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。     

K系数—多波长最小一乘回归分光光度法同时测定三组分的研究

将双波长Ｋ系分光光度法和多波长线性回归分光光度法相结合，并采用最小一乘法准则计算回归系数，提出了一种同时测定三组分的新方法，即Ｋ系数－多波长最小一乘回归分光光度法。     

K系数－多波长最小—乘回归分光光度法同时测定三组分的研究

将双波长Ｋ系数分光光度法和多波长线性回归分光光度法相结合，并采用最小一乘法准则计算回归系数，提出了一种同时测定三组分的新方法，即Ｋ系数－多波长最小一乘回归分光光度法。该法不需要吸收曲线交点和等吸收点，且能较好地克服异常点的影响，得到比最小二乘法更好的回归稳健性。方法用于测定模拟混合色素样品和饮料样品中的柠檬黄、胭脂红、果绿，相对误差为—３．６％～２．８％，回收率为９６．３％～１０４．８％，变异系数小于２．７９％。     

序贯数论优化法在化学定量分析中的应用

将序贯数论优化(SNTO)法用于二维多组分数据的约束背景双线性化(CBBL)和基于最小中位数平方(LMS)法的稳健多元校正的优化过程,以进行多组分同时测定。对于所选数学函数和在CBBL中与变步长广义模拟退火(VSGSA)法进行了比较, SNTO法都取得了令人满意的结果。同时, 对于多元校正, 将基于SNTO的LMS法用于模拟和实际体系, 结果表明LMS法是一种稳健的算法。SNTO法的原理清晰, 且易于实现,是一种较实用的全局优化方法。     

模拟退火神经网络用于药物液相色谱梯度分离条件的优化

模拟退火神经网络用于药物液相色谱梯度分离条件的优化。使用均匀设计法以乙腈在线性梯度展开时的初始浓度和线性梯度的斜率为优化参数，对六种药物混合体系进行优化。采用退火神经网络方法建立了有效的分离条件预测模型。对神经网络模型所预测的最佳分离条件进行试验，分离结果满意。模拟退火神经网络可有效地用于药物液相色谱分离条件的优化。     

萃取溶剂选择的计算机辅助分子设计中的数学方法

萃取精馏是分离沸点相近或具有恒沸组成混合物的一种重要方法。选择最优的萃取剂是提高生产能力和降低能耗的根本途径。萃取剂选择的计算机辅助分子设计（CAMD）是基于性质估算的方法即通过CAMD来生成一组具有期望性质的分子，然后再对候选分子进行筛选。目前，CAMD法的研究主要可分为生成一验证、数学优化和组合优化。生成一验证法通常使用启发式的策略，是基于知识的，无法克服基团的组合爆炸问题，也不能保证结果的最优性；数学优化方法，包括混合整数非线性规划（MINLP）和混合整数线性规划（MILP），可用于表达非线性或线性的结构．性质关系，但准确表达结构-性质关系还有困难；组合优化法是使用遗传算法或模拟退火算法、禁忌搜索等的方法，理论上可克服以上问题。本文评述了以上三种方法在萃取精馏溶剂选择领域中的用途、原理及其工业化所面临的主要问题。     

一种新的化学计量学方法——蚁群虎法

蚁群算法是一种全新的仿生算法，具有智能搜索，全局优化，稳健笥强，分布式计算，易与其它方法结合等优点，是求解复杂的组合优化问题的有力工具。本文对蚁群算法的基本原理，数学模型，应用领域以及进展情况进行了介绍。     

近红外光谱结合LSTSVM算法判别不同等级烟叶

本文用近红外光谱结合最小二乘双胞胎支持向量机(LSTSVM)算法建立了烟叶等级分类模型。从三个等级共210个烟叶样品中,取出120个样品作为建模集,剩余90个样品作为预测集。为了建立最优模型,对光谱预处理方法和模型参数进行筛选优化,最优模型对预测集样品的平均识别率为95.56%,结果表明该方法可以作为烟叶等级分类的一种有效方法。此外,将该算法与SIMCA、PLS-DA、SVM等三种常见的模式识别算法进行了比较,结果表明基于样品的原始光谱,同等条件下,LSTSVM算法的预测效果优于其他三种算法。     

遗传算法在分析化学中的应用

遗传算法是基于自然界生物进化基本法进而发展起来的一类新算法，在优化过程中，它无需体系的选验知识，能在许多局部较优中找到全局最优点，是一种全局最优化方法，能有效地处理复杂的非线性问题，有广阔的发展前景，目前在分析化学领域已经有多方面应用，本文简要地介绍遗传算法的原理及其在分析化学等方面的若干应用。     

退火遗传算法在液相色谱线性梯度分离条件优化中的应用

对于复杂样品的反相高效液相色谱分离,一般需在不同的流动相洗脱模式下进行。流动相线性梯度淋洗在分离方法发展中得到了广泛的应用。遗传算法作为一种新的全局优化搜索算法,已广泛应用于计算机科学等领域、作者也已成功地将遗传算法应用于液相色谱分离条件优化。本文将模拟退火演化方法引入遗传算法,并将退火遗     

稳健线性回归法探讨分析精度与浓度之间的关系

研究分析精度与浓度之间的关系对于分析温度的质量控制具有重要意义。本文以土壤及水系沉积中微量元素为例,研究了不同实验室之间分析精度与被测组分浓度之间的关系。应用重新加权迭代最小二乘法（ＩＲＬＳ）成功地对分析结果的标准偏差与含量之间的关系进行了线性拟合,表明线性关系良好,与经典最小二乘法比较发现,ＩＲＬＳ法具有很好的稳健性,受离群值的影响小,回归结果也更符合实际。     

Robust and non-parametric statistics in the evaluation of figures of merit of analytical methods. Practices for students

A set of laboratory practices is proposed in which evaluation of the quality of the analytical measurements is incorporated explicitly by applying systematically suitable methodology for extracting the useful information contained in chemical data. Non-parametric and robust techniques useful for detecting outliers have been used to evaluate different figures of merit in the validation and optimization of analytical methods. In particular, they are used for determination of the capability of detection according to ISO 11843 and IUPAC and for determination of linear range, for assessment of the response surface fitted using an experimental design to optimize an instrumental technique, and for analysis of a proficiency test carried out by different groups of students. The tools used are robust regression, least median of squares (LMS) regression, and some robust estimators as median absolute deviation (m.a.d.) or Huber estimator, which are very useful as an alternatives to the usual centralization and dispersion estimators.     

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near‐infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high‐quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near‐infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure‐activity relationship problems.     

An improved optimization strategy and its application to clustering analysis.

In this paper, a new optimization strategy is put forward which locates as many potential unimodal regions as possible in the search space. The potential optima can be further explored by a global optimization method for searching in the identified unimodal regions. The proposed strategy was evaluated by the optimization of test functions. The results obtained by this approach are comparable with those achieved by variable step size generalized simulated annealing (VSGSA) and a genetic algorithm (GA). Finally, we used this strategy in a clustering analysis of a tobacco data set.     

Simultaneous Spectrophotometric Determination of Imipramine Hydrochloride with Chlordiazepoxide and Nortriptyline Hydrochloride with Fluphenazine Hydrochloride

Spectrophotometry was used with multivariate calibration to simultaneously determine compounds in mixtures. Two antidepressant mixtures were investigated: imipramine hydrochloride and chlordiazepoxide and nortriptyline hydrochloride and fluphenazine hydrochloride. Considerable spectral overlap and large differences in component concentrations were challenges. Since this type of analysis is often performed using complex algorithms, a simple strategy was used here for the simultaneous determination of both mixture components by classical least squares, principal component regression, and partial least squares. Experimental design was used to select the optimum parameters including the wavelength range, sampling interval, software, and derivative order. Accuracy was enhanced by proper wavelength selection. In addition, derivatives of the raw spectra improved the selectivity. The standard deviation, deviation of mean recovery from 100%, and prediction ability of the models were used as the responses. In respect to these terms, first-order derivatization of the spectra and a sampling interval of 1?nm provided the best results. In particular, the low concentration compounds in the mixtures (chlordiazepoxide and fluphenazine) were determined more accurately with precision lower than 3%. The strategy was used for the quality control of pharmaceuticals containing the mixtures without chemical pretreatment.     

模糊聚类-偏最小二乘回归光度法同时测定地质样品中金铂钯铑的研究

将模糊聚类分析与偏最小二乘法相结合,对地质样品中吸收光谱严重重叠的贵金属多组份体系进行解析,较好地解决了计算光度分析中校准模型的优化问题,使计算结果的精度得到了显著提高,分析结果的相对误差小于 10%,标准偏差小于 0.67,明显优于一般偏最小二乘 (PLS)法。采用小锍试金法消除样品中贱金属元素的干扰,其回收率为 92%～107%,标准偏差为 0.10～0.67;相对标准偏差为 4.7%～11.0%。并对影响聚类效果的参数选择作了讨论。     

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.     

Experimental design and multivariate calibration in the development, set-up and validation of a differential pulse polarographic and UV spectrophotometric method for the simultaneous plasmatic determination of the therapeutic metronidazole-pefloxacin combination

Partial least squares regression (PLS1 and PLS2) and GOLPE variable selection procedures were used for the treatment of differential pulse polarographic and UV spectrophotometric data obtained from the analysis of the therapeutic combination of metronidazole and pefloxacin. The analytical method used for the determination was set up using experimental design strategies (Doehlert's design, full factorial design, fractional face center cube design, etc.) and by involving the simultaneous optimization of several responses (desirability function). Method validation was also performed, determining accuracy, precision, linearity and range, detection and quantification limits and robustness. The quantitative prediction abilities in determining metronidazole and pefloxacin plasma levels of the PLS1 and PLS2 models were tested on spiked plasma samples and good results were obtained (metronidazole, 97.5%, RSD = 4.8%, n = 3; pefloxacin, 100.6%, RSD = 3.6%, n = 3). The use of multivariate calibration was particularly useful for spectrophotometric quantification because of the highly overlapping spectra of the binary mixture.