偏最小二乘紫外分光光度法同时测定丁烯二酸的顺反异构体

将偏最小二乘法用于紫外分光光度分析,在pH=1.4的磷酸溶液中,同时测定了丁烯二酸的顺、反异构体。确定了测定的最佳波长范围为190～268nm;测得23个混合标样的吸光度值用于建立模型,顺、反丁烯二酸的浓度范围为3.0～14.0mg/L和1.0～13.0mg/L。所建立的测定二者模型的相关系数分别为0.9951和0.9983;平均回收率分别为100.8%和100.7%;均方根误差(RMSE)分别为0.3667和0.2233;预测相对误差(REP)分别为5.05%和3.49%。对3个批次反丁烯二酸样品的测定结果与高效液相色谱法的测定结果进行比较,经成对t检验表明,两种方法的测定结果无显著性差异。     

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。     

偏最小二乘法用于铽、钍、铒的同时测定

偏最小二乘法用于紫外可见光度分析测定多组分间相互作用较显著的铽、钍、铒稀土体系得到较好的结果.考查了波长和间隔选取对计算精度的影响,与卡尔曼滤波法对比,证明了本法的优越性和广泛适用性.     

偏最小二乘分光光度法同时测定扑热息痛等五组分含量

本文将偏最小二乘分光光度法应用于感冒液中扑热息痛、扑尔敏、咖啡因、对氨基酚和愈创木酚甘油醚五组分含量的分析研究,介绍了基本原理和具体的分析步骤。实验结果表明,偏最小二乘分光光度法对样品各组分的平均回收率在96．4％－105．0％之间,样品不经分离即可同时测定,取得了满意的分析结果。     

应用最小二乘法于壳聚糖测定的数据处理

应用紫外分光光度法和旋光法分别测定了壳聚糖的含量.在建立壳聚糖浓度与吸光度(紫外分光光度法)及壳聚糖浓度与旋光度(旋光法)之间的线性方程中,采用最小二乘法(LS法)对相关数据进行了处理.试验结果所示:用LS法所得上述两种测定方法的拟合曲线得出的测定结果,与常规方法所得曲线相比,具有更高的精密度和准确度.再者,两种测定方法的拟合曲线相比较,紫外分光光度法的曲线具有更高的精密度.     

偏最小二乘光度法同时测定多种酚的研究及应用

利用Ｃｕ（Ⅱ）吡啶能与酚形成稳定的三元配合物的特点，研究了Ｃｕ（Ⅱ）－吡啶－酚三元显色新体系，并以偏最小二乘法建立模型预测，同时测定了模拟水样和环境水样中的对苯二酚、间苯二酚、邻苯三酚和对硝基苯酚，取得满意效果。     

构建支持向量机-偏最小二乘法为药物构效关系建模

为研究药物构效关系积累样本数据的过程中,需为小样本建模。此时较易造成过拟合,影响模型的预测性能和稳定性。为此可用偏最小二乘(PLS)法从样本数据中成对地提取最优成分,消除自变量间的复共线性,并有效的降维,然后应用最小二乘支持向量机对成对成分进行非线性回归,并以基于误差修正的策略调整,使之更有效地表达自、因变量间的非线性关系。由此构建为EB-LSSVM-PLS算法,所建模型的预报精度高,稳定性良好。将其应用于新型黄烷酮类衍生物的QSAR建模,效果令人满意,其泛化性能优于其它方法。     

Flow-Injection Simultaneous Chemiluminescence Determination of Ascorbic Acid and L-Cysteine with Partial Least Squares Calibration

In this paper, a flow-injection chemiluminescence system is proposed for simultaneous determination of ascorbic acid and L-cysteine with partial least squares calibration. This method is based on the fact that both AA and Cys can quantitatively reduce Fe³⁺ to Fe²⁺, and that the reaction rates of AA and Cys with Fe³⁺ are different. The reduced product Fe²⁺ was detected with the luminol-Fe²⁺–O₂ CL system. The CL intensity was measured and recorded at different reaction times of Fe³⁺ with AA and Cys, and the obtained data was processed by the chemometric approach of partial least squares. The experimental calibration set was composed of 16 sample solutions using an orthogonal calibration design for two component mixtures. The calibration curve was linear over the concentration range of 0.066µgmL^–1 and 0.440µgmL^–1 for ascorbic acid and L-cysteine, respectively. The proposed method was successfully applied to the simultaneous determination of both analytes in pharmaceutical formulations and human urine samples.     

偏最小二乘法用于荧光光度法同时测定铈,镨,铽

本文将偏最小二乘法(PLS)用于荧光光度法同时测定铈、镨、铽。对荧光光度法的测定条件及PLS法中系列校准样品的实验设计及测量波长点的选择等进行了试验和讨论。所建立的方法用于天然混合稀土氧化物中铈、镨、铽的测定,获得较满意的结果     

Near-Infrared Spectroscopy Analytical Model Using Ensemble Partial Least Squares Regression

A novel ensemble-based feature selection method was developed which is designated as ensemble partial least squares regression coeffientents (EPRC). It was composed of two steps: generating a series of different single feature selectors and aggregating them to reach a consensus. Specifically, the bootstrap resampling approach was used to generate a diversity of single feature selectors, and the absolute values of the regression coefficients of the partial least squares (PLS) model were used to rank the features. Next, these feature rankings out of single feature selectors were aggregated by the weighted-sum approach. Finally, coupled with the regression model, the features selected by EPRC were evaluated through cross validation and an independent test set. By experiments of constructing the spectroscopy analysis model on three near infrared spectroscopy (NIRS) datasets, it was shown that the EPRC located key wavelengths, gave a promotion to regression performance, and was more stable and interpretable to the domain experts.     

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.