In multisensor fusion, several sources of information are combined in order to increase the estimation quality for the quantity of interest. This activity finds many applications from tactical missile defense to self-driving cars and the estimation of variables difficult to measure such as analyte concentrations in chemical processes. In industrial applications, it is common to employ laboratory analysis that provides more accurate measurements but usually at slower rates, with significant delays and requiring the involvement of highly skilled personnel as well as considerable capital and operational costs. In this context, soft sensors and online analyzers are often introduced in the process to provide more frequent and updated measurements, as additional sources of information for the variables of interest. To take advantage of all these sources, they need to be properly fused. In this article, two fusion schemes are proposed and tested: one version of the classic tracked Bayesian fusion (TBF) scheme and a novel modification of the track-to-track algorithm, designated as bias-corrected track-to-track fusion (BCTTF). Among other features, the proposed methodologies are able to handle multirate and irregularly sampled data, measurements with different quality and measurements delay. The two fusion schemes were tested and compared using real plant data, where it was possible to verify that BCTTF presents better prediction performance and higher alarm identification sensitivity. This algorithm also produces a smoother estimated signal. The analysis of the figures of merit lead us to recommend the use of BCTTF as a fusion algorithm under multirate sensor fusion conditions.