傅里叶变换用于铁和锌的同时光度测定

研究了傅里叶变换技术用于铁锌二组分的同时分光光度测定，采用傅里叶变换对吸光度数据进行预处理，再结合目标转换因子分析或偏最小二乘分析，结果较普通的目标转换因子分析或偏最小二乘法有显著改善。以傅里叶变换－偏最小二乘法就用于实际铝合金样品中铁和锌的同时测定，结果令人满意。     

荧光光度法同时测定邻苯二酚、间苯二酚与对苯二酚

将一种直接信号校正(DOSC)-小波包变换(WPT)-偏最小二乘法(PLS)(DOSC-WPT-PLS)新方法用于解析荧光光谱严重重叠的邻苯二酚?间苯二酚和对苯二酚混合物,并对其进行测定。该法将DOSC、WPT及PLS 3种方法结合从而提高了获取特征信息的能力和回归质量。DOSC方法用于除去与浓度无关的结构噪音。利用WPT的时域和频域局部化的特点改进了除噪质量和数据压缩及信息提取能力。PLS方法用于多变量校准和噪音消除。处理该3种组分的荧光光谱数据,并实现了3种化合物的同时测定。设计了PDOSCWPTPLS程序执行相关计算,并对以上3种化学计量学方法进行了比较,其总体相对预测标准偏差分别为4.3%、7.7%、11.5%,结果表明DOSC-WPT-PLS法优于WPT-PLS法和PLS法。将该法用于测定自来水中邻苯二酚?间苯二酚和对苯二酚的含量,其回收率分别为99%~110%?95%~108%和98%~104%,结果满意。     

小波变换用于重叠色谱峰组分信息的提取

提出了一个小波变换用于提取重叠色谱中组分信息的理论公式，并将它应用于三组分和五组分重叠色谱实验数据的解析。结果表明：在定定分离度范围内，当相邻色谱峰峰宽相近时，根据此公式的计算结果，可以将各组分的信息从重叠色谱峰信号中提取出来。洒工作对小波变换用于重叠峰的解析具有重要意义。     

二维色谱／光谱重叠峰的定性定量方法研究（Ⅱ）

提出了一种新的联用色谱定性定量方法－－投影降秩分辨法，该法可不经对重叠组分的逐－－分辩直接对目标组分进行定性定量分析，以实际分析体系大气飘尘中多环芳烃菲、蒽、萤蒽和芘为目标组分，成功地进行了直接定性定量分析。     

热解析法测汞仪的校准

建立了热解析法测汞仪的校准方法。比较了传统冷原子吸收测汞仪和热解析法测汞仪的测量原理及计算方式,指出了JJG 548–2004 《测汞仪检定规程》不适用于热解析法测汞仪的原因。选取固体和液体两种标准物质,对热解析法测汞仪的示值误差、检出限、重复性等主要计量特性及技术指标进行校准。选用DMA–80型热解析法测汞仪对该校准方法进行了验证,其示值误差为5.3%,检出限为0.007 ng,重复性为1.4%。经验证,上述结果均未超出设定指标,各计量特性符合方法要求。该方法校准后的计量特性及技术指标较为合理,校准方法切实可行,适用于热解析法测汞仪的校准。     

联用色谱数据的双窗口因子分析

利用组分光谱的特征信息,发展了一种能直接对联用色谱重叠峰中组分进行定性定量分析的新方法──双窗口因子分析（dualwindowfactoranalysis,DWPA）。该法可从多组分重叠峰中定性目标组分,且在未经其它组分的分辨下可直接对目标组分的光谱、色谱进行分辨。因此更适应于联用色谱对复杂体系中待测组分的定性定量分析。用该法成功地对4组分重叠峰进行了分析,实验结果令人满意。     

校正变换矩阵法及其在中芳香化合物同时测定中的应用

将目标变换因子分析中的自由浮动思想引入多元校正中，提出了一种新的多组分同时校正法－－校正变换矩阵法。将该法应用于紫外分光光度法同时测定五种芳香族化合物，结果令人满意。与目标因子分析法和卡尔曼波法比较，本法具有更好的校正准确度。     

用四种离散变换主组分回归方法分辨重叠吸收光谱

将离散小波变换、小波包变换、傅里叶变换和离散余弦变换与主组分回归方法结合构成4种离散变换主组分回归方法,编制了离散变换主组分回归方法的计算程序。将离散变换主组分回归方法用于处理对硝基甲苯、对硝基酚和对硝基苯胺混合物的重叠紫外吸收光谱数据。结果表明,离散变换主组分回归方法优于主组分回归方法,试样质量浓度的预测值与实际值的相对预测标准误差由3.81%降至约1.11%。     

校准工作矩阵——目标因子分析光度法同时测定氨基酸混合体系

本提出一种应用传目标因子分析法的实用技术。通过确认一个相应的校准工作矩阵可对混合样品中的和组分进行同时直接定量。以二批六组分氨基酸混合样品（各组分浓度为２．０７２～”〕．１７０μｇ／ｍＬ０的分析表明，各组分绝大多数的回收率均在１００±１０％之内。该技术尤其适用于组成情况清楚的混合样品的例行分析。     

小波变换-主成分回归分光光度法同时测定邻苯二酚、间苯二酚和对苯二酚三组分

根据小波变换具有将信号分频的特点,本文提出了将小波变换与主成分回归(PCR)相结合的一种多元校正算法。该法能更有效地去除噪声,提取有用信息,并将其用于分析邻苯二酚、间苯二酚、对苯二酚三组分体系。实验结果表明,本法比直接用主成分回归处理效果好,得到的平均相对误差从2．24％降低到1．19％。     

一种新的目标转换因子分析算法用于混合物红外光谱中纯组分光谱的解析

本文针对运用目标转换因子分析从混合物红外光谱中解析出纯组分光谱的有关限制,进行了有效的改进。把抽象正交特征光谱与以单一向量为初始向量的目标转换因子分析相结合,提出了一种新的算法,结果令人满意。     

基于计算最大差异光谱的目标转换因子分析用于解析混合物红外光谱

基于因子分析的目标转换法[1]利用抽象光谱构成的矩阵具有使纯物种光谱向自身逐步逼近的性质[2],用于混合物红外光谱的解析,简单明确[3]．在进一步的探索中,我们发现该算法对原始光谱的构成具有较强的依赖性．本文特别构造了这样的体系,即在混合物样品中剔除了某一物种占优势的样品,应用新算法后,避免了对采样的依赖性．1方法与原理根据自模拟曲线分辨算法（SelfModelingCurveResolution,SMCR）[4~6],纯物种光谱可写成式中,Akj为抽象正交光谱,Xik为系数,Fij为纯物种光谱,n为物种数．根据吸光度不能为负这一限制条件,可以…     

Enhancement of analyte detectability in isotachophoresis exploiting spectrophotometric diode‐array detection with chemometric data processing

The potentialities of capillary ITP combined with diode‐array detection (DAD) with subsequent chemometric data processing have been investigated in this work. A series of different migration configurations were created using model analytes, interferents and appropriate spacers. Special attention has been paid not only to constituents migrating in fully developed ITP zones but also to the spike mode of ITP migration. The purity assessment and identity confirmation of model analytes migrating in both modes were performed by means of multivariate curve resolution and target transformation factor analysis (TTFA). Their successful applications have revealed a smart way to increase in the analytical information obtained by ITP separation even in the instance of trace analysis.     

Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra

Temperature constrained cascade correlation networks (TCCCNs) are computational neural networks that configure their own architecture, train rapidly, and give reproducible prediction results. TCCCN classification models were built using the Latin-partition method for five classes of pathogenic bacteria. Neural networks are problematic in that the relationships among the inputs (i.e., mass spectra) and the outputs (i.e., the bacterial identities) are not apparent. In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA). Without validation of the classification model, it is impossible to ascertain whether the bacteria are classified by peaks in the mass spectrum that have no causal relationships with the bacteria, but instead randomly correlate with the bacterial classes. Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs. A multiple output TCCCN model achieved classification accuracies of 96 +/- 2% and exhibited improved performance over multiple single output TCCCN models. Chemical ionization mass spectra were obtained from in situ thermal hydrolysis methylation of freeze-dried bacteria. Mass spectral peaks that pertain to the neural network classification model of the pathogenic bacterial classes were obtained by sensitivity analysis. A significant number of mass spectral peaks that had high sensitivity corresponded to known biomarkers, which is the first time that the significant peaks used by a neural network model to classify mass spectra have been divulged. Furthermore, TTFA furnishes a useful visual target as to which peaks in the mass spectrum correlate with the bacterial identities.     

Calibration model transfer for near-infrared spectra based on canonical correlation analysis

In order to solve the calibration transformation problem in near-infrared (NIR) spectroscopy, a method based on canonical correlation analysis (CCA) for calibration model transfer is developed in this work. Two real NIR data sets were tested. A comparative study between the proposed method and piecewise direct standardization (PDS) was conducted. It is shown that the transfer results obtained with the proposed method based on CCA were better than those obtained by PDS when the subset had sufficient samples.     

Improved calibration transfer between near-Infrared (NIR) spectrometers using canonical correlation analysis

The application of mobile near-infrared (NIR) spectrometers in field measurements is growing. Calibration transfer techniques offer simple solutions for enabling models constructed on benchtop instruments for use on mobile spectrometers. Since different types of spectrometers with different components, scanning ranges and resolutions cause great differences in the spectral response, calibration transfer is difficult to apply. In this paper, we focus on calibration transfer among benchtop, portable and handheld spectrometers by a method of calibration transfer based on canonical correlation analysis (CTCCA). Its capability was illustrated by the example of a group of NIR spectra dataset for predicting reducing sugars, total sugar, and nicotine contents in tobacco leaves. The experimental results showed that the transferability of CTCCA was superior to other conventional calibration transfer methods, including piecewise direct standardization, spectral space transformation, calibration transfer based on independent component analysis, and calibration transfer based on the weight matrix. Moreover, the best transfer results were obtained in the three cases by canonical correlation analysis method executing transfer while the spectra were not interpolated, which shows that this approach has the advantage of easy implementation for calibration transfer. Therefore, CTCCA without interpolation calculation offers a new and simple solution for transferring the spectra acquired by mobile spectrometers to the optimized spectral models built on benchtop devices to improve the accuracy of the results. Additionally, the results show that the benchtop spectrometer is more suitable as the master instrument for calibration transfer with more accurate prediction than using a portable device as the master.     

Time shift correction in second-order liquid chromatographic data with iterative target transformation factor analysis

When the generalized rank annihilation method (GRAM) is applied to liquid chromatographic data with diode-array detection, an important problem is the time shift of the peak of the analyte in the test sample. This problem leads to erroneous predictions. This time shift can be corrected if a time window is selected so that the chromatographic profile of the analyte in the test sample is trilinear with the peak of the analyte in the calibration sample. In this paper we present a new method to determine when this condition is met. This method is based on the curve resolution with iterative target transformation factor analysis (ITTFA). The calibration and test matrices are independently decomposed into profiles and spectra, and aligned before GRAM is applied. Here we study two situations: first, when the calibration matrix has one analyte and second, when it has two analytes. When the calibration matrix has two analytes, we selectively determine the time window for the analyte to be quantified. There were considerably fewer prediction errors after correction.     

Two-site property of thenoyltrifluoroacetone inhibiting succinate-ubiquinone reductase.

It is proved by using the Dixon plot and the Lineweaver-Burk plot that thenoyltrifluoroacetone (TTFA) has two inhibitive sites affecting the reduction of ubiquinone catalyzed by succinate-ubiquinone reductase. The high affinity site (inhibited at the concentration of thenoyltrifluoroacetone less than 20 mumol/L) shows noncompetitive with substrate Q2, while the low affinity site (inhibited at the concentration of TTFA over 20 mumol/L) shows competitive. It is suggested that both the reducing steps of Q----QH-. and QH-.----QH2 are inhibited by thenoyltrifluoroacetone.     

Calibration in TLC/HPTLC using the Michaelis-Menten function

Summary The Michaelis-Menten function describes a non linear relationship and is a possible calibration curve in TLC. Usually the linearized forms are used but the problem of uncontrolled error propagation arises from data transformation and hence incorrect calibration. Nonlinear regression analysis is therefore used in this paper. The results of erroneous transformation according to Lineweaver-Burk and to Eadie are compared with the results of non-linear regression by means of three sets of measured data or data given in the literature.     

Multivariate versus univariate calibration for nonlinear chemiluminescence data: Application to chromium determination by luminol-hydrogen peroxide reaction

Multivariate calibration is tested as an alternative to model chromium(III) concentration versus chemiluminescence registers obtained from luminol-hydrogen peroxide reaction. The multivariate calibration approaches included have been: conventional linear methods (principal component regression (PCR) and partial least squares (PLS)), nonlinear methods (nonlinear variants and variants of locally weighted regression) and linear methods combined with variable selection performed in the original or in the transformed data (stepwise multiple linear regression procedure). Both the direct and inverse univariate approaches have been also tested.

The use of a double logarithmic transformation previous to the linear regression has been also evaluated. A new double logarithmic transformation previous to the linear regression is proposed in order to avoid the effect of the noise in the calibration model. Pre-processing, optimization and prediction ability of the multivariate calibration models has been studied at nine different experimental conditions including batch and FIA measurements. Box-plots, PCA and cluster analysis have been employed to test the prediction ability of the different models tested. Nonlinear PCR and nonlinear PLS provide the best results. Real samples have been analyzed and compared with the reference method. The results confirm the successful use of the proposed methodology.