一类用于多元光度分析的小波基主成分回归法

通过将变尺度小波分解滤噪与特征信息提取相结合,提出一类新的多元光度分析算法－小波基主成分回归（ＰＣＲＷ）方法。该法可有效地减小主成分向量残留噪声所引 误差显著提高多元校正准确性。将其用于分析氯霉素、醋酸对塞米松、尼泊金乙脂体系、得到满意的回收率,与ＰＣＲ法相比,分析结果的总平均相对误差从３．３８％降低到０．８３％。     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优     

主成分回归用于分光光度法同时测定6种食品添加剂

山梨酸、苯甲酸钠、香兰素、NaNO2、NaNO3和糖精钠的紫外吸收光谱严重重叠,不经预先分离很难进行单一组分的直接测定.报道了一种同时测定上述6种食品添加剂的分光光度法,这种方法是基于在pH 2.85的Britton-Robinson(B-R)缓冲溶液中对该6种食品添加剂混合溶液进行光度测定,所得的重叠光谱数据用主成分回归(PCR)进行建模,并用该模型对未知样品浓度进行预报.该方法可以不经分离同时测定食品样品中的多种添加剂.     

小波变换-主成分回归分光光度法同时测定邻苯二酚、间苯二酚和对苯二酚三组分

根据小波变换具有将信号分频的特点,本文提出了将小波变换与主成分回归(PCR)相结合的一种多元校正算法。该法能更有效地去除噪声,提取有用信息,并将其用于分析邻苯二酚、间苯二酚、对苯二酚三组分体系。实验结果表明,本法比直接用主成分回归处理效果好,得到的平均相对误差从2．24％降低到1．19％。     

主成分分析-支持向量回归建模方法及应用研究

将主成分分析(PCA)用于近红外光谱的特征提取,并与支持向量回归(SVR)相结合,实现了主成分分析-支持向量回归(PCA-SVR)用于近红外光谱定量分析的建模方法。与单纯的SVR方法相比,不仅提高了运算速度,而且提高了模型的预测准确度。将PCA-SVR方法用于烟草样品中总糖和总挥发碱含量的测定,所得结果的预测均方根误差分别为1.323和0.0477;回收率分别为91.8%~112.6%和88.9%~120.2%。     

连续小波变换-独立成分回归算法及其在多组分分析中的应用

采用连续小波变换（CWT）对光谱数据进行处理，用独立成分分析（ICA）进行特征提取，再用回归分析方法对被测组分进行测定，建立了连续小波变换一独立成分回归（CWT-ICR）方法。方法用于肉样品中水分、脂肪和蛋白质多组分的同时测定，所得结果与化学法测得结果相符。     

pH滴定—主成分回归法同时测定酚类同系物

本文利用ｐＨ滴定－主成分回归法同时测定了苯酚，邻苯二酚，邻工本三酚的混合物，结果良好，通过交差验证法，本文还对采样区间和采样间隔作了详细讨论。     

pH滴定-主成分回归法同时测定酚类同系物

本文利用pH滴定－主成分回归法同时测定了苯酚、邻苯二酚、邻苯三酚的混合物，结果良好。通过交差验证法，本文还对采样区间和采样间隔作了详细讨论。     

主成分回归-紫外光度法同时测定食用香料麦芽酚和乙基麦芽酚

报道了一种快速、简便的同时测定食用香料麦芽酚、乙基麦芽酚光度法,方法基于在pH2.87的B R缓冲溶液中对麦芽酚和乙基麦芽酚两组分混合溶液进行光度测定,所得的重叠波谱数据用主成分回归法(PCR)、经典最小二乘法(CLS)和偏最小二乘法(PLS)等化学计量学方法进行处理,结果表明主成分回归法(PCR)的预报误差最小。对样品进行测定,获得了较好的定量分析结果。麦芽酚和乙基麦芽酚的线性范围均为1.0～20.0mg·L-1;检出限分别为0.4347和0 5589mg·L-1。     

主成分回归残差神经网络校正算法用于近红外光谱快速测定汽油辛烷值

根据汽油辛值预测体系本身的非线性特点,提出主成分回归残差神经网络校正算法（principal component regression residual artificial neural network,PCRRANN)用于近红外测定汽油辛烷值的预测模型校正,该方法给合了主成分回归算法（PC）,与经典的线性校正算法（PLS（Partial Least Square),PCR, 以及非线性PLS（NPLS,Non-linear PLS)等相比,预测明显的改善,文中还讨论了PCR主成分数及训练参数对预则模可能的影响。     

Construction of stable multivariate calibration models using unsupervised segmented principal component regression

In multivariate spectral calibration by principal component regression (PCR), the principal components (PCs) are calculated from the response data measured at all employed instrument channels; however some channels are redundant and their responses do not possess useful information. Thus, the extracted PCs possess mixed information from both useful and redundant channels. In this work, we propose a segmentation approach based on unsupervised pattern recognition to identify the most informative spectral region and then to construct a stable multivariate calibration model by PCR. In this method, the instrument channels are clustered into different segments via Kohonen self‐organization map. The spectral data of each segment are then subjected to PCA and the derived PCs are used as input variables for an inverse least square (ILS) regression model employing stepwise selection of the informative PCs. The proposed method was evaluated by the analysis of four simulated and six experimental data sets. It was found that our proposed method can model the above data sets with prediction errors lower than conventional partial least squares (PLS) and PCR methods. In addition, the prediction ability of our method was better than the previously reported models for these data sets. Copyright © 2011 John Wiley & Sons, Ltd.     

Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis

ABSTRACT

It is well known that bromodomain-containing protein 4 (BRD4) has been thought as a promising target utilized for treating various human diseases, such as inflammatory disorders, malignant tumours, acute myelogenous leukaemia (AML), bone diseases, etc. For this study, molecular dynamics (MD) simulations, binding free energy calculations, and principal component analysis (PCA) were integrated together to uncover binding modes of inhibitors 8P9, 8PU, and 8PX to BRD4(1). The results obtained from binding free energy calculations show that van der Waals interactions act as the main regulator in bindings of inhibitors to BRD4(1). The information stemming from PCA reveals that inhibitor associations extremely affect conformational changes, internal dynamics, and movement patterns of BRD4(1). Residue-based free energy decomposition method was wielded to unveil contributions of independent residues to inhibitor bindings and the data signify that hydrogen bonding interactions and hydrophobic interactions are decisive factors affecting bindings of inhibitors to BRD4(1). Meanwhile, eight residues Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asn140, and Ile146 are recognized as the common hot interaction spots of three inhibitors with BRD4(1). The results from this work are expected to provide a meaningfully theoretical guidance for design and development of effective inhibitors inhibiting of the activity of BRD4.     

Use of principal component analysis and a spectral mapping technique for the evaluation of the antifungal activity of anthracene-based synthetic dyes

The antifungal activity of 14 anthracene-based synthetic dyes and 6 reference compounds was measured on 36 fungal strains and the data matrix was evaluated separately by principal component analysis (PCA) and using a spectral mapping technique (SPM). The dimensionality of the maps of principal component loadings and variables and the selectivity maps was reduced to two by non-linear mapping. Except for two compounds, the dyes showed marked antifungal activity. Calculations proved that both the strength and selectivity of the biological effect of anthracene-based dyes were highly dependent on the chemical structure of the dye and on the type of fungi. PCA and SPM revealed different aspects of the antifungal activity, therefore, their simultaneous application in future quantitative structure–activity relationship studies is highly recommended.     

Hidden information in principal component analysis of ToF‐SIMS data: On the use of correlation loadings for the identification of significant signals and structure elucidation

In this paper, an improved approach to interpret results of principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) spectra is presented. Signals are typically observed in different intensity ranges in a single ToF‐SIMS spectrum due to different sensitivity factors and surface concentrations. This can complicate the PCA interpretation, because loadings are reported to be strongly affected by these intensity changes. In contrast, it is shown here that correlation loadings are unaffected by these differences. In particular, correlation loadings were successfully used to identify signals with relatively low intensity but high significance. These signals may be overlooked when only loadings are used. This is particularly true in failure analysis, where ToF‐SIMS is used to screen for initially unknown signals that may be relevant for the characteristics/failure of a product. As a model study, the concept was applied to investigate ageing of Li‐ion batteries by ToF‐SIMS. In this data set, the significance of impurities that affect the quality of Li‐ion batteries was identified only by correlation loadings, whereas the loadings were found to overestimate the influence of other matrix signals. In addition, correlation loadings aid in the chemical identification and helped to successfully assign unknown peaks.     

Simultaneous spectrophotometric determination of ethinylestradiol and levonorgestrel by partial least squares and principal component regression multivariate calibration

Two spectrophotometric methods for the determination of Ethinylestradiol (ETE) and Levonorgestrel (LEV) by using the multivariate calibration technique of partial least square (PLS) and principal component regression (PCR) are presented. In this study the PLS and PCR are successfully applied to quantify both hormones using the information contained in the absorption spectra of appropriate solutions. In order to do this, a calibration set of standard samples composed of different mixtures of both compounds has been designed. The results found by application of the PLS and PCR methods to the simultaneous determination of mixtures, containing 4–11 μg ml⁻¹ of ETE and 2–23 μg ml⁻¹ of LEV, are reported. Five different oral contraceptives were analyzed and the results were very similar to that obtained by a reference liquid Chromatographic method.     

Cross‐validation in PCA models with the element‐wise k‐fold (ekf) algorithm: theoretical aspects

Cross‐validation has become one of the principal methods to adjust the meta‐parameters in predictive models. Extensions of the cross‐validation idea have been proposed to select the number of components in principal components analysis (PCA). The element‐wise k‐fold (ekf) cross‐validation is among the most used algorithms for principal components analysis cross‐validation. This is the method programmed in the PLS_Toolbox, and it has been stated to outperform other methods under most circumstances in a numerical experiment. The ekf algorithm is based on missing data imputation, and it can be programmed using any method for this purpose. In this paper, the ekf algorithm with the simplest missing data imputation method, trimmed score imputation, is analyzed. A theoretical study is driven to identify in which situations the application of ekf is adequate and, more importantly, in which situations it is not. The results presented show that the ekf method may be unable to assess the extent to which a model represents a test set and may lead to discard principal components with important information. On a second paper of this series, other imputation methods are studied within the ekf algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

Spectrophotometric study of zirconocene/polymethylalumoxane catalytic systems: principal component analysis and parametric modeling

Transformation of electronic absorption spectra of zirconocene catalytic systems Ph₂CCpFluZrCl₂-polymethylalumoxane (MAO) and rac-Me₂Si(2-Me,4-PhInd)₂ZrCl₂-MAO (Flu is fluorenyl, Ind is indenyl) in toluene was studied upon a change in the ratio of reactants Al_MAO/Zr from 0 to 3000 mol mol⁻¹. Analysis of the spectroscopic data using statistical methods determined the number of reaction products in each system. A reaction model including three equilibria and being common for the both systems was proposed. Effective equilibrium constants and absorption spectra of individual reaction products were determined by parametric self-modeling of the experimental spectra. Published in Russian in Izvestiya Akademii Nauk. Seriya Khimicheskaya, No. 10, pp. 2257–2264, October, 2005.     

New insights into the meaning and usefulness of principal component analysis of concatenated trajectories

A comparison between different conformations of a given protein, relating both structure and dynamics, can be performed in terms of combined principal component analysis (combined‐PCA). To that end, a trajectory is obtained by concatenating molecular dynamics trajectories of the individual conformations under comparison. Then, the principal components are calculated by diagonalizing the correlation matrix of the concatenated trajectory. Since the introduction of this approach in 1995 it has had a large number of applications. However, the interpretation of the eigenvectors and eigenvalues so obtained is based on intuitive foundations, because analytical expressions relating the concatenated correlation matrix with those of the individual trajectories under consideration have not been provided yet. In this article, we present such expressions for the cases of two, three, and an arbitrary number of concatenated trajectories. The formulas are simple and show what is to be expected and what is not to be expected from a combined‐PCA. Their correctness and usefulness is demonstrated by discussing some representative examples. The results can be summarized in a simple sentence: the correlation matrix of a concatenated trajectory is given by the average of the individual correlation matrices plus the correlation matrix of the individual averages. From this it follows that the combined‐PCA of trajectories belonging to different free energy basins provides information that could also be obtained by alternative and more straightforward means. © 2014 Wiley Periodicals, Inc.     

Generalization of multivariate optical computations as a method for improving the speed and precision of spectroscopic analyses

Multivariate optical computations (MOCs) offer improved analytical precision and increased speed of analysis via synchronous data collection and numerical computation with scanning spectroscopic systems. The improved precision originates in the redistribution of integration time from spurious channels to informative channels in an optimal manner for increasing the signal‐to‐noise ratio with multivariate analysis under the constraint of constant total analysis time. In this work, MOCs perform the multiplication and addition steps of spectral processing by adjusting the integration parameters of the optical detector or adjusting the scanning profile of the tunable optical filter. Improvement in the precision of analysis is achieved via the implicit optimization of the analytically useful signal‐to‐noise ratio. The speed improvements are realized through simpler data post‐processing, which reduces the computation time required after data collection. Alternatively, the analysis time may be significantly truncated while still seeing an improvement in the precision of analysis, relative to competing methods. Surface plasmon resonance (SPR) spectroscopic sensors and visible reflectance spectroscopic imaging were used as test beds for assessing the performance of MOCs. MOCs were shown to reduce the standard deviation of prediction by 15% compared to digital data collection and analysis with the SPR and up to 45% for the imaging applications. Similarly, a 30% decrease in the total analysis time was realized while still seeing precision improvements. Copyright © 2008 John Wiley & Sons, Ltd.