Syntheses and Studies of PEG-b-PNIPA Block Polymers

ＳｙｎｔｈｅｓｅｓａｎｄＳｔｕｄｉｅｓｏｆＰＥＧ┐ｂ┐ＰＮＩＰＡＢｌｏｃｋＰｏｌｙｍｅｒｓＣＡＯＷｅｉ－ｘｉａｏ＊＊ａｎｄＺＨＡＮＧＴａｏ（ＣｏｌｅｇｅｏｆＣｈｅｍｉｓｔｒｙａｎｄＭｏｌｅｃｕｌａｒＥｎｇｉｎｅｒｉｎｇ，ＰｅｋｉｎｇＵｎｉｖｅｒｓｉｔ...     

Synthesis,Characterization and DNA-Binding Studies of Nickel(Ⅱ) Mixed-Polypyridyl Complex

Ｓｙｎｔｈｅｓｉｓ，ＣｈａｒａｃｔｅｒｉｚａｔｉｏｎａｎｄＤＮＡ－ＢｉｎｄｉｎｇＳｔｕｄｉｅｓｏｆＮｉｃｋｅｌ（Ⅱ）Ｍｉｘｅｄ－ＰｏｌｙｐｙｒｉｄｙｌＣｏｍｐｌｅｘＪＩＮＬａｎ，ＹＡＮＧＰｉｎ（ａＳｔａｔｅＫｅｙＬａｂｏｒａｔｏｒｙｏｆＣｏｏｒｄｉｎ...     

Studies on the Binding of Tris(1,10-phenanthroline) Nickel(Ⅱ) to Calf Thymus DNA

ＳｔｕｄｉｅｓｏｎｔｈｅＢｉｎｄｉｎｇｏｆＴｒｉｓ（１，１０－ｐｈｅｎａｎｔｈｒｏｌｉｎｅ）Ｎｉｃｋｅｌ（Ⅱ）ｔｏＣａｌｆＴｈｙｍｕｓＤＮＡＪＩＮＬａｎ，ＹＡＮＧＰｉｎ．．ａｎｄＬＩＱｉｎｇ－ｓｈａｎ（ＳｔａｔｅＫｅｙＬａｂｏｒａｔｏｒｙｏｆＣｏｏｒ...     

BOND LENGTHS OF BUCKY-BALLS C_(60), C_(240), C_(540), AND C_(960)

ＢＯＮＤＬＥＮＧＴＨＳＯＦＢＵＣＫＹ－ＢＡＬＬＳＣ_（６０），Ｃ_（２４０），Ｃ_（５４０），ＡＮＤＣ_（９６０）￥ＬｅｉＬＩＵ；ＫａｉＴａｉＣＨＥＮ；ａｎｄＹｕＦｅｎＬＩ（ＤｅｐａｒｔｍｅｎｔｏｆＰｈｙｓｉｃｓ，ＦｕｄａｎＵｎｉｖｅｒｓｉｔｙ，Ｓｈａｎ...     

Studies on a New System of Lithium Fast Ion Conductors Based on Kaolinite and LiZr_2(PO_4)_3

ＳｔｕｄｉｅｓｏｎａＮｅｗＳｙｓｔｅｍｏｆＬｉｔｈｉｕｍＦａｓｔＩｏｎＣｏｎｄｕｃｔｏｒｓＢａｓｅｄｏｎＫａｏｌｉｎｉｔｅａｎｄＬｉＺｒ２（ＰＯ４）３ＨＵＡＮＧＪｉａｎ－ｄｏｎｇ＊＊，ＬＩＮＢｉｎ，ＷＡＮＧＷｅｎ－ｊｉ（ＤｅｐａｒｔｍｅｎｔｏｆＣｈ...     

Non－isothermalKineticsofThermalDecompositionReactionsofSchiffBaseComplexesofAminoAcidswithTranistionMetals（I）

Ｎｏｎ－ｉｓｏｔｈｅｒｍａｌＫｉｎｅｔｉｃｓｏｆＴｈｅｒｍａｌＤｅｃｏｍｐｏｓｉｔｉｏｎＲｅａｃｔｉｏｎｓｏｆＳｃｈｉｆｆＢａｓｅＣｏｍｐｌｅｘｅｓｏｆＡｍｉｎｏＡｃｉｄｓｗｉｔｈＴｒａｎｉｓｔｉｏｎＭｅｔａｌｓ（Ｉ）ＬＩＳｈｕ－ｌａｎ，ＬＩＵＤｅ－...     

Studies on the Glycosides in the Leaves ofOplopanax elatus NAKAI(Ⅰ)

ＳｔｕｄｉｅｓｏｎｔｈｅＧｌｙｃｏｓｉｄｅｓｉｎｔｈｅＬｅａｖｅｓｏｆＯｐｌｏｐａｎａｘｅｌａｔｕｓＮＡＫＡＩ（Ⅰ）ＷＡＮＧＧｕａｎｇ－ｓｈｕ，ＺＨＡＯＣｈｕｎ－ｆａｎｇａｎｄＸＵＪｉｎｇ－ｄａ（ＢｅｔｈｕｎｅＵｎｉｖｅｒｓｉｔｙｏｆＭｅｄｉｃａｌＳ...     

Keyword Index Vol1.5,1994

ＫｅｙｗｏｒｄＩｎｄｅｘＶｏｌ．５，１９９４ＡＢＩｎｌｔｌｏＣＩ（９９５）ＡＢＩｎｌｔｌｏｃａｌｃｕｌａｔｉｏｎ（５ｏ７）Ａｂｓｏｒｐｔｉｏｎｔｒａｐ（７３）Ａｃｃｅｌｅｒａｔｏｒｍａｓｓｓｐｅｃｔｒｏｍｅｔｒｙ（８７３）（ｉｃ４３）Ａｃｅｔｅｑｅｎ...     

CuX／bpy催化体系中甲基丙烯酸甲酯的原子转移自由基聚合

讨论了以α－溴代丙酸乙酯（ＥＰＮ－Ｂｒ）为引发剂、卤化亚铜（ＣｕＸ）／联二吡啶（ｂｐｙ）为催化剂，８０℃下，甲基丙烯酸甲酯（ＭＭＡ）地不同溶剂中的原子转移自由基聚合（ＡＴＲＰ）反应。通过对催化剂ＣｕＢｒ或ＣｕＣｌ及几种溶剂的考察，发现在８０℃下ＥＰＮ－Ｂｒ／ＣｕＢｒ／ｂｐｙ能有效控制丙烯酸甲酯（ＭＡ）的本体ＡＴＲＰ反应，但并不能很好地控制ＭＭＡ在ＥＡｃ中的聚合反应为一可控聚合过程，引发效率为０．８     

Quantum Chemistry Studies on the Heteropoly Blues of Molybdosilic Series with α-Keggin Structure

ＱｕａｎｔｕｍＣｈｅｍｉｓｔｒｙＳｔｕｄｉｅｓｏｎｔｈｅＨｅｔｅｒｏｐｏｌｙＢｌｕｅｓｏｆＭｏｌｙｂｄｏｓｉｌｉｃＳｅｒｉｅｓｗｉｔｈα－ＫｅｇｇｉｎＳｔｒｕｃｔｕｒｅＦＵＱｉａｎｇ，ＹＡＮＧＭｉｎｇ，ＣＨＥＮＢｉｎ，ＷＡＮＣＥｎ－Ｂｏ，ＸＩＥＤｅ－...     

CmⅠ奇宇称光谱能级的模式识别研究

应用新的模式识别方法PCA－BPN（PrincipalComponentAnalysis－BackPropagationNetwork）指认CmⅠ奇宇称未知能级，支持了前人应用传统的KNN（KNearestNeighbors）等模式识别方法及对传神经网络方法（CounterPropagationNetwork，CPN）对大部分谱线的指认，进一步确认了这些组态的归属；鉴别了KNN等与CPN不同的预报结果，纠正CPN的某些错误分类，并以可视非线性映照分类器加以佐证     

Classification of Cm I energy levels using PCA-BPN and PCA-NLM

Two pattern recognition (PR) techniques, principal component analysis-back propagation networks (PCA-BPN) and principal component analysis-nonlinear mapping (PCA-NLM), have been applied to the problem of classifying unknown energy levels of the first spectrum of curium (Cm I) according to their configurations. In comparison, with those reported by early PR techniques and counter propagation neural networks (CPN's), PCA-BPN has been demonstrated to possess much more prediction accuracy as to its performance on test sets. Obtained results further confirm the most previous assignments with these energy levels given by some early PR techniques and CPN. Moreover, the obtained results definitely reassign some energy levels' electronic configurations which were ambiguously conjectured in previous work.     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

Characterization of Galician (N.W. Spain) quality brand potatoes: a comparison study of several pattern recognition techniques

Authenticity is an important food quality criterion and rapid methods to guarantee it are widely demanded by food producers, processors, consumers and regulatory bodies. The objective of this work was to develop a classification system in order to confirm the authenticity of Galician potatoes with a Certified Brand of Origin and Quality (CBOQ) 'Denominación Específica: Patata de Galicia' and to differentiate them from other potatoes that did not have this CBOQ. Ten selected metals were determined by atomic spectroscopy in 102 potato samples which were divided into two categories: CBOQ and non-CBOQ potatoes. Multivariate chemometric techniques, such as cluster analysis and principal component analysis, were applied to perform a preliminary study of the data structure. Four supervised pattern recognition procedures [including linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA) and multilayer feed-forward neural networks (MLF-ANN)] were used to classify samples into the two categories considered on the basis of the chemical data. Results for LDA, KNN and MLF-ANN are acceptable for the non-CBOQ class, whereas SIMCA showed better recognition and prediction abilities for the CBOQ class. A more sophisticated neural network approach performed by the combination of the self-organizing with adaptive neighbourhood network (SOAN) and MLF network was employed to optimize the classification. Using this combined method, excellent performance in terms of classification and prediction abilities was obtained for the two categories with a success rate ranging from 98 to 100%. The metal profiles provided sufficient information to enable classification rules to be developed for identifying potatoes according to their origin brand based on SOAN-MLF neural networks.     

Differentiation of red wines using an electronic nose based on surface acoustic wave devices

An electronic nose, utilizing the principle of surface acoustic waves (SAW), was used to differentiate among different wines of the same variety of grapes which come from the same cellar. The electronic nose is based on eight surface acoustic wave sensors, one is a reference sensor and the others are coated by different polymers by spray coating technique. Data analysis was performed by two pattern recognition methods; principal component analysis (PCA) and probabilistic neuronal network (PNN). The results showed that electronic nose was able to identify the tested wines.     

Authentication of Galician (N.W. Spain) quality brand potatoes using metal analysis. Classical pattern recognition techniques versus a new vector quantization-based classification procedure

The objective of this work was to develop a classification system in order to confirm the authenticity of Galician potatoes with a Certified Brand of Origin and Quality (CBOQ) and to differentiate them from other potatoes that did not have this quality brand. Elemental analysis (K, Na, Rb, Li, Zn, Fe, Mn, Cu, Mg and Ca) of potatoes was performed by atomic spectroscopy in 307 samples belonging to two categories, CBOQ and Non-CBOQ potatoes. The 307 x 10 data set was evaluated employing multivariate chemometric techniques, such as cluster analysis and principal component analysis in order to perform a preliminary study of the data structure. Different classification systems for the two categories on the basis of the chemical data were obtained applying several commonly supervised pattern recognition procedures [such as linear discriminant analysis, K-nearest neighbours (KNN), soft independent modelling of class analogy and multilayer feed-forward neural networks]. In spite of the fact that some of these classification methods produced satisfactory results, the particular data distribution in the 10-dimensional space led to the proposal of a new vector quantization-based classification procedure (VQBCP). The results achieved with this new approach (percentages of recognition and prediction abilities > 97%) were better than those attained by KNN and can be compared advantageously with those provided by LDA (linear discriminant analysis), SIMCA (soft independent modelling of class analogy) and MLF-ANN (multilayer feed-forward neural networks). The new VQBCP demonstrated good performance by carrying out adequate classifications in a data set in which the classes are subgrouped. The metal profiles of potatoes provided sufficient information to enable classification criteria to be developed for classifying samples on the basis of their origin and brand.     

Chemometric characterisation of Basque and French ciders according to their polyphenolic profiles

Polyphenolic compositions of Basque and French ciders were determined by HPLC-DAD following thiolysis, in order to characterise and differentiate these beverages and then develop a classification system capable of confirming the authenticities of both kinds of cider. A data set consisting of 165 cider samples and 27 measured features was evaluated using multivariate chemometric techniques, such as cluster analysis and principal component analysis, in order to perform a preliminary study of data structure. Supervised pattern recognition techniques such as linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA), and multilayer feed-forward artificial neural networks (MLF-ANN) attained classification rules for the two categories using the chemical data, which produced satisfactory results. Authentication systems obtained by combining two of these techniques were proposed. We found that SIMCA and LDA or KNN models achieved 100% hit-rates, since LDA and KNN permit the detection of every Basque cider and SIMCA provides a model for Basque cider that excludes all French ciders. Polyphenolic profiles of the ciders provided enough information to be able to develop classification rules for identifying ciders according to their geographical origin (Basque or French regions). Chemical and organoleptic differences between these two types of cider are probably due to the original and distinctive cidermaking technologies used for their elaboration. Using polyphenic profiles, about 80% of French ciders could be distinguished according to their region of origin (Brittany or Normandy). Although their polyphenolic profiles did not provide enough information to achieve an authentication system for Breton and Norman ciders.Abbreviations AVI Avicularin - CQA Caffeoylquinic acid - CAF Caffeic acid - CAT (+)-catechin - CT-1, -2, -3 Unknown flavan-3-ols - DPn Average degree of polymerization of procyanidins - EC (–)-epicatechin - HCA-7 Ferulic acid - HCA-1, -2 ,-3, 4, -5, -6 Unknown hydroxycinnamic acids - HYP Hyperin - IQC Isoquercitrin - PC Total procyanidins - PCM p-Coumaric acid - PCQ p-Coumaroylquinic acid - PL Phloretin - PLG Phloridzin - PLXG phloretin-2-O-xyloglucoside - PPO Polyphenoloxidase - QCE Quercetin - QCI Quercitrin - RUT Rutin - CA Cluster analysis - KNN K-nearest neighbours - LDA Linear discriminant analysis - MLF-ANN Multilayer feed-forward-artificial neural network - PCA Principal component analysis - PC1 First principal component - PC2 Second principal component - PC3 Third principal component - RMSE Root medium square error - SD Standard deviation - SIMCA Soft independent modelling of class analogy - DAD Diode array detector - HPLC High Performance Liquid Chromatography - ND Not detected     

Discrimination of Chinese traditional soy sauces based on their physico-chemical properties

This work aimed to classify the categories (produced by different processes) and brands (obtained from different geographical origins) of Chinese soy sauces. Nine variables of physico-chemical properties (density, pH, dry matter, ashes, electric conductivity, amino nitrogen, salt, viscosity and total acidity) of 53 soy sauce samples were measured. The measured data was submitted to such pattern recognition as cluster analysis (CA), principal component analysis (PCA), discrimination partial least squares (DPLS), linear discrimination analysis (LDA) and K-nearest neighbor (KNN) to evaluate the data patterns and the possibility of differentiating Chinese soy sauces between different categories and brands. Two clusters corresponding to the two categories were obtained, and each cluster was divided into three subsets corresponding to three brands by the CA method. The variables for LDA and KNN were selected by the Fisher F-ratio approach. The prediction ability of all classifiers was evaluated by cross-validation. For the three supervised discrimination analyses, LDA and KNN gave 100% predications according to the sample category and brand.     

A program for calculating Mahalanobis distances using principal component analysis

A technique for pattern recognition analysis of near-infrared reflectance spectra is described. Classification of samples is achieved by using the Mahalanobis distances of spectra in a principal component subspace. Probability levels for class membership are determined from the Chi-squared distribution.