Detection Of Characteristic Volatile Compounds From Mildew Wheat Based On The Nanoscaled Colorimetric Sensor Technology

As a grain,wheat reserve is the first in China.Wheat is rich and balanced in nutrients,and it is ground to the flour that can be processed into a variety of delicious foods.Therefore,wheat?flour?is very popular among people.Above all,the quality of wheat is closely related to human health.However,wheat has poor resistance to mildew during storage and transportation,and it is easy to cause the germination and mildew of wheat under mild storage conditions.At present,the detection of the degree of mildew in wheat mainly relies on sensory evaluation methods and instrumental analysis.Each of them has the disadvantages of low accuracy and high cost,and cannot form a set of identification criteria that can quantitatively analyze the degree of mildew in wheat.In summary,it is urgent to establish a rapid and accurate identification method for the degree of wheat mildew.In this study,a novel nanoscaled chemo dyes based on sensor was prepared by nanometering the selected colorimetric materials sensitive to wheat mildew gas,and the visible/near-infrared spectral information of the wheat Inoculated with different bacteria?Aspergillus glaucus and Aspergillus candidus?was collected based on the cross-colorimetric sensor.The spectral information is combined with a variety of multivariate analysis methods to screen and analyze the spectral variables to achieve quantitative analysis of moldy wheat.The main research contents are as follows:?1?Screening analysis of characteristic volatile gas during moldy wheat.SPME-GC-MS has been used to analyze the changes of volatile components of wheat with different mildew degree?fresh wheat,moldy wheat stored for 3d,5d,7d and 11d?.The results showed that 55 kinds of volatile components were detected from five different degrees of mildew?different storage time?.There were significant differences in volatile gases in wheat during different storage periods.The contents of four substances including n-hexane,3-octanone,1-octene-3-ol and 2-methylbutanal were compared and it is found that the change trend of 3-octanone and 1-octene-3-ol is obviously increasing and stable.Therefore,3-octanone and 1-octene-3-ol can become important index for the change of volatile substances in wheat during storage,and be used as characteristic components in wheat mildew process.In the experiment,the olfactory visualization system was used to screen the colorimetric dyes sensitive to the volatile gas 1-octene-3-ol and 3-octanone among these colorimetric materials including fluoroboronoids and porphyrins.And the selected materials were fabricated into a colorimetric sensor to complete the discrimination of mildewed wheat.The experimental results showed that NO₂BDP has a large response value to 1-octene-3-ol,and NO₂BrBDP has a good discriminating ability for 3-octanone.?2?Modification of the colorimetric sensor.The experiment used the soap-free emulsification method to synthesize a nanoscaled microsphere polystyrene-acrylic acid and the nanopolymer was nacoscaling materials NO₂BDP and NO₂BrBDP to produce nano-sensors with high specific sensitivity.The sensor is optimized by changing the mass ratio of the nanopolymer and the colorimetric materials,the size of the particle size,the choice of the surfactant,etc.,and is combined with the non-nano colorimetric materials NO₂BDP and NO₂BrBDP to form a cross sensor.It established models for different concentrations of mildew gas 1-octene-3-ol and 3-octanone.Compared the model results,the correlation between the color component difference and the concentration of the mold gas is analyzed.The results show that the nanosensor NO₂BDP shows better potential in detecting the moldy gas of 1-octene-3-ol in wheat,especially in the B component,the correlation is R²=0.8078 and RMSE=3.05g/L between the B component and the 1-octene-3-ol concentration.The correlation coefficient R² between the G component and the 3-octanone concentration obtained by the nano-doped NO₂BrBDP sensor reached 0.8324,and RMSE=1.65 g/L.?3?Mildewed wheat detection based on nanoscaled colorimeric sensor technology.The fluoroboronazole compounds NO₂BDP and NO₂BrBDP sensitive to the wheat mildew marker 1-octene-3-ol and 3-octanone were selected as materials for normal colorimetric sensor,and the two colorimetric materials NO₂BDP and NO₂BrBDP were optimized by nano-polymer PSA to fabricate a nanoscaled sensor.KNN recognition model and LDA recognition model were established by combining principal component analysis.The results show that when K is 3 and the number of principal components is9,the LDA model established by normal colorimetric sensors has the best recognition effect.The recognition rates of cablibration set and prediction set are 90%and 83.33%respectively.When the number of principal components is 5,the prediction result of the LDA model is the best,the recognition rate of the calibration set and the recognition rate of the prediction set is 100%.?4?Quantitative analysis of the number of moldy wheat colonies based on nanoscaled colorimetric spectroscopy.Experiments were carried out to determine the target strains of wheat infected with Aspergillus glaucus and Aspergillus candidus.The two kinds of mold were inoculated into sterile wheat to cultivate wheat samples,and the number of colonies were determined by plate colony counting method.Then,the sensor system was constructed with four colorimetric materials:NO₂BrBDP,NO₂BDP,nano-NO₂BrBDP and nano-NO₂BDP.The spectral information of each wheat sample with different mold amount was collected by visible/near-infrared technology,and the spectral information was pre-processed by multivariate analysis.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 band of wheat collected by the nano-NO₂BrBDP+nano-NO₂BDP sensor.And the square root of the cross-validation of the calibration set is 0.4236 lgcfu,and the prediction set was 0.4444 lgcfu;the correlation coefficient between the measured value and the predicted value in the training set is0.9783,and the prediction set is 0.9811.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 nano-NO₂BrBDP+nano-NO₂BDP sensor.The RMSECV of the calibration set was 0.4349 lgcfu,and the RMSEP of the prediction set was 0.5545 lgcfu,the Rc in the calibration set was 0.9801,and the Rp in the predicted set was 0.9772.