (2019). Potential of electronic nose based on temperature-modulated metal oxide gas sensors for detection of geographical origin of spices. Innovative Food Technologies, 6(2), 219-231. doi: 10.22104/jift.2018.3048.1735 [In Persian]
[36] Hayati, M., & Mohebi, Z. (2007). Application of artificial neural networks for temperature forecasting. International Journal of Electrical and Computer Engineering, 1(4), 662-666.
[37] 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(4), 761-770. doi: 10.22059/ijbse.2017.60273
[38] Teimouri, N., Omid, M., Mollazade, K. and Rajabipour, A., 2015. An Artificial Neural Network‐Based Method to Identify Five Classes of Almond According to Visual Features. Journal of Food Process Engineering, 39(6): 625-635.