This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints.T...This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints.The goal of this study was to provide a reliable tool for assessing the quality of sesame oil.The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200-330 nm.Various classification and prediction methods,including linear discrimination analysis(LDA),support vector machines(SVM),soft independent modeling of class analogy(SIMCA),partial least squares regression(PLSR),support vector machine regression(SVR),and back-propagation neural network(BPNN),were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil.The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil,achieving an accuracy of 96.15%,a sensitivity of 97.87%,and a specificity of 80%.For quantitative analysis,BPNN yielded the best prediction results,with an R^(2) value of 0.99,RMSEP of 2.34%,and RPD value of 10.60(LOD of 8.60%and LOQ of 28.67%).Overall,the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.展开更多
基金supported by the project number of“China Agricultural Research System funded by the Ministry of Agriculture”CARS-14,the Key Project of Science and Technology of Henan Province (201300110600)the“Double First-Class”Project for Postgraduate Academic Innovation Enhancement Programme of Henan University of Technology (HAUTSYL2023TS16)Education and Teaching Reform Research and Practice Project in School of International Education,Henan University of Technology (GJXY202407).
文摘This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints.The goal of this study was to provide a reliable tool for assessing the quality of sesame oil.The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200-330 nm.Various classification and prediction methods,including linear discrimination analysis(LDA),support vector machines(SVM),soft independent modeling of class analogy(SIMCA),partial least squares regression(PLSR),support vector machine regression(SVR),and back-propagation neural network(BPNN),were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil.The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil,achieving an accuracy of 96.15%,a sensitivity of 97.87%,and a specificity of 80%.For quantitative analysis,BPNN yielded the best prediction results,with an R^(2) value of 0.99,RMSEP of 2.34%,and RPD value of 10.60(LOD of 8.60%and LOQ of 28.67%).Overall,the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.
