# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 1 Introduction
These technologies are typically characterized by their ability to provide simple, rapid, non-destructive, portable, and highly specific detection. They also offer good reproducibility and are cost-effective compared to traditional analytical techniques [36]. There are some reviews and research publications in which the different applications of the E-nose and E-tongue have been reported over the years in the food industry. For instance, these systems have been implemented for the early detection of microorganisms (meat, milk, etc. ), authenticity of food and beverages, evaluation of food quality (e.g., fish, wine, meat, beer, milk, water, drinks), monitoring food processing, and the identification of ripening states in fruits such as apples, pears, peaches, apricots, citrus fruits, grapes, strawberries, mangoes, and other tropical fruits [35; 37; 38; 39; 40; 41; 42]. However, using these systems combined with chemometric methods to determine pesticide residues in fruits and vegetables has yet to be explored. [PERSON] et al., in 2021, used an E-nose based on MOS sensors to detect the diazinon pesticide in cherry samples, obtaining 96% of the variation in the data for toxic and non-toxic sweet cherries [43]. Similarly, [PERSON] and collaborators implemented a commercial system (PEN3) with ten metal oxide semiconductor (MOS) sensors, which was used to detect pesticide residues such as cypermethrin and chlorpyrifos in apple samples, achieving a good classification of the pesticide residues using principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) [44]. [PERSON] et al., used a self-made E-nose to obtain qualitative responses to different pesticides [45]. Likewise, studies have been conducted on detecting pesticide residues in fruits such as strawberries, peaches, and sour cherries [8; 46]. Different investigations have been carried out regarding the use of E-tongues, which have been focused on the efficient detection of pesticide residues in fruits. Still, it is worth mentioning that these studies have been carried out using biosensors in most cases. For example, [PERSON] et al. used an ultrasensitive aptamer biosensor based on cationic polymer and gold nanoparticles for the detection of the organophosphate family pesticide malathion in apples, achieving a detection limit of 5.2 pM (0.001 ug kg\({}^{-1}\)) [47; 48]. In another study, an electrochemical nanosensor based on fluorine-doped tin oxide (FTO) was developed for the detection of chlorpyrifos with gold nanoparticles (AuNP) and anti-chlorpyrifos antibodies (chl-Ab). The FTO-AuNPs sensor was successfully used to detect chlorpyrifos in standard and actual samples up to 10 nM for apple and cabbage and further 50 nM for pomergnate [49]. A monoenzymatic impedimetric acetylcholineinesterase biosensor has been reported in the literature to sensitively detect carbamate and organophosphate compounds with a very rapid response [50]. However, with recent advances in nanotechnology, metal-based enzyme-free sensors, and corresponding metal oxides have been widely reported. Recent studies demonstrate that CuO nanostructures have been applied in pesticide detection thanks to their ability to bind to phosphate groups in organophosphates [51]. This study aims to validate the use of E-tongue and E-nose technologies as innovative tools for detecting and classifying pesticide residues in various fruits. The fruits selected are among the most widely cultivated in the Santander region, making them ideal candidates for evaluating the effectiveness and practicality of these devices as alternatives to traditional pesticide detection methods, such as GC-MS. The evaluation focused on the key advantages of the E-tongue and E-nose, including non-destructive analysis, portability, speed, and cost-effectiveness. This study began with training and validation phases, followed by testing with new data to assess the devices' capabilities.