目标识别因子分析解析完全未知混合体系的研究

本文以目标识别因子分析为基础, 将因子分析与聚类分析相结合, 给出了从完全未知混合体系中提取纯物种光谱的新方法。所得纯物种光谱经光谱检索作定性判别, 然后利用外标法对混合物中的各组份进行标定。该方法用于计算机模拟体系及三组份实际混合体系的红外光谱解析, 结果令人满意。     

分枝定界算法用于有机混合物的同时定性定量分析

分枝定界法用于多组分同时定性定量分析，只需解析一份试样测得的数据，即可同时得到待测样品中所含组分的种类、数目及含量，具有简单、快速、准确等优点。此文对分枝定界法在判据应用方面进行了改进，应用四个判据，解决了最佳子集难判断的问题。建立了精蒽中不经分离同时测定蒽、菲和咔唑的方法。     

混合物定性定量分析的分枝定界算法

已知被测体系可能存在的物种范围, 但其确含何种物质及相应浓度必须经分析方可确定, 实为分析化学实践中常见的一类分析体系。基于分枝定界原理, 本文提出一种直接对其进行定性定量解析的光谱多元分析算法, 可显著减少计算量,却无失去真实信息的风险。对实际紫外可见及荧光混合物光谱进行解析, 均获满意结果。     

中药药对的化学成分研究川芎-赤芍挥发油的GC/MS分析

中药药对是复方的最小组成单位,具有中药配伍的基本特点. 药对化学是复方化学的核心内容. 联用色谱和化学计量学方法为中药复杂体系的分离与分辨提供了强有力的工具. 采用GC-MS法分离测定中药药对川芎-赤芍、 单味药川芎和赤芍的挥发油成分,并对其中的重叠色谱峰采用化学计量学解析方法(CRM)进行分辨,得到药对和各单味药的纯色谱曲线和质谱图,药对川芎-赤芍、 单味药川芎和赤芍分别分辨出82,78和57个色谱峰,通过质谱库对分辨的纯组分进行定性,分别得到61,52和33个定性结果,占总含量的90.18%,95.14%和95.82%.     

动态分析解析解方法研究可逆吸附及不可逆吸附在多相催化反应中的作用

提出了一个多相催化反应中可逆吸附物种及不可逆吸附物种同时参与反应的动态分析解析解新模型。在同时考虑轴向扩散、不可逆物种吸附速率以及可逆物种和不可逆物种表面反应速率的情况下,通过解析解对实验曲线进行拟合计算,即可求出各反应的速率常数。在不同简化条件下,由解析解可自然得到稳态方程、不可逆物种表面反应流出曲线解析解以及迎头吸附流出曲线解析解等。通过理论计算,考察了各速率参数对流出曲线的影响。     

非线性主成分回归分光光度法同时测定复方制剂中两组分的含量

构造多组分体系的非线性模型,并用主成分回归法来解析,建立了同时测定两组分的非线性主成分回归分光光度法,方法用于复方苯甲酸制剂中苯甲酸和水杨酸含量的测定,结果优于线性主成分回归分光光度法。     

气相色谱/质谱-化学计量学法分析测定药对桃仁-红花挥发油

药对是中药配伍中最基本、最常用的形式,具有中药配伍的基本特点。药对化学是复方化学的核心内容。联用色谱和化学计量学方法是分析中药复方复杂体系的有效工具。采用GC/MS法分离测定了药对桃仁-红花、单味药桃仁和红花的挥发油成分,并对其重叠色谱峰采用化学计量学解析法进行了分辨,得到药对和各单味药的纯色谱曲线和质谱。药对桃仁-红花、单味药桃仁和红花分辨出的色谱峰,通过质谱库对其进行定性,分别得到84、27和52个定性结果,占总含量的92.06%、89.43%和94%。实验结果表明:桃仁-红花挥发油成分与单味药桃仁和红花的存在较大差别,也不是两者挥发油成分之加和。     

辣根过氧化物酶反应的化学动力学分析

用辣根过氧化物酶来处理废水中的苯酚和氯酚为许多人所关注。采用停流快速混合技术，利用快速扫描紫外可见分光光度计得到辣根过氧化物酶催化过氧化氢氧化苯酚聚合过程的瞬态光谱数据。对此测量数据矩阵用正交投影及遗传算法解析后，确定体系中产生紫外吸收的物种数，及辣根过氧化物酶中间体形式的纯光谱信息，进而解出反应过程中各组分的动力学曲线。     

全二维气相色谱-飞行时间质谱/氢火焰离子化检测器对煤直接液化循环溶剂的定性与定量分析

建立了全二维气相色谱-飞行时间质谱/氢火焰离子化检测器(GC×GC-TOF MS/FID)对煤直接液化循环溶剂(CDLRS)定性定量的分析方法。采用TOF MS和FID两种检测器同时采集数据,并结合谱库检索、标准物质保留值对照、谱图解析、标准质谱图对照、全二维谱图特征以及提取化合物分子离子等定性方法,将TOF MS检测数据定性,然后将定性的烃类化合物以z值分类法分为18类;应用Chroma TOF数据处理软件将TOF MS数据的定性分类结果应用到FID的检测数据中,对TOF MS和FID采集的数据色谱峰面积归一化处理,实现CDLRS的半定量分析。GC×GC/FID定量结果显示:煤直接液化循环溶剂中饱和烃和芳烃分别占45.805%、53.938%,其中饱和烃主要为二环烷烃及三环烷烃,含量依次为14.644%、18.021%;芳烃主要为一环烷苯和二环烷苯,含量依次为19.759%、16.528%。该方法为CDLRS的定性定量提供了一种有效的分析方法。     

利用目标试验因子分析法确定化学反应的级及速率常数

利用目标试验因子分析（TTFA）结合数值遗传算法（NGA），解析反应过程中在线测得的动力学谱－光谱数据矩阵，可在未知各组分纯光谱及动力学模型情况下同时求解出各组分的纯光谱，反应级数及速率常数，提出用近似计算法计算各组分的动力学谱，使该方法能适用于在任意反应级数的体系。针对两步连续反应模型，对反应物，中间体和最终产物均有吸收及某一种组分没有吸收的体系的矩阵进行了处理，表明该方法均能适用。利用该方法对邻苯二甲酸二甲酯在碱性介质中的水解反应及日落黄水溶液的电解降解反应过程中测得的数据矩阵进行解析，均获得了可靠结果。     

Comparative Study of Variable Selection Using Genetic Algorithm with Various Types of Chromosomes

In this study,different methods of variable selection using the multilinear step-wise regression(MLR) and support vector regression(SVR) have been compared when the performance of genetic algorithms(GAs) using various types of chromosomes is used.The first method is a GA with binary chromosome(GA-BC) and the other is a GA with a fixed-length character chromosome(GA-FCC).The overall prediction accuracy for the training set by means of 7-fold cross-validation was tested.All the regression models were evaluated by the test set.The poor prediction for the test set illustrates that the forward stepwise regression(FSR) model is easier to overfit for the training set.The results using SVR methods showed that the over-fitting could be overcome.Further,the over-fitting would be easier for the GA-BC-SVR method because too many variables fleetly induced into the model.The final optimal model was obtained with good predictive ability(R2 = 0.885,S = 0.469,Rcv2 = 0.700,Scv = 0.757,Rex2 = 0.692,Sex = 0.675) using GA-FCC-SVR method.Our investigation indicates the variable selection method using GA-FCC is the most appropriate for MLR and SVR methods.     

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.     

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…     

Structural Characterization and Retention Time Prediction for Components of Essential Oil of Meconopsis Integrifolia Flowers

A molecular structural characterization (MSC) method called reduced molecular electronegativity-distance vector (MEDVR) was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression (SMR) and partial least square (PLS) methods, a model with the correlation coefficient (R1) of 0.987 and the standard deviation (SD1) of 1.377 could be obtained. Then through multiple linear regression (MLR), another model with the correlation coefficient (R2) of 0.989 and standard deviation (SD2) of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique (SMR), 8 vectors were selected to build up another model with its correlation coefficient (R3) and standard deviation (SD3) of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out (LOO) procedure, and the correlation coefficients (QCV) were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.     

有机酸对卷烟感官风格和品质的影响研究

测定了25种成品卷烟的15种有机酸含量,采用逐步回归分析法建立了卷烟感官风格品质与有机酸之间的关系模型。根据逐步回归模型选择的变量,评价影响卷烟香韵表现与感官品质的关键有机酸成分。将回归模型成功地应用于试制卷烟香韵表现和感官品质得分预测,证明了模型的有效性及有机酸对卷烟香韵表现和感官品质的解释能力。研究结果表明,异戊酸、丙二酸、苯乙酸和柠檬酸等有机酸对卷烟香韵表现或感官品质存在正面或负面的不同影响。     

Spectrophotometric study of complexation equilibria with H-point standard addition and H-point curve isolation methods

The use of H-point curve isolation (HPCIM) and H-point standard addition methods (HPSAM) for spectrophotometric studies of complex formation equilibria are proposed. One step complex formation, two successive stepwise and mononuclear complex formation systems, and competitive complexation systems are studied successfully by the proposed methods. HPCIM is used for extracting the spectrum of complex or sum of complex species and HPSAM is used for calculation of equilibrium concentrations of ligand for each sample. The outputs of these procedures are complete concentration profiles of equilibrium system, spectral profile of intermediate components, and good estimation of conditional formation constants. The reliability of the method is evaluated using model data. Spectrophotometric studies of murexide-calcium, dithizone-nickel, methyl thymol blue (MTB)-copper, and competition of murexide and sulfate ions for complexation with zinc, are used as experimental model systems with different complexation stoichiometries and spectral overlapping of involved components.     

Determination of ascorbic acid in pharmaceutical preparations by near infrared reflectance spectroscopy

We developed a method for determination of ascorbic acid in pharmaceutical preparations containing various excipients by using near infrared diffuse reflectance spectroscopy and two different calibration methods, viz. stepwise multiple linear regression (SMLR) and partial least-squares (PLS) regression, which provided comparable results and resulted in prediction errors of 1-2%. However, the PLS method provided somewhat better results with the more complex samples.     

Determination of galactosamine impurities in heparin samples by multivariate regression analysis of their <Superscript>1</Superscript>H NMR spectra

Heparin, a widely used anticoagulant primarily extracted from animal sources, contains varying amounts of galactosamine impurities. Currently, the United States Pharmacopeia (USP) monograph for heparin purity specifies that the weight percent of galactosamine (%Gal) may not exceed 1%. In the present study, multivariate regression (MVR) analysis of ¹H NMR spectral data obtained from heparin samples was employed to build quantitative models for the prediction of %Gal. MVR analysis was conducted using four separate methods: multiple linear regression, ridge regression, partial least squares regression, and support vector regression (SVR). Genetic algorithms and stepwise selection methods were applied for variable selection. In each case, two separate prediction models were constructed: a global model based on dataset A which contained the full range (0–10%) of galactosamine in the samples and a local model based on the subset dataset B for which the galactosamine level (0–2%) spanned the 1% USP limit. All four regression methods performed equally well for dataset A with low prediction errors under optimal conditions, whereas SVR was clearly superior among the four methods for dataset B. The results from this study show that ¹H NMR spectroscopy, already a USP requirement for the screening of contaminants in heparin, may offer utility as a rapid method for quantitative determination of %Gal in heparin samples when used in conjunction with MVR approaches.     

Calibration of the complex matrix effects on the sampling of polycyclic aromatic hydrocarbons in milk samples using solid phase microextraction

Solid phase microextraction (SPME), a simple, fast and promising sampling technique, has been widely used for complex sample analysis. However, complex matrices could modify the absorption property of coatings as well as the uptake kinetics of analytes, eventually biasing the quantification results. In the current study, we demonstrated the feasibility of a developed calibration method for the analysis of polycyclic aromatic hydrocarbons (PAHs) in complex milk samples. Effects of the complex matrices on the SPME sampling process and the sampling conditions were investigated. Results showed that short exposure time (pre-equilibrium SPME, PE-SPME) could increase the lifetime of coatings, and the complex matrices in milk samples could significantly influence the sampling kinetics of SPME. In addition, the optimized sampling time, temperature and dilution factor for PAHs were 10 min, 85 °C and 20, respectively. The obtained LODs and LOQs of all the PAHs were 0.1–0.8 ng/mL and 1.4–4.7 ng/mL, respectively. Furthermore, the accuracy of the proposed PE-SPME method for milk sampling was validated by the recoveries of the studied compounds in two concentration levels, which ranged from 75% to 110% for all the compounds. Finally, the proposed method was applied to the screening of PAHs in milk samples.