## 3 Results  
The average spectra of the considered classes are shown in Figure 3. Although a direct comparison of intensities is not feasible on unprocessed spectra, certain differences in the intensities of specific peaks can be recognized using simple visual inspection, especially in the fingerprint zone from 1100 cm\({}^{-1}\) to 500 cm\({}^{-1}\). A detailed interpretation of the reported profiles is challenging, given the complexity of the matrix and the strong influence of water on the spectroscopic signals. In general, the reported profiles reflect well the expected composition of the samples, mainly constituted by water, proteins, sugars and carbohydrates, crude fiber components, as well as phenolics, chlorophylls and porphyrins, carotenoids, Vitamins (A, C, K), glucosinolates, choline, and betaine. Starting from the broad band centered at 3360 cm\({}^{-1}\), it is primarily attributed to the O-H stretch of water, overlapping with vibrational bands associated with the O-H stretching modes of sugars, phenolics, Vitamin C and N-H stretch in proteins and indole moiety of glucosinolates [40]. Peaks from \(\sim\)2920 cm\({}^{-1}\) to \(\sim\)2860 cm\({}^{-1}\) represent symmetric and antisymmetric C-H stretching vibrations, respectively. The signal at 1740 cm\({}^{-1}\) is linked to the C=O stretching of chlorophylls and pectins [41]. The broad spectral band at 1640 cm\({}^{-1}\) results from several combined effects of O-H bending, H-O-H bending of water, to the Amide I band of proteins and the C=N-sulphate moiety, which result in strong IR bands between 1630 and 1690 cm\({}^{-1}\)[42]. The signal at 1515 cm\({}^{-1}\) is attributed to the stretching of Amide II of proteins. However, within the range of 1500-1200 cm\({}^{-1}\), absorptions are associated with CH\({}_{2}\) cellulose bending modes and various pectin and chlorophyll vibration modes. Signals in the 1200-950 cm\({}^{-1}\) range reflect asymmetric stretching of the C-O-C glycosidic linkage and C-C and C-O ring stretching modes of polysaccharides [43]. Again, in the range of \(\sim\)1495 to \(\sim\)1265, signals related to S=O of glucosinolates can be found. Furthermore, signals at 1600 and 1100 cm\({}^{-1}\) hint at the existence of aromatic compounds, flavonoids, and phenols. The IR spectra collected on the various samples were subjected to different classification problems, which were solved using both SIMCA and PLS-DA. In particular, both approaches were used to test whether it is possible to classify Class Mugnoli (constituted by all the individuals belonging to Class A, B, and C) vs. all the other investigated ecotypes (Classes D, E, F, G, and H modeled as four individual categories). Afterward, the same classification problems were re-evaluated, considering the three accessions of Mugnoli as individual classes, in order to test whether the variability among them was high enough to require their individual modeling. Furthermore, in order to find out significant differences between Mugnoli landrace and the Brassicaceae most commonly found on the market (Class E, G, and H), PLS-DA has been used to investigate whether it is possible to discriminate between Class Mugnoli (constituted by all the individuals belonging to Class A, B and C) vs. the three Classes of Commercial Brassicaceae, i.e., the individual classes of E, G, and H.  
More details on the PLS-DA and SIMCA models are reported below in the respective paragraphs. Regardless of the classifier used, different spectral preprocessings were tested on data:  
- Mean centering (MC)  
- Standard Normal Variate (SNV) (+MC)  
- First Derivative (D1) (+MC)  
- Second Derivative (D2) (+MC)  
- SNV + D1 (+MC)  
- SNV + D2 (+MC)  
As it is possible to see from the list above, combinations of preprocessing methods were tested (e.g., SNV + D1), meaning that they were applied to the data in the same order  
Figure 3: MIR spectra of the collected and analyzed samples, averaged according to the class belonging. they are listed.