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Grain storage pests can reduce the weight, quality and nutritional health index of grain and its products, and the way of grain pest detection in China is still dominated by manual detection. To meet the needs of modern detection of grain storage pests, gas chromatography-mass spectrometry (GC-MS) was used to obtain the main specific volatile organic compounds (VOCs) of Tribolium castaneum (Herbst), screened multiple metal oxide gas sensors with the obtained compounds as reference. Then the air chamber for sensor response was optimally designed and an electronic nose detection device for grain storage pests was developed based on the composed sensor array. The device collected odor information from three experimental subjects, including T.castaneum, the flour during infestation of T.castaneum and the flour during infestation of Latheticus oryzae Waterhouse. Relative change values and relative integration values of the response curve corresponding to each sensor as the original feature matrix (10×2). Principal component analysis (PCA) and partial least squares regression algorithm (PLSR) were used to optimize the original feature matrix. Finally, a predictive regression model was built to forecast the population density of T.castaneum and Latheticus oryzae Waterhouse in flour. Two GC-MS studies were carried out for the purpose to collect 12 distinct volatile chemical compounds of T.castaneum that were not found in other grain insects or stored grains. The number of sensors was reduced from 10 to 8, and the contribution of the two principal components of the T.castaneum samples was increased to 79.4%. The odor of the flour itself would be a great interference to the electronic nose detection of T.castaneum, and under the condition of no flour interference, the electronic nose device can discriminate between samples with varying insect population densities. The PLSR-based prediction model was highly effective in predicting the number of T.castaneum in flour (correction set: correlation coefficient r=0.88, root mean square error (RMSE) was 8.09;validation set: correlation coefficient r=0.89,RMSE was 7.75);the prediction model was also highly effective in predicting the number of Latheticus oryzae Waterhouse in flour (correction set: correlation coefficient r=0.94, RMSE was 5.85;validation set: r=0.94, RMSE was 6.08). The research results indicated that the device can meet the needs of distinguishing samples with different insect densities in stored grains and had reliable stability. This method also provided a method reference for detecting other pests in stored grain.