# Convolutional Neural Network for Measurement of Suspended Solids and Turbidity
## 3 Results and Discussion
However, the two smaller concentrations (Classes 0 and 1) had lower precisions. To improve this problem and test repeatability, we repeated and expanded the measurement with a different sample, paprika, and a smartphone with a different operating system (iPhone 6, IOS system). In order to improve and validate our results, we analyzed a set of liquid samples made with paprika. This additional set of samples incorporated two new classes (1/2 and 3/4) into the CNN training process, which significantly reduced the measuring error of classes 0 and 1. Additionally, Table 5 shows TSS values for the paprika samples with each color illumination dataset. In view of the training of the new classes, the CNN + MLR improved its accuracy to 98.2400% for the TSS values with the white dataset. It should be highlighted that between TSS and turbidity there is a correlation due to the coefficient of proportionality that creates a linear regression between them. This coefficient of proportionality between TSS and turbidity depends on the geometric and optical properties of the suspended solids (i.e., size, shape, refraction index, mass density) [40; 41; 42]. In other words, the samples of fish feed and paprika have the same concentration of TSS, but have different concentrations of turbidity since they have a different coefficient of proportionality. In order to estimate the turbidity values in the liquid samples, the system was trained and validated using reference values measured with another instrument. These turbidity reference values were measured with a HACH DR900 colorimeter, within the operating range 0-263 NTU, and are shown in Table 6. The reference measurements were replicated six times to obtain the standard deviation of the device. Note that when making measurements near 200 NTU with the HACH DR900, the standard deviations increased, due to it being adjusted with a calibration curve using the reading obtained with the 200 NTU formazin standard. Additionally, according to the user manual, the instrument error is \(\pm\)21 NTU [43]. Table 6 shows the TSS and turbidity reference values for the fish feed and paprika samples. The turbidity values estimated by our method are shown in Table 7 for the different color datasets. The white dataset showed the lowest standard deviation values among all color datasets. It had a maximum standard deviation of \(\pm\)13.68 NTU for the fifth class and, in turn, had a lower standard deviation than the instrument used as reference. In addition, for the fish feed samples, the white color presented an error of 9.84% compared to 11.64% for red, 12.55% for blue, and 42.50% for green. Therefore, the 0-263 NTU range is appropriate for aquatic environments, as the safe turbidity level for aquatic life should not exceed 25 NTU [44]. For the turbidity measurement, the standard deviation in our proposed method was \(\pm\)6.98 NTU and an accuracy of 90.16% for the white dataset, which was the best color dataset for the fish feed samples. Additionally, Table 7 shows the turbidity values estimated by our method for paprika samples, and it can be noted that by training the new classes, the CNN + MLR improved its accuracy to 97.20% for turbidity values using the white dataset. In order to test our method, eight new samples with paprika were prepared with fractional concentrations of TSS. To validate the proposed method, these samples were estimated without training the CNN or the MLR for these TSS values. The TSS values estimated by the CNN + MLR are shown in Table 8, with an accuracy of 96.88% for the white dataset. The turbidity values estimated by the proposed method are shown in Table 9, with an accuracy of 96.14% for the white dataset. The CNN + MLR were validated for extra concentration samples.