Compared to conventional imaging spectrometry, computational imaging spectrometry has the advantages of high throughput snapshot imaging etc. But because of the presence of the dispersive element, computational imaging spectrometry suffers the effect of spectral line bending. To study the effect of spectral line bending on signal acquisition, spatialspectral aliasing and reconstructed result in computational imaging spectrometry, combined with the principle of computational imaging spectrometry and reconstruction algorithm, the relative peak signaltonoise ratio of the reconstructed image and the maximal error of the reconstructed spectral curve with different spectral offset were calculated and analyzed. The experimental result showed that spectral offset of the signal acquired by the detector will change the degree of spatialspectral aliasing. The reconstructed results with spectral line bending exhibit distinct errors compared with no spectral line bending. And both sides of the reconstructed spectral curve tend to smooth. In order to reconstruct the object scene with high accuracy, spectral offset should be no more than half a pixel for computational imaging spectrometry with 10 nm resolution.