You will have to be more specific?  What parameters?  Linearity, specificity, accuracy, precision...  See ICH Q2(R1) and take your pick!

Hi, I agree with Bruce, what parameters? in addition of ICH R, I attach Guidance from Eurachem as your reference

Hello,

If you have two (or more) parameters and want to investigate a certain outcome (e.g. optimized productions) in relation to these I would recommend to use a design of experiments approach. But if this is the correct approach depends on the parameters and your application.

Maybe you can consider the recursive least squares algorithm (RLS). RLS is the recursive application of the least squares (LS) regression algorithm, so that each new data point is taken in account to modify (correct) a previous estimate of the parameters from some linear (or linearized) correlation thought to model the observed system. The method allows for the dynamical application of LS to time series acquired in real-time. As with LS, there may be several correlation equations with the corresponding set of dependent (observed) variables. For RLS with forgetting factor (RLS-FF), acquired data is weighted according to its age, with increased weight given to the most recent data.

Years ago, while investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS-FF algorithm to estimate the parameters from the KLa correlation, used to predict the O2 gas-liquid mass-transfer, hence giving increased weight to most recent data. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). The power dissipated by agitation was accessed by a torque meter (pilot plant). The proposed (adaptive) control algorithm compared favourably with PID. Simulations assessed the effect of numerically generated white Gaussian noise (2-sigma truncated) and of first order delay. This investigation was reported at (MSc Thesis):

Thesis Controlo do Oxigénio Dissolvido em Fermentadores para Minimi...