# THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts
## 5 Conclusions  
In this study, millimeter wave measurements to select healthy, rotten, shriveled and empty hazelnuts samples have been made by using an experimental setup designed to perform measures in transmission configuration. Measurements were carried out by selecting nine frequency values between 36 and 40 GHz. Two different methods are compared to analyze the same data: one based on some trivial statistics and another based on neural networks.  
Both systems can distinguish between healthy and unhealthy hazelnuts with sufficient accuracy. The two systems have different development costs in terms of computational capabilities and development. These costs, higher in the case of neural networks, are widely  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Neuron** \\ **Index** \\ \end{tabular} } & \begin{tabular}{c} **Hazelnuts** \\ **in Each** \\ **Neuron** \\ \end{tabular} & \begin{tabular}{c} **Only** \\ **Healthy** \\ \end{tabular} & \begin{tabular}{c} **Only** \\ **Rotten** \\ \end{tabular} & \begin{tabular}{c} **Only** \\ **Shriveled** \\ \end{tabular} & \begin{tabular}{c} **Only** \\ **Empty** \\ \end{tabular} & \begin{tabular}{c} **\%** \\ **Healthy** \\ \end{tabular} & \begin{tabular}{c} **\%** \\ **Rotten** \\ \end{tabular} & \begin{tabular}{c} **\%** \\ **Shriveled** \\ \end{tabular} & \
\begin{tabular}{c} **\%** \\ **Empty** \\ \end{tabular} &
\begin{tabular}{c} **\%** \\ **Error** \\ \end{tabular} \\ \hline
1 & 28 & 0 & 12 & 4 & 12 & 65.43\% & 12.34\% & 5.55\% & 16.66\% & 0.6\% \\ \hline
2 & 105 & 105 & 0 & 0 & 0 & & & & \\ \hline
3 & 27 & 0 & 7 & 5 & 15 & & & & \\ \hline
4 & 2 & 1 & 1 & 0 & 0 & & & & \\ \(\mathrm{tot}\) & 162 & 106 & 20 & 9 & 27 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: 65.43% of the hazelnuts were healthy, 12.34% were rotten, 5.55% were shriveled and finally, 16.66% were empty with a sorting error of 0.6%.  
compensated by the enormous possibilities for further development and greater depth of selection capabilities (such as differentiation between different types of defects). The system based on the statistical algorithm may be cost-effective only for systems aimed at a coarser sorting and with a specific task: eliminating all the bad ones or selecting all the good ones.  
The method based on neural networks (the so-called Self-Organizing Map, which exploits [PERSON]'s algorithm) allows the achievement of both objectives and has potential still under study but at a higher implementation cost. The proposed methods have both proven to be able to discriminate with high accuracy, showing different features.  
Further studies are currently underway in order to test the neural network on a larger data set, also trying to improve the performance of the setup by reducing acquisition times through the implementation of control software.  
Conceptualization: A.D., E.G., M.G. and L.S., Data curation: M.G. and L.S.; Methodology: S.G.; Resources: F.L.; Roles/Writing--original draft: M.G. and L.S.; and Writing--review & editing: A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.