Soft analyzers have been increasingly accepted as an alternative to physical ones in the chemical industry to infer and improve the product quality. In this study, an adaptive least-squares support vector regression (ALSSVR) algorithm is proposed for the issue of nonlinear multi-input−multi-output process modeling and applied to soft chemical analyzer development. The ALSSVR algorithm adopts the moving window scheme and a two-stage recursive learning framework to trace the time-varying dynamics of a process. The useless sample (i.e., a node of analyzer model), while not the oldest one, is selectively deleted from the model topology, using the fast leave-one-out cross-validation criterion. Consequently, the updated model can exhibit good generalization ability and trace the process characteristics effectively. Besides, a variable moving window is proposed, so its size can be adaptively adjusted, relative to process changes. The ALSSVR-based soft analyzer is then applied to an industrial fluidized catalytic...