# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 3 Results
### Data Analysis with Different Pesticides in Fruits
#### 3.1.1 E-Nose  
Figure 3 shows the results of the PCA analysis applied to organic fruit samples and the fruit with pesticides. According to the results, plum reached a variance of 97.27% (Figure 3A), strawberry 96.37% (Figure 3B), apple 97.64% (Figure 3C), and cape gooseberry 96.07% (Figure 3D), achieving good classification and discrimination of organic fruit and contaminated samples. Each value in question is derived from the cumulative variances associated with the first and second principal components (PC1 and PC2). This confirms that the E-nose can detect significant differences in the volatile profiles of fruits depending on the pesticide used. However, in the case of the plum, there was a misclassified measurement between one of the Funlate samples with Amistar, and the same arises with the cape gooseberry, which presented an overlap between the categories of Curzate, Daconil, and Preza, as observed in Figures 3D and 4D based on PCA and LDA analysis. Although Daconil, Curzate, and Preza are pesticides that belong to different chemical classes (organochlorines, acetamides, and anthranilamides) and contain different active ingredients (Table 1) and mechanisms of action, there are some similarities in terms of their chemical properties and their use in pest and disease control in agriculture. The application of these pesticides on fruits may lead to similar patterns of volatile organic compounds (VOCs), which could result in overlaps in PCA and LDA plots when analyzing volatile profiles with the E-nose. This can occur due to the chemical degradation of the pesticides or secondary compounds formed during the application of these products. Additionally, the fruits may emit their own VOCs in response to the chemical or oxidative stress caused by the pesticides. These VOCs may include alcohols, aldehydes, ketones, esters, terpenes, and other compounds that are produced as part of normal metabolic pathways or as a direct response to stress [66; 67; 68].  
Figure 4 illustrates similar behavior using the LDA classifier, where a separation of the organic fruit category is observed concerning fruit contaminated with the pesticide. In the same way, there was an overlap in the case of cape gooseberry with respect to Daconil, Curzate, and Preza. On the other hand, less dispersion in the data concerning that of the PCA was obtained (see Figure 3). The significant overlap between the samples of cape gooseberry treated with Daconil, Curzate, and Preza in the LDA analysis may be attributed to several factors related to the ripening stage, the chemical composition of the pesticides, the physiological response of the fruit to these treatments, the extraction temperature of the VOCs, or the detection capabilities of the E-nose, as the sensors may not be sufficiently specific to distinguish between these compounds. The LDA method was used together in this study, since PCA analysis may be used to reduce the dimensionality of the data, followed by LDA to further reduce the dimensions and optimize the separation between classes for classification tasks. The classification method resulted in a level of 90% accuracy.