Principal Component Analysis or something alike for nonlinear Regression

Hello, code of estimated parameters of an non-linear differential equation model is

      options=optimset('MaxFunEvals', 10000, 'MaxIter', 10000, 'TolFun', 0.0001, 'TolX',0.0001,'Display','on');
      [k_optim, resnorm, residual, exitflag, output, lambda, jacobian] = lsqnonlin(f,k0,lb,ub,options);

From a theoretical point of view: Can I use the Jacobian matrix from my output to perform what is called Principal Component Analysis for the world of linear regression models? For those models you use the covariance Matrix of the observables, because this appears in the equation for the Variance of the parameters. In case of non-linear Models the parameter-variance is defined as J_T * J (but Im not sure), so I thought I might could use the one from my output. Is anyone sure and knows respective code in Matlab to do this?