完全未知混合体系的纯物种光谱辨析

基于因子分析的迭代目标转换算法,本文提出一种从完全未知混合物体系中获取纯物种光谱的解析方法.该法简单明瞭,不增加任何困难即可拓广至含n组份的混合体系,对光谱峰形状亦无任何附加限制要求.先采用计算机模拟技术对该法进行了校验,继用于紫外可见与荧光光谱的实际混合体系的解析,均获满意结果.     

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。     

线性规划—导数分光光度法同时测定钴,镍,铜,锌,铁的研究

1 引言 近年来,随着计算机的普及和发展,用化学计量学方法来解析光谱数据,实现毋需分离的多组分同时测定的报道迅速增加,但一般仅限于用来解析基本光谱数据。本文提出用线性规划算法解析一阶导数光谱数据的新方法,对以PAR为显色剂的Co、Ni、Cu、Zn、Fe五组分混合体系的同时测定进行了研究,结果表明,该法既保持了导数分光光度法的高灵敏度,又利用了线性规划算法解决了普通导数光谱难以解析的5组分同时测定问题,对标准合成样品进行了测定,结果令人满意。     

完全未知混合体系中纯组份的红外光谱解析

提出一种新的目标转化因子分析，其初始向量的构造基于实际最大差异光谱，无需分离即可从红外混合体系中解析出纯组份光谱。该算法简单易懂，不必迭代，运算量小，应用于模拟及实际体系，结果令人满意。     

胶束增溶光度法同时测定铂、铑、钯和金的研究

本文研究了贵金属Pt、Rh、Pd和Au在SnCl_2-BRB-AG-乳化剂OP离子缔合物体系中显色反应的条件,采用偏最小二乘回归法对上述体系重迭光谱进行解析及数据处理。结果表明,该体系灵敏度高,表观摩尔吸光系数可达1.74×10～6～2.47×10～6L·mol～-1·cm～-1;体系稳定性较好。应用本文方法对混合试样中Pt、Rh、Pd、Au进行同时测定,取得较满意的结果。     

双峰倍增配平导数—同步荧光法同时测定苯酚和间苯二酚的研究

本文提出用双峰倍增配平法计算解析苯酚和间苯二酚混合体系的一阶导数－同步荧光光谱，测定结果的回收率，精密度和两组分间浓度比范围均统一阶导数－同步荧光法优方法用于雷琐辛涂剂和水样中苯酚与间苯二酚的测定，结果令人满意。     

基于子空间重合判断的混合醇组分光谱识别方法

针对光谱分析中传统的特征峰比对法对物质定性检测存在的不足,采用基于子空间重合判断的混合光谱模式识别方法对9种混合醇组分进行定性检测。此法将测量到的待定性混合醇光谱信息视为向量,通过计算混合醇组分拉曼光谱与标准样品数据库拉曼光谱的子空间夹角,并依据子空间夹角变化,排列筛选出含有最少标准样品数目的子空间,该子空间所包含的标准样品组成即为待定性混合醇组分组成,从而成功实现了9种混合醇组分的定性识别,识别率100%。该法具有测试时间短、测试结果准确、与被测物含量无关、操作简便等优点,适用于多组分混合体系的定性识别。     

神经网络与紫外光谱法同时测定芳香类化合物

测定了苯酚、苯胺、α萘胺、间二硝基苯及对氧基苯甲醛混合体系的紫外光谱，用神经网络法直接对混合体系紫外光谱数据进行处理，成功地测定了混合体系各组分含量，结果良好     

完全重叠色谱峰的二维微分色谱解析算法

利用完全重叠的色谱峰中同时消失的两组分的浓度变化速率差异，通过二维数据微分色谱法获取被掩盖组份的近似光谱，以解析完全重叠的色谱体系，将该法应用于维生素Ｃ和菸酸两组份完全重叠的二维色谱之分辨，结果令人满意，分辨光谱与标准光谱重合甚好。     

偏最小二乘-同步荧光光谱法同时测定两种蒽环类抗生素

将偏最小二乘法(PLS)用于同步荧光光谱严重重叠的多柔比星(doxorubicin, DOX)和柔红霉素(daunorubicin, DNR)两组分混合体系进行波谱解析, 建立了该混合体系含量同时测定的新方法. 在pH 3.45 B-R缓冲溶液中, 波长差Δλ=55 nm时, 用测得的25个混合标样的同步荧光原始光谱、一阶导数光谱值建立模型. DOX和DNR在质量浓度为0.05～3.0 μg/mL范围内呈现良好的线性关系, 所建立的测定二者模型的相关系数分别为0.9897和0.9909; 平均回收率分别为101.0%和101.4%; 预测均方根误差(RMSEP)分别为0.1400和0.1395; 预测相对标准误差(SEP)分别为0.1541和0.1525. 该方法可应用于尿液样品的分析测定.     

Decomposition of multicomponent mass spectra using Bayesian probability theory

We present a method for the decomposition of the mass spectra of mixed gases using Bayesian probability theory. The method works without any calibration measurement and therefore applies also to the analysis of spectra containing unstable species. For the example of mixtures of three different hydrocarbon gases the algorithm provides concentrations and cracking coefficients of each mixture component and also their confidence intervals. The amount of information needed to obtain reliable results and its relation to the accuracy of our analysis are discussed.     

From theory of spectra to standardless analysis of molecular objects

The authors discuss the methodology of quantitative analysis of pure substances and mixtures by optical spectra (IR, Raman, UV, etc.) without using samples of standard composition (standardless molecular spectral analysis). An algorithm of quantitative mixture analysis using the reduction of ideal spectra to real ones and computational algorithms of the determination of component concentrations in a mixture taking into account error distribution is proposed. The applicability of the method is estimated using computational experiments parsed and conclusions about the specific features of its work are drawn.     

Analysis of multicomponent mass spectra applying Bayesian probability theory

In this paper we develop a method for the decomposition of mass spectra of gas mixtures, together with the relevant calibration measurements. The method is based on Bayesian probability theory. Given a set of spectra, the algorithm returns the relative concentrations and the associated margin of confidence for each component of the mixture. In addition to the concentrations, such a data set enables the derivation of improved values of the cracking coefficients of all contributing species, even for those components for which the set does not contain a calibration measurement. This latter feature also allows one to analyze mixtures that contain radicals in addition to stable molecules. As an example, we analyze and discuss the mass spectra obtained from the pyrolysis of azomethane, which contain the radical CH3 apart from nitrogen and C1- and C2-hydrocarbons.     

Weighted two-band target entropy minimization for the reconstruction of pure component mass spectra: Simulation studies and the application to real systems

A method is proposed, on the basis of a recently developed algorithm--Band Target Entropy Minimization (BTEM)--to reconstruct mass spectra of pure components from mixture spectra. This method is particular useful in dealing with spectral data with discrete features (like mass spectra). Compared to the original BTEM, which has been applied to differentiable spectroscopies such as Fourier-transfer infrared spectroscopy (FTIR), ultraviolet (UV), Raman, and nuclear magnetic resonance (NMR), the latest modifications were obtained through: (1) Reformulating the objective function using the peak heights instead of their derivatives; (2) weighting the abstract vector VT to reduce the effect of noise; (3) using a two-peak targeting strategy (tBTEM) to deal with strongly overlapping peaks; and (4) using exhaustive search to locate all the component spectra. A set of 50 multi-component mass spectra was generated from ten reference experimental pure component spectra. Many of the compounds chosen have common MS fragments and therefore, many of the pure component spectra have considerable intensity in same data channels. In addition, a set of MS spectra from a real system with four components was used to examine the newly developed algorithm. Successful reconstruction of the ten component spectra of the simulated system and the four component spectra of the real system was rapidly achieved using the new tBTEM algorithm. The advantages of the new algorithm and its implication for rapid system identification of unknown mixtures are readily apparent.     

Kalman filter quantitative resolution of shifted ESCA spectra after optimal alignment by simulated annealing

Summary The quantitative separation of overlapped responses accomplished by the Kalman filter algorithm is deteriorated by the errors in the model due to shifts of the component peaks. This is particularly true for spectral resolution in Electron Spectroscopy for Chemical Analysis (ESCA). A computational approach for the quantitative resolution of overlapping ESCA spectra when there is loss of collinearity between the pure component peaks and the mixture peak, has been developed. The procedure makes itera tive use of the Kalman filter for resolving the mixture spectrum with the component spectra aligned according to some values of the position parameters, and of the simulated annealing algorithm to find the optimal alignment. The performance of simulated annealing in pursuing this task, has been compared with those of simplex and steepest descent, by analyzing a set of overlapped synthetic spectra and some ESCA spectra of powder mixtures of lead compounds.     

Decomposition of mixtures' spectra by multivariate curve resolution of rapidly acquired TOCSY experiments

A method is presented that allows for retrieving 1D spectra of the individual components of a mixture from a sparsely acquired 2D‐TOCSY spectrum. The decomposition of the 2D‐TOCSY data into pure 1D traces is achieved using a non‐negative matrix factorization algorithm, also known as multivariate curve resolution analysis. Here, we show that the algorithm can be applied to data processed in the direct dimension only. Thus, our method can be applied to non‐linearly sampled experiments or data acquired with few indirect points. An example is shown for the spectra of a mixture of six amino acids, acquired in 15 min. Copyright © 2010 John Wiley & Sons, Ltd.     

Estimation of component spectra by the principal components method

The principal components method enables component spectra from pigment mixtures to be estimated by evaluating the eigenvectors of the second moment matrix. The components are linear combinations of these eivenvectors, but cannot be identified unambiguously. With the conditions of non-negativity of spectral values and of concentrations, this ambiguity can be limited; components spectra for 2 and 3 components were calculated earlier. In the present work, maximal dissimilarity of component spectra is assumed as a further condition. An algorithm based on linear programming is described; it enables any number of components to be estimated from eigenvectors of the second moment matrix with better reliability than previously.     

Computer-assisted library search system for identification of unknown mass spectra

The library search system described identifies a single component or two components in the unknown pure or mixture mass spectra by comparing them with a large data base of reference spectra. A preliminary search is based on the spectral interpretation and the main search is based on the probability of peak appearance. The performance of this system was tested on 254 pure spectra and 88 mixture spectra. The percentages of successful search by using the NIH/EPA/MSDC data base were 75% for the pure spectra, and 63% for both the first and second components in mixture spectra. The percentages of successful search improved to 94% for the first components of the mixture spectra and 77% for both first and second components of the mixture spectra, when conditions for measurement of reference spectra were the same as those for the unknown spectra.     

Calibration transfer of near‐infrared spectra for extraction of informative components from spectra with canonical correlation analysis

A new calibration transfer method that applies canonical correlation analysis (CCA) to transfer the informative components extracted from a spectral dataset is proposed to reduce the interference of noise, background and non‐predicted properties. This method employs the partial least squares method to extract the informative components related to the predicted properties from the raw spectra and then corrects the informative components based on CCA. The performance of this algorithm was tested using three pairs of spectra batches: two pairs of corn spectra and one pair of tri‐component solvent spectra. The results showed that this method can significantly reduce prediction errors compared with CCA and piecewise direct standardization. Copyright © 2014 John Wiley & Sons, Ltd.