植物样品中单宁的微波溶出快速测定法研究

研究了利用微波代替一般的沸水浴处理试样的方法。对不同植物样品中单宁的浸提测定，与常规方法做了对照，并讨论了微波功率、时间、酸度等因素对测定结果的影响。研究表明，此法可大大地缩短测定时间，提高测定效率，节省人力、物力，不污染环境，便于大批量样品的测定，相对标准偏差≤１．４％，测定结果较为满意。为植物样品中单宁的测定，提供了一个快速、简便、准确的分析方法，并可借鉴于其它样品的测定。     

连续流动氢化物发生—原子吸收光谱法测定地质样品中痕量硒

经试验研究，选定了分析地质样品中痕量硒的测定体系，建立了切实可行的分析方法，该方法不需分离，直接测定，灵敏度高，干扰少，方法测定下限可达０．０２μｇ／ｇ，进行了部分样样分析，结果令人满意。     

能量色散一X荧光能谱法同时测定工业硅中铝钙铁硅

研究了能量色散-X荧光法测定工业硅的方法，提出以硼酸作外壳，粉末直接压片测定，应用于实际测定，速度快、准确度高，与国家标准方法比较，无显著性差异。     

铍试剂Ⅲ为指示剂分光光度法测定盐中碘

食品中碘的测定常用铬酸钾氧化比色法，此法费时且灵敏度不高。也有动力学光度法，灵敏度高，但测定的多为I^-，测定食盐中的碘还需经过处理。指示剂氧化褪色法可直接测定食盐中的碘。本文试验表明在硫酸介质中，溴化钾存在下，碘酸根使铍试剂Ⅲ褪色，椐此建立了测定     

氧化铝微柱预富集分光光度法测定天然水体中微量磷酸根

提出了以活性氧化铝微柱预富集分光光度法测定天然水体中的磷酸根，系统研究了氧化铝微柱的吸附性能，确定了分离富集磷酸根的最佳条件，并且对光度法测定条件进行了优化。本法用于天然水体中微量磷酸根的测定，结果满意。     

离子色谱法测定酸雨中的阳离子

用离子色谱法测定酸雨中的Na^ 、NH4^ 、K^ 、Mg^2 、Ca^2 等阳离子，并同时用原子吸收分光光度法进行了对比实验。结果表明，利用离子色谱法测定阳离子，方法简便、快捷，能一次同时测定多种离子，测定结果和原子吸收法相符。     

微乳液介质—雷氏盐浊度法测定汞的研究

研究了以乳化剂ＯＰ／正丁醇／正庚烷／水微乳液为介质，雷氏盐浊度法测定汞。微乳液的引入，使沉淀颗 粒分散均匀，体系稳定性显著提高，分析条件得到改善，体系可稳定１１ｈ。以４００ｕｍ为测定波长，汞量在０．０４～２．４ ｍｇ／Ｌ范围内线性关系良好。对痕量汞的测定可采用巯基葡聚糖凝胶（ＳＤＧ）进行分离富集，提高了测定的选择性和 灵敏度，用于实际样品测定，结果令人满意。     

三波长分光光度法测定曙红墨水中的染料

用三波长分光光度法测定了曙红墨水中的染料。墨水稀释液中的弱酸性红Ａ在５３６，５１６，４９７ｎｍ处测定，酸性红Ｇ在６０８，５４６，４５３．５ｎｍ处测定。本法操作简便，结果准确。     

Cu（Ⅱ）—茜素红S—甲基紫—聚乙烯醇体系光度测定痕量铜

提出了Ｃｕ（Ⅱ）－茜素红Ｓ－甲基紫－聚乙烯醇三元配合物体系光度法测定Ｃｕ（Ⅱ）的新方法，在聚乙烯醇存在的下，Ｃｕ（Ⅱ）－茜素红Ｓ－甲基紫形成稳定的有色配合物，通过测定该化合物的吸光度而测定Ｃｕ（Ⅱ）含量，测定了天然水样，试验表明，方法有较好的灵敏度和选择性，精密度可靠，适用于水样中痕量Ｃｕ（Ⅱ）的测定。     

固定化过氧化物酶在过氧化物测定中的应用

采用聚丙烯酰胺凝胶迭氮法固定了辣根过氧化物酶，其作为催化剂用于荧光法测定过氧化物，并探讨了固定酶测定过氧化物的最佳条件，如溶液pH值、温度、反应时间、荧光剂用量等。结果表明：酶固定化后，反应pH值范围变宽，为pH5.0-7.0和7.5-9.0，最佳pH为7.8左右；酶的热稳定性与储存稳定性也都得到提高，在室温下便可用固定酶进行长时间测定，且可较长时间保存。采用固定酶制成的酶柱用于HPLC测定过氧化物，固定酶可反复使用，简化了测定操作，并降低了成本。     

多元线性回归同时单点pH滴定法研究

本文将多元线性回归分析用于单点ＰＨ滴定中，同时测定极弱酸或碱混合物含量。对 十个五元 极弱碱混合样进行了测定，获得满意结果。     

分光光度法同时测定人发中的钙、锌

将多元线性回归分析用于分光光度法同时测定人发中钙、锌元素的含量,讨论了该方法的原理,考察了最佳实验条件,建立了回归方程和线性方程。对6个模拟发样和2个人发样品进行了测定,结果令人满意。     

A new method of estimating monomer reactivity ratios in copolymerization by linear regression methods

Sequential gas-liquid chromotographic analysis of the reaction mixture throughout a copolymerization reaction in conjuction with the improved curve-fitting I (integrated form) method, which accounts for measurements errors in both variables, allows accurate estimation of the monomer reactivity ratios. In this article an alternative method is presented for estimating r values in copolymerization with linear regression only, which is especially suited to cases in which one or two of the r values is close to 1. In these cases the improved curve-fitting I method tends to converge slowly because of the numerical instability of the integrated copolymerization equation. The use of the new method is illustrated for the estimation of the r values for ethylene and vinyl acatate in benzene at 35 kg/cm² and 62°C. The linear regression method was also tried on other copolymerizations and the results are compared with those obtained from the improved curve-fitting I method. The limits of the applicability of the linear regression method were determined by simulated sequential sampling experiments. It appears that the new method is applicable when the product of the r values is between 0.001 and 2, provided both monomer conversions are large enough compared with the measurements error.     

Comparison of Several Methods of Chromatographic Baseline Removal with a New Approach Based on Quantile Regression

The article is intended to introduce and discuss a new quantile regression method for baseline detrending of chromatographic signals. It is compared with current methods based on polynomial fitting, spline fitting, LOESS, and Whittaker smoother, each with thresholding and reweighting approach. For curve flexibility selection in existing algorithms, a new method based on skewness of the residuals is successfully applied. The computational efficiency of all approaches is also discussed. The newly introduced methods could be preferred to visible better performance and short computational time. The other algorithms behave in comparable way, and polynomial regression can be here preferred due to short computational time.     

Development and Application of Chemometric Methods in Near Infrared Spectral Analysis

In recent 10 years, like other disciplines influenced by the fast development of PC technique, chemometrics has been used in many analytical methods, especially in instrumental analysis. This article describes applications and comparison of multivariate linear regression (MLR), principal component analysis (PCA), principal component regression (PCR), partial least square (PLS), neural network (ANN), fuzzy and model recognition. A better calibration method can be a great help to improve the efficiency of the routine analytical work.     

Prediction of Antoine constants using a group contribution method

Vapor pressure is one of the important properties necessary for process design. Antoine equation has been widely used to evaluate the vapor pressures. This article deals with the proposal of a method for predicting the Antoine constants using a group contribution method. The 1817 compounds treated here are grouped into six classes. The group contribution parameters for each class have been determined using a regression analysis. The group contribution method with help of experimental normal boiling point has good results for predicting the vapor pressures.     

Improving the performance of SOMFA by use of standard multivariate methods

Self-Organizing Molecular Field Analysis (SOMFA) comes with a built-in regression methodology, the Self-Organizing Regression (SOR), instead of relying on external methods such as PLS. In this article we present a proof of the equivalence between SOR and SIMPLS with one principal component. Thus, the modest performance of SOMFA on complex datasets can be primarily attributed to the low performance of the SOMFA regression methodology. A multi-component extension of the original SOR methodology (MCSOR) is introduced, and the performances of SOR, MCSOR and SIMPLS are compared using several datasets. The results indicate that in general the performance of SOMFA models is greatly improved if SOR is replaced with a more sophisticated regression method. The results obtained for the Cramer (CBG) dataset further underline the fact that it is a very poor benchmark dataset and should not be used to evaluate the performance of QSAR techniques.     

广义二面角在定量结构/活性相关分析中的应用

化合物的结构与其活性具有相关性 .建立相关性需要由结构提取特征 ,并运用这些特征作为自变量去构造数学模型 ,以便预测未知的化合物及其活性 .目前 ,在诸多 3D- QSAR方法中 ,应用最为广泛的是 Co MFA( Comparasive Molecular Field Analysis)方法 [1～ 3] ,但新近的研究发现 ,Co MFA方法对于某类化合物 (如苯胺类化合物 )适应性较差 ,但分子在三维空间投影面积及量化参数的引入可大大改善所得结果[4 ] .本文从另一角度 (即广义二面角 )研究了分子在三维空间的形状 ,得到了满意的结果 .1　实验部分本文选用 34个 1 - [( 2 - Hydroxy…     

快速多模型回归分析方法的研究

逐步回归[1]是变量选择的常用方法．由逐步回归法得到的数学表达式不一定是最优数学模型，所以有其局限性．假如有m个变量x；，x。，……，x。，我们希望得到分别包含1个变量、2个变量、……、直至m个变量的最优回归方程，可以采用组合算法C：，对每个子集的所有组合进行回归分析，然后从C：个回归方程中选出最优的数学模型．由于随着变量个数m的增加，总的回归次数以2”的形式倍增，运算量大，时间长．此时可采用快速多模型（LeaPsandbounds）回归分析［‘j．此法为一种快速多模型回归，据此可以得到一组含有不同变量个数的最优方程．该…     

Opening the kernel of kernel partial least squares and support vector machines

Kernel partial least squares (KPLS) and support vector regression (SVR) have become popular techniques for regression of complex non-linear data sets. The modeling is performed by mapping the data in a higher dimensional feature space through the kernel transformation. The disadvantage of such a transformation is, however, that information about the contribution of the original variables in the regression is lost. In this paper we introduce a method which can retrieve and visualize the contribution of the variables to the regression model and the way the variables contribute to the regression of complex data sets. The method is based on the visualization of trajectories using so-called pseudo samples representing the original variables in the data. We test and illustrate the proposed method to several synthetic and real benchmark data sets. The results show that for linear and non-linear regression models the important variables were identified with corresponding linear or non-linear trajectories. The results were verified by comparing with ordinary PLS regression and by selecting those variables which were indicated as important and rebuilding a model with only those variables.