Non-destructive test to detect adulteration of rice using gas sensors coupled with chemometrics methods
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Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq
Department of Electric Technology, Electronics and Automation, University of Extremadura, Avda. de Elvas S/n, 06006, Badajoz, Spain
Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland
Department of Biosystems Engineering, University of Tehran, Tehran 11365-4117, Iran
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Final revision date: 2023-05-01
Acceptance date: 2023-05-09
Publication date: 2023-07-10
Corresponding author
Marek Gancarz   

Faculty of Production and Power Engineering, University of Agriculture in Krakow, Poland
Int. Agrophys. 2023, 37(3): 235-244
  • E-nose can identified quick and non-destructively pure and mixed rice
  • Chemometric methods were used for the data analysis of the sensor arrays
  • Chemometrics and ANN were used simultaneously to classify the samples
In order to accurately determine and evaluate the odour of rice, it is necessary to identify the substances that affect that odour and to develop methods to determine their amounts. For more than three decades, researchers have been studying the factors that produce and influence the aroma of rice. An electronic nose can be used to detect the volatile compounds of rice, while an olfactory machine is capable of classifying and detecting the variety, origin, and storage time of rice with a high degree of efficiency. This study aimed to investigate the efficacy of electronic noses and other chemometric methods such as principal component analysis, linear discriminant analysis, and the Artificial Neural Network as a cost-effective, rapid, and non-destructive method for the detection of pure and adulterated rice varieties. Therefore, an electronic nose equipped with nine metal oxide semiconductor sensors with low power consumption was used. The results showed that the amount of variance accounted for by PC1 and PC4 was 98% for the samples used. Also, the classification accuracy of the linear discriminant analysis and Artificial Neural Network methods were 100%, respectively. The Support Vector Machines method (including Nu-SVM and C-SVM) was also used, which, in all its functions except the polynomial function, produced 100% accuracy in terms of training and validation.
The authors declare that there is no conflict of interest regarding the publication of this paper. Data availability statement: The datasets used and/or analysed during the current study are available from the corresponding author if a reasonable request is made.
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