基于分子相互作用力场分析的多氯代二苯并二噁英定量结构-色谱保留关系

提出了基于分子相互作用力场(MIF)、应用偏最小二乘(PLS)与多区组偏最小二乘(MBPLS)分析相结合,建立并检验多氯代二苯并二噁英(PCDD)定量结构-色谱保留关系(QSRR)模型的研究方法。分别以表征van der Waals、氢键和疏水效应的C3、H和DRY探针,计算75种PCDD的分子相互作用力场,并与其气相色谱Kov偄ts保留指数进行PLS与MBPLS分析,建立了拟合与预测效果良好的QSRR模型。其中MBPLS模型相关系数r2为0.998;交叉验证的相关系数q2为0.994。采用投影变量重要性方法判断了各种效应在PCDD色谱保留中的贡献。结果表明:van der Waals作用的影响最大,其次为疏水效应,而氢键效应影响较小。     

仪器噪声在近红外光谱偏最小二乘模型内的传播效应

在实际多元校正应用中有很多因素会影响偏最小二乘(PLS)模型的预测效果,作为光谱数据本源的仪器噪声是其中的重要影响因素。以往的研究工作多使用各种滤波器或平滑方法来降低仪器噪声的影响,然而对于仪器噪声如何影响偏最小二乘的建模过程和模型预测能力鲜有报道。该文阐述并论证了仪器噪声怎样通过第一个隐变量的计算被引入模型中,经过对偏最小二乘计算过程的理论推导,论述了噪声的引入对偏最小二乘权重向量、载荷向量计算具有累积效应,并随着后续隐变量的计算不断在模型中传递,从而对偏最小二乘模型产生影响。同时对偏最小二乘模型的预测误差进行理论分解,将其划分为无噪理想模型本身的误差和由噪声传播导致的误差。结果表明,仪器噪声不仅会降低偏最小二乘模型的预测性能,还会影响偏最小二乘模型的最优复杂度选择。     

两种梅花香气成分的分析及QSRR研究

采用固相微萃取(SPME)-气相色谱质谱(GC-MS)联用技术分析两种梅花的香气成分,通过保留指数与质谱解析相结合,分别对化合物进行定性分析.采用偏最小二乘回归(PLS)及多元线性回归(MLR)方法分别建立定量结构-色谱保留关系(QSRR)预测模型,并对训练集及测试集中化合物的保留指数进行预测.该研究为建立有效的GC-MS定性方法提供了一定的依据.     

Bagging偏最小二乘和Boosting偏最小二乘算法的金银花醇沉过程近红外光谱定量模型预测能力研究

建立金银花醇沉过程中稳健的近红外光谱( Near infrared spectroscopy,NIR)定量模型,为金银花醇沉过程的快速评价提供方法。研究基于金银花醇沉过程绿原酸的 NIR 数据,通过建立 Bagging 偏最小二乘(Bagging-PLS)模型、Boosting偏最小二乘(Boosting-PLS)模型与偏最小二乘(Partial Least Squares,PLS)模型,实现对模型性能比较;在此基础上,采用组合间隔偏最小二乘法( Synergy interval partial least squares,siPLS)和竞争自适应抽样( Competitive adaptive reweighted sampling,CARS )法分别对光谱进行变量筛选,建立模型,实现了对模型预测性能的考察。实验结果表明, Bagging-PLS和Boosting-PLS(潜变量因子数设为10)的预测性能均优于 PLS 模型。在此基础上,两批样品采用 siPLS 筛选变量,第一个批次金银花筛选波段820~1029.5 nm和1030~1239.5 nm,第二个批次金银花醇沉筛选波段为820~959.5 nm和960~1099.5 nm;采用CARS方法变量筛选,两批样品分别选择5折交叉验证和10折交叉验证,取交叉验证均方根误差( RMSECV)值最小的子集作为最终变量筛选的结果。经过变量筛选的两批金银花醇沉过程中的绿原酸含量Bagging-PLS和Boosting-PLS模型的预测均方根误差(RMSEP)值降低了0.02~0.04 g/L,预测相关系数提高了4%~5%。综上,Baggning-PLS和Boosting-PLS算法可作为金银花醇沉过程NIR定量模型的快速预测方法。     

利用理论化学描述符预测多氯联苯的贝类净化

利用理论化学描述符和偏最小二乘法（PLS）对多氯联苯（PCBs）在贝类perna viridis和dreissena polymorpha体内的净化速率常数（kd）分别进行模拟分析,获得两个定量结构-活性相关模型（QSAR）.模型的交叉验证相关系数（Q2cum）分别为0.501,0.756,标准偏差为别为0.084,0.076,模型具有较高的预测能力和可靠性.模型中具有重要意义的参数包括平均分子极化率（α）,分子体积（MV）,分子质量（MW）,分子表面积（S）及总能（TE）.这些参数表明范德华力在贝类净化PCBs的过程中起到关键作用,PCBs在贝类体内的净化机制可能为PCBs在生物相和水相间的分配作用.     

基于氨基酸描述符SVHEHS的ACE抑制肽QSAR研究

收集20种天然氨基酸的457种理化性质,按照疏水、电性特征、氢键贡献和立体特征分类后,对它们分别进行主成分分析(Principal component analysis,PCA),得到一个新的氨基酸残基结构描述符SVHEHS.用该描述符分别对血管紧张素转化酶(AngiotensinⅠconverting enzyme,ACE)抑制二肽、三肽、四肽进行序列表征,并用来与生物活性建立偏最小二乘(Partial least square regression,PLS)模型.ACE抑制二肽、三肽、四肽模型的相关系数、交叉验证相关系数、 均方根误差、外部验证相关系数分别为0.607,0.507,0.587,0.783;0.852,0.813,0.232,0.839;1,1,0,0.935.由此说明,采用SVHEHS描述符建立的PLS模型拟合、预测能力均较好,可用于血管紧张素转化酶抑制肽的定量构效关系研究.     

粒子群最小二乘支持向量机结合偏最小二乘法用于芝麻油质量的鉴别

结合粒子群最小二乘支持向量机(PSO-LSSVM)与偏最小二乘法(PLS)提出一种基于气相色谱技术的新方法,对芝麻油进行真伪鉴别,并对掺伪品中掺假比例进行定量分析。采用主成分分析法(PCA)对857个样本的脂肪酸色谱数据进行分析,优选主成分作为最小二乘支持向量机(LSSVM)的输入向量。利用粒子群算法(PSO)优化LSSVM,构建芝麻油掺伪鉴别的两级分类模型,同时运用PLS建立掺伪芝麻油中掺伪油脂的定量校正模型,两级分类模型的准确率分别达到了100%和98.7%,定量分析模型的平均预测标准偏差(RMSEP)为3.91%。结果表明,本方法的鉴别准确性和模型泛化能力均优于经典的BP神经网络和支持向量机(SVM),可用于食用油脂加工和流通环节的质量控制,为食用油质量的准确鉴定提供了一条有效途径。     

遗传算法用于偏最小二乘方法建模中的变量筛选

利用全局搜索方法－遗传算法（genetic algorithms,GA)对近红外光谱分析中的波长变量进行筛选,再用偏最小二乘方法（patrial least squares,PLS）建立分析校正模型。对两类样品的近红外光谱分析应用实例表明,这种选取变量进行校正的方法,不仅简化、优化了模型,而且增强了所建模型的预测能力,尤其适用于单纯PLS较以校正关联的体系。     

支持向量机用于多氯代萘毒性的定量构效研究

用偏最小二乘法(PLS)和留一交叉验证从90多个量子化学参数中筛选出极化率、分子量、部分原子上的净电荷、静电势等作为描述符,应用支持向量机(SVM)对20个多氯代萘同系物的三组毒性数据分别建立了定量构效关系模型.所得模型的交叉验证相关系数的平方分别为0.805、0.890、0.936.并将偏最小二乘法建模所得结果与之进行比较,结果表明,SVM预报能力优于PLS.     

独立分量分析预处理法提高苹果糖度模型预测精度研究

为了提高苹果近红外光谱糖度预测模型精度,利用独立分量分析方法(ICA)对苹果近红外光谱进行了预处理,并且建立了糖度的偏最小二乘(PLS)预测模型。结果表明,独立分量分析不但能分离出噪声信号,而且所分离出来的光谱信号也比原始光谱信号光滑。在预处理后的最佳PLS糖度模型校正时的相关系数rc和标准偏差SEC分别为0.9549和0.3361,用于预测时的相关系数rp和标准偏差SEP分别为0.9071和0.4355。与普通的平均处理法的PLS模型相比,其精度有所提高,且模型更加简洁。     

Stacked partial least squares regression analysis for spectral calibration and prediction

Two novel algorithms which employ the idea of stacked generalization or stacked regression, stacked partial least squares (SPLS) and stacked moving‐window partial least squares (SMWPLS) are reported in the present paper. The new algorithms establish parallel, conventional PLS models based on all intervals of a set of spectra to take advantage of the information from the whole spectrum by incorporating parallel models in a way to emphasize intervals highly related to the target property. It is theoretically and experimentally illustrated that the predictive ability of these two stacked methods combining all subsets or intervals of the whole spectrum is never poorer than that of a PLS model based only on the best interval. These two stacking algorithms generate more parsimonious regression models with better predictive power than conventional PLS, and perform best when the spectral information is neither isolated to a single, small region, nor spread uniformly over the response. A simulation data set is employed in this work not only to demonstrate this improvement, but also to demonstrate that stacked regressions have the potential capability of predicting property information from an outlier spectrum in the prediction set. Moisture, oil, protein and starch in Cargill corn samples have been successfully predicted by these new algorithms, as well as hydroxyl number for different instruments of terpolymer samples including and excluding an outlier spectrum. Copyright © 2009 John Wiley & Sons, Ltd.     

Algorithms for multi‐group PLS

Several approaches of investigation of the relationships between two datasets where the individuals are structured into groups are discussed. These strategies fit within the framework of partial least squares (PLS) regression. Each strategy of analysis is introduced on the basis of a maximization criterion, which involves the covariances between components associated with the groups of individuals in each dataset. Thereafter, algorithms are proposed to solve these maximization problems. The strategies of analysis can be considered as extensions of multi‐group principal components analysis to the context of PLS regression. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust partial least squares regression: Part I,algorithmic developments

The work summarized in this paper presents the first part of a three‐paper series on robust partial least squares (RPLS) regression. Motivated by recent research activities in this area, this part provides a detailed algorithmic analysis of associated techniques, showing that existing work (i) may not represent a true robust formulation of partial least squares (PLS), (ii) may lead to convergence problems or (iii) may be insensitive to a certain type of outlier. On the basis of this analysis, Part I introduces a new conceptual RPLS algorithm that overcomes the deficiencies of existing work. The second part of this work details this new RPLS technique, compares its peformance with existing RPLS methods and provides an analysis on the computational efficiency and sensitivity of these algorithms. Whilst the first two parts of this work discuss algorithmic developments of RPLS, the final part concentrates on practical issues of RPLS implementations. This third part is devoted to practitioners of chemistry and chemical engineering covering a wide range of applications involving a calibration experiment, the analysis of recorded data from an industrial debutanizer process and data from a number of Raman spectroscopy experiments. Copyright © 2007 John Wiley & Sons, Ltd.     

Averaged and weighted average partial least squares

Two alternative partial least squares (PLS) methods, averaged PLS and weighted average PLS, are proposed and compared with the classical PLS in terms of root mean square error of prediction (RMSEP) for three real data sets. These methods compute the (weighted) average of PLS models with different complexity. The prediction abilities of the alternative methods are comparable to that of the classical PLS but they do not require to determine how many components should be included in the model. They are also more robust in the sense that the quality of prediction depends less on a good choice of the number of components to be included. In addition, weighted average PLS is also compared with the weighted average part of LOCAL, a published method that also applies weighted average PLS, with however an entirely different weighting scheme.     

Canonical partial least squares—a unified PLS approach to classification and regression problems

We propose a new data compression method for estimating optimal latent variables in multi‐variate classification and regression problems where more than one response variable is available. The latent variables are found according to a common innovative principle combining PLS methodology and canonical correlation analysis (CCA). The suggested method is able to extract predictive information for the latent variables more effectively than ordinary PLS approaches. Only simple modifications of existing PLS and PPLS algorithms are required to adopt the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.     

Rapid Determination of Cotton Content in Textiles by Near-Infrared Spectroscopy and Interval Partial Least Squares

Textile products must be marked by fabric type and composition on the label and cotton is by far the most important fiber in the industry and often needs fast quantitative analysis. The corresponding standard methods are very time-consuming and labor-intensive. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and interval-based partial least squares (iPLS) for determining cotton content in textiles. Three types of partial least square (PLS)-based algorithms were used for experimental measurements. A total of 91 cloth samples with cotton content ranging from 0 to 100% (w/w) were collected and all compositions are commercially available on the market in China. In all cases, the original spectrum axis was split into 20 subintervals. As a result, three final models, i.e., the iPLS model on a single subinterval, the backward interval partial least squares (biPLS) model on the region remaining six subintervals, and the moving window partial least squares (mwPLS) model with a window of 75 variables, achieved better results than the full-spectrum PLS model. Also, no obvious differences in performance were observed for the three models. Thus, either iPLS or mwPLS was preferred considering their simplicity, which suggested that iPLS and mwPLS combined with NIR technique may have potential for the rapid determination of the cotton content of textile products with comparable accuracy to standard procedures. In addition, this approach may have commercial and regulatory advantages that avoid labor-intensive and time-consuming chemical analysis.     

Specificity of the proteasome cleavage to the antigen protein

In the MHC classⅠmolecule binding antigenic peptides processing and presentation pathway,the ubiquitin-proteasome system plays a key role in degrading the protein substrate.For the purpose of studying the specificities of proteasomal cleavage sites,partial least squares method is used to predict the proteasomal cleavage sites,and the predictive accuracy of the model is 82.8%.The specificities of the cleavage sites and the adjacent positions come from the contribution of the amino acids of the samples to the...     

Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares

Changeable size moving window partial least squares (CSMWPLS) and searching combination moving window partial least squares (SCMWPLS) are proposed to search for an optimized spectral interval and an optimized combination of spectral regions from informative regions obtained by a previously proposed spectral interval selection method, moving window partial least squares (MWPLSR) [Anal. Chem. 74 (2002) 3555]. The utilization of informative regions aims to construct better PLS models than those based on the whole spectral points. The purpose of CSMWPLS and SCMWPLS is to optimize the informative regions and their combination to further improve the prediction ability of the PLS models. The results of their application to an open-path (OP)/FT-IR spectra data set show that the proposed methods, especially SCMWPLS can find out an optimized combination, with which one can improve, often significantly, the performance of the corresponding PLS model, in terms of low prediction error, root mean square error of prediction (RMSEP) with the reasonable latent variable (LVs) number, comparing with the results obtained using whole spectra or direct combination of informative regions for a compound. Regions consisting of the combinations obtained can easily be explained by the existence of IR absorption bands in those spectral regions.     

Simultaneous Determination of Dipyrone and Papaverine in Pharmaceutical Formulation using PLS Regression and UV Spectrophotometry

Abstract

A combination of sodium dipyrone and papaverine hydrochloride is used as an analgesic and antispasmodic drug. A simple and rapid procedure is proposed for simultaneous determination of these drugs in commercial formulations (Melpaz^®) based on partial least squares (PLS) regression and UV spectrophotometric measurements in the range of 218–300 nm. The calibration set was built with 25 solutions in concentrations ranging from 15.0–35.0 mg ml^?1 for dipyrone and from 0.5–1.5 mg ml^?1 for papaverine in methanol. The relative standard deviation (RSD) was 1.05% for dipyrone and 1.55% for papaverine in pharmaceutical formulations. The percent of relative recovery was 95.9% for dipyrone and 95.2% for papaverine. Figures of merit, such as accuracy, precision, sensitivity and adjust were also determined. The methodology was validated by using an independent method, based on high performance liquid chromatography (HPLC).