Document Type : Research Paper

Authors

1 Associate Professor, Mechanical Engineering of biosystems Department, Faculty of Agriculture, Razi University, Kermanshah, Iran

2 PhD Student, Mechanical Enginnering of Biosystems Department, Faculty of Agriculture, University of Mohaghegh Ardabili , Ardabil-Iran

3 Associate Professor, Mechanical Engineering of Biosystems Department, Faculty of Agriculture, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

Introduction As the most important source of calories and protein, bread has a special role and importance in the nutrition of the country, and its cheapness has caused it to replace other food items in the diet in recent years. The increase in bread consumption in the low-income and vulnerable groups has been more intense due to the low volume of supply of other food products and the excessive and continuous increase in the price of other alternative products. Flat bread has the highest consumption statistics among other breads in Iran. One of their types is Barbari bread, which is the second most consumed bread after lavash bread in Iran. Therefore, the health and quality of consumed Barbari bread is of particular importance. For this purpose, this study was conducted with the aim of the effect of storage time of Barbari bread based on the characteristic of smell using the olfactory machine system based on eight metal oxide semiconductor sensors.
Materials and Methods An odor machine system based on eight MOS sensors was carried out in order to investigate the effect of bread storage time based on odor characteristics. Designed system includes data acquisition system, sensors, shield of sensors, sample container, power supply, connections, electric valves, air pump and air filter. The sensor array was consisted of the 8 MOS sensors that each one reacts to specific volatile compounds. These sensors are widely used in olfactory machines because of their high chemical stability, high durability, low response to moisture and affordable prices. These are the most commonly used sensors in electronic nose system. Sensors are the main components of an electronic nose system therefor it is necessary to select the able sensors to detect differences among samples. In order to carry out the test, the sample was placed in sample container and in the baseline correction step (150 seconds), clean air was passed through the sensors to transmit the response of sensor array to steady state. At the injection step (180 seconds), the sample headspace was transmitted and passed through sensors chamber. Output voltage of each sensor depends on the type of sensor and its sensitivity. At the cleaning step (150 seconds) the clean air was passed through sensors to get the sensor array responsive to a stable state. Also, at this step the pump removed the odor remaining inside the sample container and system is prepared for the next test. The signals obtained from the sensors were recorded and then pre-processed.
Results and Discussion The olfactory machine system based on eight metal oxide semiconductor (MOS) sensors was investigated with the PCA pattern recognition method due to the storage time of Barbari bread at four different temperatures. The data obtained from the signals processing with fractional method were used as input of PCA. The results of principal component analysis with two components PC1 and PC2 are 95, 90, 86 and 85%, respectively, for Barbari bread that is stored at room temperature (in the table), refrigerator temperature (4°C), room temperature (in foil) And the temperature of the freezer was placed, it showed. The results obtained from the QDA analysis to determine the quality of Barbari bread at 4 °C for 9 days, at room temperature (in foil) and (on the table) for 5 days and Barbari bread at the freezer temperature of the refrigerator (-18 °C) for 15 days of storage with classification accuracy of 98.52, 96, 100 and 97.35% respectively. The results of LDA analysis for the signals obtained from the olfactory machine, in the classification of the duration of storage of Barbari bread at refrigerator temperature, room temperature (in the table), room temperature (in foil) and refrigerator freezer temperature, respectively, with classification accuracy of 79.26 and 85.33, 78.67 and 75.22 percent were obtained. Also, according to the output obtained from the loading linear graphs and the radar graph, the smell of Barbari bread has the most and the least effect on the MQ9 sensor and the TGS813 sensor, respectively
Conclusion. An olfactory machine system based on eight metal oxide semiconductor (MOS) sensors was investigated for the Barbari bread time retention effect at four different temperatures. The results of principal component analysis with two components PC1 and PC2, for Barbari bread at room temperature (the table), refrigerator temperature, room temperature (in foil) and It showed that they were exposed to freezing temperatures. The results obtained from QDA analysis to detect the quality of Barbari bread at 4°C in room temperature (in foil) and (in the table) and refrigerator freezer temperature respectively. The results of LDA analysis for the classification of Barbari bread at refrigerator temperature, room temperature (in the table), room temperature (in foil) and freezer temperature of the refrigerator. The was obtained. Also, according to the output obtained from the loading linear graphs and the radar graph, the smell of Barbari bread has the most and the least effect on the MQ9 sensor and the TGS813 sensor, respectively.

Keywords

Main Subjects

  1. Afkari-Sayyah, A. H., Karami, H., and Khorramifar, A. 2023. Evaluation ability of the electronic nose to detect the ripening time of walnuts. Journal of Environmental Science Studies, 8(3), 7004-7010. (in Persian with English abstract).
  2. Ayari, F., Mirzaee- Ghaleh, E., Rabbani, H., and 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).
  3. Ayari, F., Mirzaee-Ghaleh, E., Rabbani, H., and Heidarbeigi, K. 2018b. Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat). International Journal of Food Properties, 21(1), 1670-1679. ‏
  4. Ayari, F., Mirzaee‐Ghaleh, E., Rabbani, H., and Heidarbeigi, K. 2018a. Using an E‐nose machinefor detection the adulteration of margarine in cow ghee. Journal of Food ProcessEngineering, 41(6), e12806. ‏
  5. Davidou, S., Le Meste, M., Debever, E., and Bekaert, D., 1996, A contribution to the study of staling of white bread:Effect of water and hydrocolloid. Food Hydrocolloids 10, 375-383
  6. Ghasemi-Varnamkhasti, M., Mohtasebi, S.S., Siadat, M., Lozano, J., Ahmadi, H., Razavi,S. H. and Dicko, A. 2011. Aging fingerprintcharacterization of beer using electronic nose.Sensors and Actuators B: Chemical, 159, 51-59.
  7. Ghayour asli, M. A., Hadad Khodaparast, M. H., and Karimi, M. 2008. Effect of Alpha amylase and Ascorbic acid on rheological properties of doughand specific volume of strudel bread. Iranian Food Science and Technology Research Journal, 4(2). (in Persian with English abstract).
  8. Giannou, V., Kessoglou, V., and Tzia, c., 2003, Quality and safety haracteristics of bread made from frozen dough.Food Science & Technology, 14, 99–108.
  9. Goesaert, H., Brijs, K., Veraverbeke, W. S., Courtin, C. M., Gebruers, K. and Delcour, J. A., 2005, Wheat flourconstituents: how they impact bread quality, and how to impact their functionality”, Trends Food Science Technology, )16(,12–30.
  10. Hajinezhad, M., Ghasemi-varnamkhasti, M., and Aghbashlo, M. 2016. Classification of different floral origin honey samples using a machine olfaction system. Iranian Journal of Biosystems Engineering, 47(3), 415-423. (in Persian with English abstract).
  11. Hajinezhad, M., Mohtasebi, S. S., Ghasemi-Varnamkhasti, M., and Aghbashlo, M. (2017). Detecting Adulteration in Lotus Honey Using a Machine Olfactory System. Journal of Agricultural Machinery, 7(2), 439-450. (in Persian with English abstract).
  12. Heidarbeigi, H., Mohtasebi, S.S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, SH., andRezaei K. 2014. Detection of adulteration in saffron samples using electronic nose. InternationalJournal of Food Properties, 18(7): 1391-1401.
  13. Jooyandeh, H., 2009, Evaluation of physical and sensory properties of Iranian Lavash flat bread supplemented withprecipitated whey protein (PWP), African Journal of Food Science, 3(2), 028-034.
  14. Kashwan, K.R., and Bhuyan, M. 2005. Robuest electronic- nose system with temperature and humidity drift compensation for tea and spice flavor discrimination, Asian Conference. Sensors International, New Technology. Pharm. Biomed. Research. – Proc., Vol, pp. 154-158
  15. Keramat-Jahromi, M., Mohtasebi, S. S., Mousazadeh, H., Ghasemi-Varnamkhasti, M., rafiee, S., and Savand-Roumi, E. (2019). Evaluation of a Machine Olfaction to Classify the Quality of Dried Date Fruit by Electrohydrodynamic, Hot Air, and the Hybrid Drying Techniques. Iranian Journal of Biosystems Engineering, 50(1), 241-251. (in Persian with English abstract).
  16. Khodamoradi, F., Mirzaee- Ghaleh, E., Dalvand, M., and Sharifi, R. 2019. Classification of savory (Satureja hortensis L.) based on the level of used urea fertilizer consumed using an olfactory machine. Iranian Journal of Medicinal and Aromatic Plants Research, 35(5), 789-801. (in Persian with English abstract).
  17. Khodamoradi, F., Mirzaee-Ghaleh, E., Dalvand, M. J., and Sharifi, R. 2021. Classification of basil plant based on the level of consumed nitrogen fertilizer using an olfactory machine. Food Analytical Methods, 14, 2617-2629. ‏
  18. Kiani, S., Minaei, S., and Ghasemi-Varnamkhasti, M. (2018). Real-time aroma monitoring of mint (Mentha spicata L.) leaves during the drying process using electronic nose system. Measurement, 124, 447-452.‏
  19. Kohajdová, Z., Karovičová, J., and Schmidt, Š. 2009. Significance of Emulsifiers andHydrocolloids in Bakery Industry. Acta Chimical Slovaca, 2(1): 46-61.
  20. Langkvist, M., and Loutfi, A. 2011. Unsupervised feature learning for electronic nose data appliedto Bacteria Identification in Blood, Deep Feature. Learn unsupervised Learn. pp.1-7.
  21. Li, M., Cha, D. J., Lai, Y., Villaruz, A. E., Sturdevant, D. E., and Otto, M. 2007. The antimicrobial peptide‐sensing system aps of Staphylococcus aureus. Molecularmicrobiology, 66(5), 1136-1147. ‏
  22. Mahmoudi, A., Omid, M., Aghagolzadeh, A., and Borgayee, A. M. 2006. Grading of Iranian's export pistachio nuts based on artificial neural networks. International Journal of Agriculture and Biology (Pakistan), 8(3). ‏
  23. Mohamadiyan, N., Ziaiifar, A. M., Mirzaee- Ghaleh, E., Kashaninejad, M., and karami, H. 2023. Application of electronic nose for detecting fraud in lemon juice with the help of multivariate analysis techniques. Journal of Researches in Mechanics of Agricultural Machinery, 12(2), 103-114. (in Persian with English abstract).
  24. Mohammad-Razdari, A., Ghasemi-Varnamkhasti, M., Yoosefian, S. H., Siadat, M., Izadi, Z., and Rostami, S. 2018. Detection of pumpkin puree adulteration in tomato paste using a gas sensor array. Innovative Food Technologies, 6(1), 137-148.
  25. Mortazavi, S. A., Sheikholeslami, Z., and Ghiafe Davoodi, M. 2015. Effect of Guar gum and amylase enzymes on quality part baked frozen Barbari bread. Iranian Food Science and Technology Research Journal, 11(5), 508-520. (in Persian with English abstract).
  26. Ozgoli, H., Mohtasebi, S. S., Hosseinpour, S., and Hosseinpour-Zarnaq, M. 2023. Investigating meat and oil quality in chicken nuggets using electronic nose and image processing techniques. Iranian Journal of Biosystems Engineering, 54 (2),1-14. (in Persian with English abstract).
  27. Pearce, C. L., Sims Jr, H. P., Cox, J. F., Ball, G., Schnell, E., Smith, K. A., and Trevino, L. 2003. Transactors, transformers and beyond: A multi‐method development of a theoretical typology of leadership. Journal of Management development, 22(4), 273-307. ‏
  28. Pourfarzad, A., Khodaparast, M. H. H., Karimi, M., Mortazavi, S. A., Davoodi, M. G., Sourki, A. H., and Razadegan Jahromi, S. H. 2011. Effect of polyols on shelf‐life and quality of flat bread fortified with soy flour. Journal of Food Process Engineering, 34(5), 1435-1448.
  29. Rajabzadeh, N. 2007. Bread technology. Tehran University Printing and Publishing Institute, pages 7-3 and 409-437. (in Persian with English abstract).
  30. Rasooli Sharabiani, V., and Khorramifar, A. 2022. Recognition and classification of pure and adulterated rice using the electronic nose. Journal of Environmental Science Studies, 7(2), 4904-4910. (in Persian with English abstract).
  31. Rosell, C. M., and Gómez, M. 2007. Frozen dough and partially baked bread: an update. Food Reviews International, 23(3), 303-319. ‏
  32. Rusinek, R., Gancarz, M., and Nawrocka, A. 2020. Application of an electronic nose with novel method for generation of smell-prints for testing the suitability for consumption of wheat bread during 4-day storage. LWT, 117, 108665.‏
  33. Rutolo, M. F., Iliescu, D., Clarkson, J. P., and Covington, J. A. 2016. Early identification of potato storage disease using an array of metal-oxide based gas sensors. Postharvest Biology and Technology, 116, 50-58. ‏
  34. Samadi M. Saidlo S. Rouhani A., and Nikbakht M. A. 2022. Application of electronic nose system based on support vector machine algorithm to detect the purity of peppermint essential oil. Journal of Researches in Mechanics of Agricultural Machinery, 11(1), 41-49. (in Persian with English abstract).
  35. Sanaeefar, A., Mohtasebi, S. S., Ghasemi Varnamkhasti, M., and Ahmadi, H. 2014. Evaluation of machine olfaction system (electronic nose) based on metal oxide semiconductor (MOS) sensors in detecting aroma fingerprint changes of banana storage. Innovative Food Technologies, 1(3), 29-38.
  36. Sanaeifar, A., Mohtasebi, S. S., Ghasemi-Varnamkhasti, M., and Ahmadi, H. 2016.Application of MOS based electronic nose for the prediction of banana qualityMeasurement, 82, 105-114. ‏
  37. Sanaeifar, A., ZakiDizaji, H., Jafari, A., and Guardia, M.d. 2017. Early detection of contaminationand defect in foodstuffs by electronic nose: A review. TrAC Trends in Analytical Chemistry, 97,257-271.
  38. Taheri Garavand, A., Mirzaee- Ghaleh, E., and Ayari, F. 2020. Intelligent Classification of Fresh Chicken Meat from Frozen-Thawed Using Olfactory Machine. Journal of Food Technology and Nutrition, 17(Spring), 13-22. (in Persian with English abstract).
  39. Zakaria, A., Shakaff, A. Y. M., Masnan, M. J., Saad, F. S. A., Adom, A. H., Ahmad, M. N., and Kamarudin, L. M. 2012. Improved maturity and ripeness classifications of magnifera indica cv. harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor. Sensors, 12(5), 6023-6048.‏
  40. Kheiralipour, K., and Sheikhi, N. 2021. Material and energy flow in different bread baking types. Environment, development and sustainability, 23, 10512-10527.‏