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1.
偏最小二乘法测定复方乙酰水杨酸片中的有效成分   总被引:3,自引:0,他引:3  
将偏最小二乘法(PLS)与近红外漫反射光谱法相结合,对复方乙酰水杨酸片进行无损非破坏定量分析.建立了最佳的数学校正模型,比较了样品中3种有效成分(乙酰水杨酸、非那西丁和咖啡因)同时测定和单独测定时的主成分数对PLS定量预测能力的影响,预测了未知样品。3种有效成分同时测定和单独测定建立的PLS模型具有相同的主成分数,PLS预报浓度与参考浓度具有相近的标准偏差,说明用PLS法同时测定3种组分的含量是可行的。  相似文献   

2.
前期研究工作提出了以预测均方根相对误差最小为回归目标的方法(Minimization of prediction relative error,MPRE),它能使得预测结果的均方根相对误差更小.偏最小二乘法(Partial least squares,PLS)是以预测均方根误差为回归目标,能使得预测结果的均方根误差更小.基于多模型结合的思想,提出将MPRE与PLS相结合的双模型结合多元校正方法.本方法步骤为:(1)分别采用MPRE与PLS法对校正集建模;(2)计算阈值;(3)分别采用已建立好的MPRE与PLS模型进行预测;(4)将预测结果与阈值进行比较,得到预测结果.通过对酒精的近红外光谱与汽油紫外光谱进行定量分析结果表明,本方法可进一步减小预测均方根误差与相对误差.  相似文献   

3.
光谱分析技术由于具有简单、快速、无损等优势,在复杂体系的定性和定量分析中得到了广泛应用。然而光谱中往往包含成百上千的波长点,有些波长点与研究的目标性质并不相关,加大了计算量并降低了模型的预测准确度。因此,在建立模型前需要进行变量选择。最小绝对收缩与选择算子(LASSO)可将回归系数收缩为0,进而达到变量选择的目的。该研究将LASSO用于三元调和油样品近红外光谱和生物样品拉曼光谱的变量选择,基于偏最小二乘(PLS)和多元线性回归(MLR)模型,分别对香油和肌氨酸的含量进行定量分析,并与无信息变量消除-PLS(UVE-PLS)、蒙特卡罗结合UVE-PLS(MCUVE-PLS)和随机检验-PLS(RT-PLS)3种变量选择方法进行比较。结果表明,基于LASSO的变量选择方法保留的变量数最少,运算速度最快。对三元调和油样品,LASSO-PLS预测的准确度最高;对生物样品,LASSO-MLR预测的准确度最高。因此,基于LASSO的变量选择算法有望在光谱分析领域中得到良好应用。  相似文献   

4.
邵学广  陈达  徐恒  刘智超  蔡文生 《中国化学》2009,27(7):1328-1332
偏最小二乘法(PLS)在近红外光谱(NIR)定量分析中占有重要地位,但预测结果往往容易受到样本分组和奇异样本等因素的影响,稳健性不强。多模型PLS (EPLS)方法在模型稳健性上得到提高,然而它无法识别样本中存在的奇异样本。为了同时提高模型的预测准确性和稳健性,本文提出了一种根据取样概率重新取样的多模型PLS方法,称为稳健共识PLS(RE-PLS)方法。该方法通过迭代赋权偏最小二乘法(IRPLS)计算样本回归残差得到每个校正集样本的取样概率,然后根据样本的取样概率来选择训练子集建立多个PLS模型,最后将所有PLS模型的预测结果平均作为最终预测结果。该方法用于两种不同植物样品的近红外光谱建模,并与传统的PLS及EPLS方法进行比较。结果表明该方法可以有效的避免校正集中奇异样本对模型的影响,同时可以提高预测精确度和稳健性。对于含有较多奇异样本的,复杂近红外光谱烟草实际样本,利用简单PLS或者EPLS方法建模预测效果不是很理想,而RE-PLS凭借其独特优势则有望在这种复杂光谱定量分析中得到广泛的应用。  相似文献   

5.
偏最小二乘算法( Partial least squares, PLS)可以很好地解决分析数据中的变量共线性问题,在光谱分析,尤其是近/中红外及拉曼光谱的定量分析中应用广泛。针对PLS存在的有效信息提取和噪声抑制问题,提出一种变量聚类重加权的PLS算法。通过对光谱的各波数变量进行聚类并分别建模,然后集成为全谱模型。通过对计算并赋予各子类不同的权重,根据对模型的贡献对变量进行重加权,从而提高算法的预测精度。汽油中的辛烷值预测和烟草中的烟碱含量预测两组近红外数据验证表明,所提出算法优于经典的PLS算法,其RMSEP在两组数据中分别降低32%和22%,在光谱数据的定量分析中具有潜在的应用优势。  相似文献   

6.
石油焦中微量元素对其作为预焙阳极的性能起着决定性的作用。首先,通过基于LIBS光谱构建用于石油焦中铁(Fe)和铜(Cu)定量分析的PLS校正模型。然后,考察了不同光谱预处理(归一化、多元散射校正、标准正态变换、一阶导数和二阶导数)以及变量选择算法(粒子群优化算法和变量重要性投影)对PLS校正模型预测性能的影响。建立了一种基于激光诱导击穿光谱(Laser-induced breakdown spectroscopy, LIBS)结合偏最小二乘(Partial least squares, PLS)的石油焦中微量元素定量分析方法。结果显示,与其他PLS校正模型相比,基于二阶导数和变量重要性投影的PLS模型对Fe的预测性能最优,最优的交叉验证相关系数(R-squared cross validation,R2cv)为0.966 7,均方根误差(Root mean squared error cross validation, RMSEcv)为10.282 1 mg/kg,预测集的相关系数(R-squared prediction,R2p)为0.86...  相似文献   

7.
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。  相似文献   

8.
应用异烟肼片粉末的近红外漫反射光谱数据分别结合偏最小二乘法(PLS)和径向基神经网络(RBFNN)建立定量分析模型,并用所建模型对预测集样品进行了预测,结果表明:应用RBFNN所建立的定量分析模型优于PLS模型,相关系数(r)值由0.99593提高到0.99734,交互验证均方根误差(RMSECV)值由0.00523下降到0.00423,预测均方根误差(RMSEP)值由0.00614下降到0.00501。  相似文献   

9.
利用多模型共识偏最小二乘法(cPLS)建立新生儿苯丙酮尿症(PKU)的红外光谱筛查模型,比较PLS和cPLS模型的性能。对原始光谱进行一阶微分预处理,分别用PLS和cPLS建立干血片中苯丙氨酸浓度的定量校正模型,各运行40次,以预测均方根误差(RMSEP)、平均相对误差(MRE)和预测准确率(Acc)为指标,考察两种模型对独立测试集的预测效果。PLS模型的RMSEP、MRE、Acc的平均值和标准差分别为103.3、0.32、97.1和30.0、0.07、4.4;而cPLS模型的RMSEP、MRE、Acc的平均值和标准差分别为88.4、0.26、99.3和19.8、0.04、2.4。cPLS较PLS模型预测更准确,稳定性更好,更适于建立PKU的红外光谱筛查模型。  相似文献   

10.
复杂样品近红外光谱定量分析模型的构建方法   总被引:3,自引:0,他引:3  
针对复杂样品近红外光谱分析中校正集的设计问题, 探讨了标准样品参与复杂样品建模的可行性. 通过标准样品和复杂基质样品共同构建的偏最小二乘(PLS)模型, 考察了波段筛选和建模参数对预测结果的影响. 结果表明, 采用PLS方法建立定量模型时, 校正集样品性质应该尽量与预测集样品相似, 当样品的性质相差较大时, 适当增加校正集样品的差异性可使模型具有更强的预测能力. 同时, 波段优选对提高预测结果的准确性具有重要的意义.  相似文献   

11.
This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.  相似文献   

12.
化学计量学非线性偏最小二乘算法进展评述   总被引:11,自引:0,他引:11  
吴晓华  陈德钊 《分析化学》2004,32(4):534-540
评述了偏最小二乘法(PLS)近年来非线性化的进展以及重要算法的原理、特性和应用。一类是对自变量进行非线性前处理的方法,包括二次变换、Chebychev变换和基于机理的变换;另一类是基于内部非线性映射的PLS,主要有多项式映射、样条函数映射、由遗传算法辅助实施的任意非线性映射和神经网络映射;介绍了具有类似功能的正交网模型。本文还就算法的普适性、稳健性和简易性,及其非线性化的演变进程予以评述、总结和展望。  相似文献   

13.
《Analytical letters》2012,45(9):2073-2083
Abstract

A consensus regression approach based on partial least square (PLS) regression, named as cPLS, for calibrating the NIR data was investigated. In this approach, multiple independent PLS models were developed and integrated into a single consensus model. The utility and merits of the cPLS method were demonstrated by comparing its results with those from a regular PLS method in predicting moisture, oil, protein, and starch contents of corn samples using the NIR spectral data. It was found that cPLS was superior to regular PLS with respect to prediction accuracy and robustness.  相似文献   

14.
Carbamazepine (CBZ) undergoes enzyme biotransformation through epoxidation with the formation of its metabolite, carbamazepine-10,11-epoxide (CBZE). A simple chemometrics-assisted spectrophotometric method has been proposed for simultaneous determination of CBZ and CBZE in plasma. A liquid extraction procedure was operated to separate the analytes from plasma, and the UV absorbance spectra of the resultant solutions were subjected to partial least squares (PLS) regression. The optimum number of PLS latent variables was selected according to the PRESS values of leave-one-out cross-validation. A HPLC method was also employed for comparison. The respective mean recoveries for analysis of CBZ and CBZE in synthetic mixtures were 102.57 (+/-0.25)% and 103.00 (+/-0.09)% for PLS and 99.40 (+/-0.15)% and 102.20 (+/-0.02)%. The concentrations of CBZ and CBZE were also determined in five patients using the PLS and HPLC methods. The results showed that the data obtained by PLS were comparable with those obtained by HPLC method.  相似文献   

15.
《Analytical letters》2012,45(2):340-348
Synchronous 2D correlation spectroscopy was first proposed to select informational spectral intervals in PLS calibration. The proposed method could extract the spectral intervals related to analyte. The results of its application to NIR/PLS determination of quercetin in extract of Ginkgo biloba leaves showed that the proposed method could find out an optimized region with which one could improve the performance of the corresponding PLS model, in terms of low prediction error, root mean square error of prediction (RMSEP), and comparing with the result obtained using whole spectra and interval PLS.  相似文献   

16.
A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. However, the repeatability of the number of selected variables was improved significantly.  相似文献   

17.
成忠  诸爱士 《分析化学》2008,36(6):788-792
针对光谱数据峰宽、局部效应显著、含有噪音、变量个数多及彼此间常存在严重的复共线性等问题,改进和设计一种光谱数据局部校正方法:基于窗口平滑的段式正交信号校正方法,并将之结合偏最小二乘回归,以实现光谱数据的预处理及定量分析。通过NIPALS算法初始化将滤去的正交成分,以近邻分段方式进行逐个波长点的正交信号校正。而后将去噪后的光谱矩阵作为新的自变量阵,通过偏最小二乘回归构建其与性质参变量间的校正模型。通过小麦近红外漫反射光谱数据的应用实验结果表明,本方法正交成分估计稳定,去噪明显,模型的预报性能优于其它方法,PLS成分数减少,模型更加简洁。  相似文献   

18.
19.
Carbamazepine (CBZ) and phenytoin (PHT) are two antiepileptic drugs which are used simultaneously. In this paper a partial least-squares (PLS) calibration method is described for the simultaneous spectrophotometric determination of CBZ and PHT in plasma. Standard binary mixtures of CBZ and PHT have been resolved by application of PLS-1 to their UV spectra. Then, the binary standard solutions, spiked to plasma, were prepared and after the extraction of the drugs, their corresponding UV spectrum were analyzed by PLS regression to calculate the concentration of drugs in unknown plasma. A leave one out cross-validation procedure was employed to find the optimum numbers of latent variables using PRESS. A HPLC method was also applied for simultaneous determination of two drugs in the plasma and in methanol. The mean recoveries obtained by PLS were 98.4 and 98.2 for CBZ and PHT and those obtained by HPLC were 100.1 and 101.7, respectively. Although, the HPLC method showed better performance than PLS, it was found that the results obtained by PLS were comparable with those obtained by HPLC method.  相似文献   

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