Feasibility of using electronic nose and artificial intelligence to identify wheat varieties

Document Type : Research Paper

Authors

1 Department of Agricultural Machinery Engineering, Sonqor Faculty of Agriculture, Razi University, Kermanshah, Iran

2 Department of Agricultural Machinery Engineering, Sonqor Faculty of Agriculture, Razi University, Kermanshah, Iran.

3 Department of Biosystem Mechanical Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran..

Abstract

Wheat is an important grain product that constitutes about 20% of the calories consumed by the human population around the world. Due to the strong dependence of wheat growth and yield on its variety, the choosing the right variety for cultivation in different soil and water conditions is very important. In this research, the feasibility of using an e-nose system along with artificial intelligence based on metal oxide semiconductor sensors (MOS) as a non-destructive tool for the separation and identification of three varieties of wheat with the names: Salari dry wheat, Quds blue wheat and local red wheat is evaluated. Support vector machine (SVM), artificial neural network (ANN) and principal component analysis (PCA) were the methods used to achieve this goal. The obtained results showed that TGS822 and TGS2620 sensors play the most role and TGS813 and TGS2610 sensors play the least role in wheat variety detection. ANN analysis method with 91.7% accuracy showed better result than SVM method (75% accuracy) in identifying and classifying wheat varieties. In the meantime, the PCA method showed a relatively good performance in separating and identifying wheat varieties with 77% of the total variance of the total data. Also, the results showed that wheat of Salari dry wheat variety had different aromatic compounds with other two varieties of Quds blue wheat and local red wheat. The proper performance of the e-nose in the separation of wheat varieties can indicate the promising application of this technology in the separation and identification of wheat varieties.

Keywords

Main Subjects

Introduction

Wheat is an important cereal crop that accounts for about 20% of the calories consumed by the human population. Global wheat production reached 796 million tons in 2023. Given the great diversity in wheat varieties, choosing the right variety for cultivation in different soil and water conditions is of great importance, the growth and yield of wheat is strongly related to its variety. Old methods such as measuring yield and performance indicators in examining wheat varieties are a time-consuming method. On the other hand, recent and non-destructive methods for identifying wheat varieties are the hyperspectral imaging method and the electronic nose machine combined with artificial intelligence. Much research has been conducted in the field of using the hyperspectral imaging method in identifying wheat varieties. On the other hand, the e-nose machine, as a cheaper, faster and easier method, can be a more suitable method for identifying wheat varieties. Therefore, the aim of this research is to assess the feasibility of identifying three wheat varieties with the help of the smell machine and artificial intelligence.

Method

In order to conduct this experiment, three wheat varieties were prepared: Salari dry wheat, Quds blue wheat and local red wheat cultivated in Sonqor County. An e-nose device equipped with 10 metal oxide semiconductor (MOS) sensors was used to conduct the experiments. To perform the olfactory tests, first 5 grams of the samples were placed in a closed container (sample chamber) for 30 minutes. The sample chamber was connected to the e-nose device and data was collected. 8 repetitions were considered for each sample. A fractional method was used to correct the baseline. The preprocessed data were analyzed using Support Vector Machine, Artificial Neural Network, and Principal Component Analysis using Unscrambler V 9.7 and Matlab 2015a software.

Results

According to the results obtained from this research, it can be said that TGS822 and TGS2620 sensors showed the highest response and TGS813 and TGS2610 sensors showed the lowest response to the aromatic compounds of three wheat varieties. Which indicates the presence of organic vapor compounds in the aromatic compounds of wheat. The results of the classification of three wheat varieties, including Salari dry wheat, Quds blue wheat and local red wheat by SVM classification method, showed that this method was able to classify the three tested wheat cultivars well with an accuracy of 75%. Define and recognize. Also, according to the results of the confusion matrix, the SVM classification method was able to separate and classify all the samples into Salari dry wheat and Quds blue wheat with 100% accuracy. The disturbance matrix obtained from the neural network for three wheat varieties showed that the ANN method was able to classify and separate the three tested wheat varieties with an overall accuracy of 91.7%. to do. The artificial neural network analysis method was the best type of analysis with the identification accuracy of 91.7. According to the results obtained from this research, it can be said that e-nose in combination with artificial intelligence is a suitable method for detecting and identifying wheat cultivars.

Conclusion

In this research, an e-nose system based on ten metal oxide semiconductor (MOS) sensors was used to identify and distinguish three varieties of wheat, including Salari dry wheat, Quds blue wheat and local red. Support vector machine (SVM), artificial neural network (ANN) and principal component analysis (PCA) were used to identify and classify wheat varieties. The artificial neural network analysis method was the best type of analysis with the identification accuracy of 91.7. According to the results obtained from this research, it can be said that e-nose in combination with artificial intelligence is a suitable method for detecting and identifying wheat cultivars.

Author Contributions

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability Statement

Data available on request from the authors.

Acknowledgements

This research was carried out in Razi University, Kermanshah-IRAN. Therefore, the authors are thankful to Razi University for their supporting.

 

Ethical considerations

The study was approved by the research Committee of the University of Razi. The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest

References

Aghili nategh, N., Anvar, A., Dalvand,M. J. (2020). Detection of Ripeness Grades of Strawberry Using an E-nose. Journal of Researches in Mechanics of Agricultural Machinery, 9(2), 71-80. (In Persian).
Adab, H., Farajzadeh, M., Filekash, A., & Esmaili, R. (2015). preparation of autumn rapeseed yield map using perceptron neural network, case study: Sabzevar city. Quarterly scientific research journal of geographical space, 13th year, number 02, page -173.( In Persian)
Bagherpour, H.,& Shamohammadi, S. (2023). Identification of different wheat cultivars using two deep convolutional neural networks. The 15th National & 1st International Congress of Biosystem Mechanical Engineering & Agricultural Mechanization, Tehran. (In Persian).
FAO. FOASTAT, Food and Agriculture Data. 2023. Available online: http://www.fao.org/faostat .
Guo, Z., Guo, C., Chen, Q., Ouyang, Q., Shi, J., El-Seedi, H. R., & Zou, X. (2020). Classification for Penicillium expansum spoilage and defect in apples by e-nose combined with chemometrics. Sensors, 20(7), 2130.‏
Javaheri, M.A. (2022). Identification of bread and durum wheat cultivars suitable for planting in the Arzoiyeh region of Kerman province. Agricultural Sciences Research in Dry Areas, 4(1), 78-95. . (In Persian).
Jiang,X., Bu,Y, Han,L., Tian,J., Hu,X., Zhang,X., Huang,D., Luo,H. (2023). Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning. Food Control, 150(),
Khorramifar, A., Rasekh, M., Karami, H., Malaga-Toboła, U., & Gancarz, M. (2021). A machine learning method for classification and identification of potato cultivars based on the reaction of MOS type sensor-array. Sensors, 21(17), 5836.‏
Karami, H., Rasekh, M., & Mirzaee‐Ghaleh, E. (2020). Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. Journal of Food Processing and Preservation, 44(9), e14696.‏
Liu,W., Liu,Y., Honga,f., Lia,J.,, Jianga,Q., Konga,L., Liu,C., Zheng,L. (2025). Identification of varieties of wheat seeds based on multispectral . Food Physics,2,1-12.
Mu, F., Gu, Y., Zhang, J., & Zhang, L. (2020). Milk source identification and milk quality estimation using an e-nose and machine learning techniques. Sensors, 20(15), 4238.‏
Majidi, M., Separi, M., Ahmadi, M. S., & Nasrolahbeigi, M. B. (2019). Evaluation of yield and yield components of new wheat cultivars in comparison with Sardari wheat. Plant Production and Genetics, 1(1 ), 45-52. (In Persian).
Que, H., Zhao, X., Sun, X., Zhu, Q., Huang, M. (2023). Identification of wheat kernel varieties based on hyperspectral imaging technology and grouped convolutional neural network with feature intervals.Infrared Physics & Technology,Volume 131, 104653.
Song, S., Yuan, L., Zhang, X., Hayat, K., Chen, H., Liu, F., & Niu, Y. (2013). Rapid measuring and modelling flavour quality changes of oxidised chicken fat by e-nose profiles through the partial least squares regression analysis. Food chemistry, 141(4), 4278-4288.‏
Sharma,A., Singh,T.,  Mittal Garg,N. (2024). Nondestructive Identification of Wheat Seed Variety and Geographical Origin Using Near-Infrared Hyperspectral Imagery and Deep Learning.Journal of Chemosensor,38(10).
Sun,Q ., Zhang,R., Liu,Y., Niu, L., Liu,H., Ren, P., Xu, B., Guo,B.,, Zhang, B. .(2024). Insights into “wheat aroma”: Analysis of volatile components in wheat grains cultivated in saline–alkali soil. Food Research International, Volume 183,  114211
Rasooli Sharabiani, V., Kisalaei, A., & Khorramifar, A. (2024). Using E-nose to Detect Potato Varieties using Chemometric Methods. Journal of Environmental Science Studies, 8(4), 7358-7364. (In Persian).
Roshan Moghaddam, M., Amiri Chayjan, R., & Aghili Nategh, N. (2021). Assessment and Comparison of Changes in Aroma, Color and Vitamin C of Dried Orange Slices in Freeze and Hot Air Dryers. Agricultural Mechanization, 6(3), 41-47. (In Persian).
Tyagi, B. S., Foulkes, J., Singh, G., Sareen, S., Kumar, P., Broadley, M. R., ... & Singh, G. P. (2020). Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches. Agronomy, 10(3), 417.‏
Tahri, K., Tiebe, C., El Bari, N., Hübert, T., & Bouchikhi, B. (2017). Geographical classification and adulteration detection of cumin by using electronic sensing coupled to multivariate analysis. Procedia technology, 27, 240-241.‏
Wei Liu,W., Liu,Y., Hong,F., Li,J., Jiang, Q., Kong,L., Liu,C., Zheng,L.(2024). Identification of varieties of wheat seeds based on multispectral imaging combined with improved YOLOv5.Preprint.
Zheng, X. Z., Lan, Y. B., Zhu, J. M., Westbrook, J., Hoffmann, W. C., & Lacey, R. E. (2009). Rapid identification of rice samples using an e-nose. Journal of Bionic Engineering, 6(3), 290-297.
Zhang,K., Zhang,C., Gao,L., Zhuang,H., Feng,T., & Xu,G.(2021). Analysis of volatile flavor compounds of green wheat under different treatments by GC-MS and GC-IMS. Journal of  Food Biochemistry, 1-17.
Iranian Journal of Biosystem Engineering
Volume 56, Issue 2
September 2025
Pages 1-12

Files

History

  • Receive Date: 06 February 2025
  • Revise Date: 06 May 2025
  • Accept Date: 19 May 2025

Share

How to cite

Statistics