Near　infrared　(NIR)　spectroscopy　has　the　characteristics　of　rapid　processing,　nondestructive　analysis　and　on-line　detection.　This　technique　has　been　widely　used　in　the　fields　of　quantitative　determination　and　substance　content　analysis.　However,　for　complex　NIR　spectral　data,　most　traditional　machine　learning　models　cannot　carry　out　effective　quantitative　analyses　(manifested　as　underfitting;　that　is,　the　training　effect　of　the　model　is　not　good).　Small　amounts　of　available　data　limit　the　performance　of　deep　learning-based　infrared　spectroscopy　methods,　while　the　traditional　threshold-based　feature　selection　methods　require　more　prior　knowledge.　To　address　the　above　problems,　this　paper　proposes　a　competitive　adaptive　reweighted　sampling　method　based　on　dual　band　transformation　(DWT-CARS).　DWT-CARS　includes　four　types　in　total:　CARS　based　on　integrated　two-dimensional　correlation　spectrum　(i2DCOS-CARS),　CARS　based　on　difference　coefficient　(DI-CARS),　CARS　based　on　ratio　coefficient　(RI-CARS)　and　CARS　based　on　normalized　difference　coefficient　(NDI-CARS).　We　conducted　comparative　experiments　on　three　datasets;　compared　to　traditional　machine　learning　methods,　our　method　achieved　good　results,　demonstrating　that　this　method　has　considerable　prospects　for　the　quantitative　analysis　of　near-infrared　spectroscopic　data.　To　further　improve　the　performance　and　stability　of　this　method,　we　combined　the　idea　of　integrated　modeling　and　constructed　a　partial　least　squares　model　based　on　Monte　Carlo　sampling　for　the　samples　obtained　by　CARS　(DWT-CARS-MC-PLS).　Through　comparative　experiments,　we　verified　that　the　integrated　model　could　further　enhance　the　accuracy　and　stability　of　the　results.