Abstract
This article describes the possibility of an electronic device coupled with chemometric methods to detect and discriminate between mint treated with an insecticide containing deltamethrin and the untreated mint. A multisensor system is designed and realized mainly by a commercial metal oxide (MOS) gas sensors array, a data acquisition board, and a personal computer coupled with chemometric methods to achieve the objective. In each experiment, data were collected for 510 s using the multi-sensor system. Then, the principal component analysis (PCA) statistical data projection method and the support vector machine (SVM) machine learning method were exploited to prove the ability of our laboratory prototype to differentiate untreated mint from deltamethrin mint treated. The data projection with principal component analysis algorithm indicates that this method can classify the data with 98% of the variance by the first three main components (PC1, PC2, and PC3) with remarkable separation between mint groups while that the machine support vector (SVM) method was able to distinguish samples with a success rate of 95%. As such, this work offers the ability to identify the mint treated from untreated one using a simple, fast, and inexpensive multi-sensor system.
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