## 3 Results  
The RBF-based SVM algorithm is highly susceptible to the different magnitudes of the individual features submitted for learning. Therefore, the features all need to be standardized so that one feature is not predominant compared to the rest [31]. In our case, the features were the integer reflectance intervals corresponding to the individual half-widths of the previously selected lasers. The standardization was performed by subtracting the mean of all observations from each observation and dividing by their standard deviation. This yielded a distribution with the mean at zero and a standard deviation equal to one. Even though all measurements were previously normalized after the data collection process, the use of additional standardization raised the classification result by 2% and 4% for white and brown eggs, respectively. However, this should be performed on a case-by-case basis, and it is best to test which of the scaling measures returns the highest quality classification. It is also important to check for outliers in the dataset, as they will disrupt not only the standardization process but also the learning process. In our dataset, there were no outliers, so it was possible to start learning. Since the dataset was not large, this provided the possibility of generating subsets consisting of all combinations without repetition from one to nineteen of all selected wavelengths. The combinations were ranked, and the top twenty positions are shown in Tables 3 and 4 for the white and brown eggshells, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ranking** & **Number of** & **Combination of Parameters** & **5 CV** & **5 CV SD** \\
**Position** & **Wavelengths** & **Combination of Parameters** & **Mean (\%)** & **5 CV SD** \\ \hline
1 & 7 & A10, A11, A12, A13, A14, A15, A0 & 82.473 & 7.683 \\
2 & 6 & A11, A12, A13, A14, A14, A0 & 82.438 & 8.068 \\
3 & 5 & A12, A13, A14, A15, A0 & 82.431 & 7.965 \\
4 & 7 & A11, A12, A13, A14, A15, A16, A0 & 82.421 & 8.247 \\
5 & 8 & A10, A11, A12, A13, A14, A15, A16, A0 & 82.271 & 8.249 \\
6 & 9 & A4, A7, A8, A11, A12, A13, A14, A15, A0 & 82.118 & 8.561 \\
7 & 4 & A11, A12, A13, A0 & 82.061 & 10.84 \\
8 & 7 & A5, A11, A12, A13, A14, A15, A0 & 81.958 & 8.375 \\
9 & 7 & A6, A11, A12, A13, A14, A15, A0 & 81.931 & 9.018 \\
10 & 3 & & & & \\
11 & 8 & A9, A10, A11, A12, A13, A14, A15, A0 & 81.921 & 10.986 \\
12 & 7 & A8, A11, A12, A13, A14, A15, A0 & 81.921 & 8.058 \\
13 & 8 & A8, A11, A12, A13, A14, A15, A0 & 81.871 & 8.463 \\
14 & 8 & A4, A8, A11, A12, A13, A14, A15, A0 & 81.825 & 8.654 \\
15 & 5 & & & & \\
16 & 4 & A12, A13, A16, A0 & 81.744 & 10.881 \\
17 & 8 & A6, A8, A11, A12, A13, A14, A15, A0 & 81.723 & 8.451 \\
18 & 8 & A8, A10, A11, A12, A13, A14, A15, A0 & 81.723 & 8.451 \\
19 & 8 & A5, A8, A11, A12, A13, A14, A15, A0 & 81.68 & 8.671 \\
20 & 6 & A8, A12, A13, A14, A15, A0 & 81.671 & 8.088 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top 20 ranking for multiple wavelength usage for white eggshells, without hyperparameter optimization. The quality of classification with hyperparameter optimization was then tested for each subset of features, and those with the highest results were selected. A new ranking for white and brown eggshells is shown in Table 5 and Table 6, respectively. This activity was important because an overabundance of features can lead to noise in the best solution, and too few features can lead to underfitting the classification model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ranking** & **Number of** & **Combination of Parameters** & **5 CV** & **5 CV SD** \\
**Position** & **Wavelengths** & **Combination of Parameters** & **Mean (\%)** & **5 CV SD** \\ \hline
1 & 2 & A15, A0 & 73.514 & 8.112 \\  
A14, A15 & 72.238 & 13.728 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top 20 ranking for multiple wavelength usage for brown eggshells without hyperparameter optimization.