Functionalization of Hydrocolloids: Principal Component Analysis Applied to the Study of Correlations Between Parameters Describing the Consistency of Hydrogels

This work was part of a pure research project on the functionalization of three families of hydrocolloids: cellulose derivatives, carrageenates, and alginates. Principal component analysis (PCA), a powerful statistical method, was used to demonstrate the relations existing among these different parameters that describe the consistency of hydrogels and their spreadability. This approach therefore provides a basis for modeling hydrogel consistency. PCA also afforded a classification of hydrogels that demonstrated the remarkable adhesiveness of very stiff gels based on cellulose derivatives and sodium or potassium alginates. The corresponding semifluid gels and all the gels based on carrageenates and mixed sodium-calcium alginates, whatever their spreadability, were found to be very poorly adhesive. Generalized to all the many colloids currently marketed, this approach can be used to set up a databank for the formulation of mucoadhesive excipients.     

Three-way principal component analysis applied to noodles sensory data analysis

The results presented in this paper are issued from the study and the interpretation of a 3-way data matrix constituted from the sensory analysis of wheat noodles. The aim of this work was to provide a complementary understanding of internal relationships between the chemical composition of the noodles and sensory attributes such as color, surface smoothness, elasticity or chewiness. The application of the Tucker3 algorithm involving the noodles composition, the sensory attributes and the assessors as the three modes, facilitates the interpretation of the differences among the types of noodles and also the estimation of the effect of different sources of variability on the sensory evaluation. A joint interpretation of the first and of the second mode (noodles and sensory attributes) allows to link appearance and texture attributes with the composition of the noodles.     

Principal component analysis applied to filtered signals for maintenance management

This paper presents an approach for detecting and identifying faults in railway infrastructure components. The method is based on pattern recognition and data analysis algorithms. Principal component analysis (PCA) is employed to reduce the complexity of the data to two and three dimensions. PCA involves a mathematical procedure that transforms a number of variables, which may be correlated, into a smaller set of uncorrelated variables called ‘principal components’. In order to improve the results obtained, the signal was filtered. The filtering was carried out employing a state–space system model, estimated by maximum likelihood with the help of the well‐known recursive algorithms such as Kalman filter and fixed interval smoothing. The models explored in this paper to analyse system data lie within the so‐called unobserved components class of models. Copyright © 2009 John Wiley & Sons, Ltd.     

On the random differential quadrature (RDQ) method: consistency analysis and application in elasticity problems

Differential Quadrature (DQ) is an efficient derivative approximation technique but it requires a regular domain with uniformly arranged nodes. This restricts its application for a regular domain only discretized by the field nodes in a fixed pattern. In the presented random differential quadrature (RDQ) method however this restriction of the DQ method is removed and its applicability is extended for a regular domain discretized by randomly distributed field nodes and for an irregular domain discretized by uniform or randomly distributed field nodes. The consistency analysis of the locally applied DQ method is carried out, based on it approaches are suggested to obtain the fast convergence of function value by the RDQ method. The convergence studies are carried out by solving 1D, 2D and elasticity problems and it is concluded that the RDQ method can effectively handle regular as well as irregular domains discretized by random or uniformly distributed field nodes.     

Improvement of mechanical properties of recycled plastic blends via optimizing processing parameters using the Taguchi method and principal component analysis

In this paper, the Taguchi method and principal component analysis (PCA) are used to improve the mechanical properties of recycled polypropylene (PP) blends in injection moulding procedure, with detailed assessments performed on each method and comparison was made based on results of both the methods. The experimental design was carried out by adopting a L9-3⁴ Taguchi orthodoxy array (OA), which has four controllable factors (i.e., melt temperature, mould temperature, injection speed and packing pressure) at three levels. Injection moulded specimens made from different compositions of virgin-recycled PP were tested to determine the optimal conditions for the injection moulding procedure. The effects of the processing parameters and the proportion of recycled plastic in composites on the mechanical properties were investigated, the optimal conditions for desired properties were obtained and then verified. The appropriate blending ratio of virgin and recycled plastic was evaluated. The results reveal that deteriorations in the mechanical properties of products produced from recycled plastic can be improved by optimizing the processing parameters during the injection moulding procedure.     

基于主成分分析的护听器优选方法研究

为了探究不同护听器对抽水蓄能电站内不同工作场所的降噪效果及适用情况,以便于工作人员根据不同需要选择合适的护听器,根据112种护听器的插入损失测试结果,应用主成分分析(Principal Component Analysis, PCA)对数据进行分析。结合某抽水蓄能电站10个工作场所的现场测试结果,得出文中所测试的112种护听器大部分适用于该蓄水电站中1#发电机隔声罩内、1#水车室外、1#水车室内、2#尾水锥管室外、2#尾水锥管检修门、3#尾水锥管室外和3#尾水锥管检修门7个工作场所,其他场所需要有针对性地选择适合的护听器。该文同时可以为其他不同工作场所情况下护听器的选择提供借鉴。     

Using principal component analysis to monitor spatial and temporal changes in water quality

Chemical, biological and physical data monitored at 12 locations along the Passaic River, New Jersey, during the year 1998 are analyzed. Principal component analysis (PCA) was used: (i) to extract the factors associated with the hydrochemistry variability; (ii) to obtain the spatial and temporal changes in the water quality. Solute content, temperature, nutrients and organics were the main patterns extracted. The spatial analysis isolated two stations showing a possible point or non-point source of pollution. This study shows the importance of environmental monitoring associated with simple but powerful statistics to better understand a complex water system.     

声信号的主分量分析应用于滚动轴承故障诊断

利用声信号来进行故障诊断具有"采集比较容易,非接触式测取,设备简单,速度快,无须事先粘贴传感器,不影响设备正常工作,易于实现早期预报和在线监测,疳可在不易测量振动信号的场合得到广泛应用"等优点.由于外界噪声的影响,有效信息的提取较为困难.采用主分量分析对传声器测取的声信号进行了预处理;在此基础上应用基于Morlet小波变换的包络分析和频谱分析来提取故障特征向量,并以滚动轴承为例进行实验.结果表明,这是诊断滚动轴承早期故障的一种可选方法.     

Cost function analysis applied to the determination of aorta parameters

It has been shown by Fich and Welkowitz that a model of the aorta which is tapered in area and elastic constant and is essentially reflectionless is a good representation for the calculation of pressure and flow wave propagation in the aorta. If one then measures a steady state pressure transfer function in the aorta and relates it to the steady state pressure transfer function of the model, it is possible to adjust the physical parameter values that appear in the model until the transfer functions coincide. By this means it is possible to determine the physical parameters of the aorta.

Both analog and digital data techniques were developed for efficient handling of the data. Matching of the transfer functions was accomplished by finding the ‘best’ set of parameters that minimized a weighted error cost function derived from the magnitude and phase of the transfer functions.     

Degrees of freedom for the residuals of a principal component analysis — A clarification

Various conflicting proposals for degrees of freedom associated with the residuals of a principal component analysis have been published in the chemometrics-oriented literature. Here, a detailed derivation is given of the ‘standard’ formula from statistics. This derivation intends to be more accessible to chemometricians than, for example, the impeccable, but condensed proof that was published by John Mandel in a relatively unknown paper (J. Res. Nat. Bur. Stand., 74B (1970) 149–154). The derivation is presented in the form of a two-stage recipe that also appears to apply to more complex multiway models like the ones considered by Ceulemans and Kiers (Br. J. Math. Stat. Psych., 59 (2006) 133–150).     

Simultaneous optimization of the microextraction of coffee volatiles using response surface methodology and principal component analysis

It is well known that no single experimental condition can be found under which the extraction of all the volatile compounds in a gas chromatographic analysis of roasted coffee beans by headspace-solid phase microextraction (HS-SPME) is maximized. This is due to the large number of peaks recorded. In this work, the scores vector of the first principal component obtained from PCA on chromatographic peak areas was used as the response to find the optimal conditions for simultaneous optimization of coffee volatiles extraction via response surface methodology (RSM). This strategy consists in compressing several highly correlated peak areas into a single response variable for a central composite design (CCD). RSM was used to identify an optimal factor combination that reflects a compromise between the partially conflicting behavior of the volatiles groups. This simultaneous optimization approach was compared with the desirability function method. The versatility of the PCA-RSM methodology allows it to be used in other chromatographic applications, resulting in an interpretable procedure to solve new analytical problems.     

Feasibility study of use of recycled High Density Polyethylene and multi response optimization of injection moulding parameters using combined grey relational and principal component analyses

Usability of recycled High Density Polyethylene (HDPE) as substitute for virgin HDPE is investigated. Optimization design of the injection moulding parameters for recycled HDPE products is presented. Tensile, compressive and flexural strengths are selected to evaluate the process performance and the corresponding moulding parameters are melt temperature, holding pressure, injection time, and holding time. Optimal combination of injection moulding parameters is determined using Grey relational analysis. The principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics. Tensile, compressive and flexural strengths of the recycled HDPE are found close to that of virgin HDPE. Thus, recycled HDPE is a good substitute for virgin HDPE. Optimal combination of the process parameters for the multi-performance characteristics of the recycled HDPE is the set with melt temperature at 240 °C, holding pressure at 255 N/m², injection time at 0.6 s and holding time at 30 s.     

基于主成分分析与BP网络的锚杆支护方案优选

巷道特征与锚杆支护之间可以看作是一种非线性映射关系,用一般的数学方法难以表达巷道支护方案与其影响因素之间的非线性映射关系.神经网络已广泛应用于锚杆支护方案优选,并取得较好的效果.基于单一神经网络预测锚杆支护方案存在一些不足,构建了主成分分析与BP网络相结合的巷道锚杆支护方案优选模型.利用主成分分析对神经网络的输入数据进行预处理,使输入数据减少且不相关,加快网络的收敛速度,并且预测精度均在90%以上.研究结果表明:将主成分分析与BP神经网络结合优选巷道的锚杆支护方案,具有很高的预测精度;与单一BP神经网络相比,提高了预测精度．     

改进的求解独立成分分析参数的算法及应用

提出改进的求解独立成分分析参数的算法。该算法采用平均场近似原理,通过改变模型参数的限制条件,解决传统ICA算法中不能解决的问题。在图像特征的提取中,将模型中的混合矩阵和源信号均限制为非负,使提取的特征更为独立。利用语音信号、仿真图形及剑桥大学ORL人脸数据进行实验,从实验结果可以看出,改进的算法能有效地解决源信号数大于观测信号数的问题,同时该算法能分离出更独立的人脸五官特征,对ORL人脸识别结果分析表明,识别率从原来的87%左右提高到95%左右,也说明该算法比传统的算法更有利于进行图像特征提取及识别。     

TOF-SIMS and principal component analysis characterization of the multilayer surface grafting of small molecules: antibacterial furanones

Furanone compounds (fimbrolides) have attracted interest as antibacterial compounds for use in human health care, for instance, as an antibacterial coating for medical devices to combat device-centered infections. To ensure effectiveness for extended periods of time, they must be immobilized covalently onto a device surface; in this study, this was done via azide/nitrene chemistry and photochemical coupling. However, the detection and quantification of surface-immobilized small molecules such as furanones presents a considerable analytical challenge, yet is necessary for optimization of coatings and reliable interpretation of biological responses. We have utilized the surface sensitivity and chemical specificity of time-of-flight secondary ion mass spectrometry (TOF-SIMS) to characterize each step of the grafting sequence. On account of the complexity of the data, principal component analysis (PCA) was used to interpret and compare spectra. The results demonstrate the utility of TOF-SIMS with PCA for the detection of the surface-grafted small molecules azidoaniline and a brominated furanone; imaging of the bromine ion peaks also enabled assessment of grafting uniformity. Thus, successful multilayer coating and furanone grafting was observed, and substantial and uniform coverage of furanone molecules on the surface. Even multiple grafting steps involving, in the present case, two low molecular weight compounds can readily be disentangled by PCA. The utility of TOF-SIMS analysis with PCA is particularly well illustrated in the present case by the grafting of the furanone molecules, which did not yield a singular unique peak in the positive ion mass spectra, whereas the collective spectral changes elucidated by PCA provided unambiguous verification of successful grafting of this low molecular weight compound.     

Multiple imputation and maximum likelihood principal component analysis of incomplete multivariate data from a study of the ageing of port

A multivariate data matrix containing a number of missing values was obtained from a study on the changes in colour and phenolic composition during the ageing of port. Two approaches were taken in the analysis of the data. The first involved the use of multiple imputation (MI) followed by principal components analysis (PCA). The second examined the use of maximum likelihood principal component analysis (MLPCA). The use of multiple imputation allows for missing value uncertainty to be incorporated into the analysis of the data. Initial estimates of missing values were firstly calculated using the Expectation Maximization algorithm (EM), followed by Data Augmentation (DA) in order to generate five imputed data matrices. Each complete data matrix was subsequently analysed by PCA, then averaging their principal component (PC) scores and loadings to give an estimation of errors. The first three PCs accounted for 93.3% of the explained variance. Changes to colour and monomeric anthocyanin composition were explained on PC1 (79.63% explained variance), phenolic composition and hue mainly on PC2 (8.61% explained variance) and phenolic composition and the formation of polymeric pigment on PC3 (5.04% explained variance). In MLPCA estimates of measurement uncertainty is incorporated in the decomposition step, with missing values being assigned large measurement uncertainties. PC scores on the first two PCs after multiple imputation and PCA (MI+PCA) were comparable to maximum likelihood scores on the first two PCs extracted by MLPCA.     

Blockchain applied to the construction supply chain: A case study with threat model

The construction industry has long faced the challenge of introducing collaborative systems among multiple stakeholders. This challenge creates a high level of rigidity in terms of processing shared information related to different processes, robust holistic regulations, payment actualizations, and resource utilization across different nodes. The need for a digital platform to cross-connect all stakeholders is necessary. A blockchain-based platform is a prime candidate to improve the industry in general and the construction supply chain (CSC) in particular. In this paper, a literature review is presented to establish the main challenges that CSC faces in terms of its effects on productivity and efficiency. In addition, the effect of applying blockchain platforms on a case study is presented and analyzed from performance and security level. The analysis aims to emphasize that blockchain, as presented in this paper, is a viable solution to the challenges in the CSC regardless of the risks associated with the security and robustness of the flow of information and data protection. Moreover, a threat analysis of applying a blockchain model on the CSC industry is introduced. This model indicates potential attacks and possible countermeasures to prevent the attacks. Future work is needed to expand, quantify, and optimize the threat model and conduct simulations considering proposed countermeasures for the different blockchain attacks outlined in this study.     

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.     

基于两被联件振动信号概率密度和PCA的螺栓松动识别方法研究

螺栓松动是一种常见且具有潜在危害的机械故障。考虑到螺栓松动会导致被联接件结合部动力参数发生变化,提出了一种基于两被联接件振动信号的松动识别方法。所提方法首先计算两信号的概率密度,并对概率密度曲线进行网格化处理生成概率矩阵,继而对概率矩阵进行主元分析(PCA),在合并两路信号经主元分析后所得投影矩阵之后,再次进行主元分析和投影。设计了两种识别方式,方式1首先按上述过程进行已知样本训练以得到各松动状态投影点,识别时根据所得投影点与各状态投影点间的欧式距离进行判断;方式2使用螺栓紧固状态时所得样本数据和现场实测数据直接按上述过程进行计算,并根据PCA特性设计了松动判别条件。试验验证表明所提方法能够准确区分不同松动状态,且识别方式2操作简便,无需故障样本,易于实际应用。     

独立分量分析在说话人识别技术中的应用

独立分量分析方法能够将线性混合信号进行分离,得到统计独立的源信号,能用于提取组合语音的特征基函数。倒谱矢量符合ICA变换的假设条件,用ICA方法对MFCC特征进行转换得到ICA特征基,继而用于说话人识别,建立了一个基于独立分量分析的说话人识别系统。实验结果表明,在噪声环境下此系统具有更高的识别率。