# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 4 Discussion  
Nowadays, most studies have focused on using the E-nose to detect pesticides in fruits (apples [44], sweet cherries [43], peaches [46]). In the study conducted by [PERSON]  
Figure 20: Validation of data obtained with E-nose (**A**,**B**) and E-tongue (**C**,**D**) in cape gooseberry. [PERSON] et al. [46], an E-nose composed of eight chemoresistive sensors was used to detect organophosphate pesticide residues in peaches (_Prunus persica_) and distinguish between three concentrations (1, 2, and 3 ppm) as well as pesticide-free fruit. The sensor responses were recorded using PCA, achieving a variance of 99.8% with two principal components. Another study by [PERSON] et al. [43] demonstrated the capability of an E-nose composed of 10 MQ and TGS reference sensors to detect Diazinon pesticide residues in cherries and differentiate between four stages of fruit maturity. The authors employed three data analysis methods: PCA, LDA, and ANN. PCA score plots of PC1 and PC2 captured 90-96% of the variance in the data for toxic and non-toxic sweet cherries, while ANN and LDA achieved 100% classification accuracy. In another study, [PERSON] et al. [44] evaluated the performance of a commercial PEN3 E-nose equipped with 10 metal oxide semiconductor (MOS) sensors for detecting pesticides in apple samples. The fruit samples were treated with two pesticides containing cypermethrin and chlorpyrifos at four different concentrations and one mixture. PCA and LDA methods successfully differentiated between control and contaminated samples at varying concentrations. Although both methods were effective, PCA exhibited the best discrimination ability. The SVM method demonstrated high accuracy, ranging from 94% to 97% for training datasets and from 90% to 93% for testing datasets, concluding that the commercial E-nose is a useful tool for pesticide residue detection. In a more recent study, an E-nose composed of 11 gas sensors was fabricated, allowing for differentiation between pesticide-contaminated cherries (Korban) and non-contaminated cherries. Four classification algorithms (random forest classifier, extra trees classifier, decision tree classifier, and k-nearest neighbor (kNN)) were employed, with the extra trees classifier achieving the most satisfactory results, with a classification accuracy of 94.30%, a sensitivity of 93.00%, and a specificity of 95.60% [70]. Regarding E-tongues, there is limited literature focused on pesticide detection in fruits. However, some biosensors made from various materials have been used to detect pesticides (malathion and cadusafos) in laboratory-prepared matrices (water, food). In terms of classification, sensitivity, and specificity, studies on pesticide detection in fruits using E-noses show promising results. Additionally, the ability of these devices to differentiate between various types of pesticides and concentrations, along with their high sensitivity and specificity, makes them promising tools for enhancing safety and quality control in the food industry [71; 72; 73]. However, to date, no studies have been reported in the literature that implement both an E-nose and tongue simultaneously to detect pesticides in different fruits and concentrations. Only one study conducted by the same authors [74] managed to discriminate and classify newly cultivated and organic fruits using both technologies. Both devices demonstrated the ability to discriminate and classify the studied samples, although the E-tongue exhibited greater data dispersion and some overlap between the analyzed data classes. Therefore, the distinction between the study proposed in this research and the others mentioned above lies in using both technologies to detect pesticides in various fruits characteristic of the region compared to organic fruit that was free of contamination. Additionally, different performance metrics of the E-nose and E-tongue were evaluated using the best classification model reported in Table 4, followed by the evaluation of the metrics for the best models.