多维校正用于复杂体系定量分析和小分子与DNA相互作用机理研究
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摘要
本文作者对化学计量学中多维校正用于定量分析的几个重要问题进行了方法探索和应用研究。同时,我们也利用了二阶校正方法对小分子与DNA的相互作用机理进行了探讨。本论文内容主要涉及以下几个方面：
1.提出了一种新的三线性分解算法-稳健信息自提取不对称三线性分解算法(RISEATD)。该方法把整体最小二乘理论融入到三线性模型的分解过程中,能快速有效地提取有用信息。同时该算法结合了平行因子分析(PARAFAC-ALS)和交替三线性分解方法(ATLD)的迭代特点,用一种不对称的方式分辨求解三维数据阵中三个潜在的载荷矩阵。该算法的显著特点就是当体系噪声大或共线性强时,分辨得到的感兴趣分析物的图谱都是十分稳定的,并且具有较快的收敛速度。本文还利用模拟的荧光光谱数据阵和真实的激发-发射荧光光谱数据对方法进行了测试,并与传统的PARAFAC, PARAFAC-ALS和ATLD算法计算结果进行了比较。结果表明,RISEATD方法在三维数据分解中具有优越的性能。
2.利用化学计量学中具有“二阶优势”的二阶校正方法,与三维激发-发射荧光光谱相结合,以“数学分离”代替“化学分离”,提出了采用荧光分析直接测定血浆和药片样中的盐酸特拉唑嗪含量的新方法。运用PARAFAC,交替惩罚三线性分解算法(APTLD)和RISEATD对三维荧光数据进行解析,最终实现了血样中盐酸特拉唑嗪的定量测定。该方法快速简便,无需复杂的样品预处理,花费成本低廉,定量结果满意。同时,PARAFAC, APTLD和RISEATD与标准加入法相结合,实现了对实际样品药片中盐酸特拉唑嗪的定量测定。所得结果与标准的色谱方法比较,结果令人满意。
3.本章提出了一种激发发射矩阵荧光与二阶校正方法结合,在血浆和尿液中检测右美沙芬和奎尼丁的方法。由于这两种药物的荧光与血浆和尿液基质的光谱相互叠加严重重叠,因而未经分离直接用光谱的方法来检测血样和尿样中的右美沙芬和奎尼丁几乎是不可能的。本章利用了三种二阶校正方法,PARAFAC、自加权交替三线性分解算法(SWATLD)和APTLD的二阶优势,利用数学分离来代替化学分离,实现严重干扰下对右美沙芬和奎尼丁定量测定。
4.随着现代高阶分析仪器和数据采集技术的的发展,特别是二阶校正方法在处理三维数据阵时的应用,研究药物与DNA的相互作用成为可能。即使该混合物中存在着很复杂的化学平衡,也可以很方便的预测感兴趣的组分和DNA的相互作用机制。对于二阶校正方法最值得注意的优势在于,对三维阵的分解通常是唯一的,可以直接分辨出复杂体系中感兴趣组分的相对浓度和光谱图。本章采用紫外和荧光分析结合二阶校正方法对吡柔比星与DNA的相互作用进行了研究。荧光测数据采用PARAFAC算法和交替归一加权残差(ANWE)算法进行解析,可分辨得到动力学平衡体系中各组分的激发、发射光谱以及相对浓度,为吡柔比星与DNA的相互作用机制的研究提供了更为直观的有用信息。这对抗癌药物的抗癌机理、以及新型药物的设计合成方面都有很大的帮助。
5.随着农药大面积、持续的使用，农作物、蔬菜、水果甚至动物体内都可能有农药残留,人们的生命和健康造成威胁。所以研究农药对DNA的潜在损伤作用对于保证人体免受农药的危害具有一定的实际意义。本文利用三维荧光结合二阶校正方法PARAFAC和APTLD对西维因和DNA的相互作用进行了研究,为西维因和DNA的相互作用机制的研究提供了很有用的信息。
6.最小支持向量机(LS-SVM)以其优越的性能在多元校正建模中得到越来越广泛的应用。然而,它的性能在很大的程度上还依赖于模型误差的同质性和数据集分布的均一性。该工作探讨了多元校正建模中的训练集样品的代表性和最优化样品加权问题。由于多元校正的样品光谱空间的多维性和复杂性以及样品选取过程中的不确定性,准确估计训练集样品在整个样品空间的代表性尚存在一定困难。为解决以上问题,同时考虑到样品的代表性很难通过考察单个样品进行估计,我们把全局优化样品加权的思想和最小支持向量机相结合,提出了最优化样品加权最小支持向量机这一新算法。该算法通过对原来的训练集样品进行非负加权,在校正建模过程中同时考虑了模型的复杂性和预测能力,最优样品权重通过粒子群优化算法搜索获得。将该算法应用于真实的标准数据集的结果表明,在原始校正样品的代表性较差时,最优化样品加权最小支持向量机算法确实能够很好地改善模型的预测性能。
7.在对光谱数据的多元校正建模中,传统的波长变量选择方法对某些波长的舍弃将导致有用信息的丢失。为了获得更加灵活的变量选择和建模,以粒子群优化算法为基础,提出了一种变量加权版本的最小支持向量机用于多元校正中光谱变量的选择。变量加权的策略旨在不人为删除和保留变量,允许变量的非负加权。采用粒子群优化算法实现非负的变量加权实质上可视为对波长变量的某种最优化重新刻度。若使用粒子群优化算法同时优化模型其它参数则使得变量加权的支持向量机变成一个无需人为调节参数的全自动建模方法,因此将比传统的变量选择及建模方法有更多的灵活性,且更智能化,运算速度快。将该算法应用于真实的标准数据集的结果表明变量加权最小支持向量机方法确实能在多元校正模型中实现对变量的最优化刻度,保留更多的结构信息,从而帮助得到训练和预测能力更优且智能化的回归模型。
The research work focuses on mulity-way calibration for the quantitative analysis in complex chemical systems and studies on the mechanism for the interactions of small molecules with DNA.
The robust information self-extracting asymmetric trilinear decomposition algorithm (RISEATD) has been developed, based on the total least squares principle and an asymmetric way with partial bilinearization to calculate the three underlying profile matrices in the resolution of the trilinear model. It can obtain useful information quickly. The new proposed algorithm combines the way of decomposition for PARAFAC-alternating least squares (PARAFAC-ALS) and alternating trilinear decomposition (ATLD). The results obtained by simulated data and real excitation-emission spectral data sets have shown that RISETLD method retains the second-order advantage of quantification for analyte(s) of interest even in the presence of potentially unknown interferents even when the noise and colinearity are high Comparing with PARAFAC, PARAFAC-ALS and ATLD algorithms, the developed method can supply acceptable results.
The collection of EEM fluorescence spectra of a mixture and combination with second-order calibration methods can quantify the analytes even in the presence of uncalibrated interferences that has been called the "second-order advantage". With the property of "mathematical separation" to displace "chemical separation", three second-order calibration methods based on PARAFAC, the alternating penalty trilinear decomposition (APTLD) algorithms, and RISEATD respectively, have been utilized for the direct determination of terazosin hydrochloride (THD) in human plasma samples, coupled with the excitation-emission matrix fluorescence spectroscopy. Meanwhile, the three algorithms combing with the standard addition procedures have been applied for the determination of terazosin hydrochloride in tablets and the results were validated by the high-performance liquid chromatography with fluorescence detection.
Three second-order calibration methods were presented to allow accurate and reliable quantitative analysis of dextromethorphan and quinidine in human plasma and urine samples at biological fluids by excitation-emission matrices fluorescence. PARAFAC, self-weighted alternating trilinear decomposition (SWATLD) and APTLD algorithms were applied and the performances of the three methods were compared. It has been found that all the methodologies could obtain good results.
With the development of high-order analytical instruments and chemometric algorithms, it becomes easier to obtain and resolve multi-dimensional data from complex systems. The combination of excitation-emission matrix fluorescence and second-order calibration methods could provide a powerful tool for studies of parallel competitive binding reactions of many chemical components with DNA in the presence of interferents. The relative equilibrium concentrations of the component can be directly obtained. In this paper, UV-vis spectroscopy and fluorescence were combined to study the binding of DNA with the anthacycline antibiotic drug pirarubicin (THP). Ethidium bromide (EB) as the fluorescence probe was used to study the competitive binding interactions of THP with DNA by excitation -emission fluorescence matrices coupled with PARAFAC and the alternating normalization-weighted error algorithm (ANWE) with the second-order advantage. The relative equilibrium concentrations of EB-DNA, EB and THP in the equilibrium system can be directly obtained, which makes it possible to determine the reaction pattern of different interacting pairs in a mixture medium.
The pesticide is used indiscriminately. It may residue in fruits, vegetables and ground and surface waters that lead to pose a potential hazard for consumers, so the toxicity has raised the public concern about the ecosystem and human health. Therefore, the investigation of genotoxicity and genetic damage via the interaction of DNA with insecticide is very important. Competitive binding interactions of carbaryl and the fluorescence probe EB with DNA have been studied by excitation-emission fluorescence spectroscopy to obtain a three-dimension excitation-emission fluorescence data array. The second-order calibration methods based on PARAFAC and APTLD algorithms were used to resolve the data array obtained.
Least squares-support vector machine (LS-SVM) has been introduced into multivariate calibration by many investigators for its attractive features and promising empirical performance. However, the performance of models is strongly dependent upon the homogeneity of the model errors and the uniformity of the data sampling. The representation of training samples for multivariate calibration has been discussed and the concept of weighted sampling has been introduced to multivariate calibration. Due to the high-dimensionality and complexity of spectral data space and the uncertainty involved in sampling process, the representation of training samples in the whole sample space is difficult to evaluate and select representative training samples for multivariate calibration depends largely on experiential methods. If the training samples fail to represent the sample space, sometimes the predictions of new samples can be degraded. In order to solve this problem, a new algorithm for multivariate calibration is developed by combining optimized sampling and least squares-support vector machine, where the original training samples are non-negatively weighted and the complexity and the ability of prediction of the model are considered simultaneously. Two real data sets are investigated and the results demonstrate that sample-weighted least squares-support vector machine models can improve the ability of prediction for a model when the representation of original calibration sample is poor.
For multivariate calibration, all of the wavelength variables might carry more or less molecular information, it seems more advisable to investigate all the possible variables rather than traditional variable selection. Based on particle swarm optimization algorithm, a more flexible variable selection and modeling method, variable-weighted least squares-support vector machine is proposed. The strategy of variable weighting allows non-negative weights of variables rather than removing or reserving any variables. Using particle swarm optimization (PSO) to seek the non-negative weights of variables can be seen as an optimized rescaling of the variables in certain sense. If employing PSO to search for the other parameters in the model of least squares-support vector machine at the same time, the variable-weighted least squares-support vector machine would become a total-automatically modeling approach and therefore be more flexible and intelligent than traditional variable methods.
1.提出了一种新的三线性分解算法-稳健信息自提取不对称三线性分解算法(RISEATD)。该方法把整体最小二乘理论融入到三线性模型的分解过程中,能快速有效地提取有用信息。同时该算法结合了平行因子分析(PARAFAC-ALS)和交替三线性分解方法(ATLD)的迭代特点,用一种不对称的方式分辨求解三维数据阵中三个潜在的载荷矩阵。该算法的显著特点就是当体系噪声大或共线性强时,分辨得到的感兴趣分析物的图谱都是十分稳定的,并且具有较快的收敛速度。本文还利用模拟的荧光光谱数据阵和真实的激发-发射荧光光谱数据对方法进行了测试,并与传统的PARAFAC, PARAFAC-ALS和ATLD算法计算结果进行了比较。结果表明,RISEATD方法在三维数据分解中具有优越的性能。
2.利用化学计量学中具有“二阶优势”的二阶校正方法,与三维激发-发射荧光光谱相结合,以“数学分离”代替“化学分离”,提出了采用荧光分析直接测定血浆和药片样中的盐酸特拉唑嗪含量的新方法。运用PARAFAC,交替惩罚三线性分解算法(APTLD)和RISEATD对三维荧光数据进行解析,最终实现了血样中盐酸特拉唑嗪的定量测定。该方法快速简便,无需复杂的样品预处理,花费成本低廉,定量结果满意。同时,PARAFAC, APTLD和RISEATD与标准加入法相结合,实现了对实际样品药片中盐酸特拉唑嗪的定量测定。所得结果与标准的色谱方法比较,结果令人满意。
3.本章提出了一种激发发射矩阵荧光与二阶校正方法结合,在血浆和尿液中检测右美沙芬和奎尼丁的方法。由于这两种药物的荧光与血浆和尿液基质的光谱相互叠加严重重叠,因而未经分离直接用光谱的方法来检测血样和尿样中的右美沙芬和奎尼丁几乎是不可能的。本章利用了三种二阶校正方法,PARAFAC、自加权交替三线性分解算法(SWATLD)和APTLD的二阶优势,利用数学分离来代替化学分离,实现严重干扰下对右美沙芬和奎尼丁定量测定。
4.随着现代高阶分析仪器和数据采集技术的的发展,特别是二阶校正方法在处理三维数据阵时的应用,研究药物与DNA的相互作用成为可能。即使该混合物中存在着很复杂的化学平衡,也可以很方便的预测感兴趣的组分和DNA的相互作用机制。对于二阶校正方法最值得注意的优势在于,对三维阵的分解通常是唯一的,可以直接分辨出复杂体系中感兴趣组分的相对浓度和光谱图。本章采用紫外和荧光分析结合二阶校正方法对吡柔比星与DNA的相互作用进行了研究。荧光测数据采用PARAFAC算法和交替归一加权残差(ANWE)算法进行解析,可分辨得到动力学平衡体系中各组分的激发、发射光谱以及相对浓度,为吡柔比星与DNA的相互作用机制的研究提供了更为直观的有用信息。这对抗癌药物的抗癌机理、以及新型药物的设计合成方面都有很大的帮助。
5.随着农药大面积、持续的使用，农作物、蔬菜、水果甚至动物体内都可能有农药残留,人们的生命和健康造成威胁。所以研究农药对DNA的潜在损伤作用对于保证人体免受农药的危害具有一定的实际意义。本文利用三维荧光结合二阶校正方法PARAFAC和APTLD对西维因和DNA的相互作用进行了研究,为西维因和DNA的相互作用机制的研究提供了很有用的信息。
6.最小支持向量机(LS-SVM)以其优越的性能在多元校正建模中得到越来越广泛的应用。然而,它的性能在很大的程度上还依赖于模型误差的同质性和数据集分布的均一性。该工作探讨了多元校正建模中的训练集样品的代表性和最优化样品加权问题。由于多元校正的样品光谱空间的多维性和复杂性以及样品选取过程中的不确定性,准确估计训练集样品在整个样品空间的代表性尚存在一定困难。为解决以上问题,同时考虑到样品的代表性很难通过考察单个样品进行估计,我们把全局优化样品加权的思想和最小支持向量机相结合,提出了最优化样品加权最小支持向量机这一新算法。该算法通过对原来的训练集样品进行非负加权,在校正建模过程中同时考虑了模型的复杂性和预测能力,最优样品权重通过粒子群优化算法搜索获得。将该算法应用于真实的标准数据集的结果表明,在原始校正样品的代表性较差时,最优化样品加权最小支持向量机算法确实能够很好地改善模型的预测性能。
7.在对光谱数据的多元校正建模中,传统的波长变量选择方法对某些波长的舍弃将导致有用信息的丢失。为了获得更加灵活的变量选择和建模,以粒子群优化算法为基础,提出了一种变量加权版本的最小支持向量机用于多元校正中光谱变量的选择。变量加权的策略旨在不人为删除和保留变量,允许变量的非负加权。采用粒子群优化算法实现非负的变量加权实质上可视为对波长变量的某种最优化重新刻度。若使用粒子群优化算法同时优化模型其它参数则使得变量加权的支持向量机变成一个无需人为调节参数的全自动建模方法,因此将比传统的变量选择及建模方法有更多的灵活性,且更智能化,运算速度快。将该算法应用于真实的标准数据集的结果表明变量加权最小支持向量机方法确实能在多元校正模型中实现对变量的最优化刻度,保留更多的结构信息,从而帮助得到训练和预测能力更优且智能化的回归模型。
The research work focuses on mulity-way calibration for the quantitative analysis in complex chemical systems and studies on the mechanism for the interactions of small molecules with DNA.
The robust information self-extracting asymmetric trilinear decomposition algorithm (RISEATD) has been developed, based on the total least squares principle and an asymmetric way with partial bilinearization to calculate the three underlying profile matrices in the resolution of the trilinear model. It can obtain useful information quickly. The new proposed algorithm combines the way of decomposition for PARAFAC-alternating least squares (PARAFAC-ALS) and alternating trilinear decomposition (ATLD). The results obtained by simulated data and real excitation-emission spectral data sets have shown that RISETLD method retains the second-order advantage of quantification for analyte(s) of interest even in the presence of potentially unknown interferents even when the noise and colinearity are high Comparing with PARAFAC, PARAFAC-ALS and ATLD algorithms, the developed method can supply acceptable results.
The collection of EEM fluorescence spectra of a mixture and combination with second-order calibration methods can quantify the analytes even in the presence of uncalibrated interferences that has been called the "second-order advantage". With the property of "mathematical separation" to displace "chemical separation", three second-order calibration methods based on PARAFAC, the alternating penalty trilinear decomposition (APTLD) algorithms, and RISEATD respectively, have been utilized for the direct determination of terazosin hydrochloride (THD) in human plasma samples, coupled with the excitation-emission matrix fluorescence spectroscopy. Meanwhile, the three algorithms combing with the standard addition procedures have been applied for the determination of terazosin hydrochloride in tablets and the results were validated by the high-performance liquid chromatography with fluorescence detection.
Three second-order calibration methods were presented to allow accurate and reliable quantitative analysis of dextromethorphan and quinidine in human plasma and urine samples at biological fluids by excitation-emission matrices fluorescence. PARAFAC, self-weighted alternating trilinear decomposition (SWATLD) and APTLD algorithms were applied and the performances of the three methods were compared. It has been found that all the methodologies could obtain good results.
With the development of high-order analytical instruments and chemometric algorithms, it becomes easier to obtain and resolve multi-dimensional data from complex systems. The combination of excitation-emission matrix fluorescence and second-order calibration methods could provide a powerful tool for studies of parallel competitive binding reactions of many chemical components with DNA in the presence of interferents. The relative equilibrium concentrations of the component can be directly obtained. In this paper, UV-vis spectroscopy and fluorescence were combined to study the binding of DNA with the anthacycline antibiotic drug pirarubicin (THP). Ethidium bromide (EB) as the fluorescence probe was used to study the competitive binding interactions of THP with DNA by excitation -emission fluorescence matrices coupled with PARAFAC and the alternating normalization-weighted error algorithm (ANWE) with the second-order advantage. The relative equilibrium concentrations of EB-DNA, EB and THP in the equilibrium system can be directly obtained, which makes it possible to determine the reaction pattern of different interacting pairs in a mixture medium.
The pesticide is used indiscriminately. It may residue in fruits, vegetables and ground and surface waters that lead to pose a potential hazard for consumers, so the toxicity has raised the public concern about the ecosystem and human health. Therefore, the investigation of genotoxicity and genetic damage via the interaction of DNA with insecticide is very important. Competitive binding interactions of carbaryl and the fluorescence probe EB with DNA have been studied by excitation-emission fluorescence spectroscopy to obtain a three-dimension excitation-emission fluorescence data array. The second-order calibration methods based on PARAFAC and APTLD algorithms were used to resolve the data array obtained.
Least squares-support vector machine (LS-SVM) has been introduced into multivariate calibration by many investigators for its attractive features and promising empirical performance. However, the performance of models is strongly dependent upon the homogeneity of the model errors and the uniformity of the data sampling. The representation of training samples for multivariate calibration has been discussed and the concept of weighted sampling has been introduced to multivariate calibration. Due to the high-dimensionality and complexity of spectral data space and the uncertainty involved in sampling process, the representation of training samples in the whole sample space is difficult to evaluate and select representative training samples for multivariate calibration depends largely on experiential methods. If the training samples fail to represent the sample space, sometimes the predictions of new samples can be degraded. In order to solve this problem, a new algorithm for multivariate calibration is developed by combining optimized sampling and least squares-support vector machine, where the original training samples are non-negatively weighted and the complexity and the ability of prediction of the model are considered simultaneously. Two real data sets are investigated and the results demonstrate that sample-weighted least squares-support vector machine models can improve the ability of prediction for a model when the representation of original calibration sample is poor.
For multivariate calibration, all of the wavelength variables might carry more or less molecular information, it seems more advisable to investigate all the possible variables rather than traditional variable selection. Based on particle swarm optimization algorithm, a more flexible variable selection and modeling method, variable-weighted least squares-support vector machine is proposed. The strategy of variable weighting allows non-negative weights of variables rather than removing or reserving any variables. Using particle swarm optimization (PSO) to seek the non-negative weights of variables can be seen as an optimized rescaling of the variables in certain sense. If employing PSO to search for the other parameters in the model of least squares-support vector machine at the same time, the variable-weighted least squares-support vector machine would become a total-automatically modeling approach and therefore be more flexible and intelligent than traditional variable methods.
引文
[1]梁逸曾,俞汝勤.分析化学手册(第十分册),化学计量学.北京：化学工业出版社,2000
[2]俞汝勤.化学计量学导论.长沙：湖南教育出版社,1991
[3]梁逸曾.白灰黑复杂多组分分析体系及其化学计量学算法.长沙：湖南科技出版社,1997
[4]高鸿.分析化学前沿.北京：科学出版社,1991
[5]许禄,邵学广.化学计量学方法.北京：科学出版社,2004
[6]陆晓华.化学计量学.武汉：华中理工大学出版社,1997
[7]许禄.化学计量学：一些重要方法的原理及应用.北京：科学出版社,2004
[8]倪永年.化学计量学在分析化学中的应用.北京：科学出版社,2004
[9]Yu R Q. Chemometrics in China. Chemometrics and Intelligent Laboratory Systems,1992,14(1):23-29
[10]Wold S. Chemometrics:what do we mean with it, and what do we want from it? Paper of InC INC'94. Http://www.Emsl.pnl.gov:2080/docs/incinc/homepage. html
[11]Kowalski B R. Analytical chemistry as an information science. Trends in Analytical Chemistry,1981,1(3):71-74
[12]Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation. In Parallel Distributing Processing, Explorations in the Microstructure of Cognition, Vol.1:Foundations, Rumelhart D E and McClelland J L (eds). Cambridge, MA:MIT Press,1986,318-362
[13]Werbos P. Beyond regression:new tools for prediction and analysis the behavioral sciences. [PHD dissertation], Cambridge, MA:Harvard,1974
[14]Vapnik V N. The nature of statistical learning theory. New York:Springer-Verlag,1995
[15]Vapnik V N. Statistical learning theory. New York:John Wiley and Sons,1998
[16]高志明,李井会,高礼让等.小波分析在化学中的应用进展.化学进展,2002,12(2)：179-191
[17]Cao L, Harrington P B, Harden C S, et al. Nonlinear wavelet compression of ion mobility spectra from ion mobility spectrometers mounted in an unmanned aerial vehicle. Analytical Chemistry,2004,76(4):1069-1077
[18]Juan A, Tauler R, Dyson R, et al. Spectroscopic imaging and chemometrics:a powerful combination for global and local sample analysis. Trends in Analytical Chemistry,2004,23(1):70-79
[19]Friedman J H. Stochastic gradient boosting. Computational Statistics and Data Analysis,2002,38(4):367-378
[20]Breiman L. Bagging predictors. Machine Learning,1996,24(2):123-140
[21]Breiman L, Friedman J H, Olshen R A, et al. Classification and regression trees. Belmont CA:Wadsworth International Group,1984
[22]Breiman L. Randomizing outputs in increase prediction accuracy. Machine Learning,2000,40(3):229-24
[23]Fraga C G, Prazen B J, Synovec R E. Objective data alignment and chemometric analysis of comprehensive two-dimensional separations with run-to-run peak shifting on both dimensions. Analytical Chemistry,2001,73(24):5833-5840
[24]Trevisan M G, Poppi R J. Determination of doxorubicin in human plasma by excitation-emission matrix fluorescence and multi-way analysis. Analytica Chimica Acta,2003,493(1):69-81
[25]Fraga C G, Bruckner C A, Synovec R E. Increasing the number of analyzable peaks in comprehensive two-dimensional separations through chemometrics. Analytical Chemistry,2001,73(3):675-683
[26]Mohimen A, Dobo A, Hoerner J K, et al. A chemometric approach to detection and characterization of multiple protein conformers in solution using electrospray ionization mass spectrometry. Analytical Chemistry,2003,75(16): 4139-4147
[27]Fransson M, Sparen A, Lagerholm B, et al. On-line process control of liquid chromatography. Analytical Chemistry,2001,73(7):1502-1508
[28]Lopez-Diez E C, Goodacre R. Characterization of microorganisms using UV resonance Raman spectroscopy and chemometrics. Analytical Chemistry,2004, 76(3):585-591
[29]Vogt F, Mizaikoff B. Introduction and application of secured principal component regression for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing. Analytical Chemistry,2003,75(13): 3050-3058
[30]Mayr T, Igel C, Liebsch G, et al. Cross-reactive metal ion sensor array in a micro titer plate format. Analytical Chemistry,2003,75(17):4389-4396
[31]Rodriguez EMR, Alaejos M S, Romero C D. Chemometric studies of several minerals in milks. Journal of Agricultural and Food Chemistry,1999,47(4): 1520-1524
[32]Einax J. Chemometrics in environmental chemistry statistical methods. Berlin Germany:Springer-Verlag,1995
[33]Rios P, Stuart J A, Grant E. Plastics disassembly versus bulk recycling: engineering design for end-of-life electronics resource recovery. Environmental Science and Technology,2003,37(23):5463-5470
[34]Mannhold R, Hrogsgaard P, Larscn H, et al. Methods and principles in medicinal chemistry. Weinheim Germany:Wiley-VHC,1995
[35]Hergert L A, Escandar G M. Spectrofluorimetric study of the β-cyclodextrin-ibuprofen complex and determination of ibuprofen in pharmaceutical preparations and serum. Talanta,2003,60(2-3):235-246
[36]Espinosa-Mansilla A, Pe A, Mu D L, et al. Photoinduced spectrofluorimetric determination of fluoroquinolones in human urine by using three- and two-way spectroscopic data and multivariate calibration. Analytica Chimica Acta,2005, 531(3):257-266
[37]Hansch C, Fujita T. Classical and Three-Dimensional QSAR in Agrochemistry. In:ACS Symposium Series 606. Washington DC:American Chemical Society, 1995:23-56
[38]Rodriguez-Cuesta M J, Boque R, Rius F X, et al. Determination of carbendazim, fuberidazole and thiabendazole by three-dimensional excitation-emission matrix fluorescence and parallel factor analysis. Analytica Chimica Acta,2003,491(1): 47-56
[39]Chaudry U A, Popelier P L. Estimation of pKa using quantum topological molecular similarity descriptors:application to carboxylic acids, anilines and phenols. Journal of Organic Chemistry,2004,69(2):233-241
[40]Lakshminarayanan S, Mhatre P, Gudi R. Identification of bilinear models for chemical processes using canonical variate analysis. Industrial and Engineering Chemistry Research,2001,40(20):4415-4427
[41]Shen M, Wagner M S, Castner D G, et al. Multivariate surface analysis of plasma-deposited tetraglyme for reduction of protein adsorption and monocyte adhesion. Langmuir,2003,19(5):1692-1699
[42]梁逸曾,俞汝勤.化学计量学在我国的发展.化学通报,1999,10(1)：14-19
[43]吴海龙,梁逸曾,俞汝勤.分析化学计量学.分析实验室,1999,11(2)：94-102
[44]Lohnes M T, Guy R D, Wentzell P D. Window target-testing factor analysis: Theory and application to the chromatographic analysis of complex mixtures with multiwavelength fluorescence detection. Analytica Chimica Acta,1999, 389(1-3):95-113
[45]Liang Y Z, Leung A K M, Chau F T. A roughness penalty approach and its application to noisy hyphenated chromatographic two-way data. Journal of Chemometrics,1999,13(5):511-524
[46]Xu Q S, Liang Y Z. On the equivalence of window factor analysis and orthogonal projection resolution. Chemometrics and Intelligent Laboratory Systems.1999,45(1-2):335-338
[47]Maeder M. Evolving factor analysis for the resolution of overlapping chromatographic peaks. Analytical Chemistry,1987,59(3):527-530
[48]Malinowski E R. Window factor analysis:Theoretical derivation and application to flow injection analysis data. Journal of Chemometrics,1992,6(1):29-40
[49]Gampp H, Maeder M, Meyer C J. Calculation of equilibrium constants from multiwavelength spectroscopic data—Ⅲ:Model-free analysis of spectrophotometric and ESR titrations. Talanta,1985,32(12):1133-1139
[50]Maeder M, Zuberbuhler A D. The resolution of overlapping chromatographic peaks by evolving factor analysis. Analytica Chimica Acta,1986,181(2): 287-291
[51]Kvalheim O M, Liang Y Z. Heuristic evolving latent projections:resolving two-way multicomponent data.1. Selectivity, latent-projective graph, data scope, local rank, and unique resolution. Analytical Chemistry,1992,64(8):936-946
[52]Liang Y Z, Kvalheim O M, Keller H R, et al. Heuristic evolving latent projections:resolving two-way multicomponent data.2. Detection and resolution of minor constituents. Analytical Chemistry,1992,64(8):946-953
[53]Jiang J H, Sasic S, Yu R Q, et al. Resolution of two-way data from spectroscopic monitoring of reaction or process systems by parallel vector analysis (PVA) and window factor analysis (WFA):inspection of the effect of mass balance, methods and simulations. Journal of Chemometrics,2003,17(3):186-197
[54]Sanchez F C, Bogaert B, Rutan S C, et al. Multivariate peak purity approaches. Chemometrics and Intelligent Laboratory Systems,1996,34(2):139-171
[55]Karjalainen E. The spectrum reconstruction problem:Use of alternating regression for unexpected spectral components in two-dimensional spectroscopies. Chemometrics and Intelligent Laboratory Systems,1989,7(1-2): 31-38
[56]Tauler R, Casassas E. Spectroscopic resolution of macromolecular complexes using factor analysis:Cu (Ⅱ)-polyethyleneimine system. Chemometrics and Intelligent Laboratory Systems,1992,14(1-3):305-317
[57]Liang Y Z, Kvalheim O M. Heuristic evolving latent projections:Resolving hyphenated chromatographic profiles by component stripping. Chemometrics and Intelligent Laboratory Systems,1992,64,20(2):115-125
[58]Manne R, Shen H L, Liang Y Z. Subwindow factor analysis. Chemometrics and Intelligent Laboratory Systems,1999,45(1-2):171-176
[59]Shen H L, Manne R, Liang Y Z, et al. Local resolution of hyphenated chromatographic data. Chemometrics and Intelligent Laboratory Systems,1999, 45(1-2):323-328
[60]Jiang J H, Ozaki Y, Kleimann M, et al. Resolution of two-way data from on-line Fourier-transform Raman spectroscopic monitoring of the anionic dispersion polymerization of styrene and 1,3-butadiene by parallel vector analysis (PVA) and window factor analysis (WFA). Chemometrics and Intelligent Laboratory Systems,2004,70(1):83-92
[61]Windig W, Guilment J. Interactive self-modeling mixture analysis. Analytical Chemistry,1991,63(14):1425-1432
[62]Jiang J H, Liang Y Z, Ozaki Y. On simplex-based method for self-modeling curve resolution of two-way data. Chemometrics and Intelligent Laboratory Systems,2003,65(1):51-65
[63]Wang J H, Liang Y Z, Yu R Q, et al. Local chemical rank estimation of two-way data in the presence of heteroscedastic noise:A morphological approach. Chemometrics and Intelligent Laboratory Systems,1996,32(2):265-272
[64]Juan A, Bogaert B, Sanchez F C, et al.. Application of the needle algorithm for exploratory analysis and resolution of HPLC-DAD data. Chemometrics and Intelligent Laboratory Systems,1996,33(2):133-145.
[65]Juan A, Vander H Y, Taule R, et al. Assessment of new constraints applied to the alternating least squares method. Analytica Chimica Acta,1997,346(3): 307-318
[66]Malinowski E R. Automatic window factor analysis:A more efficient method for determining concentration profiles from evolutionary spectra. Journal of Chemometrics,1996,10(4):273-279
[67]Gemperline P J. A priori estimates of the elution profiles of the pure components in overlapped liquid chromatography peaks using target factor analysis. Journal of Chemical Information and Computer Sciences,1984,24(4): 206-212
[68]Vandeginste B G M, Derks W, Kateman G. Multicomponent self-modelling curve resolution in high-performance liquid chromatography by iterative target transformation analysis. Analytica Chimica Acta,1985,173(1):253-264
[69]Paatero P. Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems,1997,37(1):23-35
[70]Gargallo R, Tauler R, Sanchez F C, et al. Validation of alternating least-squares multivariate curve resolution for chromatographic resolution and quantitation. Trends in Analytical Chemistry,1996,15(7):279-286
[71]Elbergali A K, Brereton R G, Rahmani A. Influence of the method of calculation of noise thresholds on wavelength selection in window factor analysis of diode array high-performance liquid chromatography. The Analyst,1996,121(5): 585-590
[72]Sanchez F C, Rutan S C, Gil Garcia M D, et al. Resolution of multicomponent overlapped peaks by the orthogonal projection approach, evolving factor analysis and window factor analysis. Chemometrics and Intelligent Laboratory Systems,1997,36(2):153-164
[73]Ritter C, Gilliard J A, Cumps J, et al. Corrections for heteroscedasticity in window evolving factor analysis. Analytica Chimica Acta,1996,318(2): 125-136
[74]Elbergali A, Nygren J, Kubista M. An automated procedure to predict the number of components in spectroscopic data. Analytica Chimica Acta,1999, 379(1-2):143-158
[75]Xie Y L, Hopke P K, Paatero P J. Positive matrix factorization applied to a curve resolution problem. Journal of Chemometrics,1998,12(6):357-364
[76]Shao X G, Cai W S. Resolution of multicomponent chromatograms by window factor analysis with wavelet transform preprocessing. Journal of Chemometrics, 1998,12(2):85-93
[77]Sanchez F C, Vandeginste B G M, Hancewicz T M, et al. Resolution of Complex Liquid Chromatography-Fourier Transform Infrared Spectroscopy Data. Analytical Chemistry,1997,69(8):1477-1484
[78]Lilley K A, Wheat T E. Drug identification in biological matrices using capillary electrophoresis and chemometric software. Journal of Chromatography B,1996,683(1):67-76
[79]Statheropoulos M, Smaragdis E, Tzamtzis N, et al. Principal component analysis for resolving coeluting substances in gas chromatography-mass spectrometry doping control analysis. Analytica Chimica Acta,1996,331(1-2):53-61.
[80]Thurston T J, Brereton R G, Foord D J, et al. Monitoring of a second-order reaction by electronic absorption spectroscopy using combined chemometric and kinetic models. Journal of Chemometrics,2003,17(6):313-322.
[81]Joaquim C G, Esteves da S, Adelio A S C M, et al. Simultaneous use of evolving factor analysis of fluorescence spectral data and analysis of pH titration data for comparison of the acid-base properties of fulvic acids. Analytica Chimica Acta, 1996,318(3):365-372
[82]Koons J M, Ellis P D. Applicability of factor analysis in solid state NMR. Analytical Chemistry,1995,67(23):4309-4315
[83]Grabaric B S, Grabaric Z, Tauler R, et al. Application of multivariate curve resolution to the voltammetric data Factor analysis ambiguities in the study of weak consecutive complexation of metal ion with ligand. Analytica Chimica Acta,1997,341(2-3):105-120
[84]Bijlsma S, Boelens H F M, Hoefsloot H C J, et al. Constrained least squares methods for estimating reaction rate constants from spectroscopic data. Journal of Chemometrics,2002,16(1):28-40
[85]Winding W, Hornak J P, Antalek B. Multivariate Image Analysis of Magnetic Resonance Images with the Direct Exponential Curve Resolution Algorithm (DECRA):Part 1. Algorithm and Model Study. Journal of Magnetic Resonance, 1998,132(2):298-306
[86]Antalek B, Hornak J P, Windig W. Multivariate Image Analysis of Magnetic Resonance Images with the Direct Exponential Curve Resolution Algorithm (DECRA):Part 2. Application to Human Brain Images. Journal of Magnetic Resonance,1998,132(2):307-315
[87]Andrew J J, Hancewicz T M. Rapid analysis of raman image data using two-way multivariate curve resolution. Applied Spectroscopy,1998,52(6):797-804
[88]Diaz-Cruz M S, Tauler R, Casassas E, et al. Application of multivariate curve resolution to voltammetric data. Part 1. Study of Zn (Ⅱ) complexation with some polyelectrolytes. Journal of Electroanalytical Chemistry,1995,393(1-2):7-16
[89]Mendieta J, Diaz-Cruz M S, Tauler R, et al. Application of multivariate curve resolution to voltammetric data. Ⅱ. Study of metal-binding properties of the peptides. Analytical Biochemistry,1996,240(1):134-141
[90]Ren S X, Gao L. The speciation of Copper (Ⅱ) / Ethylenediamine / Oxalate system by evolving factor analysis. Journal of Automatic Chemistry,1995, 17(5):17-177
[91]Grung B, Kvalheim O M. Detection and quantification of embedded minor analytes in three-way multicomponent profiles by evolving projections and internal rank annihilation. Chemometrics and Intelligent Laboratory Systems, 1995,29(2):213-221
[92]Furusjooe E, Danielsson L G, Koenberg E, et al.. Evaluation techniques for two-way data from in situ fourier transform mid-infrared reaction monitoring in aqueous solution. Analytical Chemistry,1998,70(9):1726-1734
[93]Zhu E Y, Barnes R M. A simple iteration algorithm for PLS regression. Journal of Chemometrics,1995,9(1):363-372
[94]Rannar S, Lindgren F, Geladi P, et al. A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1:Theory and algorithm. Journal of Chemometrics,1994,8(2):111-126
[95]Rannar S, Geladi P, Lindgren F, et al. A PLS kernel algorithm for dta sets with many variables and fewer objects. Part Ⅱ:Cross validation, missing data and examples. Journal of Chemometrics,1995,9:459-470
[96]Zhang M H, Xu Q S, Massart D L. Averaged and weighted average partial least squares. Analytical Chimica Acta,2004,504(2):279-289
[97]Suykens J A K, Vandewale J. Least squares support vector machine classifiers. Neural Processing Letters,1999,9(3):293-300
[98]Chauchard F, Cogdill R, Roussel S, et al. Application of LS-SVM to non-linear phenomena in NIR spectroscopy:development of a robust and portable sensor for acidity prediction in grapes, Chemometrics and Intelligent Laboratory Systems,2004,71 (1):141-150
[99]Borin A, Ferrao M F, Mello C, et al. Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta,2006,579(1):25-32
[100]Ferrao M F, Godoy S C, Gerbase A E, et al. Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR. Analytica Chimica Acta,2007,595 (1):114-119
[101]Niazi A, Ghasemi J, Zendehdel M. Simultaneous voltammetric determination of morphine and noscapine by adsorptive differential pulse stripping method and least-squares support vector machines, Talanta,2007,74(1):247-254
[102]Liu F, Zhang F, Jin Z L, et al. Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy. Analytica Chimica Acta,2008,629 (1): 56-65.
[103]Booksh K S, Kowalski B R. Theory of analytical chemistry. Analytical Chemistry,1992,66(15):782A-791A
[104]Wilson B E, Sanchez E, Kowalski B R. An improved algorithm for the generalized rank annihilation method. Journal of Chemometrics,1989,3(3): 493-498
[105]Li S, Hamilton C, Gemperline P J. Generalized rank annihilation method using similarity transformations. Analytical Chemistry,1992,64(6):599-607
[106]Faber K, Lorber A, Kowalski B R. Generalized rank annihilation method: Standard errors in the estimated eigenvalues if the instrumental errors are heteroscedastic and correlated. Journal of Chemometrics,1997,11(2):95-109
[107]Sanchez E, Kowalski B R. Generalized rank annihilation factor analysis. Analytical Chemistry,1986,58(2):496-499
[108]Sanchez E, Kowalski B R. Tensorial resolution:a direct trilinear decomposition. Journal of Chemometrics,1990,4(1):29-45
[109]Li S, Gemperline P J. Eliminating complex eigenvectors and eigenvalues in multiway analyses using the direct trilinear decomposition method. Journal of Chemometrics,1993,7(1):77-88
[110]Lin Z, Booksh K S, Burgess L W, et al. A second-order fiber optic heavy metal sensor employing second-order tensorial calibration. Analytical Chemistry,1994, 66(15):2552-2560
[111]Booksh K S, Lin Z, Wang Z, et al. Extension of trilinear decomposition method with an application to the flow probe sensor. Analytical Chemistry,1994,66(15): 2561-2569
[112]Henshow J M, Burgess L W, Booksh K S, et al. Multicomponent determination of chlorinated hydrocarbons using a reaction-based chemical sensor.1. Multivariate calibration of fujiwara reaction products. Analytical Chemistry, 1994,66(20):3328-3336
[113]Gui M, Rutan S C, Agbodian A. Kinetic detection of overlapped amino acids in thin-layer chromatography with a direct trilinear decomposition method. Analytical Chemistry,1995,67(18):3293-3299
[114]Millican D W, McGrown L B. Fluorescence lifetime selectivity in excitation-emission matrices for qualitative analysis of a two-component system. Analytical Chemistry,1989,61(6):580-583
[115]Millican D W, McGown L B. Fluorescence lifetime resolution of spectra in the frequency domain using multiway analysis. Analytical Chemistry,1990,62(20): 2242-2247
[116]Scarmino I, Kubista M. Analysis of correlated spectral data. Analytical Chemistry,1993,65(4):409-416
[117]Appelolof C J, Davidson E R. Strategies for analyzing data from video fluorometric monitoring of liquid chromatographic effluents. Analytical Chemistry,1981,53(13):2053-2056
[118]Linder M, Sundberg R. Precision of prediction in second-order calibration with fous on bilinear regression method. Journal of Chemometrics,2002,16(1): 12-27
[119]Linder M, Sundberg R. Second-order bilinear calibration:the effects of vectorising the data matrices of the calibration set. Chemometrics and Intelligent Laboratory Systems,2002,63(2):107-116
[120]Damiani P C, Nepote A J, Bearzotti M, et al. A test field for the second-order advantage in bilinear least-squares and parallel factor analyses:fluorescence determination of ciprofloxacin in human urine. Analytical Chemistry,2004, 76(10):2798-2806
[121]Smilde A K, Doornbos D A. Three-way methods for the calibration of chromatographic systems:Comparing PARAFAC and three-way PLS. Journal of Chemometrics,1991,5(3):345-360
[122]Bro R. Multi-way analysis in the food industry, PhD Thesis, University of Amsterdam and Royal Veterinary and Agricultural university, Denmark,1998
[123]Mitchell B C, Burdick D S. Slowly converging PARAFAC sequences:Swamps and two-factor degeneracies. Journal of Chemometrics,1994,8(2):155-168
[124]Bro R. PARAFAC:Tutorial and applications. Chemometrics and Intelligent Laboratory Systems,1997,38(2):149-171
[125]Rayens W S, Mitchell B C. Two-factor degeneracies and a stabilization of PARAFAC. Chemometrics and Intelligent Laboratory Systems,1997,38(2): 173-181
[126]Krijnen W P. The Analysis of three-way Arrays by Constrained PARAFAC Methods, Leiden:Dswo Press,1993
[127]Harshman R A. Foundations of the PARAFAC procedure:Models and conditions for an'explanatory'multi-modal factor analysis. UCLA Working Papers in Phonetics,1970,16(1):1-84
[128]Carroll J D, Chang J J. Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition. Psychometrika,1970,35(3):283-319
[129]Jiang J H, Wu H L, Chen Z P, et al. Coupled vectors resolution method for chemometric calibration with three-way data. Analytical Chemistry,1999, 71(19):4254-4262
[130]Jiang J H, Wu H L, Li Y, et al. Alternating coupled vectors resolution (ACOVER) method for trilinear analysis of three-way data. Journal of Chemometrics,1999, 13(6):557-578
[131]Jiang J H, Wu H L, Li Y, et al. Three-way data resolution by alternating slice-wise diagonalization (ASD) method. Journal of Chemometrics,2000,14(1): 15-36
[132]Li Y, Jiang J H, Wu H L, et al. Alternation coupled matrics resolution method for three-way array analysis. Chemometrics and Intelligent Laboratory Systems, 2000,52(1):33-43
[133]Chen Z P, Wu H L, Yu R Q. On the self-weighted alternating trilinear decomposition algorithm-the property of being insensitive to excess factors used in calculation. Journal of Chemometrics,2001,15(5):439-453
[134]Chen Z P, Li Yang, Yu R Q. Pseudo alternating least squares algorithm for trilinear decomposition. Journal of Chemometrics,2001,15(3):149-167
[135]Chen Z P, Wu H L, Li Y, et al. A novel constrained PARAFAC algorithm for second-order linear calibration. Analytica Chimica Acta,2000,423(1):187-196
[136]Cao Y Z, Chen Z P, Mo C Y, et al. A PARAFAC algorithm using penalty diugonalization error (PDE) for three-way data array resolution. The Analyst, 2000,125(12):2303-2310
[137]Wu H L, shibukawa M, Oguma K. An alternating trilinear decomposition method with application to calibration of HPLC-DAD for simultaneous determination of overlapped chlorinated aromatic hydrocarbous. Journal of Chemometrics,1998,12(1):1-26
[138]Xia A L, Wu H L, Fang D M, et al. Alternating penalty trilinear decomposition for second-order calibration with an application to interference-free analysis of excitation-emission matrix fluorescence data. Journal of Chemometrics,2005, 19(2):65-76
[139]Xia A L, Wu H L, Zhu S H, et al. Determination of psoralen in human plasma by using excitation-emission matrix fluorescence coupled to second-order calibration. Analytical Sciences,2008,24(9):1171-1178
[140]Hu L Q, Wu H L, Ding Y J, et al. Alternating asymmetric trilinear decomposition for three-way data arrays analysis. Chemometrics and Intelligent Laboratory Systems,2006,82(1-2):145-153
[141]Xie H P, Chu X, Jiang J H, et al. Competitive interactions of adriamycin and ethidium bromide with DNA as studied by full rank parallel factor analysis of fluorescence three-way array data. Spectrochimica Acta Part A,2003, 59(4):743-749
[142]Juan A, Rutan S C, Tauler R, et al. Comparison between the direct trilinear decomposition and the multivariate curve resolution-alternating least squares methods for the resolution of three-way data sets. Chemometrics and Intelligent Laboratory Systems,1998,40(1):19-32
[143]Saurina J, Hemandez-Cassou S, Tauler R. Multivariate curve resolution and trilinear decomposition methods in the analysis of stopped-flow kinetic data for binary amino acid mixtures. Analytical Chemistry,1997,69(13):2329-2336
[144]Paatero P. Construction and analysis of degenerate PARAFAC models. Journal of Chemometrics,2000,14(3):285-299
[145]Tomasi G, Bro R. PARAFAC and missing values. Chemometrics and Intelligent Laboratory Systems,2005,75(2):163-180
[146]Lavine B, Workman J J. Chemometrics:Fundamental review. Analytical Chemistry,2004,76(12):3365-3372
[147]Bro R, Workman J J, Kowalski P R. Review of chemometrics applied to spectroscopy:1985-1995, Part Ⅲ. Multi-way analysis. Applied Spectroscopy reviews,1997,32(3):237-261
[148]Baunsgaard D, Andersson C A, Arndal A, et al. Multi-way chemometrics for mathematical separation of fluorescent colorants and colour precursors from spectrouorimetry of beet sugar and beet sugar thick juice as validated by HPLC analysis. Food Chemistry,2000,70(1):113-121
[149]JiJi R D, Andersson G G, Booksh K S. Application of PARAFAC for calibration with excitation-emission matrix fluorescence spectra of three classes of environmental pollutants. Journal of Chemometrics,2000,14(3):171-185
[150]Muroski A R, Booksh K S, Myrick M L. Single-Measurement Excitation/Emission Matrix Spectrofluorometer for Determination of Hydrocarbons in Ocean Water.1. Instrumentation and Background Correction. Analytical Chemistry,1996,68(20):3534-3538
[151]Booksh K S, Muroski A R, Myrick M L. Single-Measurement Excitation/Emission Matrix Spectrofluorometer for Determination of Hydrocarbons in Ocean Water.2. Calibration and Quantitation of Naphthalene and Styrene. Analytical Chemistry,1996,68(20):3539-3544
[152]Beltran J L, Ferrer R, Guiteras J. Multivariate calibration of polycyclic aromatic hydrocarbon mixtures from excitation-emission fluorescence spectra. Analytica Chimica Acta,1998,373(2-3):311-319
[153]Nahorniak M L, Booksh K S. Optimizing the implementation of the PARAFAC method for near-real time calibration of excitation-emission fluorescence analysis. Journal of Chemometrics,2003,17(11):608-617
[154]JiJi R D, Cooper G A, Booksh K S. Excitation-emission matrix fluorescence based determination of carbamate pesticides and polycyclic aromatic hydrocarbons. Analytica Chimica Acta,1999,397(1-3):61-72
[155]JiJi R D, Booksh K S. Mitigation of Rayleigh and Raman Spectral Interferences in Multiway Calibration of Excitation-Emission Matrix Fluorescence Spectra. Analytical Chemistry,2000,72(4):718-725
[156]Pedersen D K, Munck L, Engelsen S B. Screening for dioxin contamination in fish oil by PARAFAC and N-PLSR analysis of fluorescence landscapes. Journal of Chemometrics,2002,16(8-10):451-460
[157]Espinosa-Mansilla A, de la Pena A M, Canada-Canada F, et al. Determinations of fluoroquinolones and nonsteroidal anti-inflammatory drugs in urine by extractive spectrophotometry and photoinduced spectrofluorimetry using multivariate calibration. Analytical Biochemistry,2005,34(2):275-286
[158]Mahedero M C, Diaz N M, de la Pena A M, et al. Strategies for solving matrix effects in the analysis of sulfathiazole in honey samples using three-way photochemically induced fluorescence data.Talanta,2005,65(3):806-813
[159]Escandar G M, Gomez D G, Espinosa Mansilla A, et al. Determination of carbamazepine in serum and pharmaceutical preparations using immobilization on a nylon support and fluorescence detection. Analytica Chimica Acta,2004, 506(2):161-170
[160]Arancibia J A, Escandar G M. Two different strategies for the fluorimetric determination of piroxicam in serum. Talanta,2003,60(6):1113-1121
[161]吴海龙,龙宁,方艺峰,等.交替三线性分解算法用于水杨酸和2,52二羟基 苯甲酸的荧光法同时测定.分析试验室,2002,21(2)：44-47
[162]Jin S Y, Xu Z C, Chen J P, et al. Determination of organophosphate and carbamate pesticides based on enzyme inhibition using a pH-sensitive fluorescence probe. Analytica Chimica Acta,2004,523(1):117-123
[163]Braga J W B, Bottoli C B G, Jardim I C S F, et al. Determination of pesticides and metabolites in wine by high performance liquid chromatography and second-order calibration methods. Journal of Chromatography A,2007,1148(2): 200-210
[164]Piccirilli C, Escandar G M. Partial least-squares with residual bilinearization for the spectrofluorimetric determination of pesticides. A solution of the problems of inner-filter effects and matrix interferents. Analyst,2006,131(9):1012-1020
[165]Bijlsma S, Smilde A K. Estimating reaction rate constants from a two-step reaction:A comparison between two-way and three-way methods. Journal of Chemometrics,2000,14(5-6):541-560
[166]Tan Y X, Jiang J H, Wu H L, et al. Resolution of kinetic system of simultaneous degradations of chlorophyll a and b by PARAFAC. Analytica Chimica Acta, 2000,412(1-2):195-202
[167]Sanchez-Ponce R, Rutan S C. Steady state kinetic model constraint for multivariate curve resolution-alternating Least Squares analysis. Chemometrics and Intelligent Laboratory Systems,2005,77(1):50-58
[168]Pla F F P, Redon J F B, Valero R. A new algorithm for the kinetic data analysis. Chemometrics and Intelligent Laboratory Systems,2000,53(1):1-19
[169]Zhao Y, Wang G, Li W, Zhu Z L. Determination of reaction mechanism and rate constants of alkaline hydrolysis of phenyl benzoate in ethanol-water medium by nonlinear least squares regression. Chemometrics and Intelligent Laboratory Systems,2006,82(1-2):193-199
[170]Cruz S C, Rothenberg G, Westerhuis J A, et al. Estimating kinetic parameters of complex catalytic reactions using a curve resolution based method. Chemometrics and Intelligent Laboratory Systems,2008,91(1):101-109
[171]Garcia R A, Damiani P C, Olivieri A C. Different strategies for the direct determination of amoxicillin in human urine by second-order multivariate analysis of kinetic-spectrophotometric data. Talanta,2007,71(2):806-815
[172]Guterres M V, Volpe P L, Ferreira M C. Multiway Calibration for Creatinine Determination in Human Serum,Using the Jaffe Reaction. Applied Spectroscopy, 2004,58(1):54-60
[173]Nazi A, Ghasemi J, Yazdanipur A, PARAFAC decomposition of three-way kinetic-spectrophotometric spectral matrices based on phosphomolymbdenum blue complex chemistry for nitrite determination in water and meat samples. Analytical Letters,2005,38(14):2377-2392
[174]Nomikos P, MacGregor J F. Monitoring of batch processes using multi-way principal component analysis. AIChE Journal,1994,40(8):1361-1375
[175]Kenneth S, Dahl A, Michael J, et al. Translating third-order data analysis methods to chemical batch Processes. Chemometrics and Intelligent Laboratory Systems,1999,46(2):161-180
[176]Smilde A K, Tates A A, Boelens H F M, et al. Systematic investigation of process and product variations in a spindraw-winding process. Chemical Engineering Science,2001,56(17):4993-5002
[177]N(?)s A. The design of calibration in near infrared reflectance analysis by clustering. Journal of Chemometrics,1987,1:121-134
[178]Zemroch P J. Cluster Analysis as an experimental design generator, with application to gasoline belending experiments. Technometrics,1986,28:39-49
[179]Kennard R W, Stone L A. Computer aided design of experiments. Technometrics,1969,11:137-148
[180]Honigs D, Hieftje G M, Mark H, Hirschfeld T B. Unique sample selection via near-infrared spectral subtraction. Analitical Chemistry,1985,57:2299-2303
[181]Snee R D. Validation of regression models:methods and examples. Technometrics,1977,19(4):415-428
[182]Spiegelman C H, McShane M J, Goetz M J, et al. Theoretical justification of wavelength selection in PLS calibration:Development of a New Algorithm. Analytical Chemistry,1998,70:35-44
[183]Swierenga H, Wulfert E, de Noord O E, et al. Development of robust calibration models in near infra-red spectrometric applications, Analytical Chimica Acta, 2000,411(1):121-135
[184]Goicoechea H C, Olivieri A C. Wavelength Selection for Multivariate Calibration Using a Genetic Algorithm:A Novel Initialization Strategy. Journal of Chemical Information and Computer Sciences,2002,42 (5): 1146-1153
[185]Khanmohammadi M, Karimi M A, Ghasemi K, et al. Quantitative determination of Malathion in pesticide by modified attenuated total reflectance-Fourier transform infrared spectrometry applying genetic algorithm wavelength selection method. Talanta,2007,72(2):620-625
[186]Hoskuldsson A. Variable and subset selection in PLS regression. Chemometrics and Intelligent Laboratory Systems,2001,55 (1):23-38
[187]Esbensen K H. Multivariate Data Analysis in Practice,5th ed., CAMO Process As, Oslo,2002
[188]Centner V, Massart D L, de Noord O E et al. Elimination of Uninformative Variables for Multivariate Calibration, Analytical Chemistry,1996,68 (21): 3851-3858
[189]Hemmateenejad B, Ghavami R, Miri R, et al. Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks. Talanta,2006,,68 (4): 1222-1229
[190]Xu L, Jiang J H, Zhou Y P, et al. MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration. Chemometrics and Intelligent Laboratory Systems,2007,87(2):226-230
[191]Xu H, Liu Z C, Cai W S, et al. A wavelength selection method based on randomization test for near-infrared spectral analysis. Chemometrics and Intelligent Laboratory Systems,2009,97(2):189-193
[192]Han Q J, Wu H L, Cai C B, et al. An ensemble of Monte Carlo uninformative variable elimination for wavelength selection. Analytical Chimica Acta,2008, 612(1):121-125
[193]Norgaard L, Saudland A, Wagner J, et al. Interval partial least-squares regression (iPLS):a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy,2000,54(3):413-419
[194]Zou X, Zhao J, Li Y. Selection of the efficient wavelength regions in FT-NIRspectroscopy for determination of SSC of'Fuji'apple based on BiPLS and FiPLS models. Vibrational Spectroscopy,2007,44:220-227
[195]Goicoechea H C, Olivieri A C. A new family of genetic algorithms for wavelength interval selection in multivariate analytical spectroscopy. Journal of Chemometrics,2003,17(6):338-345
[196]Leardi R, Nφrgaard L. Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. Journal of Chemometrics,2004,18(11):486-497
[197]Jiang J H, Berry R J, Siesler H W, et al. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to midinfrared and near-infrared spectroscopic data. Analytical Chemistry,2002,74(14):3555-3565
[198]Jenkins Y, Barton J K A. sequence-specific molecular light switch:tethering of an oligonucleotide to a dipyridophenazine complex of ruthenium (11). Journal of the American Chemistry Society,1992,114(22):8736-8738
[199]Chen H F, Patel D J. Solution structure of the menogaril-DNA complex. Journal of the American Chemistry Society,1995,117(22):5901-5913
[200]Friedman A E, Chambron J C, Sauvage J P, et al. A molecular light switch for DNA:Ru(bpy)2(dppz)2+. Journal of the American Chemistry Society,1990,11 2(12):4960-4962
[201]Krotz A H, Hudson B P, Barton J K. Assembly of DNA recognition elements on an octahedral rhodium intercalator:predictive recognition of 5'-TGCA-3'by Δ —[Rh(R,R) -Me2trien]phi]3+. Journal of the American Chemistry Society, 1993,115(26):12577-12578
[202]赵晓杰,江山,陆冬生,等.用共振拉曼光谱研究电子激发态铜叶琳与D NA的相互作用.高等化学学报,1994,15(4)：600-602
[203]Hutchins R A, Crenshaw J M, Graves D E, et al. Influence of substituent modifications on DNA binding energetics of acridine-based anticancer agents. Biochemistry,2003,42 (46):13754-13761
[204]Su H, Williams P, Thompson M. Platinum anticancer drug binding to DNA detected by thickness-shear -mode acoustic wave sensor. Analytical Chemistry, 1995,67(5):1010-1013
[205]Lah J, Bezan M, Vesnaver G. Thermodynamics of berenil binding to poly[d(AT)] poly[d(AT)] and poly[d(A)]poly[d(T)] duplexes. Acta Chimica Slovenica,2001, 48(3):289-308
[206]Okhonin V, Krylova S M, Krylov S N. Nonequilibrium capillary electrophoresis of equilibrium mixtures, mathematical model. Analytical Chemistry,2004,76 (4):1507-1512
[207]David W M, Brodbelt J, Kerwin SM, et al. Investigation of quadruplex oligonucleotide-drug interactions by electrospray ionization mass spectrometry. Analytical Chemistry,2002,74(9):2029-2033
[208]Reynisson J, Schuster G B, Howerton S B, et al. Intercalation of trioxatriangulenium ion in DNA:binding, electron transfer, X-ray crystallography, and electronic structure. Journal of the American Chemistry Society,2003,125(8):2072-2083
[209]McGhee J D, Von Hippel P H. Theoretical aspects of DNA-protein interactions-cooperative and non-cooperative binding of large ligands to a one-dimensional homogeneous lattice. Journal of Molecular Biology,1974,86 (2):469-489
[210]Le Gal J M, Morjani H, Manfait M. Ultrastructural appraisal of the multidrug resistance in K562 and LR73 cell lines from fourier transform infrared spectroscopy. Cancer Research,1993,53(16):3681-3686
[211]Xie H P, Jiang J H, Cui H, et al. Competitive interaction of the antitumor drug daunorubicin and the fluorescence probe ethidium bromide with DNA as studied by resolving trilinear fluorescence data:the use of PARAFAC and its modification. Analytical and Bioanalytical Chemistry 2002,373(3):159-162.
[212]Zhang F, Zhang Q Q, Wang W G, et al. Synthesis and DNA binding studies by spectroscopic and PARAFAC methods of a ternary copper(Ⅱ) complex. Journal of Photochemistry and Photobiology A:Chemistry 2006,184 (3):241-249.
[213]Ni Y N, Lin D Q, Kokot S. Synchronous fluorescence and UV-vis spectroscopic studies of interactions between the tetracycline antibiotic, aluminium ions and DNA with the aid of the Methylene Blue dye probe. Analytica Chimica Acta, 2008,606(1):19-25
[214]Escandar G M, Olivieri A C, Faber N M, et al. Second-and third-order multivariate calibration:data, algorithms and applications. Trends in Analytical Chemistry,2007,26(7):752-765
[215]Golub G H, Van Loan C F. Analysis of the total least squares problem. SIAM Journal on Numerical Analysis,1980,17(6):883-893
[216]Van Huffel S, Vandewalle J. Analysis and solution of the nongeneric total least squares problems. SIAM Journal on Matrix Analysis and Applications,1998, 9(2):360-372
[217]魏木生,朱超.关于TLS问题.计算数学,2002,24：345-352
[218]Abraham P, Cohen A, et al.Terazosin:an effective once-daily monotherapy for the treatment of hypertension. American Journal of Medicine,1986,80(suppl 5B):29-34
[219]Mudiyala R, Ahmed A. Effect of Terazosin on Clinical Benign Prostatic Hyperplasia in Older Adults. Journal of the American Geriatrics Society,2003, 51(4):424-426
[220]Lepor H, Jones K, Williford W. Worldwide experience with alfuzosin and tamsulosin. The Journal of Urology,2000,163 (4):1134-1137
[221]Sekhar E C, Rao T R K, Sekhar K R, et al. Determination of terazosin in human plasma, using high-performance liquid chromatography with fluorescence detection. Journal of Chromatography B,1998,710 (1-2) 137-142
[222]Cheah P Y, Yuen K H, Liong M L. Improved high-performance liquid chromatographic analysis of terazosin in human plasma. Journal of Chromatography B,2000,745 (2) 439-443.
[223]Jiang C Q, Gao M X, He J X. Study of the interaction between terazosin and serum albumin:Synchronous fluorescence determination of terazosin. Journal of Chromatography B,2002,452 (2):185-189
[224]Zavitsanos A P, Alebic-Kolbah T. Enantioselective determination of terazosin in human plasma by normal phase high-performance liquid chromatography-electrospray mass spectrometry. Journal of Chromatography A, 1998,794 (1-2):45-56
[225]Bro R, Kiers H H L. A new efficient method for determining the number of components in PARAFAC modes. Journal of Chemometrics,2003,17(5): 274-286
[226]Damiani P C, Nepote A J, Bearzotti M, et al. A test field for the second-order advantage in bilinear least-squares and parallel factor analyses fluorescence determination of ciprofloxacin in human urine. Analytical Chemistry,2004, 76(10):2798-2806
[227]Olivieri A C, Faber N M. Standard error of prediction in parallel factor analysis of three-way data. Chemometrics and Intelligent Laboratory Systems, 2004, 70(1):75-82
[228]Faber K, Kowalski B R. Net analyte signal calculation in multivariate calibration. Analytical Chemistry,1997,69:1620-1626
[229]Saxberg B E H, Kowalski B R. Generalized standard addition method. Analytical Chemistry,1979,51(7):1031-1038
[230]Booksh K, Henshaw J M, Burgess L W, Kowalski B R. A second-order standard addition method with application to calibration of a kinetics-spectroscopic sensor for quantitation of trichloroethylene. Journal of Chemometrics,1995, 9(4):263-282
[231]Wu H L, Yu R Q, Shibukawa M, et al. Second-Order Standard Addition Method Based on Alternating Trilinear Decomposition. Analytical Science,2000,16(2): 217-220
[232]Comas E, Gimeno R A, Ferre J, et al. Using second-order calibration to identify and quantify aromatic sulfonates in water by high-performance liquid chromatography in the presence of coeluting interferences. Journal of Chromatography A,2003,988(2):277-284
[233]Gimeno R A, Comas E, Marce R M, et al. Second-order bilinear calibration for determining polycyclic aromatic compounds in marine sediments by solvent extraction and liquid chromatography with diode-array detection. Analytica Chimica Acta,2003,498(1-2):47-53
[234]Sinha A E, Fraga C G, Prazen B J, et al. Trilinear chemometric analysis of two-dimensional comprehensive gas chromatography-time-of-flight mass spectrometry data. Journal of Chromatography A,2004,1027(1-2):269-27
[235]Gonz'alez A G, Herrador M A, Asuero A G. Intra-laboratory testing of method accuracy from recovery assays. Talanta,1999,48(2):729-736
[236]Kanich B, Papich M G. Plasma profile and pharmacokinetics of dextromethorphan after intravenous and oral administration in healthy dogs. Journal of Veterinary Pharmacology and Therapeutics,2004,27(1):337-341
[237]Zhang Y, Britto M R,Valderhaug K R, et al. Dextromethorphan:enhancing its systemic availability by way of low-dose quinidine-mediated inhibition of cytochrome P4502D6. Clinical Pharmacology & Therapeutics,1992,51(2): 647-655
[238]Brooks B R, Thisted R A, Appel S H, et al. For the AVP-923 ALS Study Group.Treatment of＂ pseudobulbar affect in ALS with dextromethorphan/quinidine:A randomized trial. Neurology,2004,63(8): 1364-1370
[239]Panitch H S, Thisted R A, Smith R A, et al. For the pseudobulbar affect in multiple sclerosis study group andomized, controlled trial of dextromethorphan/quinidine forpseudobulbar affect in. multiple sclerosis. Annals of Neurology,2006,59 (5):780-787
[240]Thisted R A, Klaff L, Schwartz S L, et al. Dextromethorphan and Quinidine in adult patients with uncontrolled painful diabetic peripheral neuropathy:a 29-day, multicenter, open-label, dose-escalation study. Clinical Therapeutics,2006,28: 1607-1618
[241]Evaristo A, Adam B, Maria A S, et al. Dextromethorphan and Quinidine Combination for Heroin Detoxification. American Journal on Addiction,2008, 17(1):176-180
[242]Salsali M, Coutts R T, Baker G B. Analysis of dextrorphan, a metabolite of dextromethorphan, using gas chromatography with electron-capture detection, Journal of Pharmacological and Toxicological Methods,1999,41 (1):143-146
[243]Furlanut M, Cima L, Benetello P, et al. Gas-liquid chromatographic determination of dextromethorphan in serum and brain, Journal of Chromatography A,1977,140 (1):270-274
[244]Linares P, Gutierrez M C, Lazaro F, et al. Determination of benzocaine, dextromethorphan and cetylpyridinium ion by high-performance liquid chromatography with UV detection. Journal of Chromatography A,1991,558 (1):147-153
[245]Motassim N, Decolin D, Le Dinh T, et al. Direct determination of dextromethorphan and its three metabolites in urine by high-performance liquid chromatography using a precolumn switching system for sample clean-up. Journal of Chromatography B,1987,422(2):340-345
[246]Eichhold T H, McCauley-Myers D L, Khambe D A, et al. Simultaneous determination of dextromethorphan, dextrorphan, and guaifenesin in human plasma using semi-automated liquid/liquid extraction and gradient liquid chromatography tandem mass spectrometry. Journal of Pharmaceutical and Biomedical Analysis,2007,43 (2):586-600
[247]Dong Y M, Chen X F, Chen Y L, et al. Separation and determination of pseudoephedrine, dextromethorphan, diphenhydramine and chlorpheniramine in cold medicines by nonaqueous capillary electrophoresis, Journal of Pharmaceutical and Biomedical Analysis,2005,39(1):285-289
[248]Priebe W. Anthracyclines antibiotics. New analogs, methods of delivery, and mechanisms of action. Journal of American Chemical Society,1995,574(1): 259-267
[249]Naidong W, Shou W Z, Addison T, et al. Liquid chromatography/tandem mass spectrometric bioanalysis using normal-phase columns with aqueous/organic mobile phases-a novel approach of eliminating evaporation and reconstitution steps in 96-well SPE. Rapid Communications in Mass Spectrometry,2002, 16(20):1965-1975
[250]Carrasco C, Joubert A, Tardy C, et al. DNA sequence recognition by bispyrazinonaphthalimides antitumor agents. Biochemistry,2003,42(40): 11751-11761
[251]Erdem A, Ozsoz M. Interaction of the anticancer drug epirubicin with DNA. Analytica Chimica Acta,2001,437(1):107-114
[252]Pigram W J, FullerW, Hamilton L D. Stereochemistry of intercalation: interaction of daunomycin with DNA. Nat. N.Biol.1972,235(1):17-19
[253]Momparler R. L, Karon M, Siegel S E, et al. Effect of adriamycin on DNA, RNA, and protein synthesis in cell-free systems and intact cells. Cancer Research,1976,36(8):2891-2895
[254]Tewey K M, Rowe T C, Yang L, et al. Adriamycin-induced DNA damage mediated by mammalian DNA topoisomerase Ⅱ. Science,1984,226(4673): 466-468
[255]Nikolajsen R P H, Booksh K S, Hansen A M, et al. Quantifying catecholamines using muti-way kinetic modeling. Analytica Chimica Acta,2003,475(1-2): 137-150
[256]Lakowicz JR. Principles of Fluorescence Spectroscopy, third ed., Springer, New York,2006, pp.278-282
[257]Lehrer S S. Solute perturbation of protein fluorescence. Quenching of the tryptophyl fluorescence of model compounds and of lysozyme by iodide ion. Biochemistry,1971,10(17):3254-3263
[258]Carbaryl, EHC 153,1994, http://www.inchem.org/documents/ehc/ehc/ehc153.htm
[259]Cummins D J, Andrews C W. Iteratively reweighted partial least squares:A performance analysis by Monte Carlo simulation. Journal of Chemometrics, 1995,9(1):489-507
[260]Zhu E Y, Barnes R M. A simple iteration algorithm for PLS regression. Journal of Chemometrics,1995,9(1):363-372
[261]Rannar S, Geladi P, Lindgren F, et al. A PLS kernel algorithm for data sets with many variables and fewer objects. Part Ⅱ:Cross validation, missing data and examples. Journal of Chemometrics,1995,9(1):459-470
[262]Zhang M H, Xu Q S, Massart D L. Averaged and weighted average partial least squares. Analytical Chimica Acta,2004,504(2):279-289
[263]Xu L, Jiang J H, Lin W Q, et al. Optimized sample-weighted partial least squares. Talanta,2007,7(2):561-566
[264]Burbidge R, Trotter M, Buxton B, et al. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers and Chemistry,2001,26(1):5-14
[265]Yao X J, Panaye A, Fan B T, et al. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. Journal of Chemical Information and Computer Sciences,2004,44(4):1257-1266
[266]Zernov V V, Balakin K V, Pletnev I V, et al. Drug design using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. Journal of Chemical Information and Computer Sciences, 2003,43(6):2048-2056
[267]Liu H X, Zhang R S, Yao X J, et al. QSAR Study of ethyl 2-2-[(3-Methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate:An inhibitor of AP-1 and NF-KB mediated gene expression based on support vector machines. Journal of Chemical Information and Computer Sciences,2003,43(4):1288-1296
[268]Xue C X, Zhang R S, Liu H X, et al. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. Journal of Chemical Information and Computer Sciences,2004, 44(5):1693-1700
[269]Mark H. Comparative study of calibration methods for near-infrared reflectance analysis using a nested experimental design. Analytical Chemistry,1986,58(13): 2814-2819
[270]Devos O, Fanget B, Saber A I, et al. Use of a Plackett-Burman design with multivariate calibration for the analysis of polycyclic aromatic hydrocarbons in micellar media by synchronous fluorescence. Analytical Chemistry,2002,74(3): 678-683
[271]An S J, Liu W Q, Venkatesh S. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognition,2007, 40(8):2154-2162
[272]Chang M W, Lin C J, Leave-One-Out Bounds for Support Vector Regression Model Selection. Neural Computation,2005,17(5):1188-1222
[273]Eads D R, Hill D, Davis S, et al. Genetic algorithms and support vector machines for time series classification, in:B. Bosacchi, D. B. Fogel, J. C. Bezdek (Eds.), Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V. Proceedings of the SPIE,2002,4787(1): 74-85
[274]Friedrichs F, Igel C. Evolutionary tuning of multiple SVM parameters. Neurocomputing 2005,64(1):107-117
[275]Frohlich H, Chapelle O, Scholkopf B. Feature selection for support vector machines by means of genetic algorithms. in:Proceedings of the 15th IEEE International Conference on Tools with AI (ICTAI 2003), IEEE Computer Society Institute of Electrical and Electronics Engineers Inc., USA,2003, pp. 142-148.
[276]Kennedy J, Eberhart R. Particle swarm optimization. In:Proceedings of IEEE International Conference on Neural Networks. Perth, Australia,1995, 1942-1948
[277]Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan,1995,39-43
[278]Wentzell P D, Andrews D T, Walsh J M, et al. Estimation of hydrocarbon types in light gas oils and diesel fuels by ultraviolet absorption spectroscopy and multivariate calibration. Canadian Journal of Chemistry,1999,77(3):391-400
[279]Borggaard C, Thodberg H H. Optimal minimal neural interpretation of spectra. Analytical Chemistry,1992,64(5):545-551
[280]Xu Q S, Liang Y Z. Monte Carlo cross validation, Chemometrics and Intelligent Laboratory Systems,2001,56(1):1-11
[2]俞汝勤.化学计量学导论.长沙：湖南教育出版社,1991
[3]梁逸曾.白灰黑复杂多组分分析体系及其化学计量学算法.长沙：湖南科技出版社,1997
[4]高鸿.分析化学前沿.北京：科学出版社,1991
[5]许禄,邵学广.化学计量学方法.北京：科学出版社,2004
[6]陆晓华.化学计量学.武汉：华中理工大学出版社,1997
[7]许禄.化学计量学：一些重要方法的原理及应用.北京：科学出版社,2004
[8]倪永年.化学计量学在分析化学中的应用.北京：科学出版社,2004
[9]Yu R Q. Chemometrics in China. Chemometrics and Intelligent Laboratory Systems,1992,14(1):23-29
[10]Wold S. Chemometrics:what do we mean with it, and what do we want from it? Paper of InC INC'94. Http://www.Emsl.pnl.gov:2080/docs/incinc/homepage. html
[11]Kowalski B R. Analytical chemistry as an information science. Trends in Analytical Chemistry,1981,1(3):71-74
[12]Rumelhart D E, Hinton G E, Williams R J. Learning internal representations by error propagation. In Parallel Distributing Processing, Explorations in the Microstructure of Cognition, Vol.1:Foundations, Rumelhart D E and McClelland J L (eds). Cambridge, MA:MIT Press,1986,318-362
[13]Werbos P. Beyond regression:new tools for prediction and analysis the behavioral sciences. [PHD dissertation], Cambridge, MA:Harvard,1974
[14]Vapnik V N. The nature of statistical learning theory. New York:Springer-Verlag,1995
[15]Vapnik V N. Statistical learning theory. New York:John Wiley and Sons,1998
[16]高志明,李井会,高礼让等.小波分析在化学中的应用进展.化学进展,2002,12(2)：179-191
[17]Cao L, Harrington P B, Harden C S, et al. Nonlinear wavelet compression of ion mobility spectra from ion mobility spectrometers mounted in an unmanned aerial vehicle. Analytical Chemistry,2004,76(4):1069-1077
[18]Juan A, Tauler R, Dyson R, et al. Spectroscopic imaging and chemometrics:a powerful combination for global and local sample analysis. Trends in Analytical Chemistry,2004,23(1):70-79
[19]Friedman J H. Stochastic gradient boosting. Computational Statistics and Data Analysis,2002,38(4):367-378
[20]Breiman L. Bagging predictors. Machine Learning,1996,24(2):123-140
[21]Breiman L, Friedman J H, Olshen R A, et al. Classification and regression trees. Belmont CA:Wadsworth International Group,1984
[22]Breiman L. Randomizing outputs in increase prediction accuracy. Machine Learning,2000,40(3):229-24
[23]Fraga C G, Prazen B J, Synovec R E. Objective data alignment and chemometric analysis of comprehensive two-dimensional separations with run-to-run peak shifting on both dimensions. Analytical Chemistry,2001,73(24):5833-5840
[24]Trevisan M G, Poppi R J. Determination of doxorubicin in human plasma by excitation-emission matrix fluorescence and multi-way analysis. Analytica Chimica Acta,2003,493(1):69-81
[25]Fraga C G, Bruckner C A, Synovec R E. Increasing the number of analyzable peaks in comprehensive two-dimensional separations through chemometrics. Analytical Chemistry,2001,73(3):675-683
[26]Mohimen A, Dobo A, Hoerner J K, et al. A chemometric approach to detection and characterization of multiple protein conformers in solution using electrospray ionization mass spectrometry. Analytical Chemistry,2003,75(16): 4139-4147
[27]Fransson M, Sparen A, Lagerholm B, et al. On-line process control of liquid chromatography. Analytical Chemistry,2001,73(7):1502-1508
[28]Lopez-Diez E C, Goodacre R. Characterization of microorganisms using UV resonance Raman spectroscopy and chemometrics. Analytical Chemistry,2004, 76(3):585-591
[29]Vogt F, Mizaikoff B. Introduction and application of secured principal component regression for analysis of uncalibrated spectral features in optical spectroscopy and chemical sensing. Analytical Chemistry,2003,75(13): 3050-3058
[30]Mayr T, Igel C, Liebsch G, et al. Cross-reactive metal ion sensor array in a micro titer plate format. Analytical Chemistry,2003,75(17):4389-4396
[31]Rodriguez EMR, Alaejos M S, Romero C D. Chemometric studies of several minerals in milks. Journal of Agricultural and Food Chemistry,1999,47(4): 1520-1524
[32]Einax J. Chemometrics in environmental chemistry statistical methods. Berlin Germany:Springer-Verlag,1995
[33]Rios P, Stuart J A, Grant E. Plastics disassembly versus bulk recycling: engineering design for end-of-life electronics resource recovery. Environmental Science and Technology,2003,37(23):5463-5470
[34]Mannhold R, Hrogsgaard P, Larscn H, et al. Methods and principles in medicinal chemistry. Weinheim Germany:Wiley-VHC,1995
[35]Hergert L A, Escandar G M. Spectrofluorimetric study of the β-cyclodextrin-ibuprofen complex and determination of ibuprofen in pharmaceutical preparations and serum. Talanta,2003,60(2-3):235-246
[36]Espinosa-Mansilla A, Pe A, Mu D L, et al. Photoinduced spectrofluorimetric determination of fluoroquinolones in human urine by using three- and two-way spectroscopic data and multivariate calibration. Analytica Chimica Acta,2005, 531(3):257-266
[37]Hansch C, Fujita T. Classical and Three-Dimensional QSAR in Agrochemistry. In:ACS Symposium Series 606. Washington DC:American Chemical Society, 1995:23-56
[38]Rodriguez-Cuesta M J, Boque R, Rius F X, et al. Determination of carbendazim, fuberidazole and thiabendazole by three-dimensional excitation-emission matrix fluorescence and parallel factor analysis. Analytica Chimica Acta,2003,491(1): 47-56
[39]Chaudry U A, Popelier P L. Estimation of pKa using quantum topological molecular similarity descriptors:application to carboxylic acids, anilines and phenols. Journal of Organic Chemistry,2004,69(2):233-241
[40]Lakshminarayanan S, Mhatre P, Gudi R. Identification of bilinear models for chemical processes using canonical variate analysis. Industrial and Engineering Chemistry Research,2001,40(20):4415-4427
[41]Shen M, Wagner M S, Castner D G, et al. Multivariate surface analysis of plasma-deposited tetraglyme for reduction of protein adsorption and monocyte adhesion. Langmuir,2003,19(5):1692-1699
[42]梁逸曾,俞汝勤.化学计量学在我国的发展.化学通报,1999,10(1)：14-19
[43]吴海龙,梁逸曾,俞汝勤.分析化学计量学.分析实验室,1999,11(2)：94-102
[44]Lohnes M T, Guy R D, Wentzell P D. Window target-testing factor analysis: Theory and application to the chromatographic analysis of complex mixtures with multiwavelength fluorescence detection. Analytica Chimica Acta,1999, 389(1-3):95-113
[45]Liang Y Z, Leung A K M, Chau F T. A roughness penalty approach and its application to noisy hyphenated chromatographic two-way data. Journal of Chemometrics,1999,13(5):511-524
[46]Xu Q S, Liang Y Z. On the equivalence of window factor analysis and orthogonal projection resolution. Chemometrics and Intelligent Laboratory Systems.1999,45(1-2):335-338
[47]Maeder M. Evolving factor analysis for the resolution of overlapping chromatographic peaks. Analytical Chemistry,1987,59(3):527-530
[48]Malinowski E R. Window factor analysis:Theoretical derivation and application to flow injection analysis data. Journal of Chemometrics,1992,6(1):29-40
[49]Gampp H, Maeder M, Meyer C J. Calculation of equilibrium constants from multiwavelength spectroscopic data—Ⅲ:Model-free analysis of spectrophotometric and ESR titrations. Talanta,1985,32(12):1133-1139
[50]Maeder M, Zuberbuhler A D. The resolution of overlapping chromatographic peaks by evolving factor analysis. Analytica Chimica Acta,1986,181(2): 287-291
[51]Kvalheim O M, Liang Y Z. Heuristic evolving latent projections:resolving two-way multicomponent data.1. Selectivity, latent-projective graph, data scope, local rank, and unique resolution. Analytical Chemistry,1992,64(8):936-946
[52]Liang Y Z, Kvalheim O M, Keller H R, et al. Heuristic evolving latent projections:resolving two-way multicomponent data.2. Detection and resolution of minor constituents. Analytical Chemistry,1992,64(8):946-953
[53]Jiang J H, Sasic S, Yu R Q, et al. Resolution of two-way data from spectroscopic monitoring of reaction or process systems by parallel vector analysis (PVA) and window factor analysis (WFA):inspection of the effect of mass balance, methods and simulations. Journal of Chemometrics,2003,17(3):186-197
[54]Sanchez F C, Bogaert B, Rutan S C, et al. Multivariate peak purity approaches. Chemometrics and Intelligent Laboratory Systems,1996,34(2):139-171
[55]Karjalainen E. The spectrum reconstruction problem:Use of alternating regression for unexpected spectral components in two-dimensional spectroscopies. Chemometrics and Intelligent Laboratory Systems,1989,7(1-2): 31-38
[56]Tauler R, Casassas E. Spectroscopic resolution of macromolecular complexes using factor analysis:Cu (Ⅱ)-polyethyleneimine system. Chemometrics and Intelligent Laboratory Systems,1992,14(1-3):305-317
[57]Liang Y Z, Kvalheim O M. Heuristic evolving latent projections:Resolving hyphenated chromatographic profiles by component stripping. Chemometrics and Intelligent Laboratory Systems,1992,64,20(2):115-125
[58]Manne R, Shen H L, Liang Y Z. Subwindow factor analysis. Chemometrics and Intelligent Laboratory Systems,1999,45(1-2):171-176
[59]Shen H L, Manne R, Liang Y Z, et al. Local resolution of hyphenated chromatographic data. Chemometrics and Intelligent Laboratory Systems,1999, 45(1-2):323-328
[60]Jiang J H, Ozaki Y, Kleimann M, et al. Resolution of two-way data from on-line Fourier-transform Raman spectroscopic monitoring of the anionic dispersion polymerization of styrene and 1,3-butadiene by parallel vector analysis (PVA) and window factor analysis (WFA). Chemometrics and Intelligent Laboratory Systems,2004,70(1):83-92
[61]Windig W, Guilment J. Interactive self-modeling mixture analysis. Analytical Chemistry,1991,63(14):1425-1432
[62]Jiang J H, Liang Y Z, Ozaki Y. On simplex-based method for self-modeling curve resolution of two-way data. Chemometrics and Intelligent Laboratory Systems,2003,65(1):51-65
[63]Wang J H, Liang Y Z, Yu R Q, et al. Local chemical rank estimation of two-way data in the presence of heteroscedastic noise:A morphological approach. Chemometrics and Intelligent Laboratory Systems,1996,32(2):265-272
[64]Juan A, Bogaert B, Sanchez F C, et al.. Application of the needle algorithm for exploratory analysis and resolution of HPLC-DAD data. Chemometrics and Intelligent Laboratory Systems,1996,33(2):133-145.
[65]Juan A, Vander H Y, Taule R, et al. Assessment of new constraints applied to the alternating least squares method. Analytica Chimica Acta,1997,346(3): 307-318
[66]Malinowski E R. Automatic window factor analysis:A more efficient method for determining concentration profiles from evolutionary spectra. Journal of Chemometrics,1996,10(4):273-279
[67]Gemperline P J. A priori estimates of the elution profiles of the pure components in overlapped liquid chromatography peaks using target factor analysis. Journal of Chemical Information and Computer Sciences,1984,24(4): 206-212
[68]Vandeginste B G M, Derks W, Kateman G. Multicomponent self-modelling curve resolution in high-performance liquid chromatography by iterative target transformation analysis. Analytica Chimica Acta,1985,173(1):253-264
[69]Paatero P. Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems,1997,37(1):23-35
[70]Gargallo R, Tauler R, Sanchez F C, et al. Validation of alternating least-squares multivariate curve resolution for chromatographic resolution and quantitation. Trends in Analytical Chemistry,1996,15(7):279-286
[71]Elbergali A K, Brereton R G, Rahmani A. Influence of the method of calculation of noise thresholds on wavelength selection in window factor analysis of diode array high-performance liquid chromatography. The Analyst,1996,121(5): 585-590
[72]Sanchez F C, Rutan S C, Gil Garcia M D, et al. Resolution of multicomponent overlapped peaks by the orthogonal projection approach, evolving factor analysis and window factor analysis. Chemometrics and Intelligent Laboratory Systems,1997,36(2):153-164
[73]Ritter C, Gilliard J A, Cumps J, et al. Corrections for heteroscedasticity in window evolving factor analysis. Analytica Chimica Acta,1996,318(2): 125-136
[74]Elbergali A, Nygren J, Kubista M. An automated procedure to predict the number of components in spectroscopic data. Analytica Chimica Acta,1999, 379(1-2):143-158
[75]Xie Y L, Hopke P K, Paatero P J. Positive matrix factorization applied to a curve resolution problem. Journal of Chemometrics,1998,12(6):357-364
[76]Shao X G, Cai W S. Resolution of multicomponent chromatograms by window factor analysis with wavelet transform preprocessing. Journal of Chemometrics, 1998,12(2):85-93
[77]Sanchez F C, Vandeginste B G M, Hancewicz T M, et al. Resolution of Complex Liquid Chromatography-Fourier Transform Infrared Spectroscopy Data. Analytical Chemistry,1997,69(8):1477-1484
[78]Lilley K A, Wheat T E. Drug identification in biological matrices using capillary electrophoresis and chemometric software. Journal of Chromatography B,1996,683(1):67-76
[79]Statheropoulos M, Smaragdis E, Tzamtzis N, et al. Principal component analysis for resolving coeluting substances in gas chromatography-mass spectrometry doping control analysis. Analytica Chimica Acta,1996,331(1-2):53-61.
[80]Thurston T J, Brereton R G, Foord D J, et al. Monitoring of a second-order reaction by electronic absorption spectroscopy using combined chemometric and kinetic models. Journal of Chemometrics,2003,17(6):313-322.
[81]Joaquim C G, Esteves da S, Adelio A S C M, et al. Simultaneous use of evolving factor analysis of fluorescence spectral data and analysis of pH titration data for comparison of the acid-base properties of fulvic acids. Analytica Chimica Acta, 1996,318(3):365-372
[82]Koons J M, Ellis P D. Applicability of factor analysis in solid state NMR. Analytical Chemistry,1995,67(23):4309-4315
[83]Grabaric B S, Grabaric Z, Tauler R, et al. Application of multivariate curve resolution to the voltammetric data Factor analysis ambiguities in the study of weak consecutive complexation of metal ion with ligand. Analytica Chimica Acta,1997,341(2-3):105-120
[84]Bijlsma S, Boelens H F M, Hoefsloot H C J, et al. Constrained least squares methods for estimating reaction rate constants from spectroscopic data. Journal of Chemometrics,2002,16(1):28-40
[85]Winding W, Hornak J P, Antalek B. Multivariate Image Analysis of Magnetic Resonance Images with the Direct Exponential Curve Resolution Algorithm (DECRA):Part 1. Algorithm and Model Study. Journal of Magnetic Resonance, 1998,132(2):298-306
[86]Antalek B, Hornak J P, Windig W. Multivariate Image Analysis of Magnetic Resonance Images with the Direct Exponential Curve Resolution Algorithm (DECRA):Part 2. Application to Human Brain Images. Journal of Magnetic Resonance,1998,132(2):307-315
[87]Andrew J J, Hancewicz T M. Rapid analysis of raman image data using two-way multivariate curve resolution. Applied Spectroscopy,1998,52(6):797-804
[88]Diaz-Cruz M S, Tauler R, Casassas E, et al. Application of multivariate curve resolution to voltammetric data. Part 1. Study of Zn (Ⅱ) complexation with some polyelectrolytes. Journal of Electroanalytical Chemistry,1995,393(1-2):7-16
[89]Mendieta J, Diaz-Cruz M S, Tauler R, et al. Application of multivariate curve resolution to voltammetric data. Ⅱ. Study of metal-binding properties of the peptides. Analytical Biochemistry,1996,240(1):134-141
[90]Ren S X, Gao L. The speciation of Copper (Ⅱ) / Ethylenediamine / Oxalate system by evolving factor analysis. Journal of Automatic Chemistry,1995, 17(5):17-177
[91]Grung B, Kvalheim O M. Detection and quantification of embedded minor analytes in three-way multicomponent profiles by evolving projections and internal rank annihilation. Chemometrics and Intelligent Laboratory Systems, 1995,29(2):213-221
[92]Furusjooe E, Danielsson L G, Koenberg E, et al.. Evaluation techniques for two-way data from in situ fourier transform mid-infrared reaction monitoring in aqueous solution. Analytical Chemistry,1998,70(9):1726-1734
[93]Zhu E Y, Barnes R M. A simple iteration algorithm for PLS regression. Journal of Chemometrics,1995,9(1):363-372
[94]Rannar S, Lindgren F, Geladi P, et al. A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1:Theory and algorithm. Journal of Chemometrics,1994,8(2):111-126
[95]Rannar S, Geladi P, Lindgren F, et al. A PLS kernel algorithm for dta sets with many variables and fewer objects. Part Ⅱ:Cross validation, missing data and examples. Journal of Chemometrics,1995,9:459-470
[96]Zhang M H, Xu Q S, Massart D L. Averaged and weighted average partial least squares. Analytical Chimica Acta,2004,504(2):279-289
[97]Suykens J A K, Vandewale J. Least squares support vector machine classifiers. Neural Processing Letters,1999,9(3):293-300
[98]Chauchard F, Cogdill R, Roussel S, et al. Application of LS-SVM to non-linear phenomena in NIR spectroscopy:development of a robust and portable sensor for acidity prediction in grapes, Chemometrics and Intelligent Laboratory Systems,2004,71 (1):141-150
[99]Borin A, Ferrao M F, Mello C, et al. Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta,2006,579(1):25-32
[100]Ferrao M F, Godoy S C, Gerbase A E, et al. Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR. Analytica Chimica Acta,2007,595 (1):114-119
[101]Niazi A, Ghasemi J, Zendehdel M. Simultaneous voltammetric determination of morphine and noscapine by adsorptive differential pulse stripping method and least-squares support vector machines, Talanta,2007,74(1):247-254
[102]Liu F, Zhang F, Jin Z L, et al. Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy. Analytica Chimica Acta,2008,629 (1): 56-65.
[103]Booksh K S, Kowalski B R. Theory of analytical chemistry. Analytical Chemistry,1992,66(15):782A-791A
[104]Wilson B E, Sanchez E, Kowalski B R. An improved algorithm for the generalized rank annihilation method. Journal of Chemometrics,1989,3(3): 493-498
[105]Li S, Hamilton C, Gemperline P J. Generalized rank annihilation method using similarity transformations. Analytical Chemistry,1992,64(6):599-607
[106]Faber K, Lorber A, Kowalski B R. Generalized rank annihilation method: Standard errors in the estimated eigenvalues if the instrumental errors are heteroscedastic and correlated. Journal of Chemometrics,1997,11(2):95-109
[107]Sanchez E, Kowalski B R. Generalized rank annihilation factor analysis. Analytical Chemistry,1986,58(2):496-499
[108]Sanchez E, Kowalski B R. Tensorial resolution:a direct trilinear decomposition. Journal of Chemometrics,1990,4(1):29-45
[109]Li S, Gemperline P J. Eliminating complex eigenvectors and eigenvalues in multiway analyses using the direct trilinear decomposition method. Journal of Chemometrics,1993,7(1):77-88
[110]Lin Z, Booksh K S, Burgess L W, et al. A second-order fiber optic heavy metal sensor employing second-order tensorial calibration. Analytical Chemistry,1994, 66(15):2552-2560
[111]Booksh K S, Lin Z, Wang Z, et al. Extension of trilinear decomposition method with an application to the flow probe sensor. Analytical Chemistry,1994,66(15): 2561-2569
[112]Henshow J M, Burgess L W, Booksh K S, et al. Multicomponent determination of chlorinated hydrocarbons using a reaction-based chemical sensor.1. Multivariate calibration of fujiwara reaction products. Analytical Chemistry, 1994,66(20):3328-3336
[113]Gui M, Rutan S C, Agbodian A. Kinetic detection of overlapped amino acids in thin-layer chromatography with a direct trilinear decomposition method. Analytical Chemistry,1995,67(18):3293-3299
[114]Millican D W, McGrown L B. Fluorescence lifetime selectivity in excitation-emission matrices for qualitative analysis of a two-component system. Analytical Chemistry,1989,61(6):580-583
[115]Millican D W, McGown L B. Fluorescence lifetime resolution of spectra in the frequency domain using multiway analysis. Analytical Chemistry,1990,62(20): 2242-2247
[116]Scarmino I, Kubista M. Analysis of correlated spectral data. Analytical Chemistry,1993,65(4):409-416
[117]Appelolof C J, Davidson E R. Strategies for analyzing data from video fluorometric monitoring of liquid chromatographic effluents. Analytical Chemistry,1981,53(13):2053-2056
[118]Linder M, Sundberg R. Precision of prediction in second-order calibration with fous on bilinear regression method. Journal of Chemometrics,2002,16(1): 12-27
[119]Linder M, Sundberg R. Second-order bilinear calibration:the effects of vectorising the data matrices of the calibration set. Chemometrics and Intelligent Laboratory Systems,2002,63(2):107-116
[120]Damiani P C, Nepote A J, Bearzotti M, et al. A test field for the second-order advantage in bilinear least-squares and parallel factor analyses:fluorescence determination of ciprofloxacin in human urine. Analytical Chemistry,2004, 76(10):2798-2806
[121]Smilde A K, Doornbos D A. Three-way methods for the calibration of chromatographic systems:Comparing PARAFAC and three-way PLS. Journal of Chemometrics,1991,5(3):345-360
[122]Bro R. Multi-way analysis in the food industry, PhD Thesis, University of Amsterdam and Royal Veterinary and Agricultural university, Denmark,1998
[123]Mitchell B C, Burdick D S. Slowly converging PARAFAC sequences:Swamps and two-factor degeneracies. Journal of Chemometrics,1994,8(2):155-168
[124]Bro R. PARAFAC:Tutorial and applications. Chemometrics and Intelligent Laboratory Systems,1997,38(2):149-171
[125]Rayens W S, Mitchell B C. Two-factor degeneracies and a stabilization of PARAFAC. Chemometrics and Intelligent Laboratory Systems,1997,38(2): 173-181
[126]Krijnen W P. The Analysis of three-way Arrays by Constrained PARAFAC Methods, Leiden:Dswo Press,1993
[127]Harshman R A. Foundations of the PARAFAC procedure:Models and conditions for an'explanatory'multi-modal factor analysis. UCLA Working Papers in Phonetics,1970,16(1):1-84
[128]Carroll J D, Chang J J. Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition. Psychometrika,1970,35(3):283-319
[129]Jiang J H, Wu H L, Chen Z P, et al. Coupled vectors resolution method for chemometric calibration with three-way data. Analytical Chemistry,1999, 71(19):4254-4262
[130]Jiang J H, Wu H L, Li Y, et al. Alternating coupled vectors resolution (ACOVER) method for trilinear analysis of three-way data. Journal of Chemometrics,1999, 13(6):557-578
[131]Jiang J H, Wu H L, Li Y, et al. Three-way data resolution by alternating slice-wise diagonalization (ASD) method. Journal of Chemometrics,2000,14(1): 15-36
[132]Li Y, Jiang J H, Wu H L, et al. Alternation coupled matrics resolution method for three-way array analysis. Chemometrics and Intelligent Laboratory Systems, 2000,52(1):33-43
[133]Chen Z P, Wu H L, Yu R Q. On the self-weighted alternating trilinear decomposition algorithm-the property of being insensitive to excess factors used in calculation. Journal of Chemometrics,2001,15(5):439-453
[134]Chen Z P, Li Yang, Yu R Q. Pseudo alternating least squares algorithm for trilinear decomposition. Journal of Chemometrics,2001,15(3):149-167
[135]Chen Z P, Wu H L, Li Y, et al. A novel constrained PARAFAC algorithm for second-order linear calibration. Analytica Chimica Acta,2000,423(1):187-196
[136]Cao Y Z, Chen Z P, Mo C Y, et al. A PARAFAC algorithm using penalty diugonalization error (PDE) for three-way data array resolution. The Analyst, 2000,125(12):2303-2310
[137]Wu H L, shibukawa M, Oguma K. An alternating trilinear decomposition method with application to calibration of HPLC-DAD for simultaneous determination of overlapped chlorinated aromatic hydrocarbous. Journal of Chemometrics,1998,12(1):1-26
[138]Xia A L, Wu H L, Fang D M, et al. Alternating penalty trilinear decomposition for second-order calibration with an application to interference-free analysis of excitation-emission matrix fluorescence data. Journal of Chemometrics,2005, 19(2):65-76
[139]Xia A L, Wu H L, Zhu S H, et al. Determination of psoralen in human plasma by using excitation-emission matrix fluorescence coupled to second-order calibration. Analytical Sciences,2008,24(9):1171-1178
[140]Hu L Q, Wu H L, Ding Y J, et al. Alternating asymmetric trilinear decomposition for three-way data arrays analysis. Chemometrics and Intelligent Laboratory Systems,2006,82(1-2):145-153
[141]Xie H P, Chu X, Jiang J H, et al. Competitive interactions of adriamycin and ethidium bromide with DNA as studied by full rank parallel factor analysis of fluorescence three-way array data. Spectrochimica Acta Part A,2003, 59(4):743-749
[142]Juan A, Rutan S C, Tauler R, et al. Comparison between the direct trilinear decomposition and the multivariate curve resolution-alternating least squares methods for the resolution of three-way data sets. Chemometrics and Intelligent Laboratory Systems,1998,40(1):19-32
[143]Saurina J, Hemandez-Cassou S, Tauler R. Multivariate curve resolution and trilinear decomposition methods in the analysis of stopped-flow kinetic data for binary amino acid mixtures. Analytical Chemistry,1997,69(13):2329-2336
[144]Paatero P. Construction and analysis of degenerate PARAFAC models. Journal of Chemometrics,2000,14(3):285-299
[145]Tomasi G, Bro R. PARAFAC and missing values. Chemometrics and Intelligent Laboratory Systems,2005,75(2):163-180
[146]Lavine B, Workman J J. Chemometrics:Fundamental review. Analytical Chemistry,2004,76(12):3365-3372
[147]Bro R, Workman J J, Kowalski P R. Review of chemometrics applied to spectroscopy:1985-1995, Part Ⅲ. Multi-way analysis. Applied Spectroscopy reviews,1997,32(3):237-261
[148]Baunsgaard D, Andersson C A, Arndal A, et al. Multi-way chemometrics for mathematical separation of fluorescent colorants and colour precursors from spectrouorimetry of beet sugar and beet sugar thick juice as validated by HPLC analysis. Food Chemistry,2000,70(1):113-121
[149]JiJi R D, Andersson G G, Booksh K S. Application of PARAFAC for calibration with excitation-emission matrix fluorescence spectra of three classes of environmental pollutants. Journal of Chemometrics,2000,14(3):171-185
[150]Muroski A R, Booksh K S, Myrick M L. Single-Measurement Excitation/Emission Matrix Spectrofluorometer for Determination of Hydrocarbons in Ocean Water.1. Instrumentation and Background Correction. Analytical Chemistry,1996,68(20):3534-3538
[151]Booksh K S, Muroski A R, Myrick M L. Single-Measurement Excitation/Emission Matrix Spectrofluorometer for Determination of Hydrocarbons in Ocean Water.2. Calibration and Quantitation of Naphthalene and Styrene. Analytical Chemistry,1996,68(20):3539-3544
[152]Beltran J L, Ferrer R, Guiteras J. Multivariate calibration of polycyclic aromatic hydrocarbon mixtures from excitation-emission fluorescence spectra. Analytica Chimica Acta,1998,373(2-3):311-319
[153]Nahorniak M L, Booksh K S. Optimizing the implementation of the PARAFAC method for near-real time calibration of excitation-emission fluorescence analysis. Journal of Chemometrics,2003,17(11):608-617
[154]JiJi R D, Cooper G A, Booksh K S. Excitation-emission matrix fluorescence based determination of carbamate pesticides and polycyclic aromatic hydrocarbons. Analytica Chimica Acta,1999,397(1-3):61-72
[155]JiJi R D, Booksh K S. Mitigation of Rayleigh and Raman Spectral Interferences in Multiway Calibration of Excitation-Emission Matrix Fluorescence Spectra. Analytical Chemistry,2000,72(4):718-725
[156]Pedersen D K, Munck L, Engelsen S B. Screening for dioxin contamination in fish oil by PARAFAC and N-PLSR analysis of fluorescence landscapes. Journal of Chemometrics,2002,16(8-10):451-460
[157]Espinosa-Mansilla A, de la Pena A M, Canada-Canada F, et al. Determinations of fluoroquinolones and nonsteroidal anti-inflammatory drugs in urine by extractive spectrophotometry and photoinduced spectrofluorimetry using multivariate calibration. Analytical Biochemistry,2005,34(2):275-286
[158]Mahedero M C, Diaz N M, de la Pena A M, et al. Strategies for solving matrix effects in the analysis of sulfathiazole in honey samples using three-way photochemically induced fluorescence data.Talanta,2005,65(3):806-813
[159]Escandar G M, Gomez D G, Espinosa Mansilla A, et al. Determination of carbamazepine in serum and pharmaceutical preparations using immobilization on a nylon support and fluorescence detection. Analytica Chimica Acta,2004, 506(2):161-170
[160]Arancibia J A, Escandar G M. Two different strategies for the fluorimetric determination of piroxicam in serum. Talanta,2003,60(6):1113-1121
[161]吴海龙,龙宁,方艺峰,等.交替三线性分解算法用于水杨酸和2,52二羟基 苯甲酸的荧光法同时测定.分析试验室,2002,21(2)：44-47
[162]Jin S Y, Xu Z C, Chen J P, et al. Determination of organophosphate and carbamate pesticides based on enzyme inhibition using a pH-sensitive fluorescence probe. Analytica Chimica Acta,2004,523(1):117-123
[163]Braga J W B, Bottoli C B G, Jardim I C S F, et al. Determination of pesticides and metabolites in wine by high performance liquid chromatography and second-order calibration methods. Journal of Chromatography A,2007,1148(2): 200-210
[164]Piccirilli C, Escandar G M. Partial least-squares with residual bilinearization for the spectrofluorimetric determination of pesticides. A solution of the problems of inner-filter effects and matrix interferents. Analyst,2006,131(9):1012-1020
[165]Bijlsma S, Smilde A K. Estimating reaction rate constants from a two-step reaction:A comparison between two-way and three-way methods. Journal of Chemometrics,2000,14(5-6):541-560
[166]Tan Y X, Jiang J H, Wu H L, et al. Resolution of kinetic system of simultaneous degradations of chlorophyll a and b by PARAFAC. Analytica Chimica Acta, 2000,412(1-2):195-202
[167]Sanchez-Ponce R, Rutan S C. Steady state kinetic model constraint for multivariate curve resolution-alternating Least Squares analysis. Chemometrics and Intelligent Laboratory Systems,2005,77(1):50-58
[168]Pla F F P, Redon J F B, Valero R. A new algorithm for the kinetic data analysis. Chemometrics and Intelligent Laboratory Systems,2000,53(1):1-19
[169]Zhao Y, Wang G, Li W, Zhu Z L. Determination of reaction mechanism and rate constants of alkaline hydrolysis of phenyl benzoate in ethanol-water medium by nonlinear least squares regression. Chemometrics and Intelligent Laboratory Systems,2006,82(1-2):193-199
[170]Cruz S C, Rothenberg G, Westerhuis J A, et al. Estimating kinetic parameters of complex catalytic reactions using a curve resolution based method. Chemometrics and Intelligent Laboratory Systems,2008,91(1):101-109
[171]Garcia R A, Damiani P C, Olivieri A C. Different strategies for the direct determination of amoxicillin in human urine by second-order multivariate analysis of kinetic-spectrophotometric data. Talanta,2007,71(2):806-815
[172]Guterres M V, Volpe P L, Ferreira M C. Multiway Calibration for Creatinine Determination in Human Serum,Using the Jaffe Reaction. Applied Spectroscopy, 2004,58(1):54-60
[173]Nazi A, Ghasemi J, Yazdanipur A, PARAFAC decomposition of three-way kinetic-spectrophotometric spectral matrices based on phosphomolymbdenum blue complex chemistry for nitrite determination in water and meat samples. Analytical Letters,2005,38(14):2377-2392
[174]Nomikos P, MacGregor J F. Monitoring of batch processes using multi-way principal component analysis. AIChE Journal,1994,40(8):1361-1375
[175]Kenneth S, Dahl A, Michael J, et al. Translating third-order data analysis methods to chemical batch Processes. Chemometrics and Intelligent Laboratory Systems,1999,46(2):161-180
[176]Smilde A K, Tates A A, Boelens H F M, et al. Systematic investigation of process and product variations in a spindraw-winding process. Chemical Engineering Science,2001,56(17):4993-5002
[177]N(?)s A. The design of calibration in near infrared reflectance analysis by clustering. Journal of Chemometrics,1987,1:121-134
[178]Zemroch P J. Cluster Analysis as an experimental design generator, with application to gasoline belending experiments. Technometrics,1986,28:39-49
[179]Kennard R W, Stone L A. Computer aided design of experiments. Technometrics,1969,11:137-148
[180]Honigs D, Hieftje G M, Mark H, Hirschfeld T B. Unique sample selection via near-infrared spectral subtraction. Analitical Chemistry,1985,57:2299-2303
[181]Snee R D. Validation of regression models:methods and examples. Technometrics,1977,19(4):415-428
[182]Spiegelman C H, McShane M J, Goetz M J, et al. Theoretical justification of wavelength selection in PLS calibration:Development of a New Algorithm. Analytical Chemistry,1998,70:35-44
[183]Swierenga H, Wulfert E, de Noord O E, et al. Development of robust calibration models in near infra-red spectrometric applications, Analytical Chimica Acta, 2000,411(1):121-135
[184]Goicoechea H C, Olivieri A C. Wavelength Selection for Multivariate Calibration Using a Genetic Algorithm:A Novel Initialization Strategy. Journal of Chemical Information and Computer Sciences,2002,42 (5): 1146-1153
[185]Khanmohammadi M, Karimi M A, Ghasemi K, et al. Quantitative determination of Malathion in pesticide by modified attenuated total reflectance-Fourier transform infrared spectrometry applying genetic algorithm wavelength selection method. Talanta,2007,72(2):620-625
[186]Hoskuldsson A. Variable and subset selection in PLS regression. Chemometrics and Intelligent Laboratory Systems,2001,55 (1):23-38
[187]Esbensen K H. Multivariate Data Analysis in Practice,5th ed., CAMO Process As, Oslo,2002
[188]Centner V, Massart D L, de Noord O E et al. Elimination of Uninformative Variables for Multivariate Calibration, Analytical Chemistry,1996,68 (21): 3851-3858
[189]Hemmateenejad B, Ghavami R, Miri R, et al. Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks. Talanta,2006,,68 (4): 1222-1229
[190]Xu L, Jiang J H, Zhou Y P, et al. MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration. Chemometrics and Intelligent Laboratory Systems,2007,87(2):226-230
[191]Xu H, Liu Z C, Cai W S, et al. A wavelength selection method based on randomization test for near-infrared spectral analysis. Chemometrics and Intelligent Laboratory Systems,2009,97(2):189-193
[192]Han Q J, Wu H L, Cai C B, et al. An ensemble of Monte Carlo uninformative variable elimination for wavelength selection. Analytical Chimica Acta,2008, 612(1):121-125
[193]Norgaard L, Saudland A, Wagner J, et al. Interval partial least-squares regression (iPLS):a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy,2000,54(3):413-419
[194]Zou X, Zhao J, Li Y. Selection of the efficient wavelength regions in FT-NIRspectroscopy for determination of SSC of'Fuji'apple based on BiPLS and FiPLS models. Vibrational Spectroscopy,2007,44:220-227
[195]Goicoechea H C, Olivieri A C. A new family of genetic algorithms for wavelength interval selection in multivariate analytical spectroscopy. Journal of Chemometrics,2003,17(6):338-345
[196]Leardi R, Nφrgaard L. Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. Journal of Chemometrics,2004,18(11):486-497
[197]Jiang J H, Berry R J, Siesler H W, et al. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to midinfrared and near-infrared spectroscopic data. Analytical Chemistry,2002,74(14):3555-3565
[198]Jenkins Y, Barton J K A. sequence-specific molecular light switch:tethering of an oligonucleotide to a dipyridophenazine complex of ruthenium (11). Journal of the American Chemistry Society,1992,114(22):8736-8738
[199]Chen H F, Patel D J. Solution structure of the menogaril-DNA complex. Journal of the American Chemistry Society,1995,117(22):5901-5913
[200]Friedman A E, Chambron J C, Sauvage J P, et al. A molecular light switch for DNA:Ru(bpy)2(dppz)2+. Journal of the American Chemistry Society,1990,11 2(12):4960-4962
[201]Krotz A H, Hudson B P, Barton J K. Assembly of DNA recognition elements on an octahedral rhodium intercalator:predictive recognition of 5'-TGCA-3'by Δ —[Rh(R,R) -Me2trien]phi]3+. Journal of the American Chemistry Society, 1993,115(26):12577-12578
[202]赵晓杰,江山,陆冬生,等.用共振拉曼光谱研究电子激发态铜叶琳与D NA的相互作用.高等化学学报,1994,15(4)：600-602
[203]Hutchins R A, Crenshaw J M, Graves D E, et al. Influence of substituent modifications on DNA binding energetics of acridine-based anticancer agents. Biochemistry,2003,42 (46):13754-13761
[204]Su H, Williams P, Thompson M. Platinum anticancer drug binding to DNA detected by thickness-shear -mode acoustic wave sensor. Analytical Chemistry, 1995,67(5):1010-1013
[205]Lah J, Bezan M, Vesnaver G. Thermodynamics of berenil binding to poly[d(AT)] poly[d(AT)] and poly[d(A)]poly[d(T)] duplexes. Acta Chimica Slovenica,2001, 48(3):289-308
[206]Okhonin V, Krylova S M, Krylov S N. Nonequilibrium capillary electrophoresis of equilibrium mixtures, mathematical model. Analytical Chemistry,2004,76 (4):1507-1512
[207]David W M, Brodbelt J, Kerwin SM, et al. Investigation of quadruplex oligonucleotide-drug interactions by electrospray ionization mass spectrometry. Analytical Chemistry,2002,74(9):2029-2033
[208]Reynisson J, Schuster G B, Howerton S B, et al. Intercalation of trioxatriangulenium ion in DNA:binding, electron transfer, X-ray crystallography, and electronic structure. Journal of the American Chemistry Society,2003,125(8):2072-2083
[209]McGhee J D, Von Hippel P H. Theoretical aspects of DNA-protein interactions-cooperative and non-cooperative binding of large ligands to a one-dimensional homogeneous lattice. Journal of Molecular Biology,1974,86 (2):469-489
[210]Le Gal J M, Morjani H, Manfait M. Ultrastructural appraisal of the multidrug resistance in K562 and LR73 cell lines from fourier transform infrared spectroscopy. Cancer Research,1993,53(16):3681-3686
[211]Xie H P, Jiang J H, Cui H, et al. Competitive interaction of the antitumor drug daunorubicin and the fluorescence probe ethidium bromide with DNA as studied by resolving trilinear fluorescence data:the use of PARAFAC and its modification. Analytical and Bioanalytical Chemistry 2002,373(3):159-162.
[212]Zhang F, Zhang Q Q, Wang W G, et al. Synthesis and DNA binding studies by spectroscopic and PARAFAC methods of a ternary copper(Ⅱ) complex. Journal of Photochemistry and Photobiology A:Chemistry 2006,184 (3):241-249.
[213]Ni Y N, Lin D Q, Kokot S. Synchronous fluorescence and UV-vis spectroscopic studies of interactions between the tetracycline antibiotic, aluminium ions and DNA with the aid of the Methylene Blue dye probe. Analytica Chimica Acta, 2008,606(1):19-25
[214]Escandar G M, Olivieri A C, Faber N M, et al. Second-and third-order multivariate calibration:data, algorithms and applications. Trends in Analytical Chemistry,2007,26(7):752-765
[215]Golub G H, Van Loan C F. Analysis of the total least squares problem. SIAM Journal on Numerical Analysis,1980,17(6):883-893
[216]Van Huffel S, Vandewalle J. Analysis and solution of the nongeneric total least squares problems. SIAM Journal on Matrix Analysis and Applications,1998, 9(2):360-372
[217]魏木生,朱超.关于TLS问题.计算数学,2002,24：345-352
[218]Abraham P, Cohen A, et al.Terazosin:an effective once-daily monotherapy for the treatment of hypertension. American Journal of Medicine,1986,80(suppl 5B):29-34
[219]Mudiyala R, Ahmed A. Effect of Terazosin on Clinical Benign Prostatic Hyperplasia in Older Adults. Journal of the American Geriatrics Society,2003, 51(4):424-426
[220]Lepor H, Jones K, Williford W. Worldwide experience with alfuzosin and tamsulosin. The Journal of Urology,2000,163 (4):1134-1137
[221]Sekhar E C, Rao T R K, Sekhar K R, et al. Determination of terazosin in human plasma, using high-performance liquid chromatography with fluorescence detection. Journal of Chromatography B,1998,710 (1-2) 137-142
[222]Cheah P Y, Yuen K H, Liong M L. Improved high-performance liquid chromatographic analysis of terazosin in human plasma. Journal of Chromatography B,2000,745 (2) 439-443.
[223]Jiang C Q, Gao M X, He J X. Study of the interaction between terazosin and serum albumin:Synchronous fluorescence determination of terazosin. Journal of Chromatography B,2002,452 (2):185-189
[224]Zavitsanos A P, Alebic-Kolbah T. Enantioselective determination of terazosin in human plasma by normal phase high-performance liquid chromatography-electrospray mass spectrometry. Journal of Chromatography A, 1998,794 (1-2):45-56
[225]Bro R, Kiers H H L. A new efficient method for determining the number of components in PARAFAC modes. Journal of Chemometrics,2003,17(5): 274-286
[226]Damiani P C, Nepote A J, Bearzotti M, et al. A test field for the second-order advantage in bilinear least-squares and parallel factor analyses fluorescence determination of ciprofloxacin in human urine. Analytical Chemistry,2004, 76(10):2798-2806
[227]Olivieri A C, Faber N M. Standard error of prediction in parallel factor analysis of three-way data. Chemometrics and Intelligent Laboratory Systems, 2004, 70(1):75-82
[228]Faber K, Kowalski B R. Net analyte signal calculation in multivariate calibration. Analytical Chemistry,1997,69:1620-1626
[229]Saxberg B E H, Kowalski B R. Generalized standard addition method. Analytical Chemistry,1979,51(7):1031-1038
[230]Booksh K, Henshaw J M, Burgess L W, Kowalski B R. A second-order standard addition method with application to calibration of a kinetics-spectroscopic sensor for quantitation of trichloroethylene. Journal of Chemometrics,1995, 9(4):263-282
[231]Wu H L, Yu R Q, Shibukawa M, et al. Second-Order Standard Addition Method Based on Alternating Trilinear Decomposition. Analytical Science,2000,16(2): 217-220
[232]Comas E, Gimeno R A, Ferre J, et al. Using second-order calibration to identify and quantify aromatic sulfonates in water by high-performance liquid chromatography in the presence of coeluting interferences. Journal of Chromatography A,2003,988(2):277-284
[233]Gimeno R A, Comas E, Marce R M, et al. Second-order bilinear calibration for determining polycyclic aromatic compounds in marine sediments by solvent extraction and liquid chromatography with diode-array detection. Analytica Chimica Acta,2003,498(1-2):47-53
[234]Sinha A E, Fraga C G, Prazen B J, et al. Trilinear chemometric analysis of two-dimensional comprehensive gas chromatography-time-of-flight mass spectrometry data. Journal of Chromatography A,2004,1027(1-2):269-27
[235]Gonz'alez A G, Herrador M A, Asuero A G. Intra-laboratory testing of method accuracy from recovery assays. Talanta,1999,48(2):729-736
[236]Kanich B, Papich M G. Plasma profile and pharmacokinetics of dextromethorphan after intravenous and oral administration in healthy dogs. Journal of Veterinary Pharmacology and Therapeutics,2004,27(1):337-341
[237]Zhang Y, Britto M R,Valderhaug K R, et al. Dextromethorphan:enhancing its systemic availability by way of low-dose quinidine-mediated inhibition of cytochrome P4502D6. Clinical Pharmacology & Therapeutics,1992,51(2): 647-655
[238]Brooks B R, Thisted R A, Appel S H, et al. For the AVP-923 ALS Study Group.Treatment of＂ pseudobulbar affect in ALS with dextromethorphan/quinidine:A randomized trial. Neurology,2004,63(8): 1364-1370
[239]Panitch H S, Thisted R A, Smith R A, et al. For the pseudobulbar affect in multiple sclerosis study group andomized, controlled trial of dextromethorphan/quinidine forpseudobulbar affect in. multiple sclerosis. Annals of Neurology,2006,59 (5):780-787
[240]Thisted R A, Klaff L, Schwartz S L, et al. Dextromethorphan and Quinidine in adult patients with uncontrolled painful diabetic peripheral neuropathy:a 29-day, multicenter, open-label, dose-escalation study. Clinical Therapeutics,2006,28: 1607-1618
[241]Evaristo A, Adam B, Maria A S, et al. Dextromethorphan and Quinidine Combination for Heroin Detoxification. American Journal on Addiction,2008, 17(1):176-180
[242]Salsali M, Coutts R T, Baker G B. Analysis of dextrorphan, a metabolite of dextromethorphan, using gas chromatography with electron-capture detection, Journal of Pharmacological and Toxicological Methods,1999,41 (1):143-146
[243]Furlanut M, Cima L, Benetello P, et al. Gas-liquid chromatographic determination of dextromethorphan in serum and brain, Journal of Chromatography A,1977,140 (1):270-274
[244]Linares P, Gutierrez M C, Lazaro F, et al. Determination of benzocaine, dextromethorphan and cetylpyridinium ion by high-performance liquid chromatography with UV detection. Journal of Chromatography A,1991,558 (1):147-153
[245]Motassim N, Decolin D, Le Dinh T, et al. Direct determination of dextromethorphan and its three metabolites in urine by high-performance liquid chromatography using a precolumn switching system for sample clean-up. Journal of Chromatography B,1987,422(2):340-345
[246]Eichhold T H, McCauley-Myers D L, Khambe D A, et al. Simultaneous determination of dextromethorphan, dextrorphan, and guaifenesin in human plasma using semi-automated liquid/liquid extraction and gradient liquid chromatography tandem mass spectrometry. Journal of Pharmaceutical and Biomedical Analysis,2007,43 (2):586-600
[247]Dong Y M, Chen X F, Chen Y L, et al. Separation and determination of pseudoephedrine, dextromethorphan, diphenhydramine and chlorpheniramine in cold medicines by nonaqueous capillary electrophoresis, Journal of Pharmaceutical and Biomedical Analysis,2005,39(1):285-289
[248]Priebe W. Anthracyclines antibiotics. New analogs, methods of delivery, and mechanisms of action. Journal of American Chemical Society,1995,574(1): 259-267
[249]Naidong W, Shou W Z, Addison T, et al. Liquid chromatography/tandem mass spectrometric bioanalysis using normal-phase columns with aqueous/organic mobile phases-a novel approach of eliminating evaporation and reconstitution steps in 96-well SPE. Rapid Communications in Mass Spectrometry,2002, 16(20):1965-1975
[250]Carrasco C, Joubert A, Tardy C, et al. DNA sequence recognition by bispyrazinonaphthalimides antitumor agents. Biochemistry,2003,42(40): 11751-11761
[251]Erdem A, Ozsoz M. Interaction of the anticancer drug epirubicin with DNA. Analytica Chimica Acta,2001,437(1):107-114
[252]Pigram W J, FullerW, Hamilton L D. Stereochemistry of intercalation: interaction of daunomycin with DNA. Nat. N.Biol.1972,235(1):17-19
[253]Momparler R. L, Karon M, Siegel S E, et al. Effect of adriamycin on DNA, RNA, and protein synthesis in cell-free systems and intact cells. Cancer Research,1976,36(8):2891-2895
[254]Tewey K M, Rowe T C, Yang L, et al. Adriamycin-induced DNA damage mediated by mammalian DNA topoisomerase Ⅱ. Science,1984,226(4673): 466-468
[255]Nikolajsen R P H, Booksh K S, Hansen A M, et al. Quantifying catecholamines using muti-way kinetic modeling. Analytica Chimica Acta,2003,475(1-2): 137-150
[256]Lakowicz JR. Principles of Fluorescence Spectroscopy, third ed., Springer, New York,2006, pp.278-282
[257]Lehrer S S. Solute perturbation of protein fluorescence. Quenching of the tryptophyl fluorescence of model compounds and of lysozyme by iodide ion. Biochemistry,1971,10(17):3254-3263
[258]Carbaryl, EHC 153,1994, http://www.inchem.org/documents/ehc/ehc/ehc153.htm
[259]Cummins D J, Andrews C W. Iteratively reweighted partial least squares:A performance analysis by Monte Carlo simulation. Journal of Chemometrics, 1995,9(1):489-507
[260]Zhu E Y, Barnes R M. A simple iteration algorithm for PLS regression. Journal of Chemometrics,1995,9(1):363-372
[261]Rannar S, Geladi P, Lindgren F, et al. A PLS kernel algorithm for data sets with many variables and fewer objects. Part Ⅱ:Cross validation, missing data and examples. Journal of Chemometrics,1995,9(1):459-470
[262]Zhang M H, Xu Q S, Massart D L. Averaged and weighted average partial least squares. Analytical Chimica Acta,2004,504(2):279-289
[263]Xu L, Jiang J H, Lin W Q, et al. Optimized sample-weighted partial least squares. Talanta,2007,7(2):561-566
[264]Burbidge R, Trotter M, Buxton B, et al. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers and Chemistry,2001,26(1):5-14
[265]Yao X J, Panaye A, Fan B T, et al. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. Journal of Chemical Information and Computer Sciences,2004,44(4):1257-1266
[266]Zernov V V, Balakin K V, Pletnev I V, et al. Drug design using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. Journal of Chemical Information and Computer Sciences, 2003,43(6):2048-2056
[267]Liu H X, Zhang R S, Yao X J, et al. QSAR Study of ethyl 2-2-[(3-Methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate:An inhibitor of AP-1 and NF-KB mediated gene expression based on support vector machines. Journal of Chemical Information and Computer Sciences,2003,43(4):1288-1296
[268]Xue C X, Zhang R S, Liu H X, et al. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. Journal of Chemical Information and Computer Sciences,2004, 44(5):1693-1700
[269]Mark H. Comparative study of calibration methods for near-infrared reflectance analysis using a nested experimental design. Analytical Chemistry,1986,58(13): 2814-2819
[270]Devos O, Fanget B, Saber A I, et al. Use of a Plackett-Burman design with multivariate calibration for the analysis of polycyclic aromatic hydrocarbons in micellar media by synchronous fluorescence. Analytical Chemistry,2002,74(3): 678-683
[271]An S J, Liu W Q, Venkatesh S. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognition,2007, 40(8):2154-2162
[272]Chang M W, Lin C J, Leave-One-Out Bounds for Support Vector Regression Model Selection. Neural Computation,2005,17(5):1188-1222
[273]Eads D R, Hill D, Davis S, et al. Genetic algorithms and support vector machines for time series classification, in:B. Bosacchi, D. B. Fogel, J. C. Bezdek (Eds.), Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V. Proceedings of the SPIE,2002,4787(1): 74-85
[274]Friedrichs F, Igel C. Evolutionary tuning of multiple SVM parameters. Neurocomputing 2005,64(1):107-117
[275]Frohlich H, Chapelle O, Scholkopf B. Feature selection for support vector machines by means of genetic algorithms. in:Proceedings of the 15th IEEE International Conference on Tools with AI (ICTAI 2003), IEEE Computer Society Institute of Electrical and Electronics Engineers Inc., USA,2003, pp. 142-148.
[276]Kennedy J, Eberhart R. Particle swarm optimization. In:Proceedings of IEEE International Conference on Neural Networks. Perth, Australia,1995, 1942-1948
[277]Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan,1995,39-43
[278]Wentzell P D, Andrews D T, Walsh J M, et al. Estimation of hydrocarbon types in light gas oils and diesel fuels by ultraviolet absorption spectroscopy and multivariate calibration. Canadian Journal of Chemistry,1999,77(3):391-400
[279]Borggaard C, Thodberg H H. Optimal minimal neural interpretation of spectra. Analytical Chemistry,1992,64(5):545-551
[280]Xu Q S, Liang Y Z. Monte Carlo cross validation, Chemometrics and Intelligent Laboratory Systems,2001,56(1):1-11