Figure 3

MCIA analysis of core and surface samples of cheddar cheeses. (a,b) The first two axes of MCIA represents metabolomics (LC/MS and GC/MS) and microbiota composition of the industrial and artisanal core (a) and surface (b) cheeses. Different shapes (df1, diamond: GC/MS data set; df2, triangle: microbiota data set; df3, square: LC/MS data set) represent the different variables connected by lines, the length of these lines is proportional to the divergence between the data. Lines for each sample are joined at a common point, at which the covariance derived from the MCIA analysis is maximal. Color shows the 4 brands of cheeses. (c,e) Psedo-eigenvalue space representing the percentage of variance explained by each of the MCIA component for core (c) and surface (e) datasets. (d,f) Pseudo-eigenvalues space of all datasets for the core (c) and the surface (f), showing overall co-structure between three datasets and shows which dataset contributes more to the total variance.