I have a large set of of experimental data (26 batches) with multiple variables (43) sampled at certaintime points.
the data is all centres around cell growth, so it includes the nutrients, product, waste products and cell growth, so it is all highly correlated and dependant.
i have tested varying one control level and measured the variables as responses. Can I use principal component analysis to examine this data, will it be worth while? I want to see how the control level effects the process and if it changes the correlation of the data.
would it be best to build a model using the control data (I.e. where the normal process) and then use the batches with the varied paramete as the validation data? Or is there a better method? And is there away to use one or more of the variables as the desired responses (ie product quantity)