Fingerprint Patterns in Students with Learning Disability:Evidence of Sexual Dimorphism and Potential for Diagnosis in a Nigerian Population

This study assessed the sex-based relationship and prediction pattern between fingerprint patterns,ridge counts,and learning disability(LD).This cross-sectional study recruited 300 students(150 LD and 150 non-LD)aged between 3 and 29 years.The fingerprint patterns(arch,whorl,ulnar loop,and radial loop)and the ridge count:total finger ridge count(TFRC),absolute ridge count(ARC),ulnar ridge count(URC),and radial ridge count(RRC)were accessed.Students with LD showed a significantly higher whorl and a significantly lower ulnar loop than students without LD.There is a significant association of whorl pattern in the first right finger of subjects with LD compared to non-LD counterparts.TFRC,ARC,and URC were significantly higher in females with LD than non-LD females(P=0.01,0.03,and 0.001).Males with LD showed significantly lower TFRC,RRC,and URC counts than the non-LD males(P=0.02,0.01,and 0.001).TFRC can predict LD in males(odds ratio[OR]=1.010,P=0.032)and females(OR=0.993,P=0.012).Fingerprint pattern and ridge counts are sexually dimorphic in subjects with or without LD.TFRC and whorl fingerprint patterns may be vital predictive and screening tools for LD in males and females.

Authentication and distinction of Shenmai injection with HPLC fingerprint analysis assisted by pattern recognition techniques

In this paper, the feasibility and advantages of employing high performance liquid chromatographic （HPLC） fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were investigated and demonstrated. The Similarity Evaluation System was employed to evaluate the similarities of samples of Shenmai injection, and the HPLC generated chromatographic data were analyzed using hierarchical clustering analysis （HCA） and soft independent modeling of class analogy （SIMCA）. Consistent results were obtained to show that the authentic samples and the blended samples were successfully classified by SIMCA, which could be applied to accurate discrimination and quality control of Shenmai injection. Furthermore, samples could also be grouped in accordance with manufacturers. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of Shenmai injection.

Investigation and Application of Automatic Fingerprint Identification Based on Fuzzy Pattern Recognition

Fingerprint image is a typical non-restraint image that has some uncertainty, which makes it difficult to perform identification using classical approach. Therefore, fuzzy pattern recognition is applied to match individual query by searching the entire template database. The fuzzy maximum subordinate principle is used to solve shift matching. Through experimenting and analyzing, the approximate principle fuzzy method is employed by selecting fuzzy characteristics and determining the similarity function to achieve the further accuracy. Theoretical and experimental results show this approach is effective and reasonable.

Research on Intelligent Identification of PD Patterns Based on the Fingerprint Features

Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J4 value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J5 value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.

Development and Distinction of <i>Rhizoma rodgersiae</i>with HPLC-DAD Fingerprint Analysis Combined with Chemical Pattern Recognition Techniques

In this paper, the feasibility and advantages of employing high performance liquid chromatographic-photodiode array detection (HPLC-DAD) fingerprint combined with chemical pattern recognition for quality consistency evaluation of widely used Rhizoma rodgersiae (RR) were investigated and demonstrated for the first time. The Similarity Evaluation System was employed to evaluate the similarities of 10 batches of RR sample;moreover, hierarchical clustering analysis (HCA) and principal component analysis (PCA) were also successfully applied to discriminate RR samples of different regions and seasons. Our results indicated that the seasonal variation had some influence on the chemical fingerprints of this herbal drug. This approach allowed the discrimination of RR samples from different sources. The current study demonstrated that fingerprint profiling coupled with chemical pattern recognition offered a reliable and efficient way to comprehensively assess the quality consistency of the tested samples.

Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.

HPLC指纹图谱结合多成分含量测定评价香附质量

目的建立香附药材的高效液相色谱(High performance liquid chromatography,HPLC)指纹图谱,结合4种化学成分含量对不同产地及用药规格香附的质量进行综合评价。方法采集HPLC指纹图谱,并结合聚类分析(Cluster analysis,CA)和主成分分析(Principal component analysis,CA)等多种化学计量学方法对不同产地及用药规格的香附药材进行区分比较。利用HPLC法测定香附药材中香附烯酮、α-香附酮、木犀草素和阿魏酸的含量。结果CA和PCA分析结果显示,不同产地香附均可单独聚为一类,而浙江金华与其他产地香附差异最大,河南尉氏香附综合得分最高,质量较优;正交偏最小二乘法判别分析(Orthogonal partial least square-discriminant,OPLS-DA)结果显示,指纹图谱中13号峰(α-香附酮)、10号峰(香附烯酮)和14号峰是不同产地间香附质量产生差异的主要成分。广东湛江和浙江金华香附中香附烯酮与α-香附酮含量较为接近,香附烯酮含量远大于河南香附,α-香附酮含量远小于河南香附;不同产地香附中阿魏酸和木犀草素含量差异不大。去皮后香附中香附烯酮、α-香附酮和木犀草素含量降低,阿魏酸含量增加。结论不同产地间香附质量存在差异,以河南香附质量最佳。去皮前后香附化学成分相似,无明显种类差异。

Bohai crude oil identification by gas chromatogram fingerprinting quantitative analysis coupled with cluster analysis

By gas chromatogram, six crude oils fingerprinting distributed in four oilfields and four oil platforms were analyzed and the corre- sponding normal paraffin hydrocarbon （ including pristane and phytane） concentration was obtained by the internal standard methed. The normal paraffin hydrocarbon distribution patterns of six crude oils were built and compared. The cluster analysis on the normal paraffin hydrocarbon concentration was conducted for classification and some ratios of oils were used for oils comparison. The results indicated： there was a clear difference within different crude oils in different oil fields and a small difference between the crude oils in the same oil platform. The normal paraffin hydrocarbon distribution pattern and ratios, as well as the cluster analysis on the nomad paraffin hydrocarbon concentration can have a better differentiation result for the crude oils with small difference than the original gas chromatogram.

K-Means Graph Database Clustering and Matching for Fingerprint Recognition

The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.

清肝润肺止咳露高效液相色谱特征图谱、含量测定及化学模式识别的研究

目的建立清肝润肺止咳露高效液相色谱(HPLC)特征图谱,测定其中吗啡、可待因、罂粟碱、那可丁的含量,并结合化学模式识别方法,综合评价该制剂的质量。方法采用HPLC法,以甲醇-5μmoL·L^(-1)庚烷硫酸钠溶液(用磷酸调节pH至2.6)为流动相进行梯度洗脱,流速:1.0 mL·min^(-1),柱温:20℃,检测波长:220 nm,进样量:10μL。利用指纹图谱相似度评价软件,结合聚类分析(CA)、主成分分析(PCA)和偏最小二乘法判别分析(PLS-DA)对10批清肝润肺止咳露的特征图谱进行评价。结果10批清肝润肺止咳露特征图谱共确定16个共有峰,指认了5个共有峰,分别为吗啡、可待因、蒂巴因、罂粟碱、那可丁。其中4个成分在线性范围内(吗啡0.70~8.70μg、可待因0.68~8.50μg、罂粟碱0.60~7.55μg、那可丁0.38~4.80μg)均有良好的线性关系(r≥0.9997);精密度、稳定性、重复性试验的RSD均小于3.0%;平均加样回收率在97.93%~99.43%之间(RSD<3.1%);含量测定结果:吗啡43.58~76.47μg·mL^(-1),可待因16.64~78.92μg·mL^(-1),罂粟碱29.93~41.22μg·mL^(-1),那可丁14.44~33.22μg·mL^(-1)。通过CA、PCA和PLS-DA分析,可将10批清肝润肺止咳露分为2类(S3、S4、S8、S10聚为一类,其余聚为一类),并筛选出峰2、峰4、峰13、峰11(吗啡)、峰1、峰10、峰8、峰12、峰16(那可丁)9个差异性成分。结论该研究建立的HPLC特征图谱及含量测定方法简单、准确,结合化学模式识别法,可用于清肝润肺止咳露的质量控制。

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. In this work, after discussing existing techniques, we propose a new algorithm to reduce the false rejection rate during the authentication-using fingerprint. This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established;then, make the correspondence between two templates by simple probabilities calculations from a deep neural network. The merging of these operations provides very promising results both on the NIST4 international data reference and on the SOCFing database.

高效液相色谱指纹图谱结合一测多评法评价诃子药材质量

目的评价诃子药材的质量。方法建立诃子药材的高效液相色谱指纹图谱,采用相似度计算、聚类分析和主成分分析法对其质量进行评价。以柯里拉京为内参物,建立测定诃子药材中莽草酸、没食子酸、没食子酸甲酯、没食子酸乙酯、诃黎勒酸、鞣花酸、诃子酸含量的一测多评法,并将计算结果与外标法测定结果进行比较。结果18批诃子药材的指纹图谱有23个共有峰,指认出2号峰为莽草酸、7号峰为没食子酸、11号峰为没食子酸甲酯、14号峰为柯里拉京、15号峰为没食子酸乙酯、16号峰为诃黎勒酸、20号峰为鞣花酸、22号峰为诃子酸,相似度均大于0.95。聚类分析结果显示,18批诃子药材聚为2类,其中产地为青海的2批药材聚为一类,其余药材聚为一类。主成分分析结果显示,主成分特征值大于1的5个成分累积方差贡献率为84.088%。采用一测多评法计算的18批诃子药材中莽草酸、没食子酸、没食子酸甲酯、没食子酸乙酯、诃黎勒酸、鞣花酸、诃子酸的含量与外标法测定结果无显著差异(P>0.05)。结论高效液相色谱指纹图谱结合一测多评法可有效评价诃子药材的质量。

不同产地巴戟天HPLC指纹图谱及化学模式识别研究

目的:利用HPLC指纹图谱及化学模式识别对巴戟天药材进行评价。方法:采用Shodex Asahipak NH2P-504E色谱柱(4.6 mm×250 mm,5μm),以乙腈-水为流动相梯度洗脱,使用蒸发光散射检测器(ELSD)。利用相似度评价、聚类分析和主成分分析等化学模式识别方法,对不同产地的巴戟天药材进行分析。结果:所建立的HPLC指纹图谱共识别了18个共有峰,经与对照品图谱比较,鉴定其中的4个共有峰分别为果糖、蔗糖、蔗果三糖和耐斯糖。6个产地巴戟天的相似度在0.861~0.995;经聚类分析和主成分分析,6个产地巴戟天共聚为3类。结论:本研究采用的巴戟天HPLC指纹图谱及化学模式识别技术可反映出不同产地的巴戟天样品的差异,可为巴戟天的质量控制提供参考。

基于HPLC特征图谱和多成分含量评价梨干的质量

目的建立梨干的特征图谱,测定其中熊果苷、绿原酸、芦丁3种成分的质量分数,结合化学模式识别方法,综合评价其质量差异。方法采用HPLC法,乙腈-0.1%(体积分数)磷酸水溶液作为流动相进行梯度洗脱,流速:1.0 mL/min;柱温:30℃;检测波长:0~25 min,280 nm;26~90 min,350 nm;进样量:20μL。采用“中药色谱特征图谱相似度评价系统(2012版)”软件,结合聚类分析(HCA)、主成分分析(PCA)和正交偏最小二乘法-判别分析(OPLS-DA)综合评估20批梨干样本的HPLC特征图谱,同时测定其熊果苷、绿原酸、芦丁的质量分数。结果建立了20批梨干药材的特征图谱,相似度为0.959~0.997,标定了16个共有峰,同时指认了3个共有峰,分别为熊果苷、绿原酸、芦丁,其质量分数分别为16.78~66.00、47.69~260.10、1.39~5.48μg/g,绿原酸质量分数明显高于其他2种成分。通过HCA、PCA和OPLS-DA分析,把20批梨干分为2类(河北产地与非河北产地),并筛选出VIP值大于1的差异性标志成分6种,分别为峰9(绿原酸)、峰4、峰5、峰8、峰13、峰1(熊果苷)。结论本方法简便、准确,可用于梨干的质量控制。

基于指标成分及指纹图谱多模式识别的不同品种红花质量评价

目的:测定不同品种红花指标成分、建立指纹图谱,并结合化学识别模式法对其进行质量评价,建立不同品种红花质量比较方法。方法:采用高效液相色谱法(HPLC)测定32批不同品种红花的羟基红花黄色素A(HSYA)和山柰酚含量;利用“中药色谱指纹图谱相似度评价系统”(2012版)对32批红花样品进行相似度评价,并采用Origin 2022软件对其进行聚类分析,采用SIMCA 14.1软件进行主成分分析。结果:32批红花样品除3批未检出外,其余29批的HSYA质量分数为0.4%~2.5%,32批红花样品中山柰酚的质量分数为0.004%~1.060%;其HPLC指纹图谱共确定16个共有峰,指认了色氨酸、HSYA和6-羟基山柰酚-3-O-β-D-葡萄糖苷等5个成分,相似度为0.510~0.936,不同品种的红花质量差异显著。结论:通过HPLC含量测定、指纹图谱建立及多模式识别法,可实现不同品种红花的质量差异分析比较,研究结果提供了不同红花品种潜在的育种方向,对红花育种工作具有重要的参考价值。

基于HPLC指纹图谱结合化学模式识别对葡萄醋的质量评价

目的建立葡萄醋中有机酸成分的高效液相(High performance liquid phase,HPLC)指纹图谱,并结合化学模式识别对葡萄醋质量进行评价及为临床应用提供参考。方法采用HPLC法,以甲醇(A)∶0.5%磷酸水溶液(D)=1∶99洗脱,建立葡萄醋指纹图谱,同时测定琥珀酸的含量;采用相似度评价、聚类分析(Cluster analysis,CA)、主成分分析(Principal component analysis,PCA)和偏最小二乘-判别分析(Partial least squares discriminant analysis,PLS-DA)等化学模式识别对差异性特征成分进行筛选。结果15批葡萄醋样品的指纹图谱共有峰有11个,通过与混合对照品色谱峰进行比对,指认出6个共有峰,分别是酒石酸(1号峰),苹果酸(2号峰),琥珀酸(3号峰),富马酸(4号峰),绿原酸(9号峰),乙酸(11号峰);CA结果表明15批葡萄醋样品中各个化合物之间含量差异较大,即使是同一生产企业生产的不同批次葡萄醋其含量差异也较大;PCA结果显示葡萄醋共有峰含量差异的主要成分并非单一成分,而是前4种主成分共同的影响;PLS-DA结果表明筛选出的7、3、9、4号峰可作为鉴别和区分葡萄醋质量差异的标志物,其中包括共有峰3号峰(琥珀酸)、4号峰(富马酸)。结论采用指纹图谱结合化学模式识别技术可快速、有效地筛选出葡萄醋的差异性特征成分,为葡萄醋质量评价及临床应用提供参考。

麻石清肺合剂HPLC指纹图谱的建立及7种成分含量测定

目的建立麻石清肺合剂HPLC指纹图谱及测定盐酸麻黄碱等7种成分的含量。方法应用HPLC法构建12批麻石清肺合剂的指纹图谱同时进行了相似度评价,通过模式识别筛选差异性成分,对盐酸麻黄碱等7种成分进行含量测定。结果12批麻石清肺合剂样品中标定16个共有峰,相似度均>0.900,指认7个化学成分。12批样品被分为4类,筛选出8、7(苦杏仁苷)、3(盐酸麻黄碱)、9、10(甘草苷)、4(盐酸伪麻黄碱)、6号峰为差异性成分。12批麻石清肺合剂中盐酸麻黄碱、盐酸伪麻黄碱、苦杏仁苷、甘草苷、甘草酸、和厚朴酚、厚朴酚的含量分别为29.620~156.274、12.504~85.355、245.089~643.440、20.357~79.625、35.117~108.893、0.310~5.556、1.777~10.382μg·mL^(-1)。结论建立的指纹图谱和含量测定方法能够更加全面地评价麻石清肺合剂的品质。

蒙药文冠木配方颗粒指纹图谱及含量测定研究

目的建立蒙药文冠木配方颗粒的高效液相色谱方法指纹图谱及指标成分的含量测定,为评价蒙药文冠木配方颗粒的质量提供参考。方法色谱柱:COSMOSIL 5C_(18)(4.6 mm×250 mm,5μm),柱温30℃,流动相采用乙腈-0.1%磷酸水溶液,流速1 mL·min^(-1),梯度洗脱,检测波长270 nm,建立15批蒙药文冠木配方颗粒指纹图谱,同时对14种成分进行含量测定。结果建立了蒙药材文冠木配方颗粒的指纹图谱,相似度均大于0.900,共标定了21个共有峰,指认了其中14种成分,并对指认的成分进行含量测定。结论建立的指纹图谱及含量测定方法准确,稳定性好,可为蒙药材文冠木配方颗粒的质量控制提供参考。

基于HPLC指纹图谱结合化学模式识别以及多指标成分定量评价前胡药材质量

基于HPLC指纹图谱和多指标成分含量测定,并结合化学模式识别,评价不同产地前胡药材质量。采用Agilent SB-C_(18)(4.6 mm×250 mm,5μm)色谱柱,以甲醇-0.5%甲酸水为流动相进行梯度洗脱,流速为0.5 mL/min,检测波长321 nm,建立指纹图谱并对4个香豆素类成分进行定量,采用聚类分析(hierarchical clustering analysis,HCA)、主成分分析(principal component analysis,PCA)、偏最小二乘法-判别分析(partial least squares discriminant analysis,PLS-DA)对不同产地前胡药材进行质量评价。结果显示16批前胡药材HPLC指纹图谱相似度为0.966~0.999,确定了21个共有峰,共指认了9个成分。通过化学模式识别分析,将16批样品聚类为三类:以安徽宁国、亳州等为主的道地产区前胡,以湖南、贵州、重庆等为主的前胡,以及高海拔云南产区的前胡,并确立8个差异性质量标志物。多指标含量测定不同产地前胡的佛手柑内酯、白花前胡甲素、白花前胡乙素、白花前胡素E含量分别为0.0167~0.1319 mg/g、6.0696~16.2255 mg/g、0.4265~2.0100 mg/g、1.3407~3.1155 mg/g。本研究所建立的指纹图谱和含量测定方法专属性强,稳定性高,分离度良好,结合化学模式可用于前胡药材的质量评价。

柴胡-白芍药对配方颗粒指纹图谱的建立及多指标性成分含量测定

建立柴胡-白芍药对配方颗粒的HPLC指纹图谱、化学模式识别及多指标成分含量测定方法。采用COSMOSIL 5C_(18)-MS-Ⅱ(250 mm×4.6 mm, 5μm)色谱柱;乙腈-0.1%磷酸水为流动相,梯度洗脱;体积流量1.0 mL/min;检测波长分别为210、230 nm;柱温30℃。建立HPLC指纹图谱,利用相似度评价、聚类分析(clustering analysis, CA)以及主成分分析(principal component analysis, PCA)对15批柴胡-白芍药对配方颗粒指纹图谱进行研究,同时测定4种指标性成分含量。本研究建立的指纹图谱共确定15个共有峰,并指认了其中的4种成分;15批柴胡-白芍药对配方颗粒指纹图谱与对照指纹图谱的相似度均>0.90;CA和PCA均可将15批柴胡-白芍药对配方颗粒大致分为2类;多指标成分含量测定方法符合方法学考察要求,4种成分的含量均有不同程度的差异。所建方法简单易行,可为柴胡-白芍药对配方颗粒的质量控制和临床用药提供参考。