Main Article Content


In Malaysia, rice, ranked as the third most crucial crop, faces challenges due to domestic consumption outpacing production, resulting in increased instances of rice adulteration. This underscores the imperative of maintaining integrity and quality standards across the entire supply chain. This study uses an electronic nose, comprising four metal oxide semiconductor (MOS) gas sensors, and employing temperature modulation, Principal Component Analysis (PCA) and supervised machine learning (classification models) to distinguish rice varieties such as Bario, Bajong, Borneo Fragrant, Biris, and Jasmine. The study evaluated 30 classifiers based on their classification and validation accuracy. Sensor data was first extracted from the transient response of sensors output voltage, yielding a 12-dimension dataset with response times of 30 s, 50 s, and 95 s. Classification models trained from this dataset achieved classification (training) accuracy of up to 100% and validation accuracy of up to 96%, where the best performing models are subspace discriminant and kernel naïve bayes classifiers. An attempt was also made to analyze the sensor data frequency response for rice classification. Comparison between the prediction results in the transient and frequency domains showed that transient response is better suited for the classification of rice.


Electronic nose Gas sensors Machine learning Metal oxide semiconductor (MOS) Principal component analysis (PCA) Rice

Article Details

How to Cite
Jee, K. W. ., Chua, H. S. ., & Lee, H. E. . (2024). Product differentiation of Sarawak premium rice using MOS gas sensors: comparison between transient and frequency response. Future Technology, 3(3), 33–42. Retrieved from
Bookmark and Share


  1. Rahim, Farah Hanim Abdul, Nurul Nazihah Hawari, and Norhaslinda Zainal Abidin. "Supply and demand of rice in Malaysia: A system dynamics approach." Int. J. Sup. Chain. Mgt 6 (2017): 1-7.
  2. Firdaus, RB Radin, Mou Leong Tan, Siti Rahyla Rahmat, and Mahinda Senevi Gunaratne. "Paddy, rice and food security in Malaysia: A review of climate change impacts." Cogent Social Sciences 6, no. 1 (2020): 1818373.
  3. Omar, Sarena Che, Ashraf Shaharudin, and Siti Aiysyah Tumin. "The status of the paddy and rice industry in Malaysia." Khazanah Research Institute. Kuala Lumpur (2019).
  4. L. V. Estrada-Pérez, S. Pradana-López, A. M. Pérez-Calabuig, M. L. Mena, J. C. Cancilla, and J. S. Torrecilla, "Thermal imaging of rice grains and flours to design convolutional systems to ensure quality and safety," Food Control, vol. 121, 2021, doi: 10.1016/j.foodcont.2020.107572.
  5. N. Gupta, R. Singh, V. Gupta, D. P. Jain, and M. Das, "Identification of plastic rice in adulterated raw and cooked rice," Toxicol Mech Methods, vol. 33, no. 7, pp. 584-589, Sep 2023, doi: 10.1080/15376516.2023.2197490.
  6. What is Machine Learning? [Online] Available:
  7. Frequency Domain [Online] Available:
  8. H. E. Lee, Z. J. A. Mercer, S. M. Ng, M. Shafiei, and H. S. Chua, "Geo-tracing of black pepper using metal oxide semiconductor (MOS) gas sensors array," IEEE Sensors Journal, vol. 20, no. 14, pp. 8039-8045, 2020, doi: 10.1109/JSEN.2020.2981602.
  9. H. E. Lee, H. S. Chua, Z. J. A. Mercer, S. M. Ng, and M. Shafiei, "Fraud detection of black pepper using metal oxide semiconductor gas sensors," in 2021 IEEE Sensors, 31 Oct.-3 Nov. 2021, pp. 1-4, doi: 10.1109/SENSORS47087.2021.9639658.
  10. H. E. Lee, Z. J. A. Mercer, S. M. Ng, M. Shafiei, and H. S. Chua, "Metal Oxide Semiconductor Gas Sensors-based E-nose and Two-stage Classification: Authentication of Malaysia and Vietnam Black Pepper Samples," in 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), 29 May-1 June 2022, pp. 1-4, doi: 10.1109/ISOEN54820.2022.9789618.
  11. H. E. Lee, "Geo-tracing of Black Pepper using Metal Oxide Semiconductor Gas Sensors," PhD, Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia, 2022. [Online]. Available:
  12. FIGARO TGS2600 - for the detection of Air Contaminants [Online] Available:
  13. FIGARO TGS2611 - for the detection of Methane [Online] Available:
  14. FIGARO TGS2602 - for the detection of Air Contaminants [Online] Available:
  15. FIGARO TGS 2620 - for the detection of Solvent Vapors [Online] Available: