This paper addresses the problem of biomass substrate hypothetical system estimation using sigma points kalman filter (SPKF) methods. Various conventional and state-of-theart state estimation methods are compared for the estimation performance, namely the unscented Kalman filter(UKF), the central difference Kalman filter (CDKF), the square-root unscented Kalman filter (SRUKF), the square-root central difference Kalman filter (SRCDKF), the iterated unscented Kalman filter (IUKF), the iterated central difference Kalman filter (ICDKF), the iterated square root unscented Kalman filter (ISRUKF), the iterated square root central difference Kalman filter (ISRCDKF) through a biomass substrate hypothetical system with two comparative studies in terms of estimation accuracy, convergence and execution times and under constanttime and varying-time parameter constraints. In the first comparative study, the state variables are estimated from noisy measurements, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study the state variables as well as the model parameters are simultaneously estimated, and the impact of the practical challenges (measurement noise and number of estimated states/parameters) on the performances of the estimation techniques are investigated. The results of both comparative studies reveal that the ISRCDKF method provides a better estimation accuracy than the IUKF, ICDKF and ISRUKF methods; while the IUKF, ICDKF, ISRUKF and ISRCDKF methods provide improved accuracy over the UKF, CDKF, SRUKF and SRCDKF methods. The benefit of the ISRCDKF method lies in its ability to provide accuracy related advantages over other estimation methods since it re-linearizes the measurement equation by iterating an approximate maximum a posteriori estimate around the updated state, instead of relying on the predicted state. The results of the comparative studies show also that, for all the techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. The ISRCDKF, however, still provides an improved state accuracies than the other techniques even with abrupt changes in estimated states.