## 4 Conclusions  
The results of this study show that visible and near-infrared transmittance spectroscopy can capture the chemical changes of SSC, TA, and MC caused by granulation in honey pomelo. Machine vision technology can quickly estimate the external characteristics of samples, and the obtained data can be used to identify changes in mass and volume caused by granulation. Application of these two methods together allows the improved identification and detection of granulation in honey pomelo compared with the use of a single or traditional method. The results showed that multi-source data fusion was the optimal feature extraction method for granulation detection of honey pomelo, which improved the \(ARM\) values of PCA-SVM and PCA-GRNN from 95.5% to 98% and 99%, respectively. For PCA-SVM and PCA-GRNN, the \(CA\) of each class was enhanced at least from 0.9745 to 0.9947 and 0.9950, the \(CS\) was improved at least from 0.9231 to 0.9714 and 0.9750, and the \(CSP\) was improved at least from 0.9728 and 0.9815 to 0.9931 and 0.9934, respectively. Visible and near-infrared transmittance spectroscopy combined with machine vision was successfully applied for the detection of granulation in honey pomelo. The predictive performances (measured by \(CA\), \(CS\), and \(CSP\)) was compared for three classification methods (LDA, PCA-SVM, and PCA-GRNN). PCA-GRNN provided the best classification results, with strong non-linear mapping, due to its flexible network structure. The developed multi-category model combined with multi-source data fusion could be used to non-destructively detect and grade the granulation levels in fruit. [PERSON] performed the experiments, analyzed the data, and wrote the paper. [PERSON] reviewed and edited the manuscript. [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (Project No. 31901414), the Research and Development Program in Key Areas of Guangdong province (Project No. 2018B0202240001), the New Developing Subject Construction Program of Guangdong Academy of Agricultural Science (Project No. 201802 XX), the Presidential Foundation of Guangdong Academy of Agricultural Science (Project No. 201920), and the Presidential Foundation of Guangdong Academy of Agricultural Science (Project No. 202034). We are grateful for the support of the South China Agricultural University and Public Monitoring Center for Agro-Product of Guangdong Academy of Agricultural Sciences. The authors also thank the anonymous reviewers for their critical comments and suggestions to improve the manuscript. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{\
\begin{tabular}{c} **Actual** \\ **Class** \\ \end{tabular} } & \multicolumn{6}{c}{**Predicted Class**} & \multicolumn{6}{c}{**PCA-SVM**} \\ \cline{3-10}  & & & **A** & **B** & **C** & **D** & **E** & **CA** & **CS** & **CSP** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} The principal \\ components of \\ the spectral data \\ \end{tabular} } & 32 & A & 32 & 0 & 0 & 0 & 0 & 0.9948 & 1 & 0.9938 \\ \cline{2-10}  & 35 & B & 1 & 34 & 0 & 0 & 0 & 0.9948 & 0.9714 & 1 \\ \cline{2-10}  & 55 & C & 0 & 0 & 55 & 0 & 0 & 1 & 1 & 1 \\ \cline{2-10}  & 26 & D & 0 & 0 & 0 & 25 & 1 & 0.9847 & 0.9615 & 0.9882 \\ \cline{2-10}  & 52 & E & 0 & 0 & 0 & 2 & 50 & 0.9847 & 0.9615 & 0.9931 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The influence of multi-source data fusion on the predictive performance of PCA-SVM.