Data Fusion Strategy

In the case of low-level fusion strategy (Figure 11), different subsets HPLC fingerprint data matrix of rhizomes, stems, and leaves) are straightforwardly concatenated and compiled into a new chromatographic data matrix for subsequent classification model construction [45,46]. Furthermore, each subset must be totally aligned and keep all the variables on the same scale before subsets reconnection [45,46].

In the case of mid-level fusion (Figure 11), the first step of data treatment is feature selection that is based on rhizomes, stems, or leaves classification models. When compared with the raw data sets, feature selection of subsets minimizes the data content and reduces data dimensions. Subsequently, new subsets of rhizomes, stems, and leaves were rebuilt while using variables of feature selection [45]. At last, those subsets are concatenated and compiled into a final data matrix for model construction [45].

In the research, relevant variables of RF classification models were determined by the R software package Boruta [64], and VIP was used for important variables selection of OPLS-DA [65].
