PCA correlation matrix plot, sorted by similarity, might be good to see which variable has most effect in PC0/PC1. https://www.reneshbedre.com/blog/principal-component-analysis.html#perform-pca-using-scikit-learn
P.S. Multicollinearity Graphs are useful, but they are not quite PCA. Maybe first factor as amount of green, second as amount of red, third factor for amount of blue? https://www.algorhythmblog.be/2022/04/05/visualizing-multicollinearity-in-python/
PCA correlation matrix plot, sorted by similarity, might be good to see which variable has most effect in PC0/PC1. https://www.reneshbedre.com/blog/principal-component-analysis.html#perform-pca-using-scikit-learn
P.S. Multicollinearity Graphs are useful, but they are not quite PCA. Maybe first factor as amount of green, second as amount of red, third factor for amount of blue? https://www.algorhythmblog.be/2022/04/05/visualizing-multicollinearity-in-python/