Dioxin　(DXN)　is　a　kind　of　pollutant　commonly　discharged　during　municipal　solid　waste　incineration　(MSWI).　In　practical　industrial　processes,　the　concentration　of　DXN　emission　is　measured　by　using　offline　analysis,　but　this　method　is　constrained　by　long　time　lag　and　high　cost.　This　study　aims　to　develop　soft　measuring　model　for　DXN　emission　concentration　by　using　easy-to-measure　MSWI　process　variables　with　the　latent　structure　algorithm.　Three　latent　structure　algorithms,　namely,　linear　projection　to　latent　structure　(PLS),　nonlinear　kernel　PLS　(KPLS),　and　a　new　improved　general　algorithm-based　selective　ensemble　KPLS　(IGASENKPLS),　are　applied　to　build　the　DXN　estimation　model.　Results　show　that　the　latent　structure　algorithm　can　successfully　generate　DXN　models　with　good　prediction　performance.　Nonlinear　KPLS　can　extract　more　variations　from　the　dataset　than　linear　PLS,　but　IGASENKPLS　can　enhance　prediction　performance　even　further.　The　proposed　approach　demonstrates　the　feasibility　of　using　latent　structure　algorithm　to　model　DXN　emission　concentration　by　using　collinear,　nonlinear,　and　small-size　sampling　data.