## 2 Materials and Methods
### Chemometric Modeling  
Given the different nature of the two datasets, different chemometric workflows were applied. Indeed, due to the limited number of analyzed samples, morphological descriptors \begin{table}
\begin{tabular}{c c c c} \hline \hline
**GLBA** & **Plant Part** & **Descriptors** & **Expression** \\ \hline
2 & Leaf & Plant habit &
\begin{tabular}{c} Effect/semi-rect/horizontal \\ Absent/weak/medium/strong/very strong \\ Very light/light/medium/dark/very dark \\ Integer/Lobed \\ Low/medium/high \\ \end{tabular} \\
7 & Leaf & Incisions &
\begin{tabular}{c} Very superficial/medium/deep/very deep \\ Absent/weak/medium/strong/very strong \\ Absent/weak/medium/strong/very strong \\ Absent/weak/medium/strong/very strong \\ Absent/weak/medium/strong/very strong \\ Absent/medium/long \\ \end{tabular} \\
11 & Leaf & Width & Small/medium/large \\
12 & Leaf & Length of the terminal lobe & Narrow/medium/wide \\
13 & Leaf & Width of the terminal lobe & Narrow/medium/wide \\
14 & Leaf & Villous surface upper leaf & Absent/weak/medium/strong/very strong \\
15 & Leaf & Anthocyanin pigmentation & Absent/weak/medium/strong/very strong \\
16 & Root & Position & Very shallow/ shallow/medium/deep/very deep \\
17 & Root & Suborous layer of the epidermis & Absent/present \\
18 & Root & Color of the epidermis outside the soil & White/green/yellow/orange/bornee/scarlet/red/purple red/purple blue \\
19 & Root & Intensity of the color of the epidermis outside the soil & Light/medium/dark \\
20 & Root & Color of the epidermis inside the soil & White/yellow/red/purple \\
21 & Root & Color of flesh & narrow transverse elliptical/transverse \\
24 & Root & Shape in longitudinal section & elliptical/rounded/oval/squared/wide oblong/narrow \\  & & & oblong/obtaining/bar \\
25 & Root & Length & Very short/Short/medium/long/very long \\
26 & Root & Diameter & Small/medium/large \\
27 & Root & Position of the widest part of the root & above the central part/in the center/below the central part \\
28 & Root & Curvature of the main axis & Absent/present \\
29 & Root & collar shape & Very depressed/Depressed/flattened/Prominent/Very Prominent \\
30 & Root & Shape of the base & Depressed/truncated/rounded/obtuse/pointed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Morpho-agronomic descriptors defined by GIBA. were investigated by exploratory analysis, in particular by principal component analysis (PCA) [36] and hierarchical cluster analysis (HCA) using average linkage [37]. PCA is one of the most common exploratory analysis (EA) methods. It is based on the concept that a data matrix \(\mathbf{X}\) can be decomposed into a set of scores (\(\mathbf{T}\), called _principal components_) and one of the loadings (\(\mathbf{P}\)), according to the expression:  
\[\mathbf{X}=\mathbf{TP^{\prime}}+\mathbf{E} \tag{1}\]  
This allows the compression of the information present in the data into a very small sub-space and creates very useful graphs for the interpretation of similarities/differences among the analyzed samples [30]. HCA is another EA approach, often used in data mining. It allows for the creation of trees that express the degree of similarity between objects. On the other hand, e-eye analysis was applied to a greater number of samples; therefore, it was possible to apply a predictive classification method and validate models using an external set of objects. In particular, Soft Independent Modeling by Class Analogy (SIMCA) [38] was used to model each investigated category. SIMCA has been designed for the analysis of individual categories. It assumes that samples belonging to the same class have similarities that can be captured by a PCA model. Consequently, the building of a SIMCA model begins with the creation of a PCA model for each category of interest.