# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 5 Conclusions  
This pilot study was carried out to evaluate the ability to detect pesticides in fruits through an E-nose and E-tongue, where the E-nose was composed of metal oxide gas sensors, while the E-tongue was through platinum electrodes. Some difficulties were observed in differentiating the categories of Curzate, Daconil, and Preza, since a similarity of the compounds between the characteristic chemicals of those pesticides was noticed. On the other hand, it was possible to identify the pesticide categories in the previously mentioned fruits by applying the platinum electrode since, through the responses of the multivariate analysis, the variance of the dataset was high in the first two PCA components and LDA factors. It should be clarified that the PCA method was used primarily to visualize the behavior and discrimination of pesticide categories in fruits within a two-dimensional space. It is important to note that PCA was not used to classify the measurements. However, its application has also been demonstrated in optimizing machine learning methods, as it enabled extracting more meaningful and informative features from the data, thereby enhancing the performance of the predictive models. In the analysis made of data concentrations from the different types of pesticides applied to each of the fruits, it was observed that the E-nose and E-tongue could discriminate concentrations from 0.2 ppm to 3 ppm, and they were able to identify each of the categories of organic fruit and concentrations. There were slight overlaps in the strawberry samples and plum samples with pesticides, where a better percentage of accuracy was observed in the data classification using each classification method. For example, the cape gooseberry fruit obtained 100% success rate. Nevertheless, the E-nose obtained good success rates in the classification of strawberries and plums, reaching percentages close to those of the E-tongue, indicating that both sensory systems could be useful in evaluating quality in producing different types of fruits. In this study, the electronic tongue was highlighted as a promising tool for pesticide detection in fruits, providing interesting results across a variety of fruit types and pesticides. Its performance results may make it ideal for applications in quality control and food safety, as it could enable producers and regulators to make informed decisions about the safety and quality of products. On the other hand, the electronic nose also proved to be effective due to its high accuracy and specificity in various fruit-pesticide combinations, which could be a valuable tool, especially in applications requiring rapid detection and a high level of specificity. Therefore, the choice between the electronic tongue and the electronic nose should be based on the specific needs of the application, considering factors such as detection speed, required accuracy, and the type of samples to be analyzed. However, it is important to continue optimizing these types of systems to have a better level of performance. As future work, it is necessary to make improvements in the analysis of certain specific pesticide substances through GC-MS to optimize the sampling and acquisition protocol with the E-tongue, and also evaluate the behavior of the responses of the gas sensors to achieve better performance in the classification of the samples. On the other hand, a data fusion of the information extracted from both equipment could help improve the results since gas sensors and electrochemical analysis would help to increase the information of the samples acquired by the measurement equipment. Conceptualization, C.M.D.A., J.K.C.G. and D.D.C.N. ; methodology, J.K.C.G. and D.D.C.N. ; validation, D.D.C.N. and J.K.C.G. ; investigation, C.M.D.A. and J.K.C.G. ; writing--original draft preparation, C.M.D.A., J.K.C.G. and D.D.C.N. ; writing--review and editing, C.M.D.A. and J.K.C.G. ; visualization, C.M.D.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.