An Intelligent Neural Network Algorithm for Uncertainty Handling in Sensor Failure Scenario of Food Quality Assurance Model

  • S.N. Deepa National Institute of Technology Arunachal Pradesh
  • N. Yogambal Jayalakshmi Dr. Mahalingam College of Engineering and Technology


The quality of food is usually tested by sensing the product odor using e-nose technique. However, in a real-time testing environment, some of the employed sensors may fail to operate, which imposes great uncertainty on the food quality assurance model. To handle the uncertainty, a support vector machine (SVM) classifier algorithm is developed to deal with the failure sensor effect using a data imputation strategy. The proposed model is evaluated experimentally by means of benchmark datasets, and validated in a realtime environment by programming an Arduino-UNO controller in the internet of things (IoT) environment.


e-nose, data imputation, quality assurance, multiclass SVM, k-nearest neighbor, IoT, Arduino UNO,


1. A. Sanaeifar, S.S. Mohtasebi, M. Ghasemi-Varnamkhasti, H. Ahmadi, J. Lozano, Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM), Czech Journal of Food Sciences, 32(6): 538–548, 2014, doi: 10.17221/113/2014-CJFS.
2. A. Sanaeifar, S.S. Mohtasebi, M. Ghasemi-Varnamkhasti, M. Siadat, Application of an electronic nose system coupled with artificial neural network for classification of banana samples during shelf-life process, [in:] 2014 International Conference on Control, Decision and Information Technologies (CoDIT), pp. 753–757, IEEE, 2014, November, doi: 10.1109/CoDIT.2014.6996991.
3. A. Sanaeifar, S.S. Mohtasebi, M. Ghasemi-Varnamkhasti, H. Ahmadi, Application of MOS based electronic nose for the prediction of banana quality properties, Measurement, 82: 105–114, 2016, doi: 10.1016/j.measurement.2015.12.041.
4. M.F. Adak, N. Yumusak, Classification of e-nose aroma data of four fruit types by ABC-based neural network, Sensors, 16(3): 304, 2016, doi: 10.3390/s16030304.
5. S. Kiani, S. Minaei, M. Ghasemi-Varnamkhasti, A portable electronic nose as an expert system for aroma-based classification of saffron, Chemometrics and Intelligent Laboratory Systems, 156(15): 148–156, 2016.
6. E. Ordukaya, B. Karlik, Quality control of olive oils using machine learning and electronic nose, Journal of Food Quality, 2017: Article ID 9272404, doi: 10.1155/2017/9272404.
7. D.R. Wijaya, R. Sarno, E. Zulaika, Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions, Data in Brief, 21: 2414–2420, 2018, doi: 10.1016/j.dib.2018.11.091.
8. J. Thorson, A. Collier-Oxandale, M. Hannigan, Using a low-cost sensor array and machine learning techniques to detect complex pollutant mixtures and identify likely sources, Sensors, 19(17): 3723, 2019, doi: 10.3390/s19173723.
9. L. Han, C. Yu, K. Xiao, X. Zhao, A new method of mixed gas identification based on a convolutional neural network for time series classification, Sensors, 19(9): 1960, 2019, doi: 10.3390/s19091960.
10. J.C. Rodriguez Gamboa, E.S. Albarracin E., A.J. da Silva, T.A.E. Ferreira, Electronic nose dataset for detection of wine spoilage thresholds, Data in Brief, 25: 104202, 2019, doi: 10.1016/j.dib.2019.104202.
11. S. Kalpana, A.L. Baghyam, Electronic-nose system for classification of fruits and freshness measurement using K-NN algorithm, International Journal of Innovative Technology and Exploring Engineering, 8(6S4): 641–644, 2019, doi: 10.35940/ijitee.F1132.0486S419.
12. M. Ghasemi-Varnamkhasti, A. Mohammad-Razdari, S.H. Yoosefian, Z. Izadi, G. Rabiei, Selection of an optimized metal oxide semiconductor sensor (MOS) array for freshness characterization of strawberry in polymer packages using response surface method (RSM), Postharvest Biology and Technology, 151: 53–60, 2019, doi: 10.1016/j.postharvbio.2019.01.016.
13. H.G.J. Voss, J.J.A. Mendes Júnior, M.E. Farinelli, S.L. Stevan, A prototype to detect the alcohol content of beers based on an electronic nose, Sensors, 19(11): 2646, 2019, doi: 10.3390/s19112646.
14. M.A. Hasan, R. Sarno, S.I. Sabilla, Optimizing machine learning parameters for classifying the sweetness of pineapple aroma using electronic nose, International Journal of Intelligent Engineering and Systems, 13(5): 122–132, 2020, doi: 10.22266/ijies2020.1031.12.
15. S. Rahman, A.S. Alwadie, M. Irfan, R. Nawaz, M. Raza, E. Javed, M. Awais, Wireless e-nose sensors to detect volatile organic gases through multivariate analysis, Micromachines, 11(6): 597, 2020, doi: 10.3390/mi11060597.
16. B. Nouri, S.S. Mohtasebi, S. Rafiee, Quality detection of pomegranate fruit infected with fungal disease, International Journal of Food Properties, 23(1): 9–21, 2020, doi: 10.1080/10942912.2019.1705851.
17. N. Aghilinategh, M.J. Dalvand, A. Anvar, Detection of ripeness grades of berries using an electronic nose, Food Science & Nutrition, 8(9): 4919–4928, 2020, doi: 10.1002/fsn3.1788.
18. J.S. Manoharan, Study of variants of extreme learning machine (ELM) brands and its performance measure on classification algorithm, Journal of Soft Computing Paradigm (JSCP), 3(2): 83–95, 2021, doi: 10.36548/jscp.2021.2.003.
19. A.P. Pandian, Performance Evaluation and comparison using deep learning techniques in sentiment analysis, Journal of Soft Computing Paradigm, 3(2): 123–134, 2021, doi: 10.36548/jscp.2021.2.006.
20. A. Kaya, A.S. Keçeli, C. Catal, B. Tekinerdogan, Sensor failure tolerable machine learning-based food quality prediction model, Sensors, 20(11): 3173, 2020, doi: 10.3390/s20113173.
21. C. Cortes, V. Vapnik, Support-vector networks, Machine Learning, 20(3): 273–297, 1995, doi: 10.1007/BF00994018.
22. C. Cheng, H. Huang, A distance-threshold kNN method for imputing medical data missing values, Journal of Advances in Computer Networks, 7(1): 13–17, 2019, doi: 10.18178/JACN.2019.7.1.265.
Mar 21, 2022
How to Cite
DEEPA, S.N.; JAYALAKSHMI, N. Yogambal. An Intelligent Neural Network Algorithm for Uncertainty Handling in Sensor Failure Scenario of Food Quality Assurance Model. Computer Assisted Methods in Engineering and Science, [S.l.], v. 29, n. 1–2, p. 105–123, mar. 2022. ISSN 2299-3649. Available at: <>. Date accessed: 28 may 2022. doi: