Rapid detection of ash content in flour was built based on the near infrared spectroscopy technology. The near infrared spectral data of flour was collected by two different near-infrared spectrometers; conventionally measured value as model data, a quantitative analysis model of flour ash content was established by partial least square regression analysis; the impact of near infrared spectrograms of flour scanned by two different near-infrared spectrometers on the model were compared. The results showed that the correlation coefficient of calibration set model of MicroNIR-1700 scanning near-infrared spectrometer was 90.69, with the root mean square error RMSECV 0.0312; the root mean square error of prediction set model RMSEP was 0.0217; the correlation coefficient of calibration set model of VERTEX70 Fourier transform near infrared spectrometer was 89.40, with the root mean square error RMSECV 0.0350; the root mean square error of prediction set model RMSEP was 0.0366. Both instruments can be used for collecting flour spectrograms, and rapid detection of ash content, while MicroNIR-1700 had good application in flour ash detection.