## 3 Results
### Estimation of the Number of Samples and Analysis of the Information  
To carry out the sampling campaigns, the estimation of the sample size required for two independent samples was made, since they are two different sensors (MT and MNT), establishing a power of the test (\(\beta\) = 0.9), a confidence level of 95% (\(\alpha\) = 0.05) and an effect size of 0.5 [25]. By using the XLSTAT tool, a minimum size of 85 was obtained as a result for each sample. However, in order to improve accuracy and ensure statistical representativeness, a final size of 100 samples per sensor (MT and MNT) was established. Additionally, the distribution of the samples was considered uniform for the four types of waste (plastic, glass, metal, and organic), with 25 records per category, establishing an adequate balance in the classification, avoiding biases during the training and testing phase of the machine learning model. Tables 2 and 3 show the mean, standard deviation, and confidence intervals for each characteristic, according to the type of material or waste used during the sampling campaign depending on the type of sensor. Figure 5: Capacitance value with non-traditional model sensor prototype (MNT) without residuals for reading. Figures 4-7 present some of the results obtained during the measurement campaigns, both in the absence of material or waste (Figures 4 and 5), and in the presence of reusable waste such as plastic, glass, and metal, as well as in various types of organic waste. Figures 4 and 6 show some of the results recorded in terms of capacitance using the MT capacitive sensor, registering a value of 21 pF in the absence of material or residue. In turn, values between 22 and 27 pF for reusable materials and values between 80 and 450 pF when dealing with organic waste. On the other hand, Figures 5 and 7 present some of the results in terms of capacitance for the MNT capacitive sensor, registering a value of 28 pF in the absence of material or residue. In turn, values between 31 and 37 pF for reusable materials and values between 150 and 1200 pF when dealing with organic waste. To visually represent the distribution of data recorded during the sampling campaigns, and estimate the variability levels for each type of residue considered as a reference, box plots were generated for each case. These diagrams also aided in evaluating the behavior of the proposed sensors and identifying outliers in the dataset. The results are presented in Figures 8 and 9. Figure 8: Plot of boxes related to capacitance [pF] for the MT sensor. Figure 7: Records with sensor—non-traditional model (MNT) for various types of waste. Additionally, the possibility was considered that the size of the sample that was established for the development of the project could have affected in some way the appearance of each of the resulting figures. However, from a practical point of view, according to [26], it is possible to consider that a box plot can be reliable as long as the sample size established for each item is equal to or greater than 20. Taking into account that 25 samples were established for each type of residue, it can be considered that this recommendation is fulfilled and with this the result of each figure can be considered reliable. Figures 9 and 10 show the box diagrams related to the capacitance [pF] recorded by the MT and MNT sensors. In them it can be seen that the behavior of the capacitance for the readings recorded by the MNT sensor describes a normal distribution for the case of the three types of reusable waste (plastic, glass, and metal), a situation that occurs in a similar way with waste corresponding to glass and metal in the case of the MT sensor. However, for the particular case of plastic in the MT sensor, it describes a positive asymmetry, describing a higher concentration of the values recorded between 50 and 75%. Figure 10: Confusion matrix obtained for the Random Forests model. Figure 9: Plot of boxes related to capacitance [pF] for the MNT sensor.