Application of the Repeated Measures (Split-Plot) Design to the Development of Processes for Drug Substances

A case study from the chemical and pharmaceutical industries is used to show the value of the repeated measures design in developing processes for drug substances. This article advocates the use of the repeated measures design in situations that commonly arise in the chemical and pharmaceutical industries when repeated measurements are taken on the same experimental unit, instead of the common practice of comparing only individual conditions, a procedure that is inappropriate in many cases. One of the most important advantages of applying the repeated measures design is the abundance of data that can be obtained by taking into account the entire response curve instead of only isolated individual points on it.     

Application of the Multivariate T2 Control Chart and the Mason-Tracy-Young Decomposition Procedure to the Study of the Consistency of Impurity Profiles of Drug Substances

A case study from the chemical and pharmaceutical industry is used to show the value of the application of the multivariate T² control chart and the Mason-Tracy-Young decomposition procedure to the study of the consistency of the impurity profiles of drug substances. The impurity profile of a drug substance is one of the most important quality characteristics, is almost always multivariate in nature, and is complicated by the relationships among the impurity levels. This prohibits examining each impurity by univariate statistical procedures. Several of the possible benefits of applying these multivariate statistical procedures to the manufacture of drug substances are illustrated through the presentation and discussion of the case study.     

Using Structural Equation Modeling (SEM) for the Study of Impurity Profiles of Drug Substances

The impurity profile is one of the most important quality characteristics of a drug substance. Although it is always desirable to determine the chemical structure of all impurities forming the impurity profile, unfortunately this is not always economically and technically feasible because of the extremely low concentrations at which some impurities may be found in the drug substance. Therefore, alternative approaches to the chemical analysis are needed for trying to determine the origin of the unidentified impurities.

In a previous study conducted by our group, based on exploratory (principal component and hierarchical cluster) analysis, we were able to suggest a hypothesis for explaining the origin of the unidentified impurities of a drug substance. However, there was still a concern that alternative hypotheses might explain the same phenomenon equally well. This article explores the application of recent developments in structural equation modeling for the systematic generation and selection of hypotheses (models) worthy of being confirmed by chemical research.     

主成分分析-二阶导数在中红外光谱定量分析中的应用

介绍了运用最小二乘法建立傅立叶变换中红外光谱定量分析模型的原理和方法。以苯甲酸和邻苯二甲酸氢钾为实验材料获取红外吸收光谱,采用MATLAB工程语言编程,分别以吸收光谱和二阶导数光谱为校正集样本,采用主成分分析方法对样本进行优选压缩,以建立了中红外光谱定量分析模型。用此模型预测混合物的含量,预测值与实际值的相对误差小于5.0%。     

主成分分析法在气调包装果蔬质量评价中的应用

用主成分分析法构造了气调包装后果蔬质量的综合评价模型,并将该方法用于实例作效果检验.结果表明用主成分分析法评价气调包装后果蔬质量的方法可行,结果客观合理.     

主成分分析法在纸或纸板质量综合评价中的应用

为了探索多元统计分析应用于纸或纸板质量综合评价的可行性和合理性,运用主成分分析法构造了纸或纸板的综合质量评价的模型,建立了一个度量纸或纸板质量好坏的综合指标值R,R值的大小较好地反映了纸或纸板的综合质量好坏.并将该方法应用于实例检验,结果表明主成分分析法评价纸和纸板的综合性质量适应性强,方法可行,结果合理.     

主成分分析在企业安全评价中的应用

主成分分析能将原有的指标重新组合成相互无关的综合指标,并由较少的综合指标反映85%以上原有指标信息。文章运用该法对某化工厂进行安全评价,以聚氯乙烯合成的生产过程中各个工段为样本,收集相关指标建立评价体系,求出综合表达式并对计算结果进行排序,从而反映出企业的安全生产状况,有助于采取有效防范措施。     

主成分分析法用于单质炸药爆轰性能评估的研究

炸药的爆轰性能是评价炸药综合毁伤能力的重要指标,是进行炸药装药设计、开发和优选工作的前提。为实现对炸药的综合爆轰性能进行合理评估,必须对炸药的5大爆轰参数（爆热、爆速、爆压、爆容、爆温）进行综合考虑和计算。尝试应用针对多元统计技术的主成分分析法,对TNT、RDX、HMX等常用单质炸药的所有爆轰参数开展了分析与评估。结果表明,炸药各爆轰参数的信息重叠度较大,主成分的贡献率可达到90.1%,在爆轰性能综合评估中可以只参考爆热和爆速。最后利用第一和第二主成分的贡献率为权数,构造了炸药爆轰性能综合评估函数;对常用炸药的强弱排序也与各自威力经验值相符,评估有效。     

Statistical Validation of Reproducibility of HPLC Peptide Mapping for the Identity of an Investigational Drug Compound Based on Principal Component Analysis

Peptide mapping is a key analytical method for studying the primary structure of proteins. The sensitivity of the peptide map to even the smallest change in the covalent structure of the protein makes it a valuable “fingerprint” for identity testing and process monitoring. We recently conducted a full method validation study of an optimized reverse-phase high-performance liquid chromatography (RP-HPLC) tryptic map of a therapeutic anti-CD4 monoclonal antibody. We have used this method routinely for over a year to test production lots for clinical trials and to support bioprocess development. One of the difficulties in the validation of the peptide mapping method is the lack of proper quantitative measures of its reproducibility. A reproducibility study may include method and system precision study, ruggedness study, and robustness study. In this paper, we discuss the use of principal component analysis (PCA) to quantitate peptide maps properly using its projected scores on the reduced dimensions. This approach allowed us not only to summarize the reproducibility study properly, but also to use the method as a diagnostic tool to investigate any troubles in the reproducibility validation process.     

二维主元分析法在心肌梗死心电信号检测中的应用

利用基于联合能量百分比搜索的二维主元分析法对12导高分辨率心电信号（ECG）进行全局特征提取和分类检测研究。所用数据取自PTB诊断数据库,包括健康状态ECG,早期心肌梗死（MI）ECG,急性期MI ECG,恢复期 MI ECG。结果表明,所用的方法能有效地融合12导ECG信号及其高频分量中的细微结构信息,与常规主元分析法相比,其平均分类检测精度可提高10.43%,与常规二维主元分析法相比,能得到维数更低的特征表示,并可获得99.46 %的平均分类检测精度。     

On the Implementation of the Principal Component Analysis–Based Approach in Measuring Process Capability

The use of principal component analysis in measuring the capability of a multivariate process is an issue initially considered by Wang and Chen (1998). In this article, we extend their initial idea by proposing new indices that can be used in situations where the specification limits of the multivariate process are unilateral. Moreover, some new indices for multivariate processes are suggested. These indices have been developed so as to take into account the proportion of variance explained by each principal component, thus making the measurement of process capability more effective. Copyright © 2011 John Wiley & Sons, Ltd.     

基于主成分分析与支持向量机的汽柴油需求预测

综合分析了影响汽柴油消费需求的关键因素,并针对其具有自相关性、复杂性、数据量大等特点,采用主成分分析法对样本数据进行降维处理,形成新的样本集。对支持向量机预测模型进行改进,在其基础之上引入时序动态因子,将上年的汽柴油需求历史数据作为时序反馈因子引入模型,从而形成新的动态反馈拟合模型,建立相应的需求预测模型。对1996~2012年的汽柴油需求预测进行实例研究,并将本文中所提方法的预测结果与灰色GM（1,1）模型、BP神经网络模型进行对比分析。结果表明本文中的主成分分析与改进支持向量机预测方法相对于GM（1,1）模型其预测误差均值分别降低了72.7%和74.86%,相对于BP神经网络其预测误差均值分别降低了81.3%和8166%,从而证明了此方法的有效性和优越性。     

主成分分析方法研究MAP对建水草芽的保鲜效果

目的 为探究气调包装对建水草芽贮藏品质的影响。方法 采用低氧气调（MAP1）、高氧气调（MAP2）和高氧结合高二氧化碳（MAP3）处理新鲜建水草芽,在4℃条件下贮藏20d,观测其品质变化。结果 与空白对照组相比,MAP1组前期可以有效维持草芽的品质,后期由于草芽进行无氧呼吸,反而会加速其品质劣变。高氧气调组能够有效保持草芽的品质,且MAP3组效果更佳,能够维持较低的质量损失率和硬度,保持较高的Vc含量和L^*值,且感官性状评价最好。主成分分析（PCA）提取出2个主成分,累计方差贡献率为87.67%,能较好地解释草芽贮藏品质的信息。L^*值、Vc含量、硬度、质量损失率和二氧化碳含量对草芽贮藏期品质的影响较大。PCA分析进一步证明高氧、高二氧化碳气调处理有助于草芽贮藏品质的稳定。结论 气调包装能够有效地提高建水草芽的贮藏品质,延长贮藏期,其中高氧结合高二氧化碳气调处理的保鲜效果最好。     

Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process

In modern manufacturing processes, massive amounts of multivariate data are routinely collected through automated in-process sensing. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio and missing values. Conventional univariate and multivariate statistical process control techniques are not suitable to be used in these environments. This article discusses these issues and advocates the use of multivariate statistical process control based on principal component analysis (MSPC-PCA) as an efficient statistical tool for process understanding, monitoring and diagnosing assignable causes for special events in these contexts. Data from an autobody assembly process are used to illustrate the practical benefits of using MSPC-PCA rather than conventional SPC in manufacturing processes.     

不同品种鲜切桃贮藏期品质变化与主成分分析

目的 探究适宜鲜切加工的桃品种.方法 比较4个品种桃经鲜切处理后的感官品质和营养成分在贮藏期间的变化趋势和规律,综合相关性分析、主成分分析构建品质综合评价模型,筛选出鲜切桃加工优势品种.结果 在贮藏期6 d内,'艳红'桃维持了较好的感官品质,质量损失率小,硬度变化缓慢,'夏之梦'中川新岛'美香'和'艳红'桃的Vc含量均显著下降(P<0.05),分别降低了75.1％,75.3％,77.5％,75.8％.相关性结果表明,鲜切桃各指标间存在不同程度的相关性,各指标间存在信息重叠.主成分分析提取的2个主成分的累积方差贡献率达到86.156％,说明其反映了原指标的大部分信息.结论 在贮藏过程中,'艳红'桃的综合得分优于其他品种,可作为较适宜鲜切加工的品种.     

基于响应面-主成分分析法优化豆渣膨化食品的配方

目的为了克服新鲜豆渣难以膨化的缺点,添加小麦面粉、玉米淀粉和水进行复配,开发出一款膨化效果较好、品质较高的豆渣膨化食品。方法将湿豆渣、小麦面粉、玉米淀粉和水按一定比例混合均匀,利用单螺杆挤压膨化机对其膨化,然后添加防腐酒,最后进行真空包装。单因素实验以500 g湿豆渣为基准量,选取小麦面粉的质量分数、玉米淀粉的质量分数和水的质量分数作为影响因素,并以膨化度、弹性、硬度、胶黏性和咀嚼度作为评价指标,再通过响应面-主成分分析法确定最佳配方。最后测定产品菌落总数和感官分数以确定防腐酒的质量分数和产品保质期。结果在小麦面粉质量分数为83%,玉米淀粉质量分数为20%,水质量分数为31%时,产品的膨化度为5.61,弹性为0.97,硬度为1178.43 g,胶黏性为935.33,咀嚼性为1028.05。添加质量分数为1%的防腐酒后并真空包装,产品在常温下的保质期至少可达6个月。结论湿豆渣与小麦面粉、玉米淀粉和水按最优配方复配后,产品膨化效果较好,品质优良。     

基于主成分分析及多维高斯贝叶斯的超声流量计故障智能诊断方法

提出了基于多维高斯贝叶斯模型的设备故障智能诊断流程,包括数据的筛选与结构化分析、数据的降维、模型的构建、诊断结果的检验与分析等。研究表明采用主成分分析方法进行降维时,不同的诊断对象在降维参数的选择方面存在较大差别,诊断效果因诊断对象和样本数量的不同而有所差异。利用公开发表的超声波流量计数据库对流程进行验证。结果显示:针对B型流量计进行280次、C型流量计进行550次智能故障诊断,故障状态的首选正确识别率分别达到99.3%和95.1%,较k-最近邻(KNN)聚类分析算法有一定的优势。     

基于改进主元分析方法的空调系统传感器故障检测和诊断研究

传感器在空调系统中主要起着监测和控制的作用,影响空调系统的正常运行,从而带来能耗增加等不良影响。本文提出了结合小波变换的数据优化,以及基于神经网络的故障诊断优化的改进主元分析方法,用于空调系统传感器故障检测和诊断研究。通过对比数据优化前后主元分析的结果,发现同样0. 850 0累计贡献率原则上,采用小波变换去除噪声后,主元个数减少了两个,蒸发器进口温度传感器的固定偏差、漂移、精度下降等故障检测效果分别提升了0. 020 7、0. 020 8、0. 041 5,风量传感器固定偏差故障检测效果提升了0. 160 6。为了进一步找出故障源,在小波变换和主元分析的基础上,将求得的主元作为神经网络的输入,对5个传感器固定偏差故障进行测试,故障诊断结果分别为0. 766 7、0. 866 7、0. 900 0、1. 000 0、1. 000 0。     

新版电器电子产品有害物质限制使用管理办法之简析

本文阐述了2016年1月6日,工业和信息化部等8部门联合公布的《电器电子产品有害物质限制使用管理办法》(第32号令)与2006年公布的《电子信息产品污染控制管理办法》(第39号)之间的扩大适用范围、扩大限制范围等差异分析,以及给我国电器电子产业结构带来的影响,督促电器电子产品升级换代.     

Multivariate analysis of the water quality variation in the Serra da Estrela (Portugal) Natural Park as a consequence of road deicing with salt

The concentration of some anions (Cl, F, NO₃, and SO₄), cations (Na, K, Ca, and Mg), pH and conductivity was determined in seven sampling points in the Serra da Estrela Mountains (Portugal) to verify if the application of road deicing with salt provokes impact in the water quality. Multivariate chemometric data analysis techniques of clustering and factor analysis were used. Preliminary analysis shows an overall conductivity increase in late spring and a decrease in late summer. A strong association between sodium chloride and the conductivity was observed by cluster analysis. Factor analysis supports the previous results and shows that the variation of the concentration of sodium chloride is the main source of the variance of the data along the year. In conclusion, the spreading of salt during the winter creates a local and seasonal impact on the water quality.