Study On Mechanism And System Of Rice Origin Identification Based On Raman Spectroscopy

China is a big country of rice production and consumption.Rice cultivation is closely related to the factors of producing area.The quantity of rice in a specific area is small and of high quality.In order to protect the geographical characteristics of rice and protect the rights and interests of consumers,effective technical means and methods are urgently needed to study the authenticity of rice origin.This study four different origin,the same kinds of rice in Heilongjiang province as the research object,the original spectrum of rice was collected by Raman spectroscopy using the original spectrum and combining different pretreatment methods to baseline correction and remove the original spectrum.Using SPA,CARS and PCA feature extraction methods,the characteristic wavelengths of rice producing areas in whole band and part band were selected as the input values of the model.The rice origin identification model based on PLS and BP neural network was established,and a rice origin identification system based on BP neural network was developed.The main research contents and conclusions are as follows:（1）In this paper,the functional group Raman spectroscopy analysis of starch,protein and fatty acid in rice was explored,and the mechanism of origin factors on"origin fingerprint characteristics"of Raman spectrum was expounded.Results show that by topography and geomorphology,the rice production factors such as temperature,moisture,illumination,soil analysis illustrates the different origin of the differences in composition of rice.Raman spectroscopy was used to analyze the functional groups of starch,protein and fatty acid in single grain rice.The properties of Raman spectrum peaks of rice were obtained as starch,protein and fat,and the manifestations were stretching and bending vibrations of corresponding molecular groups.（2）In order to solve the problem of baseline drift of Raman original spectrum in rice producing area,an improved piecewise fitting+baseline removal pretreatment method was proposed,which obtained the best result from the comparison of evaluation parameters and recognition accuracy.The results show that the best experimental parameters of Raman spectrum are as follows:integrating time is set to 4s,laser intensity is set to high,scanning times is set to10,scanning position is set to the middle part of rice.Under the parameters for the comparison of pretreatment methods,six different regions including first derivative+translation smoothing,second derivative+translation smoothing,wavelet transform+baseline removal,traditional polynomial fitting+baseline removal,improved piecewise fitting+baseline removal.Finally from the evaluation parameters and correct recognition rate comparison analysis found that the improved piecewise fitting+baseline removal result is the best.（3）Aiming at the problems of overlapping and redundancy of spectral data in rice producing area model and not being able to highlight the characteristic wavelength of producing area,taking Longjing 31 rice from Suihua,Daqing,Jiamasi and Qiqihar of Heilongjiang Province as the research object,SPA,CARS and PCA algorithms were used to extract the characteristic wavelength of rice spectrum,and the extracted characteristic wavelengths were compared with those of the main nutrients in rice.The SPA method was used to extract 46wavelength points and 11 wavelength points in the whole band 200-3400cm^-1 range and 46wavelength points and 17 wavelength points in the partial band 400-1600cm^-1 range by setting the number of variables.The CARS method was used to extract 100 wavelength points in200-3400cm^-1 and 39 wavelength points in 400-1600cm^-1.PCA method was used to extract 8wavelength points in the band range of 400-1600cm^-1 and 2800-3400cm^-1 by threshold setting method.The results showed that the wavelength points extracted by different feature methods in the band range of 400-1600cm^-1&2800-3400cm^-1 could fall on the peak of rice feature or the ascending and descending segment of left and right shoulder,but the wavelength points extracted in the band range of 200-400cm^-1&1600-2800cm^-1 were spectral noise or machine noise.PCA method was used to extract the characteristic wavelength of rice from 400-1600cm^-1 to2800-3400cm^-1,threshold method was used to extract the characteristic spectrum peak of rice producing area,and the corresponding load coefficients of the four principal components PC1,PC2,PC3 and PC4 were extracted according to the order of peak value.There are 20 wavelength points corresponding to the characteristic spectrum peaks that meet the conditions.Finally,a total of 8 characteristic wavelengths of 476,867,940,1121,1342,1384,1462 and 2914cm^-1were selected,and the main corresponding rice components were starch Raman characteristic spectrum peaks,which were consistent with the analysis of characteristic spectrum peaks of main functional groups in rice.（4）Using SPA,CARS and PCA methods to extract characteristic wavelengths as the input of the rice origin identification model,and the rice origin as the output,the rice origin identification model based on PLS and BP neural network method was established,and the validity of the model was verified in the target origin and single origin,target origin and mixed origin.The results showed that the best PLS model was 100 points of CARS,and the accuracy rate was96.9%.BP model test set has the best effect on 46 points in some bands of SPA model,and its accuracy is 99.23%.In the model of target and mixed origin,PLS model is the best with 100points of CARS method,and its accuracy rate is 95.5%.BP model test set has the best effect on46 points of partial band SPA,and its accuracy rate is 99.23%.From the above analysis,the recognition accuracy of BP model is generally higher than that of PLS model,and the input effect of 46 points in SPA band in BP model is the best,which is selected as the model input of origin identification system database.（5）An on-line rice origin identification system based on BP neural network was constructed to solve the problem of rapid non-destructive detection of rice origin by Raman spectroscopy.In the field acquisition of Raman spectrum,the fast prediction of rice target producing area was realized through the design of real-time acquisition program,database and calculation method of Raman spectrum.The results showed that the rice Raman spectrum of Wuchang and other producing areas was tested by this device,and the identification accuracy of 148 rice spectra was91.29%by using the feature modeling of 46 producing areas.It shows that the rice origin identification system is accurate and quick to operate,and can be used as a scientific research personnel and agricultural production units to carry out rice origin identification.