Fresh and stale corn samples were distinguished using Raman spectroscopy coupled with discriminant analysis. A total of 75 corn samples of the Zhengdan 958 variety were collected in Henan Province. After grinding and sieving, the powdered samples were placed in special PVC bags. Raman spectra were directly measured through the sample bags using optical fiber; polynomial smoothing, baseline correction, and first derivative methods were conducted to process the raw spectra. The discrimination model was developed with principal component discriminant analysis coupled with Mahalanobis distance. The best result was achieved when nine principal components were used with a spectral range of 914~1369 cm-1. The correct classification rates in the calibration set and the prediction set were 92.7% and 90%, respectively. Subsequently, the partial least squares discriminant analysis method was used to develop the corresponding recognition model. The best result was achieved when the number of factors was seven and the full spectral range was used. The correct classification rates in the calibration set and the prediction set were 100% and 95%, respectively. The correct classification rates obtained from the partial least squares discriminant analysis method were higher, indicating that Raman spectroscopy could be used to discriminate between fresh and stale corn rapidly and showed great potential in the quality evaluation of stored grains.