两点法确定混胺中两组分的电位滴定终点

利用两点法确定了液体推进剂混胺中两组分——三乙胺乖二甲苯胺的电位滴定终点，并与常规的弧切作图法乖二阶微分法确定的滴定终点进行了配对t检验，显示结果之间不存在显著差异，或可予校正。两点法简单快速，结果可靠，特别适宜于大量样品的快速分析。     

冷焰ICP—MS法测定高纯稀土氧化物中铁和钙的研究

用冷焰技术对高纯稀土氧化物中Ｆｅ和Ｃａ的分析方法进行了研究。估算了测定范围和检出限，探讨了内标元素对基体影响的校正作用，测定了加标回波度，对半定量法和定量法的结果作了比较。表明次序焰条件下，痕量Ｆｅ的测定是可行的，但Ｃａ的测定结果仍不理想；ＱＡ法明显优于ＴＱ法。     

混合线性分析法的原理及应用

对一种新近发展的多元校正方法－混合线性分析法（hybrid linear analysis,HLA)的原理进行了深入探讨和阐述，用该法进行了模拟体系和复方头孢氨苄制剂的分析，在主因子数确定，单位谱的获得，波长选择、适用范围等方面进行了讨论。结果表明，HLA法可给出优于偏量小二乘法（PLS）的结果，方法易行，结果准确。在复杂体系分析中具有较好的应用前景。     

中草药样品不同预处理方法对微量元素测定结果的影响

探讨了采用不同的预处理方法对中草药中微量元素含量测定结果的影响。结果表明，湿式消化法优于干式灰化法，所测中草药中锌、铜、锰、铁、铅和镉的数值较高，这可能与湿式消化法处理样品时，元素被挥发、损耗和吸附较少有关。     

不锈钢载波钝化膜的半导体性质

运用交流阻抗法和光电化学法研究了不锈钢载波钝化膜层的半导体性质。讨论了交流阻抗测试中扰动频率的变化对Mott-Schottky曲线的影响，讨论了不锈钢载波钝化膜的n/p型、杂质施主密度和平带电位等半导体性质，同时结合光电化学法对交流阻抗测试结果进行了验证分析，实验结果认为不锈钢载波钝化膜层具有与直流钝化膜层相似的半导体性质，并通过膜层的组成结构对其半导体性质作了分析。     

光电直读光谱法测定重轨钢中碳偏析的方法研究

采用类型校正法消除了光谱法与红外吸收法在测定重轨钢中碳含量时的系统误差。结合金相低倍组织检验，分析了光谱法在重轨腰部碳分析结果异常偏高的原因，并建立了相应的分析方法。该方法已用于重轨钢样的均匀性检验中，取得了良好的效果。     

样品处理方法对测定汞的影响

研究了两种样品前处理方法——湿法消解法和微波消解法对汞测定结果的影响。结果表明，用微波消解法测得的结果要比传统的湿法消解法结果较为可靠，特别是对于难分解的样品(如化妆品)，尤其在含量很低时，要尽量使用微波消解。     

痕量溴的阻抑动力学荧光法测定

基于在磷酸介质中溴对溴酸钾氧化丁基罗丹明B反应的抑制作用，建立了痕量溴的阻抑动力学荧光分析的新方法；应用单纯形最优化法确定了 实验条件，方法的检出限0.075μg/L，线性范围0.40-6.40μg/L；将该法应用于地下水、人发中溴的分析，获得满意结果，并对反应机理进行了初步探讨。     

XPS法测量铝箔表面氧化铝的厚度

介绍了一种无需溅射、无需变角，只需一次简单的ＸＰＳ（Ｘ射线光电子能谱）窄扫描，即可根据谱图中氧化态和金属态的相对强度算出铝金属表面氧化铝的厚度。用该法测量了一系列不同方法处理的铝表面氧比铝厚度，并与椭偏法及ＮＲＡ法（核反应分析法）测定结果进行了对比，结果表明，该法是一种简便准确的膜厚测量法。     

利用二进样条小波模极大方法消除毛细管电泳信号噪声的研究

根据毛细管电泳信号中噪声与真实信号的模极大值特性之间存在较大差异的特点，提出了一种利用二进样条小波模极大值法去噪的方法研究了信号的半峰宽、不同信噪比以及阈值的选取对去噪结果的影响，并对模拟含噪信号和含噪毛经电泳信号去噪前后的结果进行了比较，实验结果表明，该法能效地消除谱带较宽的毛细管电泳等信号中存在的噪声。     

卡尔曼滤波分光光度法同时测定钢中钛和钼

本文采用卡尔曼滤波(KF)递推法,以苯基荧光酮-溴化十六烷基吡啶与钛,钼的显色体系同时测定钛和钼,同时以柠檬酸和抗坏血酸掩蔽干扰,并引入误差校正因子来校正加掩蔽剂后在所测波段范围内所造成的残余背景吸收,用于实际钢样分析,获得较满意的结果。     

新极谱法实验数据的卡尔曼滤波

本文提出的"1:n+2"新卡尔曼滤波器,具有算法简单、占用计算机内存少、运行速度快等优点;同时又能有效地消除信号中的随机噪音和电容电流,讨论了"1:3"滤波器的结构性能,采用Cd⁺离子的阶梯扫描伏安法的实测数据来分析滤波结果的最优性,用于处理TI⁺、pb²⁺、In³⁺等离子的实测数据,均获得理想的滤波效果。     

The Kalman filter in analytical chemistry

During the past ten years, the means by which more information can be extracted from experimental data have become an important area of research in analytical chemistry. Digital filters have been demonstrated to have a number of applications to analytical problems. These techniques typically involve a least-squares fit of experimental data to some model of the process being filtered. One method for filtering experimental data is based on the Kalman filter, a recursive, linear digital filter first developed for use in navigation, but now used in many fields. This paper discusses the implementation of Kalman filters in analytical chemistry. The principles of state-space digital filtering are reviewed, and the development of state/space models is discussed. Discussion is focused on the discrete Kalman algorithms. Two examples are provided to demonstrate the operation of the discrete Kalman filtering algorithm. Similarities between Kalman filtering and weighted least-squares methods are considered, and the specific advantages and disadvantages of linear and nonlinear Kalman filtering approaches are evaluated. To illustrate the range of problems which benefit from use of the filter, a comprehensive literature survey of the application of Kalman filtering to chemical problems is provided.     

Comparison of robust regression methods based on least-median and adaptive kalman filtering approaches applied to linear calibration data

Five algorithms for data analysis are evaluated for their abilities to discriminate against outliers in small data sets (4–10 points). These methods included least-squares regression, the least absolute -deviation method, the least median of squares method, and two techniques based on an adaptive Kalman filter. For data sets consisting of 4–9 points with one outlier, the average errors in the estimation of the slope were found to be 18.9 % by least-squares, 17.7% by the least absolute deviation method, 0.5% by the least median of squares algorithm, 9.1% by an adaptive Kalman filter algorithm, and 0.9% by a zero-lag adaptive Kalman filter algorithm. Based on these results, the conclusion is that the zero-lag adaptive Kalman filter and the least median of squares approaches are best suited for the detection of outliers in small calibration data sets.     

卡尔曼滤波分光光度法用于钴、镍、铜、锌、镉的同时测定—5-Br-PADAP-CPB法

本文运用卡尔曼滤波(KF)递推算法,以2-(5-溴-吡啶偶氮)-5-二乙氨基苯酚(5-Br-PADAP),溴化十六烷基吡啶(CPB)同时测定了五组分混合物:Co、Ni、Cu、Zn和Cd中各元素百分含量,合成样分析结果良好。用模型数据可测百分含量相差50倍的离子混合物。     

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

The calibration model of near-infrared (NIR) spectra established using the Kalman filter-partial least square (partial least squares combined with a Kalman filter) method can be adapted to outdated equipment, environmental changes, external samples, and other applications. However, the variance of the measurement noise estimation for NIR spectrum measurements cannot be easily obtained using Kalman filter-partial least squares; therefore, the variance in the measurement noise is often assumed to be zero for the Kalman filter-partial least square calibration model, which affects the stability of the model. In this study, the measured input and output data were used effectively, and the gamma test method for estimating the measurement noise variance was used to improve the stability of the Kalman filter-partial least square calibration model. First, an accurate estimation of the measurement noise variance was obtained, and accurate modeling was then performed using Kalman filter-partial least squares. Finally, 600 abandoned drilling fluid samples were used to confirm the validity of the proposed method. The Kalman filter-partial least square and gamma test-Kalman filter-partial least square methods are compared. Testing of external samples 401–600 demonstrated that the stability of the Kalman filter-partial least square model decreased. The root mean square error of the prediction of the Kalman filter-partial least square model was 27.135, which was worse than that of the gamma test-Kalman filter-partial least square model (20.307). The validation results show that the proposed method has better stability in tracking the evolution of the NIR spectrometer’s measurement state.     

卡尔曼滤波速差动力学停流光度法同时测定铁,镍,铜

本文叙述了卡尔曼滤波速差动力学光度法同时测定多组分的原理，并与ＦＩＡ停流法及微机数据采集，、处理技术结合起来，提出了用３，５－二溴－ＰＡＤＡＰ同时测定铁、镍、铜的方法、。应用于合金试样的测定，取得了较满意的结果。     

Two-dimensional and three-dimensional fitting of enzyme kinetic data with the kalman filter

The availability of instrumentation which is capable of collecting multidimensional data has put increased demands on the data-processing methods utilized to obtain information about reaction kinetics. An enzyme-catalyzed reaction, the hydrolysis of p-nitrophenyl phosphate to p-nitrophenol, is examined so that various data-processing methods and data-collection formats can be examined and compared. The extended Kalman filter is used to obtain rate constants and values for the initial substrate concentration for three-dimensional data, and for two-dimensional kinetically perturbed data. In addition, non-linear least-squares regression with the simplex algorithm, and linear least-squares regression methods are used to analyze absorbance/time curves for this reaction. These results are compared to the results from a two-point kinetic method, and the accuracy and precision of each of the methods is evaluated. It is found that the methods based on the Kalman filter yielded results which were equivalent to or better than the results obtained from the other approaches.     

Simplex optimization of the adaptive kalman filter

A method for determining concentrations from overlapped spectral data when a complete model is not available is described. This approach combines simplex optimization with the adaptive Kalman filter to yield a method in which initial guesses for the adaptive filter are generated by the simplex algorithm. The performance of the method is demonstrated by deconvoluting overlapped synthetic data and spectral data.     

Simultaneous kinetic determinations with the kalman filter

The linear Kalman filter is successfully applied to the processing of data for simultaneous kinetic determinations. The Kalman filter offrs all of the advantages of linear least squares over traditional methods of data processing, but is simpler and more efficient than batch-mode least squares. The viability of the Kalman filter is demonstrated with synthetic data and it is then applied to the analysis of amino acid mixtures by their reaction with trinitrobenzenesulfonic acid. Mixtures of glycine and asparagine are successfully analyzed even though their pseudeo-first-order rate constants differ by a factor of only 2.5. The potential of the Kalman filter for real-time application is discussed.