Plant protein beverage adulteration occurs frequently,which may cause health problems for consumers due to the hidden allergens.Hence,a novel method was developed for authentication by ultra-performance liquid chromat...Plant protein beverage adulteration occurs frequently,which may cause health problems for consumers due to the hidden allergens.Hence,a novel method was developed for authentication by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).Almond,peanut,walnut and soybean were hydrolyzed,followed by separation by NanoLC-Triple TOF MS.The obtained fingerprints were identified by ProteinPilotTM combined with Uniprot,and 16 signature peptides were selected.Afterwards,plant protein beverages treated by trypsin hydrolysis were analyzed with UPLC-MS/MS.This method showed a good linear relationship with R2>0.99403.The limit of quantification(LOQ)were 0.015,0.01,0.5 and 0.05 g/L for almond,peanut,walnut and soybean,respectively.Mean recoveries ranged from 84.77%to 110.44%with RSDs<15%.The developed method was successfully applied to the adulteration detection of 31 plant protein beverages to reveal adulteration and false labeling.Conclusively,this method could provide technical support for authentication of plant protein beverages to protect the rights and health of consumers.展开更多
Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and ...Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and employed to classify 4 types of edible oils and castor oil with multivariate statistical methods. The results indicated that fatty acid profiles of edible oils could be used to classify the 5 kinds of oils. Meanwhile, simulated data test indicated that fatty acid profiles could be used to detect adultera-ted by 5% . Finally, a RF model was built to detect adulteration of edible oils with castor oils by fatty acid composition. The results from cross validation indicated that the oils adulterated by castor oil at low levels （5% 7V/V） could be completely separated from 4 kinds of edible oils. Therefore this model could be used to detect adulteration of 4 kinds of edible oil with castor oils.展开更多
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 High-level Talent Funding Project of Hebei Province(A202005015)Youth Top Talent Support Plan of Hebei Province.
文摘Plant protein beverage adulteration occurs frequently,which may cause health problems for consumers due to the hidden allergens.Hence,a novel method was developed for authentication by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).Almond,peanut,walnut and soybean were hydrolyzed,followed by separation by NanoLC-Triple TOF MS.The obtained fingerprints were identified by ProteinPilotTM combined with Uniprot,and 16 signature peptides were selected.Afterwards,plant protein beverages treated by trypsin hydrolysis were analyzed with UPLC-MS/MS.This method showed a good linear relationship with R2>0.99403.The limit of quantification(LOQ)were 0.015,0.01,0.5 and 0.05 g/L for almond,peanut,walnut and soybean,respectively.Mean recoveries ranged from 84.77%to 110.44%with RSDs<15%.The developed method was successfully applied to the adulteration detection of 31 plant protein beverages to reveal adulteration and false labeling.Conclusively,this method could provide technical support for authentication of plant protein beverages to protect the rights and health of consumers.
基金This work was supported by the Project of National Science ＆ Technology Pillar Plan （2012BAK08B03 ）；the National Major Project for Agro - product Quality ＆ Safety Risk Assessment （ GJFP2016006）；the National Natural Science Foundation of China （21205118 ）；the earmarked fund for China Agriculture research system （ CARS - 13 ）.
文摘Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and employed to classify 4 types of edible oils and castor oil with multivariate statistical methods. The results indicated that fatty acid profiles of edible oils could be used to classify the 5 kinds of oils. Meanwhile, simulated data test indicated that fatty acid profiles could be used to detect adultera-ted by 5% . Finally, a RF model was built to detect adulteration of edible oils with castor oils by fatty acid composition. The results from cross validation indicated that the oils adulterated by castor oil at low levels （5% 7V/V） could be completely separated from 4 kinds of edible oils. Therefore this model could be used to detect adulteration of 4 kinds of edible oil with castor oils.
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.
