## 4 Conclusions  
In this work, SK-NIRS was used to identify the maturity stage of cucumber seeds collected during five harvesting periods. Both the average NIR spectra and the second derivative of the NIR spectra showed distinct variations that agreed with the analytical results of chemical measurements. Therefore, it can be inferred that SK-NIRS is sensitive enough to be used to identify the maturity stages of cucumber seeds. In the subsequent explorative analysis, the preprocessing method of the first derivative after mean centering was proved to be efficient in distinguishing the category features of maturation stages and was used in the SIMCA and PLS-DA models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**N**} & \multicolumn{4}{c}{**SIMCA**} & \multicolumn{4}{c}{**PLS-DA**} & \multicolumn{4}{c}{**HCS**} \\ \cline{3-11}  & & **Se** & **Sp** & **P** & **Se** & **Sp** & **P** & **Se** & **Sp** & **P** \\ \hline MS1 & 90 & 1 & 1 & 1 & 0.9889 & 0.9958 & 0.9889 & 1 & 1 & 1 \\ MS2 & 52 & 1 & 0.8727 & 0.5977 & 0.9808 & 0.9455 & 0.7727 & 1 & 1 & 1 \\ MS3 & 60 & 0.9667 & 0.8727 & 0.6304 & 0.25 & 0.985 & 0.7895 & 0.9833 & 0.9888 & 0.9516 \\ MS4 & 49 & 0.9796 & 0.6871 & 0.3556 & 0.7347 & 0.8741 & 0.507 & 0.9592 & 0.9964 & 0.9792 \\ MS5 & 76 & 0.9737 & 0.9124 & 0.7708 & 0.9605 & 0.9681 & 0.9012 & 0.9868 & 1 & 1 \\ \hline \hline \end{tabular}  
* N: number of samples; HCS: hierarchical classification strategy. Se: sensitivity = TP/(TP+FN); Sp: specificity = TN/(TN+FP); P: precision = TP/(TP+FP).  
\end{table}
Table 5: Predictive performances of three multiclass classification schemes for five classes in the test set.  
A hierarchical classification strategy was proposed to address the multiclass classification problem by breaking it down into subgroups. This classification scheme consists of one root decision node (projection rule by the first PCA submodel in SIMCA) and three classification decision nodes (classification rules by three PLS-DA models). Initiating by the root node, the five classes were recursively partitioned in two subsets until all the generated subsets have only one class. The performances of these rule nodes in both the training and test phases were evaluated by means of confusion matrices. Two metrics, the classification accuracy rate and Cohen's kappa, were compared, and both showed very satisfying results. This hierarchical classification strategy was also compared to both SIMCA and PLS-DA models for direct multiclass classification, and showed superior predictive performance on the test set. Furthermore, the VIP score for each of the three PLS-DA models showed that the 1600-1800 nm NIR region, which contains absorption bands of fatty acids, was helpful in classifying samples at different maturation stages. Above all, this hierarchical multiclass classification strategy should be considered intuitive and precise for the simultaneous prediction of the maturation stages of cucumber seeds in five classes.  
All authors contributed to the conceptualization. [PERSON] performed the formal analysis, original draft preparation, and visualization. [PERSON] and [PERSON] reviewed and made relative editings. [PERSON], [PERSON], and [PERSON] contributed to the supervision. [PERSON] and [PERSON] provided resources.  
This research was funded by the program of Guangdong provincial governor (Yuecainong No. [2019] 170), the National Science and Technology Support Program of China (2018 SFD0701002), the Major Projects of Guangdong Province (2016 KZDXM028), the Science and Technology Program of Guangdong Province (2017B020206005), and the Overseas Joint Training Project for Doctoral Students of South China Agricultural University (2018 LHPY023).  
The authors are grateful for the administrative support from the South China Agricultural University and the technical guidance from the Vegetable Research Institute of the Guangdong Academy of Agricultural Sciences.  
The authors declare no conflict of interest.