# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Classification of Pesticide Concentrations in Fruits with Machine Learning Methods  
For data processing, different supervised machine learning methods were used, which were applied to calculate the accuracy and compare the measurements acquired with the E-nose and E-tongue. Table 7 illustrates that the E-nose responded better with the measurements of plum contaminated with Amistar by applying quadratic the SVM, cubic SVM, and fine k-NN  
Figure 16: LDA plots of pesticide concentrations and cape gooseberry using E-tongue: (**A**) Bricol, (**B**) Curzate, (**C**) Daconil, and (**D**) Preza. Figure 15: LDA plots of pesticide concentrations and cape gooseberry using E-nose: (**A**) Bricol, (**B**) Curzate, (**C**) Daconil, and (**D**) Preza. methods, with an accuracy of 97.1%. In the case of plum with Funlate, the fine k-NN method was applied to the dataset, obtaining a value of 94.3% success. Moreover, in strawberry, the decision trees method reached a value of 88.6% and 85.7% in the use of Across and Bricol pesticides, where the latter obtained 100% accuracy through the classification method. For this reason, the Bricol accomplished higher percentages than Across pesticide applying other methods because the gas sensor array could identify the chlorine compound present in the active compound of this pesticide. Regarding the apple, 94.3% success rate was reached by using the cubic SVM method with the pesticide Amistar, and with Funlate, an accuracy of 97.1% through the naive Bayes and fine K-NN methods, respectively. Hence, the E-nose has greater accuracy in identifying the compounds present in Funlate. On the other hand, the cape gooseberry with the Bricol pesticide obtained a better classification using the naive Bayes method, which achieved a success rate of 97.1%; Similarly, the pesticide Curzate was classified with 94.3% success rate applying the fine k-NN method. Consequently, for the Daconil pesticide in cape gooseberry fruit, 97.1% was accomplished by using the SVM method and the same classification with the Preza pesticide. Additionally, the cubic SVM method achieved an 88.6% success rate. According to the findings found through the classification methods of four pesticides studied with the E-tongue, greater accuracy was accomplished in the cape gooseberry fruit in identifying the Preza pesticide. In the same way, these classification methods were also applied to the data acquired by E-tongue, where better success percentages were obtained. For example, in plums, the Amistar pesticide was identified with a success rate of 98% through naive Bayes, and 92% in Funlate with the linear discrimination method. Concerning strawberries and using Across pesticide, the best classified method was linear discrimination, achieving an accuracy of 98%. In the same way, the Bricol pesticides with strawberries gained a 94% success rate through the cubic SVM. The above is compared with what was obtained with the E-nose, since the pesticide could be identified in the strawberry because the Bricol pesticide has an active ingredient that is mainly made up of nitrogen, oxygen, carbon, and hydrogen molecules, which have an electrochemical behavior, where the detection is possible with the platinum electrode of the E-tongue. With respect to apples, the method with the greatest accuracy in classifying the data was linear discrimination, with a value of 96% in the classification of the Amistar pesticide. For cape gooseberry, the best methods that classified pesticides were linear discrimination with a success rate of 98% with the pesticide Bricol, the naive Bayes method with Curzate, and the cubic SVM method for the Daconil pesticide. Finally, in detecting the Preza pesticide, the best results were achieved with 100% accuracy in the data classification obtained with the naive Bayes, cubic SVM, and fine K-NN methods. It should be clarified that a cross-validation method was applied to the measurements with a k-fold = 5 used for the different methods. Table 5 describes the results of the data classification methods.