A high performance liquid chromatography-ultraviolet(HPLC-UV)fingerprint method for the overall chemical analysis of edible mushrooms was established based on Auricularia heimuer for the first time,and then applied to...A high performance liquid chromatography-ultraviolet(HPLC-UV)fingerprint method for the overall chemical analysis of edible mushrooms was established based on Auricularia heimuer for the first time,and then applied to analyze 60 batches of A.heimuer,Auricularia cornea.Auricularia cornea*Yu Muer’and TremeUa fuciformis.A total of 9 characteristic peaks of A.heimuer.11 characteristic peaks of A.cornea,6 characteristic peaks of A.cornea‘Yu Muer’,and 9 characteristic peaks of I fuciformis were designated.Then,a combinatory analysis,including similarity evaluation,hierarchical cluster analysis and principal component analysis,revealed the chemical consistency and difference between samples from the same and different species.The HPLC fingerprint method established in this paper could be used to characterize the components of A.heimuer,A.cornea,A.cornea‘Yu Muer’,and T.fuciformis and discriminate the 4 edible mushrooms effectively in combination with pattern recognition analysis.展开更多
This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition ...This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.展开更多
基金This publication was financially supported by the National Key R&D Program of China(2018YFD0400202 and 2018YFD0400200).
文摘A high performance liquid chromatography-ultraviolet(HPLC-UV)fingerprint method for the overall chemical analysis of edible mushrooms was established based on Auricularia heimuer for the first time,and then applied to analyze 60 batches of A.heimuer,Auricularia cornea.Auricularia cornea*Yu Muer’and TremeUa fuciformis.A total of 9 characteristic peaks of A.heimuer.11 characteristic peaks of A.cornea,6 characteristic peaks of A.cornea‘Yu Muer’,and 9 characteristic peaks of I fuciformis were designated.Then,a combinatory analysis,including similarity evaluation,hierarchical cluster analysis and principal component analysis,revealed the chemical consistency and difference between samples from the same and different species.The HPLC fingerprint method established in this paper could be used to characterize the components of A.heimuer,A.cornea,A.cornea‘Yu Muer’,and T.fuciformis and discriminate the 4 edible mushrooms effectively in combination with pattern recognition analysis.
基金supported by the National Natural Science Foundation of China(61101179)
文摘This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.
