## 3 Results and Discussion
### Multivariate Analysis  
To put more insight into the data, multivariate analyses were performed, namely: PCA, CA, and GDA. Prior to statistical analysis [PERSON]'s correlation coefficients were calculated (Table S1) and the data matrix was standardized. Based on the data mining algorithm: Selection and elimination of variables, the following variables were characterized by the highest importance (expressed as Chi-squared) and chosen for further analysis: TBARS, hexanal, CD, and SH contents and L*, a*, b* parameters (Figure S2). All parameters of oxidation were significantly correlated (supplementary materials Table S1). The highest r Pearson's correlation coefficients were determined between SH content and TBARS (\(-0.70\)). Significant correlations were also reported between lightness and: Hexanal, TBARS, or SH content. The latter was equal to \(-0.67\). [PERSON] et al. (2017) reported in the review that oxidation changes of proteins in chicken meat affected the light scattering and thus L* values. At minimal protein denaturation, low light scattering is observed and the chicken meat has dark color. Thus, it could explain the significant correlation between lightness and protein oxidation observed in the study. The color coordinates: a* and b* were also significantly correlated. The correlations observed were proved by PCA (Figure 6a). PCA gives the possibility to compress the data to a few principal components (PCs) that can be later used to reconstruct data without any preliminary assumptions about their distribution and almost without any loss of information ([PERSON] et al., 2017; [PERSON] et al., 2017). This helps to understand the relationships between variables. Using graphical criterion, the first four PCs were extracted (three of them with eigenvalue greater than 1) and together they explained 87.3% of the total variance. The PC1 explained 42% of variance and the PC2 the 23% of variance. Based on the loaded values, showing the correlations between components and variables, it could be stated that TBARS, hexanal content, SH content, and lightness (L*) determined sample distribution along PC1 (Figure 6a). Similar results were obtained by ([PERSON] et al., 2017), who also reported high correlations between lipid, protein and myoglobin oxidation parameters in PCA. Moreover based on the PCA, the authors concluded that myoglobin, lipid, and protein oxidation reactions in rabbit meat were interlocked with each other in a certain manner, and each process seemed to facilitate one another ([PERSON] et al., 2017). The same could be stated based on the results showed in this study, since the projection of variable on the factor plane (Figure 6a) indicated complex and inseparable interrelationship among them. When oxidation of lipid occurred the oxidation reactions could be transferred to proteins fraction ([PERSON] et al., 2017; [PERSON] et al., 2017) and meat's pigment ([PERSON] et al., 2017). An inverse interaction could be also carried out ([PERSON] et al., 2017). Figure 6b showed the projection of the scores on the factor plane. PC1 enabled to separate control sample (on the right of PC1) with the highest lipid oxidation and lightness and the lowest SH content from other samples. Clove-, black seed-, and allspice-treated samples characterized with the lowest protein oxidation (the highest SH groups) among all tested extracts, thus they were on the left of PC1. PC2 was mainly correlated with a* and b* color parameters and it grouped well the samples of raw chicken meat with cloves (with the highest values of a* and b* among samples) and black seed (with the lowest values of a* and b*). The PCA is less sensitive to differentiate all samples (with allspice, bay leaf, cardamom, caraway and nutmeg). Thus, the CA was applied as another unsupervised pattern recognition technique of multivariate analysis to group samples on the basis of similarities in clusters. The Ward method and Euclidean distance between centroids were applied. The CA dendrogram enabled to separate eight clusters (Figure 6c).