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.     

Dual stacked partial least squares for analysis of near-infrared spectra

A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra. The algorithm contains two steps of stacked regression and Partial Least Squares (PLS), termed Dual Stacked Partial Least Squares (DSPLS) algorithm. First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. Several combination rules of the outer-stack step were analyzed for the proposed DSPLS algorithm. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm. Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications.     

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.     

多模型共识偏最小二乘法用于近红外光谱定量分析

将多模型共识偏最小二乘法用于近红外光谱定量分析。利用随机抽取的训练子集建立一系列偏最小二乘模型,选取其中性能较好的部分模型作为成员模型,用这些成员模型来预测未知样品。将该方法用于一组生物样本的近红外光谱与样品中人血清白蛋白、γ-球蛋白以及葡萄糖含量之间的建模研究,并与单模型偏最小二乘法了进行比较。结果 PLS对独立测试集中三种组分进行50次重复预测的平均RMSEP分别为0.1066,0.0853和0.1338,RMSEP的标准偏差分别为0.0174,0.0144和0.0416;而本方法重复预测的平均RMSEP分别为0.0715,0.0750和0.0781,RMSEP的标准偏差分别为0.0033,0.2729×10-4和0.0025。     

偏最小二乘紫外分光光度法同时测定甲基苯甲醛三种同分异构体

将偏最小二乘法(PLS)用于紫外分光光度数据的解析,建立了同时测定甲基苯甲醛3种同分异构体的模型。在230～304 nm范围内,将测得的48个样品的吸光度值作为校正集,另18个样品的吸光度值作为预测集用于建模。所建立的邻、间、对甲基苯甲醛模型的平均回收率分别为101.2%、100.2%和98.9%;均方根误差(RMSE)分别为0.2667、0.3853和0.2118;预测浓度范围分别为4.6～16.2μg/mL、5.8～17.4μg/mL和6.5～20.6μg/mL。讨论了混合物中3种同分异构体浓度比例对测定结果的影响,并确定了最佳的浓度比例范围。对模拟样品进行加标回收率试验。并通过与顺、反丁烯二酸两种同分异构体测定结果的比较,得出了有意义的结论。     

Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares,principal component analysis,and related techniques

Quantitative analysis with laser-induced breakdown spectroscopy traditionally employs calibration curves that are complicated by chemical matrix effects. These chemical matrix effects influence the laser-induced breakdown spectroscopy plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, laser-induced breakdown spectroscopy calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis techniques are employed to analyze the laser-induced breakdown spectroscopy spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis and Soft Independent Modeling of Class Analogy are employed to generate a model and predict the rock type of the samples. These Multivariate Analysis techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.     

Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares

Near-infrared (NIR) spectra in the region of 5000-4000 cm⁻¹ with a chemometric method called searching combination moving window partial least squares (SCMWPLS) were employed to determine the concentrations of human serum albumin (HSA), γ-globulin, and glucose contained in the control serum IIB (CS IIB) solutions with various concentrations. SCMWPLS is proposed to search for the optimized combinations of informative regions, which are spectral intervals, considered containing useful information for building partial least squares (PLS) models. The informative regions can easily be found by moving window partial least squares regression (MWPLSR) method. PLS calibration models using the regions obtained by SCMWPLS were developed for HSA, γ-globulin, and glucose. These models showed good prediction with the smallest root mean square error of predictions (RMSEP), the relatively small number of PLS factors, and the highest correlation coefficients among the results achieved by using whole region and MWPLSR methods. The RMSEP values of HSA, γ-globulin, and glucose yielded by SCMWPLS were 0.0303, 0.0327, and 0.0195 g/dl, respectively. These results prove that SCMWPLS can be successfully applied to determine simultaneously the concentrations of HSA, γ-globulin, and glucose in complicated biological fluids such as CS IIB solutions by using NIR spectroscopy.     

Selecting the optimum number of partial least squares components for the calibration of attenuated total reflectance-mid-infrared spectra of undesigned kerosene samples

Selecting the correct dimensionality is critical for obtaining partial least squares (PLS) regression models with good predictive ability. Although calibration and validation sets are best established using experimental designs, industrial laboratories cannot afford such an approach. Typically, samples are collected in an (formally) undesigned way, spread over time and their measurements are included in routine measurement processes. This makes it hard to evaluate PLS model dimensionality. In this paper, classical criteria (leave-one-out cross-validation and adjusted Wold's criterion) are compared to recently proposed alternatives (smoothed PLS-PoLiSh and a randomization test) to seek out the optimum dimensionality of PLS models. Kerosene (jet fuel) samples were measured by attenuated total reflectance-mid-IR spectrometry and their spectra where used to predict eight important properties determined using reference methods that are time-consuming and prone to analytical errors. The alternative methods were shown to give reliable dimensionality predictions when compared to external validation. By contrast, the simpler methods seemed to be largely affected by the largest changes in the modeling capabilities of the first components.     

Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification

Using a series of thirteen organic materials that includes novel high-nitrogen energetic materials, conventional organic military explosives, and benign organic materials, we have demonstrated the importance of variable selection for maximizing residue discrimination with partial least squares discriminant analysis (PLS-DA). We built several PLS-DA models using different variable sets based on laser induced breakdown spectroscopy (LIBS) spectra of the organic residues on an aluminum substrate under an argon atmosphere. The model classification results for each sample are presented and the influence of the variables on these results is discussed. We found that using the whole spectra as the data input for the PLS-DA model gave the best results. However, variables due to the surrounding atmosphere and the substrate contribute to discrimination when the whole spectra are used, indicating this may not be the most robust model. Further iterative testing with additional validation data sets is necessary to determine the most robust model.     

Simultaneous determination of Sb(III) and Sb(V) by partial least squares regression

A method for simultaneous spectrophotometric determination of Sb(III) and Sb(V) using multivariate calibration method is proposed. This method is based on the development of the reaction between the analytes and pyrogallol red at pH 2.00. The selection of variables was studied. A series of synthetic solutions containing different concentrations of Sb(III) and Sb(V) were used to check the prediction ability of the partial least squares model. The calibration curves were linear over the range of 0.3-3.4 and 0.3-3.0 μg ml⁻¹ for Sb(III) and Sb(V), respectively. The detection limits were 0.177 and 0.200 μg ml⁻¹ for Sb(III) and Sb(V), respectively.     

Fluorescence spectroscopic determination of triglyceride in human serum with window genetic algorithm partial least squares

Fluorescence spectrum, as well as the first and second derivative spectra in the region of 220–900 nm, was utilized to determine the concentration of triglyceride in human serum. Nonlinear partial least squares regression with cubic B‐spline‐function‐based nonlinear transformation was employed as the chemometric method. Window genetic algorithms partial least squares (WGAPLS) was proposed as a new wavelength selection method to find the optimized spectra wavelengths combination. Study shows that when WGAPLS is applied within the optimized regions ascertained by changeable size moving window partial least squares (CSMWPLS) or searching combination moving window partial least squares (SCMWPLS), the calibration and prediction performance of the model can be further improved at a reasonable latent variable number. SCMWPLS should start from the sub‐region found by CSMWPLS with the smallest root mean squares error of calibration (RMSEC). In addition, WGAPLS should be utilized within the region of smallest RMSEC whether it is the sub‐region found by CSMWPLS or region combination found by SCMWPLS. Moreover, the prediction ability of nonlinear models was better than the linear models significantly. The prediction performance of the three spectra was in the following order: second derivative spectrum < original spectrum < first derivative spectrum. Wavelengths within the region of 300–367 nm and 386–392 nm in the first derivative of the original fluorescence spectrum were the optimized wavelength combination for the prediction model. Copyright © 2012 John Wiley & Sons, Ltd.     

偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究

利用偏最小二乘将瘦肉的近红外光谱数据分别与其棕榈酸、棕榈油酸、硬脂酸、油酸、亚油酸含量建立校正模型,并用交互校验和外部检验来考查模型的可靠性.各脂肪酸模型的校正相关系数分别为0.9998、0.9844、0.9963、0.9754、0.9969,均方估计残差(RMSEC)分别为0.0231、0.0485、0.111、0.373、0.311,交互校验均方残差(RMSECV)分别为0.509、0.115、0.225、0.848、0.649.应用所建立的各脂肪酸近红外模型对瘦肉脂肪酸组成进行预测,并对各脂肪酸的预测值与气相色谱法测定值进行配对t-检验,结果表明两者差异均不显著(p>0.05).     

Borrowing information from relevant microarray studies for sample classification using weighted partial least squares

With an increasing number of publicly available microarray datasets, it becomes attractive to borrow information from other relevant studies to have more reliable and powerful analysis of a given dataset. We do not assume that subjects in the current study and other relevant studies are drawn from the same population as assumed by meta-analysis. In particular, the set of parameters in the current study may be different from that of the other studies. We consider sample classification based on gene expression profiles in this context. We propose two new methods, a weighted partial least squares (WPLS) method and a weighted penalized partial least squares (WPPLS) method, to build a classifier by a combined use of multiple datasets. The methods can weight the individual datasets depending on their relevance to the current study. A more standard approach is first to build a classifier using each of the individual datasets, then to combine the outputs of the multiple classifiers using a weighted voting. Using two quite different datasets on human heart failure, we show first that WPLS/WPPLS, by borrowing information from the other dataset, can improve the performance of PLS/PPLS built on only a single dataset. Second, WPLS/WPPLS performs better than the standard approach of combining multiple classifiers. Third, WPPLS can improve over WPLS, just as PPLS does over PLS for a single dataset.     

Gas-chromatographic fatty-acid fingerprints and partial least squares modeling as a basis for the simultaneous determination of edible oil mixtures

Partial least squares modeling and gas-chromatographic fatty-acid fingerprints are reported as a method for the simultaneous determination of cottonseed, olive, soybean and sunflower edible oil mixtures. In this work, two sets of three- and four-component combinations of oils were prepared, hydrolyzed and the obtained free fatty acids analyzed by gas chromatography (GC) without any further derivatization. The normalized percentages of the myristic (14:0), palmitic (16:0), palmitoleic (16:1), stearic (18:0), oleic (18:1), linoleic (18:2) and linolenic (18:3) acids were chromatographically measured in samples and used for constructing calibration matrix. The cross-validation method was used to select the number of factors and the proposed methods were validated by using two sets of synthetic oil mixture samples. The relative standard error for each oil in mixture samples was less than 10%. This approach allows determining possible adulteration in each of the four edible oils.     

Simultaneous kinetic spectrophotometric determination of cyanide and thiocyanate using the partial least squares (PLS) regression

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of CN⁻ and SCN⁻ ions is described. The method is based on the difference in the rate of the reaction between CN⁻ and SCN⁻ ions with chloramine-T in a pH 4.0 buffer solution and at 30 °C. The produced cyanogen chloride (CNCl) reacts with pyridine and the product condenses with barbituric acid and forms a final colored product. The absorption kinetic profiles of the solutions were monitored by measuring absorbance at 578 nm in the time range 20-180 s after initiation of the reaction with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 31 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 10.0-900.0 and 50.0-1200.0 ng mL⁻¹ for CN⁻ and SCN⁻ ions, respectively. The proposed method was successfully applied to the simultaneous determination of cyanide and thiocyanate in water samples.     

Voltammetric determination of dopamine in the presence of ascorbic and uric acids using partial least squares regression: determination of dopamine in human urine and plasma

A new differential pulse voltammetric method for dopamine determination at a bare glassy carbon electrode has been developed. Dopamine, ascorbic acid (AA) and uric acid (UA) usually coexist in physiological samples. Because AA and UA can be oxidized at potentials close to that of DA it is difficult to determine dopamine electrochemically, although resolution can be achieved using modified electrodes. Additionally, oxidized dopamine mediates AA oxidation and the electrode surface can be easily fouled by the AA oxidation product. In this work a chemometrics strategy, partial least squares (PLS) regression, has been applied to determine dopamine in the presence of AA and UA without electrode modification. The method is based on the electrooxidation of dopamine at a glassy carbon electrode in pH 7 phosphate buffer. The dopamine calibration curve was linear over the range of 1–313 μM and the limit of detection was 0.25 μM. The relative standard error (RSE %) was 5.28%. The method has been successfully applied to the measurement of dopamine in human plasma and urine.      

Optimized sample-weighted partial least squares

In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles.     

Source contributions to ambient aerosol calculated by discriminat partial least squares regression (PLS)

Partial least squares regression (PLS) is proposed for solving ir pollution source apportionment problems as an alternative method to the frequently used chemical mass balance technique. A discriminant PLS is used to calculate linear mixing proportions for a synthetic ambient aerosol data set where the truth is known. Without sacrificing orthogonality of the source profiles, PLS can resolve the emission sources and accurately predict the emission source contributions. Further extensions of the PLS approach to environmental receptor modelling are discussed.     

beta-Correction as a preprocessing method for partial least squares in simultaneous determination of zinc and lead

An error analysis of predicted values using spectral correction matrix and partial least squares (PLS) modeling is applied for the determination of Zn²⁺ and Pb²⁺ with methylthymol blue (MTB) as a metallochromic indicator. The concentration ranges for Pb²⁺ and Zn²⁺ in standard solution sets are 0.5-5.2 and 0.1-2.5 μg ml⁻¹, respectively. The experimental calibration set was composed of 20 sample solutions using a random design for two component mixtures. The absorption spectra were recorded from 400 to 700 nm. The two wavelengths, which exert the minimum error in prediction of two metal ion concentrations, are chosen according to an error analysis of different pairs of wavelengths. The effect of the pH on the sensitivity in determination of Zn²⁺ and Pb²⁺ using MTB was studied in order to choose the optimum pH (pH=6) for determination. The values of root mean square difference (RMSD) for lead and zinc using β-correction partial least squares were 0.0977 and 0.1266, respectively. The effect of diverse ions and several experimental parameters were studied. The method was used for the determination of lead and zinc in alloy samples.