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Ali Amkor
Sultan Moulay Slimane University
Electrical & Electronics Engineering

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Classification of potatoes according to their cultivated field by SVM and KNN approaches using an electronic nose Ali Amkor; Noureddine El Barbri
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5116

Abstract

In this article, we propose a homemade electronic nose to distinguish between two types of potatoes: the first type is traditionally treated with donkey and sheep manure, and the other type is treated with chicken manure. The proposed tool consists of a network of commercial metal oxide sensors, a data acquisition card, and a personal computer for data pre-processing and processing. Two methods were used, namely, support vector machines (SVM) and k-nearest neighbors (KNN) with 5-fold cross-validation and which achieved the same success rate of 97.5%. These results demonstrate that our concept, which is quick, simple, and inexpensive, can discriminate between potatoes based on the method of fertilization used in the field.
An electronic nose for insecticides detection in food: the case of alpha-cypermethrin in Swiss chard Ali Amkor; Asmae Aboulkacem; Noureddine El Barbri
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5956

Abstract

Due to the disturbingly high quantities of pesticides found in recent years, it has become essential to analyze the food composition intended for consumption. In this study and from a similar angle, we are interested in the detection of alpha-cypermethrin insecticide residues in edible Swiss chard. To this end, we suggest an electronic nose that was constructed using metal oxide gas sensors. Following data collection and pre-processing, two machine learning algorithms—principal component analysis (PCA) and support vector machine (SVM)—were used to analyze the sensor matrix data. The PCA method initially showed that the first three principal components (PCs) may account for more than 96.5% of the sample variation with a clear distinction between known groups corresponding to treated and untreated samples. The identification of untreated Swiss chard from treated one was then accomplished using the SVM method with five folds cross-validation, with a success rate of 92.3%. These results show that our suggestion, which is quick, easy, and affordable, can be utilized as an effective substitute for current methods for identifying Swiss chard that has been treated with the hazardous alpha-cypermethrin.
Co-Authors Asmae Aboulkacem Noureddine El Barbri