This paper innovatively proposes a new method for detecting mold-infected wheat by visible/near-infrared spectroscopy, and it was employed with a nanoscale colorimetric sensor as an intermediate medium to detect volatile organic compounds (VOCs). The mold-infected wheatwas stored under constant temperature and humidity conditions for different time duration to prepare experimental samples. The wheat after mold infection had different characteristic volatile gases, and visible/near-infrared spectroscopy was used to collect the spectrum information before and after the combination of volatile gas and nanosized colorimetric sensor respectively. The multi-variable analysis model was combined to predict the number of mold colonies of wheat. The Aspergillus glaucus and Aspergillus candidus were inoculated into sterile wheat to cultivate, and wheat samples were prepared by storing for 0～9 days. According to the pre-experimental study, colorimetric material 8-(4-nitrophenyl)-4,4-difluorobora-dipyrromemethane (NO2BDP) and 8-(4-nitrophenyl)-4,4-difluoro-6-bromoborodipyrrolethane (NO2BrBDP) sensitive to volatile gases of wheat was used. A nanosized sensor array was fabricated to detect these characteristic volatile gases. The experiment used the soap-free emulsification method to synthesize a nanoscaled microsphere polystyrene-acrylic acid, and it was used to couple NO2BDP and NO2BrBDP dyes for producing nanoscale colorimetric sensors with high specific sensitivity. The spectral information of each wheat sample with different mold amount was collected by visible/near-infrared technology, as well as pre-processed by multivariate analysis. The number of colonies were determined by plate colony counting method, and quantitative prediction models were respectively established for the total number of Aspergillus glaucus and Aspergillus candidus colonies.The experimental results showed that the Si-UVE-PLS model for predicting the total number of Aspergillus glaucus colonies is the best in the characteristic spectral collected by two nanosized sensor.And the square root of the cross-validation of the prediction set was 0.444 4 lgcfu; the correlation coefficient between the measured value and the predicted value was 0.981 1. The optimal model for detecting the total number of Aspergillus candidus) colonies was the Si-GA-PLS model with the spectral data collected by the sensor consisting of two nonnanosized and two nanosized sensors. The RMSECV of the prediction set was 0.554 5 lgcfu, and the Rp in the predicted set was 0.977 2. The research results showed that the combination of visible/near-infrared spectroscopy and nanoscaled sensor technology could well complete the quantitative detection of volatile gases and realize the quality monitoring of mildew wheat.