[Objective] The aim of this study was to provide a basis for distinguishing quality of rhubarb in different production areas. [Method ] X-ray diffraction patterns of rhubarbs in different production areas of Qinghai w...[Objective] The aim of this study was to provide a basis for distinguishing quality of rhubarb in different production areas. [Method ] X-ray diffraction patterns of rhubarbs in different production areas of Qinghai were obtained by X-ray diffraction analysis, and then its similarity analysis was also investigated. [ Result] The content of chemical components in rhubarbs from different production areas had differences, but its diffraction patterns and diffraction peaks had certain fingerprint characteristics. [ Conclusion] X-ray diffraction method is a fast and effective method for identifying rhubarb and other Chinese herbal medicines in different production areas.展开更多
Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artifici...Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.展开更多
A simple and facile gas chromatography-mass spectrometer （GC-MS） fingerprint of Su-He-Xiang-Wan （SHXW） was developed, the similarity analysis was conducted, and attribution of the major characteristic peaks was id...A simple and facile gas chromatography-mass spectrometer （GC-MS） fingerprint of Su-He-Xiang-Wan （SHXW） was developed, the similarity analysis was conducted, and attribution of the major characteristic peaks was identified for SHXW quality control. GC-MS analysis was performed on a QP2010 instrument （Shimadzu, Japan） equipped with a capillary column of RTX-5MS. The column temperature was initiated at 50℃, held for 5 min, increased at the rate of 3 ℃/min to 120 ℃, held for 2 min, and then increased at the rate of 4 ℃/min to 220℃, held for 10 min. Helium carrier gas was used at a constant flow rate of 1.3 mL/min. Mass conditions were ionization voltage, 70 eV; injector temperature, 250℃; ion source temperature, 250 ℃; splitting ratio, 30：1; full scan mode in the 40-500 Da mass ranges with rate of 0.2 s per scan. Attribution of the major characteristic peaks was identified for SHXW by comparing the chemical standards, references of Chinese herbal medicines and the negative controls of prescription samples （NC） of SHXW. With the help of the temperature-programmed retention indices （PTRIs） used together with mass spectra and chemical standards, 25 major characteristic peaks have been identified. Nine volatile medicinal materials were identified in the prescription of SHXW by attributing to the 27 major characteristic peaks. The results demonstrate that the proposed method is a powerful approach to quality control of complex herbal medicines.展开更多
To evaluate the quality of Polyporus umbellatus,we established a simple,repeatable and reliable method based on high performance liquid chromatography(HPLC)for specific chromatogram and fingerprint analysis,which was ...To evaluate the quality of Polyporus umbellatus,we established a simple,repeatable and reliable method based on high performance liquid chromatography(HPLC)for specific chromatogram and fingerprint analysis,which was applied to analyze samples of medicinal materials and decoction pieces collected from different regions.Finally,ten characteristic peaks were designated in the specific chromatograms and applied to the authenticate identification of P.umbellatus samples.Nine common peaks were designated in the fingerprints,and then the similarities between 32 batches of samples were calculated.Among them,eight compounds were identified by HPLC-APCI-IT-TOF-MS^(n),four of which were identified in specific chromatograms and four in fingerprints.In the present study,we,for the first time,combined HPLC specific chromatograms and fingerprints for the species identification and quality evaluation of P.umbellatus.Collectively,our findings provided a new method for establishing a comprehensive quality standard of P.umbellatus.展开更多
Frequency-hopping(FH)is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception,good confidentiality,...Frequency-hopping(FH)is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception,good confidentiality,and strong antiinterference.However,non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition,since it not only is sensitive to noise but also has non-linear,non-Gaussian,and non-stability characteristics,which make it difficult to guarantee the classification in the original signal space.Some existing classifiers,such as the sparse representation classifier(SRC),generally use an individual representation rather than all the samples to classify the test data,which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples.To address these problems,we propose a novel classifier,called the kernel joint representation classifier(KJRC),for FH transmitter fingerprint feature recognition,by integrating kernel projection,collaborative feature representation,and classifier learning into a joint framework.Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.展开更多
文摘[Objective] The aim of this study was to provide a basis for distinguishing quality of rhubarb in different production areas. [Method ] X-ray diffraction patterns of rhubarbs in different production areas of Qinghai were obtained by X-ray diffraction analysis, and then its similarity analysis was also investigated. [ Result] The content of chemical components in rhubarbs from different production areas had differences, but its diffraction patterns and diffraction peaks had certain fingerprint characteristics. [ Conclusion] X-ray diffraction method is a fast and effective method for identifying rhubarb and other Chinese herbal medicines in different production areas.
基金supported by the NSFC (U1536206,61232016,U1405254,61373133, 61502242)BK20150925the PAPD fund
文摘Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.
基金Foundation item： Projects（21275164, 21075138） supported by the National Natural Science Foundation of China
文摘A simple and facile gas chromatography-mass spectrometer （GC-MS） fingerprint of Su-He-Xiang-Wan （SHXW） was developed, the similarity analysis was conducted, and attribution of the major characteristic peaks was identified for SHXW quality control. GC-MS analysis was performed on a QP2010 instrument （Shimadzu, Japan） equipped with a capillary column of RTX-5MS. The column temperature was initiated at 50℃, held for 5 min, increased at the rate of 3 ℃/min to 120 ℃, held for 2 min, and then increased at the rate of 4 ℃/min to 220℃, held for 10 min. Helium carrier gas was used at a constant flow rate of 1.3 mL/min. Mass conditions were ionization voltage, 70 eV; injector temperature, 250℃; ion source temperature, 250 ℃; splitting ratio, 30：1; full scan mode in the 40-500 Da mass ranges with rate of 0.2 s per scan. Attribution of the major characteristic peaks was identified for SHXW by comparing the chemical standards, references of Chinese herbal medicines and the negative controls of prescription samples （NC） of SHXW. With the help of the temperature-programmed retention indices （PTRIs） used together with mass spectra and chemical standards, 25 major characteristic peaks have been identified. Nine volatile medicinal materials were identified in the prescription of SHXW by attributing to the 27 major characteristic peaks. The results demonstrate that the proposed method is a powerful approach to quality control of complex herbal medicines.
基金National Project for Standardization of Chinese Material Medica(Grant No.ZYBZH-Y-GD-13)。
文摘To evaluate the quality of Polyporus umbellatus,we established a simple,repeatable and reliable method based on high performance liquid chromatography(HPLC)for specific chromatogram and fingerprint analysis,which was applied to analyze samples of medicinal materials and decoction pieces collected from different regions.Finally,ten characteristic peaks were designated in the specific chromatograms and applied to the authenticate identification of P.umbellatus samples.Nine common peaks were designated in the fingerprints,and then the similarities between 32 batches of samples were calculated.Among them,eight compounds were identified by HPLC-APCI-IT-TOF-MS^(n),four of which were identified in specific chromatograms and four in fingerprints.In the present study,we,for the first time,combined HPLC specific chromatograms and fingerprints for the species identification and quality evaluation of P.umbellatus.Collectively,our findings provided a new method for establishing a comprehensive quality standard of P.umbellatus.
基金Project supported by the National Natural Science Foundation of China(No.61601500)
文摘Frequency-hopping(FH)is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception,good confidentiality,and strong antiinterference.However,non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition,since it not only is sensitive to noise but also has non-linear,non-Gaussian,and non-stability characteristics,which make it difficult to guarantee the classification in the original signal space.Some existing classifiers,such as the sparse representation classifier(SRC),generally use an individual representation rather than all the samples to classify the test data,which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples.To address these problems,we propose a novel classifier,called the kernel joint representation classifier(KJRC),for FH transmitter fingerprint feature recognition,by integrating kernel projection,collaborative feature representation,and classifier learning into a joint framework.Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.
