In　this　paper,　we　propose　novel　strategies　for　neutral　vector　variable　decorrelation.　Two　fundamental　invertible　transformations,　namely,　serial　nonlinear　transformation　and　parallel　nonlinear　transformation,　are　proposed　to　carry　out　the　decorrelation.　For　a　neutral　vector　variable,　which　is　not　multivariate-Gaussian　distributed,　the　conventional　principal　component　analysis　cannot　yield　mutually　independent　scalar　variables.　With　the　two　proposed　transformations,　a　highly　negatively　correlated　neutral　vector　can　be　transformed　to　a　set　of　mutually　independent　scalar　variables　with　the　same　degrees　of　freedom.　We　also　evaluate　the　decorrelation　performances　for　the　vectors　generated　from　a　single　Dirichlet　distribution　and　a　mixture　of　Dirichlet　distributions.　The　mutual　independence　is　verified　with　the　distance　correlation　measurement.　The　advantages　of　the　proposed　decorrelation　strategies　are　intensively　studied　and　demonstrated　with　synthesized　data　and　practical　application　evaluations.