A　sequential　quality　prediction　algorithm　based　on　information　increment　matrix　is　proposed　for　multi-phase　batch　processes.　It　can　overcome　the　limits　of　some　phase　partition　algorithms,　which　cannot　cope　with　the　sequence　and　dynamics　of　the　processes　and　may　inevitably　divide　the　samples　with　discontinuous　time　sequence　but　similar　characteristics　into　the　same　phase.　First,　the3D　data　is　transformed　into　2D　data　by　batch-wise　unfolding　and　splitted　into　extended　time　slices　that　equipped　with　quality　variables.　Then　a　sliding　window　is　used　to　divide　the　sub-phases　according　to　information　increment　of　extended　time　slices.　PLS　models　of　each　sub-phase　constitute　the　global　quality　prediction　strategy.　The　proposed　algorithm　takes　the　correlations　among　variables　into　consideration　and　uses　information　increment　to　capture　the　dynamics.　The　feasibility　and　effectiveness　of　the　proposed　algorithm　are　illustrated　by　a　penicillin　simulation　platform　and　an　industrial　application　of　E.　coli　fermentation,　respectively.　©　All　Right　Reserved.