RESEARCH PAPER
Vis/NIR and FTIR spectroscopy supported by machine learning techniques to distinguish pure from impure Iranian rice varieties
1
Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
2
Department of Biosystems Engineering, Faculty of Agriculture, University of Guilan, P. O. Box: 41635-1314, Rasht, Guilan, Iran
3
Department of Physical Properties of Plant Materials, Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
4
Center of Innovation and Research on Healthy and Safe Food, University of Agriculture in Kraków, Balicka 104, 30-149 Kraków, Poland
5
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116B, 30-149 Krakow, Poland
6
Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq
Final revision date: 2024-02-09
Acceptance date: 2024-02-26
Publication date: 2024-04-18
Corresponding author
Marek Gancarz
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Poland
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Poland
Int. Agrophys. 2024, 38(2): 203-211
HIGHLIGHTS
- Spectroscopy; Authenticity verification; Rice quality control; Machine learning algorithms
KEYWORDS
TOPICS
ABSTRACT
Rice is an annual plant from the family of Oryzeae, provides the main food for about 2.5 billion people. The quality of this product is under the influence of various factors. Quality control and adulteration detection are among the main issues in the rice industry for which, various methods have been developed. Some of these methods are costly or with low accuracy. Therefore, this study aimed to investigate and detect adulteration with spectroscopic devices and chemometric methods as well as neural network approach. The results of this study indicated the highest accuracy (100%) in the detection of authentic rice for Fourier-transform infrared combined with C-support vector machine (linear and polynomial functions) and visible–near–infrared device with quadratic discriminant analysis, multivariate discriminant analysis, Bayesian, and Decision Tree. The lowest accuracy was also related to support vector machine method with Sigmoid function for both devices. Principal component analysis method also provided very high accuracy for both devices (accuracy of 100% for visible–near–infrared and 99% for Fourier-transform infrared).
CONFLICT OF INTEREST
The authors declare no conflict of interest.
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