Bilinear modelling of batch processes. Part II: a comparison of PLS soft‐sensors

The on‐line monitoring of batch processes based on principal component analysis (PCA) has been widely studied. Nonetheless, researchers have not paid so much attention to the on‐line application of partial least squares (PLS). In this paper, the influence of several issues in the predictive power of a PLS model for the on‐line estimation of key variables in a batch process is studied. Some of the conclusions can help to better understand the capabilities of the proposals presented for on‐line PCA‐based monitoring. Issues like the convenience of batch‐wise or variable‐wise unfolding, the method for the imputation of future measurements and the use of several sub‐models are addressed. This is the first time that the adaptive hierarchical (or multi‐block) approach is extended to the PLS modelling. Also, the formulation of the so‐called trimmed scores regression (TSR), a powerful imputation method defined for PCA, is extended for its application with PLS modelling. Data from two processes, one simulated and one real, are used to illustrate the results. Copyright © 2008 John Wiley & Sons, Ltd.     

Double‐step block division plant‐wide fault detection and diagnosis based on variable distributions and relevant features

Large‐scale process data in plant‐wide process monitoring are characterized by two features: complex distributions and complex relevance. This study proposes a double‐step block division plant‐wide process monitoring method based on variable distributions and relevant features to overcome this limitation. First, the data distribution is considered, and the normality test method called the D‐test is applied to classify the variables with the same distribution (i.e., Gaussian distribution or non‐Gaussian distribution) in a block. Thus, the second block division is implemented on both blocks obtained in the previous step. The mutual information shared between two variables is used to generate relevant matrixes of the Gaussian and non‐Gaussian blocks. The K‐means method clusters the vectors of the relevant matrix. Principal component analysis is conducted to monitor each Gaussian subblock, whereas independent component analysis is conducted to monitor each non‐Gaussian subblock. A composite statistic is eventually derived through Bayesian inference. The proposed method is applied to a numerical system and the Tennessee Eastman process data set. The monitoring performance shows the superiority of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.     

A novel multi‐mode data processing method and its application in industrial process monitoring

Multi‐mode process monitoring is a key issue often raised in industrial process control. Most multivariate statistical process monitoring strategies, such as principal component analysis (PCA) and partial least squares, make an essential assumption that the collected data follow a unimodal or Gaussian distribution. However, owing to the complexity and the multi‐mode feature of industrial processes, the collected data usually follow different distributions. This paper proposes a novel multi‐mode data processing method called weighted k neighbourhood standardisation (WKNS) to address the multi‐mode data problem. This method can transform multi‐mode data into an approximately unimodal or Gaussian distribution. The results of theoretical analysis and discussion suggest that the WKNS strategy is more suitable for multi‐mode data normalisation than the z‐score method is. Furthermore, a new fault detection approach called WKNS‐PCA is developed and applied to detect process outliers. This method does not require process knowledge and multi‐mode modelling; only a single model is required for multi‐mode process monitoring. The proposed method is tested on a numerical example and the Tennessee Eastman process. Finally, the results demonstrate that the proposed data preprocessing and process monitoring methods are particularly suitable and effective in multi‐mode data normalisation and industrial process fault detection. Copyright © 2014 John Wiley & Sons, Ltd.     

Using Unfold‐PCA for batch‐to‐batch start‐up process understanding and steady‐state identification in a sequencing batch reactor

In chemical and biochemical processes, steady‐state models are widely used for process assessment, control and optimisation. In these models, parameter adjustment requires data collected under nearly steady‐state conditions. Several approaches have been developed for steady‐state identification (SSID) in continuous processes, but no attempt has been made to adapt them to the singularities of batch processes. The main aim of this paper is to propose an automated method based on batch‐wise unfolding of the three‐way batch process data followed by a principal component analysis (Unfold‐PCA) in combination with the methodology of Brown and Rhinehart 2 for SSID. A second goal of this paper is to illustrate how by using Unfold‐PCA, process understanding can be gained from the batch‐to‐batch start‐ups and transitions data analysis. The potential of the proposed methodology is illustrated using historical data from a laboratory‐scale sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal (EBPR). The results demonstrate that the proposed approach can be efficiently used to detect when the batches reach the steady‐state condition, to interpret the overall batch‐to‐batch process evolution and also to isolate the causes of changes between batches using contribution plots. Copyright © 2007 John Wiley & Sons, Ltd.     

Multi‐Channel Piezoelectric Quartz Crystal Sensor with Principal Component Analysis and Back‐Propagation Neural Network for Organic Pollutants from Petrochemical Plants

A multi‐channel piezoelectric quartz crystal gas sensor comprising arrays coated with various organic materials and a home‐made computer interface for data processing were prepared and employed to detect six kinds of common organic pollutants from petrochemical plants including benzene, styrene, chloroform, octane, hexene and hexyne. The principal component analysis (PCA) method was employed to select six kinds of appropriate coating materials for these organic pollutants from 22 adsorbents onto piezoelectric crystals. After performing a PCA assay, six representative coating materials, namely Polyisobutylene, Poly(dimethylsiloxane) (SE30), 4‐tert‐Butylcalix[6]arene, Cholesteryl chloroformate, C60‐Polyphenyl acetylene (C60‐PPA) and Ag(I)/cryptand‐2,2/Ethylene diamine/NH₃/Polyvinyl chloride were selected. Moreover, effects of coating load of adsorbents and concentration of pollutants were also investigated. Three kinds of recognition techniques including 2D PCA score map, radar plot and back‐propagation neural network (BPN) were employed for qualitative analysis of these organic pollutants, and a quantitative analysis method could be established by creating calibration curves for each organic pollutant. This homemade multi‐channel piezoelectric quartz crystal gas sensor showed a good detection limit of 0.068‐1.127 mg/L for these organic pollutants. The multi‐channel piezoelectric gas sensor exhibited good reproducibility with a relative standard deviation (RSD) of 1.1‐9.6%. Furthermore, this multi‐channel piezoelectric crystal detection system with BPN recognition technique was also utilized to successfully distinguish and identify each component of the mixture of organic gas samples. Multivariate linear regression (MLR) analysis was employed to quantitatively compute the concentration of species in the organic mixtures.     

Construction of stable multivariate calibration models using unsupervised segmented principal component regression

In multivariate spectral calibration by principal component regression (PCR), the principal components (PCs) are calculated from the response data measured at all employed instrument channels; however some channels are redundant and their responses do not possess useful information. Thus, the extracted PCs possess mixed information from both useful and redundant channels. In this work, we propose a segmentation approach based on unsupervised pattern recognition to identify the most informative spectral region and then to construct a stable multivariate calibration model by PCR. In this method, the instrument channels are clustered into different segments via Kohonen self‐organization map. The spectral data of each segment are then subjected to PCA and the derived PCs are used as input variables for an inverse least square (ILS) regression model employing stepwise selection of the informative PCs. The proposed method was evaluated by the analysis of four simulated and six experimental data sets. It was found that our proposed method can model the above data sets with prediction errors lower than conventional partial least squares (PLS) and PCR methods. In addition, the prediction ability of our method was better than the previously reported models for these data sets. Copyright © 2011 John Wiley & Sons, Ltd.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

Error removal by orthogonal subtraction (EROS): a customised pre‐treatment for spectroscopic data

In some applications of diffuse reflectance spectroscopy there may be substantial variability between the spectra from replicate measurements of what is nominally the same sample. A method called error reduction by orthogonal subtraction (EROS) is proposed to ameliorate the effects of this. The first step is to use principal component analysis (PCA) to identify the structure in the variability of replicate measurements. This is followed by subtraction of the modelled effects from the original spectral data matrix X by projection onto the subspace orthogonal to factors derived from the PCA. An application to the clinical diagnosis of colon lesions is presented, in which pre‐treatment of spectra using the proposed method is successful in reducing the complexity and increasing both the accuracy and interpretability of the subsequent classification model. Copyright © 2008 John Wiley & Sons, Ltd.     

Two‐way multi‐shape memory properties of peroxide crosslinked ethylene vinyl‐acetate copolymer (EVA)/polycaprolactone (PCL) blends

Rare studies have investigated on the 2‐way shape memory crosslinked blends with multiple shape memory behavior up to date. To consider the merit of commercial cost‐competitive crystalline polymers, ethylene vinyl‐acetate copolymer (EVA) / polycaprolactone (PCL) blends (60/40 and 30/70) were peroxide‐cured to form the 2‐way multi‐shape memory crosslinked blends using a melt‐blending method. Both resins were selected to have a similar controlled crosslinking degree, which allowed us to distinctly evaluate their actuation contributions from the cooling‐induced elongation (crystallization) and from the entropy‐driven elongation during cooling process, respectively. In the 2‐way process for the 60/40 system, 2 respective peaks contributed from the cooling‐induced crystallization of EVA and PCL in the cooling curves based on the strain derivate rates at various temperatures were observed. After the cooling process under the loading stress of 150 kPa, the 2‐step heating‐induced contraction process with increasing temperature started at 54.1°C above the melting temperature of PCL at 52.3°C and EVA at 78.3°C, demonstrating 2‐way multi‐shape memory behavior. The multi‐step behavior was more prominent at higher PCL composition and higher load for the 30/70 system. It was found that the entropy‐driven contribution to the overall actuation magnitude increased with increasing nominal loads due to the increased orientation of molecular networks in the blends. The current approach offers numerous possibilities in preparing 2‐way multi‐shape memory crosslinked blends.     

The research on multi‐responsive azobenzene block copolymer and its self‐assembly behavior

The azobenzene‐based amphiphilic copolymers have drawn significant attention as a kind of multi‐responsive smart materials. The demand on deeper investigation of how the azobenzene group influences the micelles formation and light‐responsive behavior on molecular level is very urgent. In this article, multi‐responsive block copolymers, poly (acrylic acid)‐block‐poly[4'‐[[(2‐Methacryloyloxy)ethyl]ethylainino]azobenzene‐co‐poly (ethylene glycol) methyl ether methacrylate] (PAA‐b‐P (AzoMA‐co‐PEGMA)), with pH‐, light‐ and reduction‐responsiveness were synthesized by the monomers of AzoMA, PEGMA and acrylic acid via reversible addition‐fragmentation chain transfer polymerization (RAFT). The amphiphilic block copolymer presented aggregation‐induced emission effect, and it was pH, light, and reduction responsive. The results showed that the micelle size decreased with the decreasing of pH within a certain range. However, the particle size of micelles increased significantly when the pH was 4. Once adding reduction agent, the micelles were disassembly. Fluorescent molecule of Nile red was selected as a hydrophobic guest molecule to study the properties of encapsulating and releasing abilities of block copolymer micelles for guest molecules. The results showed that the loading capacity of three kinds of copolymer micelles was closely related to the aggregates formed by the hydrophobic block, mainly azobenzene block. Besides, the block copolymer micelles could release a certain amount of Nile red under the irradiation of UV light, the reduction with Na₂S₂O₄ as reductant, and the exposure to alkaline environment. The mechanism of how the different status of azobenzene group influenced the self‐assembly and multi‐responsive behavior was explored on molecular level.     

Validation of the extended Tanaka column characterization protocol by multivariate analysis of chromatographic retention of low‐molecular‐weight analytes on reversed phase columns using methanol and acetonitrile as organic modifiers

The validity of the extended Tanaka column characterization procedure against the retention behavior of 101 analytes of widely differing properties chromatographed on five differing stationary phase chemistries has been established using a chemometric technique called principal component analysis (PCA). It was concluded that the simple and conveniently determined column characterization parameters covered the same space in the PCA loading plot as the retention times for the 101 differing analytes. This confirms that the ten column characterization parameters of the extended Tanaka protocol encode the same information as the retention times of the 101 analytes. Significant selectivity differences were observed between stationary phases and the mobile‐phase modifiers – MeOH and MeCN. PCA contribution plots served as a convenient way to highlight specific selectivity differences between stationary phases. logD values exhibited a poor correlation with retention indicating that retention in RP‐LC is not solely dictated by the analyte's hydrophobicity. The use of MeOH was found to generate greater selectivity differences with the five stationary phases than when MeCN is used.     

Development of spin‐dependent relativistic open‐shell Hartree–Fock theory with time‐reversal symmetry (I): The unrestricted approach

An open‐shell Hartree–Fock (HF) theory for spin‐dependent, two‐component relativistic calculations, termed the Kramers‐unrestricted HF (KUHF) method, is developed. The present KUHF method, which is formulated as a relativistic counterpart of nonrelativistic UHF, is based on quaternion algebra and partly uses time‐reversal symmetry. The fundamental characteristics of KUHF are discussed in this study. From numerical assessments, it was revealed that KUHF gives a corresponding solution to nonrelativistic UHF; furthermore, KUHF properly describes spin‐orbit interactions. In addition, KUHF can improve the self‐consistent field convergence behavior in spin‐dependent calculations, for example, for f‐block elements.     

Multi‐Channel Surface Acoustic Wave Sensors Based on Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) for Organic Vapors

A multi‐channel surface acoustic wave (SAW) detection system which is employed to detect various organic molecules in a static system was prepared using 315 MHz one‐port quartz resonators and a home‐made computer interface for signal acquisition and data process. The oscillating frequency of the quartz crystal decreases on adsorption of organic molecules on the coating materials. The principal component analysis (PCA) method with SAS software was applied to select the appropriate coating materials onto the SAW crystals for organic vapors, e.g. hexane, 1‐hexene, 1‐hexyne, 1‐propanol, propionaldehyde, propionic acid, and 1‐propylamine. A dataset for a multi‐channel sensor with 19 SAW crystals for 7 analyses was collected after comparing the correlation between the 19 coating materials and the first six principal component (PC) factor. Furthermore, linear discriminate analysis (LDA) with SPSS software and a profile discrimination map were also applied and discussed for the discrimination of these organic vapors. These organic molecules could be clearly distinguished by the six‐channel SAW static sensor. The effect of concentration for various organic vapors was investigated and discussed.     

The e‐tongue‐based classification and authentication of mineral water samples using cross‐correlation‐based PCA and Sammon's nonlinear mapping

The present paper elaborates on the design of classifiers based on cross‐correlation‐based principal component analysis (PCA) and Sammon's nonlinear mapping (NLM) using current signals obtained from electronic tongue (e‐tongue) with commercial mineral water samples available in the Indian market. The pulse‐voltammetric method is used to capture the electroanalytical/electrochemical characteristics of the sampled mineral waters by considering a real model for the liquid–electrode interface in a given e‐tongue apparatus. Then the cross‐correlation coefficients between the output and input signals are determined. Both PCA and Sammon's NLM create a subspace from high‐dimensional mineral water data by considering the principal eigenvectors and minimising the stress function, respectively. The proposed cross‐correlation‐based PCA and Sammon's classifiers establish the highest separation distance among the investigated water brands and carries out the authentication of more than one unknown sample of the same brand with a certain degree of variability with respect to their sources. Copyright © 2013 John Wiley & Sons, Ltd.     

Thermoplastic elastomers based on poly(l‐Lysine)‐Poly(ε‐Caprolactone) multi‐block copolymers

Novel thermoplastic elastomers based on multi‐block copolymers of poly(l ‐lysine) (PLL), poly(N‐ε‐carbobenzyloxyl‐l ‐lysine) (PZLL), poly(ε‐caprolactone) (PCL), and poly(ethylene glycol) (PEG) were synthesized by combination of ring‐opening polymerization (ROP) and chain extension via l ‐lysine diisocyanate (LDI). SEC and ¹H NMR were used to characterize the multi‐block copolymers, with number‐average molecular weights between 38,900 and 73,400 g/mol. Multi‐block copolymers were proved to be good thermoplastic elastomers with Young's modulus between 5 and 60 MPa and tensile strain up to 1300%. The PLL‐containing multi‐block copolymers were electrospun into non‐woven mats that exhibited high surface hydrophilicity and wettability. The polypeptide–polyester materials were biocompatible, bio‐based and environment‐friendly for promising wide applications. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2016 , 54, 3012–3018     

A nonlinear principal component analysis to study archeometric data

Statistical techniques, when applied to data obtained by chemical investigations on ancient artworks, are usually expected to recognize groups of objects to classify the archeological finds, to attribute the provenance of items compared with earlier investigated ones, or to determine whether an archaelogical attribution is possible or not. The statistical technique most frequently used in archeometry is the principal component analysis (PCA), because of its simplicity in theory and implementation. However, the application of PCA to archeometric data showed severe limitations because of its linear feature. Indeed, PCA is inadequate to classify data whose behavior describe a curve or a curved subspace of the original data space. As a consequence of it, an amount of information is lost because the multi‐dimensional data space is compressed into a lower‐dimensional subspace including principal components. The aim of this work is then to test a novel statistical technique for archeometry. We propose a nonlinear PCA method to extract maximum chemical information by plotting data on the smallest number of principal components and to answer archeological questions. The higher accuracy and effectiveness of nonlinear PCA approach with respect to standard PCA for the analysis of archeometric data are shown through the study of Apulian red figured pottery (fifth–fourth century BC) coming from some of the most relevant archeological sites of ancient Apulia (Monte Sannace (Gioia del Colle), Egnatia (Fasano), Canosa, Altamura, Conversano, and Arpi(Foggia)). Copyright © 2016 John Wiley & Sons, Ltd.     

Geographical classification of Epimedium based on HPLC fingerprint analysis combined with multi‐ingredients quantitative analysis

A reliable and comprehensive method for identifying the origin and assessing the quality of Epimedium has been developed. The method is based on analysis of HPLC fingerprints, combined with similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and multi‐ingredient quantitative analysis. Nineteen batches of Epimedium , collected from different areas in the western regions of China, were used to establish the fingerprints and 18 peaks were selected for the analysis. Similarity analysis, HCA and PCA all classified the 19 areas into three groups. Simultaneous quantification of the five major bioactive ingredients in the Epimedium samples was also carried out to confirm the consistency of the quality tests. These methods were successfully used to identify the geographical origin of the Epimedium samples and to evaluate their quality.     

Hierarchical Self‐Assembly of Poly‐Pseudorotaxanes into Artificial Microtubules

Hierarchical self‐assembly of building blocks over multiple length scales is ubiquitous in living organisms. Microtubules are one of the principal cellular components formed by hierarchical self‐assembly of nanometer‐sized tubulin heterodimers into protofilaments, which then associate to form micron‐length‐scale, multi‐stranded tubes. This peculiar biological process is now mimicked with a fully synthetic molecule, which forms a 1:1 host‐guest complex with cucurbit[7]uril as a globular building block, and then polymerizes into linear poly‐pseudorotaxanes that associate laterally with each other in a self‐shape‐complementary manner to form a tubular structure with a length over tens of micrometers. Molecular dynamic simulations suggest that the tubular assembly consists of eight poly‐pseudorotaxanes that wind together to form a 4.5 nm wide multi‐stranded tubule.     

Classifying formulations of crosslinked polyethylene pipe by applying machine‐learning concepts to infrared spectra

Crosslinked polyethylene (PEX‐a) pipes are emerging as promising replacements for traditional metal or concrete pipes used for water, gas, and sewage transport. Understanding the relationship between pipe formulation and performance is critical to their proper design and implementation. We have developed a methodology using principal component analysis (PCA) and the machine learning techniques of k‐means clustering and support vector machines (SVM) to compare and classify different PEX‐a pipe formulations based on characteristic infrared (IR) spectroscopy absorbance peaks. The application of PCA revealed that a large percentage (89%) of the total variance could be explained by the first three principal components (PC1‐PC3), with distinct clustering of the data for each formulation. By examining the contribution of the individual IR bands to the PCs, we determined that PC1 could be attributed to different peroxide crosslinkers, whereas PC2 and PC3 could be attributed to differences in the additives. Using the PCA results as input to k‐means clustering and SVM resulted in very high accuracy of classifying the different pipe formulations. Our approach highlights the advantages of using PCA and machine learning techniques to characterize different formulations of PEX‐a pipes, which is important to achieve a detailed understanding of the pipe formulation and manufacturing process. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 1255–1262     

Multi‐constituent determination and fingerprint analysis of Scutellaria indica L. using ultra high performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry

An ultra‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry method integrating multi‐constituent determination and fingerprint analysis has been established for quality assessment and control of Scutellaria indica L. The optimized method possesses the advantages of speediness, efficiency, and allows multi‐constituents determination and fingerprint analysis in one chromatographic run within 11 min. 36 compounds were detected, and 23 of them were unequivocally identified or tentatively assigned. The established fingerprint method was applied to the analysis of ten S. indica samples from different geographic locations. The quality assessment was achieved by using principal component analysis. The proposed method is useful and reliable for the characterization of multi‐constituents in a complex chemical system and the overall quality assessment of S. indica.