Currently, the traditional measuring methods ofgrain quality are mainly the traditional separation and manual inspection, which take a long time and have low efficiency. Near Infrared (NIR, 780~2 500 nm) spectral analysis technology has the advantages of a wide range of applicable samples, high accuracy of quantitative measurement, high measurement efficiency, and non-destructive testing, which is widely used in agriculture online or rapid measurement. Currently, the existing NIR instruments measuring grain quality are expensive, which prevents a wider application of this kind of device. Moreover, the predicting model is limited in applicability due to the differences ingrains in different seasons and regions. To solve these problems, in this study, a new type of NIR spectrometer system is developed to measure wheat quality. The system uses a control system developed with Python. By setting and modifying the acquisition parameters, the three steering gears and weight sensors are integrated to control the spectra data acquisition. The spectral data are preprocessed and substituted into the model to calculate the quality parameters of the target wheat samples. The principal component analysis (PCA) method removes the outlier’s spectral data. Then, the selected spectral data are preprocessed by recursive mean filtering and standard normal transformation (SNV). Finally, the optimized model is obtained with the partial least squares regression (PLS) method after competitive adaptive reweighting sampling (CARS) wavelength selection. The prediction model is currently developed for moisture, wet gluten, and whiteness of wheat. The results show that this model can effectively reduce the error caused by stray light, sample uniformity, and other effective factors. The developed NIR spectrometer system can satisfy the requirements of grain acquisition and storage.