Infrared Spectroscopy Study Of Quinoa Adulteration

Quinoa has comprehensive nutritional value and high price,which might easily lead to its adulteration.Therefore,the development of a spectroscopic method for accurate and rapid identification of adulterated quinoa is of great significance to food security and public health security.In this thesis,the combination of infrared spectroscopy and multivariate statistical analysis methods is applied to four aspects:origin identification of quinoa,adulteration detection of quinoa flour,identification of freshness and identification of aflatoxin B₁pollution.The completed work is as follows:1.Combined three-step infrared spectroscopy(FTIR,SD-IR and 2D-IR)with principal component analysis(PCA)and partial least squares discriminant analysis(PLS-DA)to identify the origin of quinoa.Spectral analysis showed that quinoa from different origins had differences in the content and structure of carbohydrates and proteins.PCA analysis realized clustering of quinoa data from different origins,and the PLS-DA analysis has achieved 92.50%validation accuracy and 90.00%prediction accuracy on the quinoa origin classification results.PLS-DA prediction results was synthetically evaluated by confusion matrix,and the precision,recall and specificity of 94.18%,94.81%and 99.70%were achieved,respectively.The results show that three-step infrared spectroscopy combined with multivariate statistical analysis method can realize the origin identification of quinoa.2.Research on detection of adulterated quinoa flour by infrared spectroscopy was carried out.Combined ATR-FTIR spectral analysis with PCA analysis,discriminant analysis(DA),regression analysis(PLSR and PCR)to study the seven adulteration types of quinoa flour.The spectral analysis results showed that the increase in the proportion of adulterant would lead to the difference in the peak intensity of adulterated quinoa flour on the relevant characteristic peaks of the infrared spectra.PCA analysis could achieve approximate clustering of pure and adulterated quinoa flour sample,but its specific adulteration concentration category couldn't be qualitatively identified.The PCA-DA analysis was carried out with PCs as new variables imported into DA.The results showed that the accuracy of PCA-DA for adulterated quinoa flour detection all reached above 90.00%.PLSR analysis has better performance than PCR analysis,and can be used for quantitative analysis of adulteration concentration of quinoa flour.The detection results of the seven adulteration types by PCA-DA analysis and PLSR analysis were consistent,indicating that PCA-DA analysis combined with PLSR regression analysis can achieve qualitative identification of quinoa flour adulteration and quantitative analysis of adulteration concentration.3.A study on the identification of aged quinoa's freshness by IR spectroscopy was carried out.Quinoa samples were aged 0.5,1,1.5,2,2.5 and 3 years by farm storage.The spectral analysis result showed that the lipids and sugars of quinoa storage substances decreased first and then increased during the aging process,while the proteins kept decreasing all the time.The 2D-IR result showed that sugars were the change sensitive factors during the aging process of quinoa.The PCA analysis could roughly distinguish quinoa with different aging degrees,and the loading plot showed that the main discrimination was based on the differences of sugars.The SIMCA analysis showed that the quinoa samples aged more than 1.5 year were successfully identified and classified.The results show that the combination of spectral analysis and PCA analysis and SIMCA analysis can be used to study the change of quinoa storage substances during aging process and the identification of its freshness can be realized.4.Infrared spectroscopy identification of quinoa contaminated with aflatoxin B₁(AFB₁)was carried out.Three categories of quinoa samples with different levels of AFB₁contamination were set up,and pure,uncontaminated quinoa was set as a control group,these above sample groups was studied by spectral analysis combined with PCA,SVM and PLSR analysis.The spectral analysis showed that with the AFB₁contamination increased,the peaks related to protein and lipid in quinoa changed.PCA analysis could roughly cluster and distinguish quinoa with different contamination levels.The effective PC variables were screened by PCA analysis and imported into the SVM classification method as new variables for analysis.The PCA-SVM analysis achieved 98.91%classification accuracy,98.91%validation accuracy and 95.83%prediction accuracy,respectively.The PLSR regression analysis method obtained better R~2and RMSE values(R_c~2=0.9941,R_v~2=0.9892,R_p~2=0.9706;RMSEC=0.1023,RMSECV=0.1401,RMSEP=0.2367;RPD=4.1573).The results showed that infrared spectroscopy analysis combined with PCA analysis,SVM classification method and PLSR regression method could realize the identification of quinoa with different AFB₁contamination levels.The work of the thesis shows that infrared spectroscopy can be used for the quinoa identification study,which can provide a reference for quinoa quality and safety monitoring and detection methods.