直观推导式演进特征投影法在拖尾重叠色谱峰解析中的应用

直观推导式演进特征投影法(HELP)已成功应用于复杂体系的重叠色谱峰解析.当色谱峰拖尾时,演进特征投影图(ELPG)显示的直线段对应的区域中可能含有前面拖尾组分的信息,据此进行HELP解析可能得不到满意结果.选择ELPG上直线段的一部分,即拖尾组分末端,前面组分的信息已基本消失的区域作为选择性区域进行HELP解析.同时,提出一种新的定量方法:用主成分分析法(PCA)分解待测组分标准样品的二维数据,将得到的“标准”色谱引入HELP的定量过程,在色谱峰拖尾或解得谱峰不平滑时,得到更准确的结果.用HELP方法解析了依诺沙星、诺氟沙星和环丙沙星三组分实验体系,结果表明,加入上述措施的HELP可有效改善拖尾重叠色谱峰的解析结果.     

多波长线性回归紫外分光光度法同时测定复方阿斯匹林片中的三组分

用改进的多波长线性回归分光光度法同时测定了复方阿期匹林片中三组分的含量,模拟样品中阿斯匹林、非那西丁、咖啡因的平均回收率分别为１００．３１％、９９．８７％、１００．７７％,相对标准偏差分别为０．３５％、０．２５％、１．３７％（ｎ＝１０）。对四个批号实际样品的测定结果与标准方法一致,适物复方阿斯匹林片的常规分析。     

基于主成分回归分析和偏最小二乘回归法的樱桃糖度检测的研究

选用烟台大樱桃为研究对象,采用便携式光谱仪对樱桃糖度进行检测,利用极差标准归一化方法和小波滤波,对其可见-近红外光谱数据进行预处理,分别运用主成分回归分析(PCR)法和偏最小二乘回归(PLSR)法建立了樱桃糖度定量分析模型,并对两种模型进行了比较。实验结果表明:在600~1 100nm波段范围内对樱桃糖度进行检测是可行的,并且PLSR模型的性能优于PCR模型。     

Analysis of dissolution profiles of iron,zinc, and manganese from complex dietary supplements by ion chromatography and chemometrics

Four commercially available formulations containing iron, zinc, and manganese were subjected to dissolution profile testing during 60 min and the dissolution was analyzed by ion chromatography. The obtained curves were analyzed directly by principal component analysis (PCA). The main trend (87.1% of variance) was connected with average dissolution percentage over the investigated time. The second component (11.2% of variance) is connected with shape of dissolution profile. All metals behave in the similar way and the differences were connected with excipients. An additional fit was completed on 12 kinetic models: first order kinetics (4 variants), Higuchi (2 variants), Hixson-Crowell (2 variants), Korsemeyer-Peppas, Logistic (2 variants), Peppas-Sahlin, Quadratic (2 variants), Weibull (3 variants), and Zero order kinetics (2 variants). The ranking of the fitting was performed by Akaike information criteria (AIC) values with additional PCA analysis on them, an approach presented in literature for the first time. The main trend (67.4% of variance) was connected with average fit. The second (14.8% of variance) is connected with differences of fitting ability to investigated dissolution curves. This methodology brought an overall look to trends and variances inside obtained data, both the profile shape and fitting ability to particular models.     

高中学业水平考试试题难度模型建构*

利用主成分分析方法厘清试题难度影响因素之间的关系,进而从问题表征、问题解决和结果输出等3个维度上建立了影响高中学业水平考试试题难度的主要因素。通过编写高中学业水平考试测试题进行实证研究,建立影响因素常见呈现类型的难易赋值规则。选用机器学习的线性回归方法建构试题难度分析模型,并使用高考化学江苏卷部分试题的实测难度数据进行校验,模型难度预测值和高考难度实测值具有较好的拟合度,表明模型具有较好的应用价值和推广意义。     

Improving the neural network method for finite element model updating using homogenous distribution of design points

In developing a neural network technique for a finite element model updating, researchers have been shown that the number of training samples and their quality, significantly affect the accuracy of the NN predication. In this study, based on the genetic algorithm (GA) method, we reduce the number of analyses required to develop the training pairs and reduce the amount of time for training the NN. In the other words, a uniform distribution of design points inside the design space will be obtained by means of this approach. To validate the efficiency of GA sample selection, random generation (RG) method is used for comparison. Two comparisons are made based on a numerical and experimental example. One is updated from 10 degrees of freedom lumped parameters system and the other is updated from a free–free beam using test data. The results indicate that the GA sample selection can reduce the number of training samples without affecting the accuracy of the NN predication. In our present study, also, the advantages of using frequency response function (FRF) data as input to the NN are kept and the drawback of having too many frequency points is overcome by the application of principal component analyses (PCA).     

DATA PREORDERING IN GENERALIZED PAV ALGORITHM FOR MONOTONIC REGRESSION

Monotonic regression （MR） is a least distance problem with monotonicity constraints induced by a partiaily ordered data set of observations. In our recent publication [In Ser. Nonconvex Optimization and Its Applications, Springer-Verlag, （2006） 83, pp. 25-33], the Pool-Adjazent-Violators algorithm （PAV） was generalized from completely to partially ordered data sets （posets）. The new algorithm, called CPAV, is characterized by the very low computational complexity, which is of second order in the number of observations. It treats the observations in a consecutive order, and it can follow any arbitrarily chosen topological order of the poset of observations. The CPAV algorithm produces a sufficiently accurate solution to the MR problem, but the accuracy depends on the chosen topological order. Here we prove that there exists a topological order for which the resulted CPAV solution is optimal. Furthermore, we present results of extensive numerical experiments, from which we draw conclusions about the most and the least preferable topological orders.     

基于IFSR异常样本剔除的落叶松木材密度近红外优化模型的研究

木材密度可以反映木材的干缩性、抗压抗拉强度等多种物理性质,是重要的木材物理特性。采用近红外光谱技术能够实现木材密度的快速预测,可克服传统检测方法耗费人力、物力、时间的弊端,但建模结果往往受异常样本的影响。为准确识别并剔除样本集中的异常样本,提出一种孤立森林结合学生化残差方法(IFSR),在利用孤立森林集成特征的优点基础上考虑样本对模型的影响度,可同时检测异常样本与强影响样本。该研究对181个落叶松木材样本的近红外光谱及其在常温下的气干密度进行了测定。通过对比多种方法预处理和特征选择方法,确定采用标准正态变量变化(SNV)+去趋势处理(DT)+均值中心化(MC)+标准化(Auto)方法进行预处理,采用竞争性自适应重加权算法(CARS)进行特征波段选择,消除噪声及无关信息对算法的影响,简化数据集,提高算法剔除异常样本的准确性。为验证IFSR方法剔除异常样本的能力,将其与蒙特卡洛交互验证(MCCV)、马氏距离(MD)等其他六种异常检测方法对比分析,建立偏最小二乘(PLS)模型对其进行异常检测性能评价。同时在上述基础上采用粒子群寻优-支持向量机回归(PSO-SVR), BP神经网络(BPNN)...     

基于半参数回归模型的最小一乘局部线性算法

根据最小一乘准则,推导出最小一乘局部线性估计的计算方法,并通过对模拟数据的计算和分析,对比最小一乘核算法和最小二乘局部线性算法,验证了最小一乘局部线性算法是一种有效的,稳健的估计方法,并且有降低边界效应的作用．     

粤东柘林湾浮游植物与生态因子的灰关联-回归分析

应用灰关联方法对 2 0 0 1年 1 0月至 2 0 0 2年 7月柘林湾海域的海水采样结果进行数据分析 ,在磷酸盐、硅酸盐等 1 1项生态因子中找出影响浮游植物密度的关键因子 .根据这个结果 ,进一步建立了以这些关键因子为自变量的线性回归模型 ,模型最后的分析结果和灰关联分析的结果一致 ,即 Fe、水温、浊度是影响柘林湾浮游植物密度的关键因子 .