遗忘因子法与其它计算方法在分光光度法中的应用研究

本文考查了常规最小二乘法、Ｐ－矩阵法、主成份回归法、偏最小二乘法、线性规划法、卡尔曼滤波以及遗忘因子法对安痛定注射液模拟样品的分析结果。从所得结果来看，遗忘因子法有一定的优点。     

偏最小二乘法用于铽、钍、铒的同时测定

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

6种化学计量学法同时分光光度测定5组分的比较研究及食品分析

优化了在表面活性剂存在下以肉桂基荧光酮同时测定铝、铜、锰、锌、钴的显色折衷条件；比较研究了偏最小二乘、岭回归、多元线性回归、多元逐步回归、目标因子分析和卡尔曼滤波法用于这一体系同时测定此５组分的优缺点，结果表明对于此类所含各组分吸收光谱重叠严重且吸光度加和性欠佳体系，上述６法中以偏最小二乘和岭回归法为最适宜；经用于九种人工合成液和四种食品分析，均获满意结果，分析两种茶叶结果也与ＩＣＰ－ＡＥＳ法一致     

因子分析-光度法同时测定α-萘酚和α-萘胺

测定了α-萘酚和α-萘胺混合体系的紫外光谱,用目标因子分析法成功地确定了混合体系的物种数、物种种类以及各物种的含量。平均回收率:α-萘酚为96.19%,α-萘胺为100.99%。并将结果同偏最小二乘法和卡尔曼滤波法做了比较。     

流动注射-阻尼最小二乘法用于多组分分析

研究了用流动注射分析进行阻尼最小二乘分光光度法同时测定锌、铜和钴的新方法。方法基于阻尼最小二乘法改进CPA法，将阻尼因子和非零截距引入CPA法，降低了P系数矩阵的病态程度和改善了校正模型的预报能力，使结果更加准确，已将方法成功用于锌铜钴混合体系的分析。     

动力学参数数目与法方程条件数的关系

多相催化反应动力学研究的基本问题是动力学方程中的参数估计，它一直是动力学研究中的活跃领域·在参数估计中最常用的方法是最小二乘法．早在1947年［刘开始用线性最小二乘法处理动力学模型的数据．1958年在线性最小二乘法中应用置信区间问题［21．1959年MarguardM出非线性最小二乘法问．前人提出如何求非线性最小二乘法的初值［4］，及加权最小二乘法的重要性问，并对最小二乘法在动力学中的应用做了综述卜］．在多相催化动力学研究中，关干线性最小二乘法遇到根本性困难的问题前人未涉及．我们曾在动力学研究中发现线性最小二乘法的法…     

线性最小二乘法在环境空气检测领域能力验证结果评价中的应用

对线性最小二乘法在环境空气检测领域能力验证结果统计评价的应用开展了研究,并比较了线性最小二乘法与En值法的评价结果。研究结果表明,最小二乘法和En值法得出的评价结果相似。最小二乘法能够对能力验证数据统计结果进行有效的评价,并可以减少因实验室不能正确评估测量不确定度对结果评价的影响。     

氨基酸混合体系的PLS分光光度法测定

本文利用亮氨酸、异亮氨酸、色氨酸、赖氨酸和苏氨酸混合物与茚三酮在 PH值 5 .2的 HAc-Na Ac缓冲溶液中发生高灵敏度的显色反应 ,建立了用偏最小二乘 (PL S)分光光度法不经分离同时测定氨基酸混合物的方法。讨论了波长范围、波长间隔对测定结果的影响 ,并同卡尔曼滤波法 (KF)和氨基酸自动分析仪结果做了比较。     

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。     

最小一乘稳健多元分析校正

本文论述最小一乘求解的多元分析校正算法，探讨了最小一乘较常规最小二乘法及其他隐健算法的优点。用计算机数值模拟及实际多组分光谱体系对方法进行了检验，展示了最小一乘法在分析化学计量学中实际应用的可行性。     

Linear calibrations in chromatography: The incorrect use of ordinary least squares for determinations at low levels,and the need to redefine the limit of quantification with this regression model

Ordinary least squares is widely applied as the standard regression method for analytical calibrations, and it is usually accepted that this regression method can be used for quantification starting at the limit of quantification. However, it requires calibration being homoscedastic and this is not common. Different calibrations have been evaluated to assess whether ordinary least squares is adequate to quantify estimates at low levels. All calibrations evaluated were linear and heteroscedastic. Despite acceptable values for precision at limit of quantification levels were obtained, ordinary least squares fitting resulted in significant and unacceptable bias at low levels. When weighted least squares regression was applied, bias at low levels was solved and accurate estimates were obtained. With heteroscedastic calibrations, limit values determined by conventional methods are only appropriate if weighted least squares are used. A “practical limit of quantification” can be determined with ordinary least squares in heteroscedastic calibrations, which should be fixed at a minimum of 20 times the value calculated with conventional methods. Biases obtained above this “practical limit” were acceptable applying ordinary least squares and no significant differences were obtained between the estimates measured using weighted and ordinary least squares when analyzing real‐world samples.     

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.     

New approach by Kriging models to problems in QSAR

Most models in quantitative structure and activity relationship (QSAR) research, proposed by various techniques such as ordinary least squares regression, principal components regression, partial least squares regression, and multivariate adaptive regression splines, involve a linear parametric part and a random error part. The random errors in those models are assumed to be independently identical distributed. However, the independence assumption is not reasonable in many cases. Some dependence among errors should be considered just like Kriging. It has been successfully used in computer experiments for modeling. The aim of this paper is to apply Kriging models to QSAR. Our experiments show that the Kriging models can significantly improve the performances of the models obtained by many existing methods.     

Appropriate calibration functions for capillary electrophoresis II. Heteroscedasticity and its consequences

If ordinary least squares regression methods are to be used, the standard deviation of the signal should not depend on the sample concentration, but this is not true in CE. Results indicate, that the signal standard deviation is approximately proportional to the sample concentration. Therefore weighted least squares regression must be used, if the standard deviation within the concentration range differs by more than the factor 50. It is advised to use this regression method down to the factor 5, where the difference to ordinary least squares calculations is still significant. This is demonstrated by comparing experimental and simulated data. These considerations are valid for other analytical techniques as well, if their characteristics of calibration and variance function are similar.     

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.     

化学计量学在我国光度分析中的进展

本文介绍了化学计量学在我国光度分析中的研究应用及某些进展。这些方法可用于消除干扰,改善选择性,实现无机、有机及药物多组分混合物以及复方制剂的同时测定。     

通用模拟退火用于稳健多元分析校正

模拟退火是一种全局优化算法，具有跨越局部最优点的机制，最小一乘是一种较常用的最小二乘更为稳健的优化准则，更适用于可能偏离正态分布的实际数据集，本文探讨了用最小一乘为准则并利用模拟退火方法同时测定多组分体系的可能性。应用于2－3组分药物体系分析，获得了满意的结果，本文还探讨了改变步长提高模拟退火算法优化精度的方法。     

Statistical comparison of calibration methods in multicomponent analysis

Spectrophotometric multicomponent analysis is considerd on the basis of inverse multivariate calibration with linear methods (ordinary least squares, principal component, ridge and partial least squares regression) and with the non-linear methods ACE and the non-linear partial least squares. The performance of the different methods is compared by paired F-tests. As an estimate of the error variance the residual mean sum of squares in the analysis of variance table is used. The comparison is demonstrated for the infrared spectrometric analysis of the hydroxyl group content of brown coal measured in diffuse reflectance. Although the error variances among the calibration methods differ gradually, the differences are much less pronounced at statistical level.