Multivariate Data Analysis Tools Research Articles

Surface-Enhanced Raman Spectroscopy for Monitoring the Biochemical Changes Due to DNA Mutations Induced by CRISPR-Cas9 Genome Editing in the Aspergillus niger Fungus.

In this study, surface-enhanced Raman spectroscopy (SERS) technique, along with principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA), is used as a simple, quick, and cost-effective analysis method for identifying biochemical changes occurring due to induced mutations in the Aspergillus niger fungus strain. The goal of this study is to identify the biochemical changes in the mutated fungal cells (cell mass) as compared to the control/nonmutated cells. Furthermore, multivariate data analysis tools, including PCA and PLS-DA, are used to further confirm the differentiating SERS spectral features among fungal samples. The mutations are caused in A. niger by the clustered regularly interspaced palindromic repeat CRISPR-Cas9 genomic editing method to improve their biotechnological potential for the production of cellulase enzyme. SERS was employed to detect the changes in the cells of mutated A. niger fungal strains, including one mutant producing low levels of an enzyme and another mutant producing high levels of the enzyme as a result of mutation as compared with an unmutated fungal strain as a control sample. The distinctive features of SERS corresponding to nucleic acids and proteins appear at 546, 622, 655, 738, 802, 835, 959, 1025, 1157, 1245, 1331, 1398, and 1469 cm-1. Furthermore, PLS-DA is used to confirm the 89% accuracy, 87.7% precision, 87% sensitivity, and 88.9% specificity of this method, and the value of the area under the curve (AUROC) is 0.67. It has been shown that surface-enhanced Raman spectroscopy is an effective method for identifying and differentiating biochemical changes in genome-modified fungal samples.

Modification of poly(L-lactic acid)-based films and evaluation of physical and antibacterial properties by using multivariate data analysis

The aim of this study is to prepare new packaging materials with improved physical and antimicrobial properties that prevent the growth of microorganisms. Poly(L-lactic acid) (PLA) based packaging films were prepared by the solvent-casting method using spruce resin (SR), epoxidized soybean oil, an essential oil mixture (calendula and clove oil), and silver nanoparticles (AgNPs). The AgNPs were synthesized by the polyphenol reduction method, using spruce resin dissolved in methylene chloride. The prepared films were tested for antibacterial activity and physical properties, such as tensile strength (TS), elongation at break (EB), elastic modulus (EM), water vapor permeability (WVP), and UV-C blocking effect. The addition of SR decreased the water vapor permeation (WVP) of the films, whereas the addition of essential oils (EOs) increased this property due to their higher polarity. The morphological, thermal, and structural properties were characterized using SEM, UV–Visible spectroscopy, FTIR, and DSC. The agar disc well method showed that SR, AgNPs, and EOs imparted antibacterial activity to the PLA-based films against Staphylococcus aureus and Escherichia coli. Multivariate data analysis tools, such as principal component and hierarchical cluster analysis, were used to discriminate PLA-based films by simultaneously evaluating their physical and antibacterial properties.

Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration

BackgroundSurface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus. ObjectivesThe main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA). MethodologySerum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS. ResultsHBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy. ConclusionsSurface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals’ serum samples.

The application of structural equation model in psychological research

The application of structural equation model in psychological research

Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients

BackgroundSurface-enhanced Raman spectroscopy (SERS) is explored to design a rapid screening method for the characterization and diagnosis of typhoid fever by employing filtrate fractions of blood serum samples obtained by centrifugal filtration with 50 KDa filters. ObjectivesThe purpose of this study, to separate the filtrate portions of blood serum samples in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire the SERS spectral features of smaller proteins more effectively which are probably associated with typhoid disease. Disease caused by Salmonella typhi diagnose more effectively by using surface-enhanced Raman spectroscopy (SERS) and multivariate data analysis tools. MethodsSERS was used as a diagnostic tool for typhoid fever by comparison between healthy and diseased samples. For this purpose, all the samples were analyzed by comparing their SERS spectral features. Over the spectral range of 400-1800cm−1, multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) are applied to diagnose and differentiate different filtrate fractions of blood serum samples of patients of typhoid fever and healthy ones. ResultsBy comparing SERS spectra of healthy filtrate with that of filtrate of typhoid sample, the SERS spectral features associated with disease development are identified including PCA is found to be efficient for the qualitative differentiation of all of the samples analyzed. Moreover, PLS-DA successfully identified and classified healthy and typhoid positive blood serum samples with 97 % accuracy, 99 % specificity, 91 % sensitivity and 0.78 area under the receiver operating characteristic (AUROC) curve. ConclusionsSurface enhanced Raman spectroscopy using silver nanoparticles SERS substrate, is found to be useful technique for the quick identification and evaluation of filtrate fractions of the blood serum samples of healthy and typhoid samples for disease diagnosis.

Quantitative analysis of ore texture breakage characteristics affected by loading mechanism: Multivariate data analysis of particle texture parameters

• MVDA can find and predict patterns in breakage. • Ore textural features have more influence on breakage compared to displacement rate. • Liberation by surface and by composition are very similar for the Malmberget deposit. Understanding and optimizing the comminution process in terms of mineral liberation, fragmentation, and fracture energy are aligned with sustainable approaches and overall international goals of green solutions. This study investigates the combined effect of material properties (ore textural features) and process factors (displacement rate) on mineral liberation, fracture energy, and fragmentation. For achieving this aim, multivariate data analysis tools are used to examine the fragmentation by compression of multiple layers of iron oxide minerals in a particle bed. The results indicate that ore textural features distinctively influence particle fragmentation, mineral liberation, and fracture energy and the ore textural effects are more pronounced compared to displacement rate.

Untargeted Metabolomics Profiling of Arabidopsis WT, lbr-2-2 and bak1-4 Mutants Following Treatment with Two LPS Chemotypes.

Plants perceive pathogenic threats from the environment that have evaded preformed barriers through pattern recognition receptors (PRRs) that recognise microbe-associated molecular patterns (MAMPs). The perception of and triggered defence to lipopolysaccharides (LPSs) as a MAMP is well-studied in mammals, but little is known in plants, including the PRR(s). Understanding LPS-induced secondary metabolites and perturbed metabolic pathways in Arabidopsis will be key to generating disease-resistant plants and improving global plant crop yield. Recently, Arabidopsis LPS-binding protein (LBP) and bactericidal/permeability-increasing protein (BPI)-related proteins (LBP/BPI related-1) and (LBP/BPI related-2) were shown to perceive LPS from Pseudomonas aeruginosa and trigger defence responses. In turn, brassinosteroid insensitive 1 (BRI1)-associated receptor kinase 1 (BAK1) is a well-established co-receptor for several defence-related PRRs in plants. Due to the lack of knowledge pertaining to LPS perception in plants and given the involvement of the afore-mentioned proteins in MAMPs recognition, in this study, Arabidopsis wild type (WT) and mutant (lbr2-2 and bak1-4) plants were pressure-infiltrated with LPSs purified from Pseudomonas syringae pv. tomato DC3000 (Pst) and Xanthomonas campestris pv. campestris 8004 (Xcc). Metabolites were extracted from the leaves at four time points over a 24 h period and analysed by UHPLC-MS, generating distinct metabolite profiles. Data analysed using unsupervised and supervised multivariate data analysis (MVDA) tools generated results that reflected time- and treatment-related variations after both LPS chemotypes treatments. Forty-five significant metabolites were putatively annotated and belong to the following groups: glucosinolates, hydroxycinnamic acid derivatives, flavonoids, lignans, lipids, oxylipins, arabidopsides and phytohormones, while metabolic pathway analysis (MetPA) showed enrichment of flavone and flavanol biosynthesis, phenylpropanoid biosynthesis, alpha-linolenic acid metabolism and glucosinolate biosynthesis. Distinct metabolite accumulations depended on the LPS chemotype and the genetic background of the lbr2-2 and bak1-4 mutants. This study highlights the role of LPSs in the reprogramming Arabidopsis metabolism into a defensive state, and the possible role of LBR and BAK1 proteins in LPSs perception and thus plant defence against pathogenic bacteria.

Laser induced fluorescence of cervical tissues: an in-vitro study for the diagnosis of cervical cancer from the cervicitis

The transformation of normal cervix to cervicitis as well as to cervical cancer is accompanied with biochemical alterations at cellular level. Laser induced fluorescence (LIF) can reflect those changes either as variations in the fluorescence intensity or as shift in the fluorescence maxima of bio fluorophores present in tissues. The curve resolved fluorescence investigation of tissues under 325 nm excitation provides collagen, bound nicotinamide adenine dinucleotide (NADH) and free NADH as the discrimination factors between normal, cervicitis and cervical cancer. Even though the fluorescence emission intensity derived from collagen fiber is comparable in both normal and cervicitis, a considerable reduction was observed for the cervical cancer tissues compared to the former. Fluorescence corresponding to bound NADH is found to be reduced during the progression from normal to cervicitis and to cervical cancer, whereas the free NADH shows an opposite trend. The principal component analysis (PCA) was performed to obtain classification of spectral data from different categories on a reduced dimensional space. Furthermore, to test the usefulness of the recorded fluorescence spectra in discriminating the malignant and non-malignant (cervicitis and normal) samples, a supervised machine learning model based on support vector machine (SVM) was built using the PCA-reduced data. The proposed SVM model was able to detect the malignant samples with a sensitivity of 94.19% and specificity of 96.51%. Moreover, the Raman spectral data from the corresponding tissue sites corroborate well with the observations derived from the fluorescence measurement. The results obtained in the present pilot study strongly suggests the potential of LIF technique combined with multivariate data analysis tool for the diagnosis of cervicitis and cervical malignancy.

Canonical Correlation Analysis With Low-Rank Learning for Image Representation.

As a multivariate data analysis tool, canonical correlation analysis (CCA) has been widely used in computer vision and pattern recognition. However, CCA uses Euclidean distance as a metric, which is sensitive to noise or outliers in the data. Furthermore, CCA demands that the two training sets must have the same number of training samples, which limits the performance of CCA-based methods. To overcome these limitations of CCA, two novel canonical correlation learning methods based on low-rank learning are proposed in this paper for image representation, named robust canonical correlation analysis (robust-CCA) and low-rank representation canonical correlation analysis (LRR-CCA). By introducing two regular matrices, the training sample numbers of the two training datasets can be set as any values without any limitation in the two proposed methods. Specifically, robust-CCA uses low-rank learning to remove the noise in the data and extracts the maximization correlation features from the two learned clean data matrices. The nuclear norm and L1 -norm are used as constraints for the learned clean matrices and noise matrices, respectively. LRR-CCA introduces low-rank representation into CCA to ensure that the correlative features can be obtained in low-rank representation. To verify the performance of the proposed methods, five publicly image databases are used to conduct extensive experiments. The experimental results demonstrate the proposed methods outperform state-of-the-art CCA-based and low-rank learning methods.

Work-Family Enrichment: Influence of Job Autonomy on Job Satisfaction of Knowledge Employees.

In order to explore the specific path of the influence of job autonomy on the satisfaction of knowledge employees, the current study deduced and established a Chain Mediation Model, which was based on the Resource-Gain-Development Model and the Conservation of Resources Theory. Primary data were gathered through questionnaire surveys at several cities in China by using a professional platform named “Wenjuanxing.” The target populations were employees with a bachelor’s degree or above, who were engaged in higher knowledge content and have mastered certain professional knowledge and skills, including technical R & D personnel, management personnel, professionals (such as accountants, lawyers, and medics) and other personnel generally recognized by the academic community. In order to improve the reliability of the sample and reduce the error caused by regional differences, the questionnaires were disseminated to involve as many cities in China as possible, such as Tianjin, Beijing, Chengdu, Wuhan, and Guangzhou. SPSS24.0 and Aoms24.0 were used as multivariate data analysis tools for statistical analysis. The results showed that job autonomy can significantly improve the satisfaction of knowledge employees; however, it cannot affect the satisfaction of knowledge employees through self-efficacy. The findings of the study also revealed that job autonomy has a positive impact on both resource source domain satisfaction and resource acceptance domain satisfaction through work-family enrichment, especially the positive emotions in the resource source domain. Job autonomy improves the self-efficacy of knowledge employees, which, in turn, improves their overall satisfaction through the work-family enrichment path.

Photoacoustic Laser System for Food Fraud Detection.

Economically motivated adulterations of food, in general, and spices, in particular, are an emerging threat to world health. Reliable techniques for the rapid screening of counterfeited ingredients in the supply chain need further development. Building on the experience gained with CO2 lasers, the Diagnostic and Metrology Laboratory of ENEA realized a compact and user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser. The sensor has been challenged with saffron adulteration. Multivariate data analysis tools indicated that the photoacoustic laser system was able to detect adulterants at mass ratios of 2% in less than two minutes.

Rapid and sensitive discrimination among carbapenem resistant and susceptible E. coli strains using Surface Enhanced Raman Spectroscopy combined with chemometric tools

BackgroundRaman spectroscopy is a powerful technique for the robust, reliable and rapid detection and discrimination of bacteria. ObjectivesTo develop a rapid and sensitive technique based on surface-enhanced Raman spectroscopy (SERS) with multivariate data analysis tools for discrimination among carbapenem resistant and susceptible E. coli strains. MethodsSERS was employed to differentiate different strains of carbapenem resistant and susceptible E. coli by using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, four strains of carbapenem resistant and three strains of carbapenem susceptible E. coli were analyzed by comparing their SERS spectral signatures. Furthermore, multivariate data analysis techniques including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were performed over the spectral range of 400–1800 cm−1 (fingerprint region) for the identification and differentiation of different E. coli strains. ResultsThe SERS spectral features associated with resistant development against carbapenem antibiotics were separated by comparing each spectrum of susceptible strains with each resistant strain. PCA and HCA were found effective for the qualitative differentiation of all the strains analysed. PLS-DA successfully discriminated the carbapenem resistant and susceptible E. coli pellets on the strain level with 99.8 % sensitivity, 100 % specificity, 100 % accuracy and 86 % area under receiver operating characteristic (AUROC) curve. ConclusionSERS can be employed for the rapid discrimination among carbapenem resistant and susceptible strains of E. coil.

Supercritical Carbon Dioxide Extraction of Allium ursinum: Impact of Temperature and Pressure on the Extracts Chemical Profile.

The aim of this study was to extract Allium ursinum L. for the first time by supercritical carbon dioxide (SC-CO2 ) as green sustainable method. The impact of temperature in the range from 40 to 60 °C and pressure between 150 and 400 bar on the quality of the obtained extracts and efficiency of the extraction was investigated. The highest extraction yield (3.43 %) was achieved by applying the extraction conditions of 400 bar and 60 °C. The analysis of the extracts was performed by gas chromatography and mass spectrometry (GC/MS). The most dominant sulfur-containing constituent of the extracts was allyl methyl trisulfide with the highest abundance at 350 bar and 50 °C. In addition, the presence of other pharmacologically potent sulfur compounds was recorded including S-methyl methanethiosulfinate, diallyl trisulfide, S-methyl methylthiosulfonate, and dimethyl trisulfide. Multivariate data analysis tool was utilized to investigate distributions of the identified compounds among the extracts obtained under various extraction conditions and yields. It was determined that the SC-CO2 extraction can by efficiently used for A. ursinum.

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry.

Multivariate data analysis tools have become an integral part of modern analytical chemistry, and principal component analysis (PCA) is perhaps foremost among these. PCA is central in approaching many problems in data exploration, classification, calibration, modelling, and curve resolution. However, PCA is only one form of a broader group of factor analysis (FA) methods that are rarely employed by chemists. The dominance of PCA in chemistry is primarily a consequence of history and convenience, but this has obscured the potential advantages of other FA tools that are widely used in other fields. The purpose of this article, which is intended for those who are already familiar with the mathematical foundations and applications of PCA, is to develop a framework to relate PCA to other commonly used FA methods from the perspective of chemical applications. Specifically, PCA is compared to maximum likelihood factor analysis (MLFA), principal axis factorization (PAF) and maximum likelihood PCA (MLPCA). Similarities and differences are highlighted with regard to the assumptions and constraints of the models, algorithms employed, and calculation of scores and loadings. Practical aspects such as data dimensionality, preprocessing, rank estimation, improper solutions (Heywood cases), and software implementation are considered. The performance of the four methods is compared using both simulated and experimental data sets. While PCA provides the most reliable estimates when measurement error variance is uniform (homoscedastic noise) and MLPCA works best when the error covariance matrix is explicitly known, MLFA and PAF have the distinct advantage of providing information about measurement uncertainty and adapting to situations of unknown heteroscedastic errors, eliminating the need for scaling. Moreover, MLFA in particular is shown to be tolerant to deviations from model linearity. These results make a strong case for increased application of other FA methods in chemistry.

Changes in Quality Characteristics of Strawberry Juice After Equivalent High Pressure, Ultrasound, and Pulsed Electric Fields Processes

Understanding the efficacy of viable emerging technologies in preserving overall quality attributes and antioxidant characteristics of fruit juices is of great interest. This study aimed to evaluate the effect of high pressure (HP), ultrasound (US), and pulsed electric fields (PEF) processes on natural microbiota inactivation, as well as changes in physicochemical attributes and phytochemical content of strawberry juice (SJ). HP at 300 MPa (1 min), US at 55 °C (3 min) and 517.1 mW/mL acoustic energy density, and PEF at 35 kV/cm (27 μs) using monopolar square pulses with 2 μs pulse width were applied, and then compared with a conventional thermal pasteurization treatment (72 °C, 15 s). The nonthermal processes were equivalent in terms of Escherichia coli (E. coli) inactivation since the selected processing conditions led to almost identical inactivation level (at least 5-log) of inoculated E. coli. The current study analyzes why these equivalent processes had different effect on SJ quality. All treatments significantly reduced the initial natural microbiota (i.e., total mesophilic aerobic bacteria and yeast-molds) below 2 log CFU/mL. No significant changes were observed on the total soluble solid content (7.83–8.00 °Brix), titratable acidity (0.79–0.84 g/100 mL), and pH (3.45–3.50; except in sonication) between SJ processed samples and the untreated ones (p > 0.05). HPP and PEF significantly promoted higher retention of total phenolic content (TPC) and radical scavenging activity (RSA) than thermal pasteurization, and significantly enhanced total anthocyanin content (TAC) compared with unprocessed SJ. HPP and PEF increased the TPC (4–5%), RSA (18–19%), and TAC (15–17%) in comparison with unprocessed SJ. Multivariate data analysis tools, i.e., principal component analysis (PCA) and hierarchical cluster analysis (HCA), were successfully applied for discrimination and classification of SJ samples based on the similarities or differences among physicochemical and phytochemical characteristics. PCA and HCA indicated that HPP- and PEF-treated samples had similar enhanced properties in terms of phytochemical content and were superior to sonicated, thermally pasteurized, and unprocessed samples. The multivariate data analysis methods were very useful to compare and classify SJ quality characteristics as a function of the processing technology. This study demonstrated that the application of the equivalent processing approach may reveal new opportunities to produce equivalent or even enhanced quality fruit juices.

On the use of PCA for Diagnostics via Novelty Detection: interpretation, practical application notes and recommendation for use

The Principal Component Analysis (PCA) is the simplest eigenvector-based multivariate data analysis tool and dates back to 1901 when Karl Pearson proposed it as a way for finding the best fitting d-1 hyperplane of a system of points in a d-dimensional (Euclidean) space. Over the time, the PCA evolved in different fields with several different names and with different scopes, but, in its essence, it is always an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Generalizing Pearson’s purpose, the knowledge derived by such an analysis is mostly used to find a subspace which effectively and efficiently summarizes the original system of points by losing a minimum amount of information. In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem which is in many cases tackled via Novelty Detection: an observation is classified as novel if it differs significantly from other observations. Novelty can, in principle, be assessed directly in the original space, but the effectiveness of the estimated novelty can be improved by taking advantage of the PCA. In this work, the traditional PCA will be compared to a robust modification that is commonly used in the field of diagnostics to face the issue of confounding influences which could affect the novelty-damage correspondence. Comparisons will be made to shed light on the main misleading aspects of PCA, and finally, define a unique, theoretically justified procedure for Diagnostics via Novelty Detection.

Workplace Interactional Demands and Work-Family Enrichment: An Investigation From the Service Sector.

Emotional labor has, so far, been found to have negative consequences for service sector employees’ personal well-being. This study strives to look at the positive aspect of emotional labor in the interactive jobs. This research focuses on employees’ psychological needs fulfillment through workplace interactions. The current research is an effort to highlight the importance of workplace interactions through fulfilling the employees’ need for relatedness and personal accomplishment, which triggers positive resources that can be transferred from work domain to home domain and might induce work-family enrichment. Primary data were gathered through self-administered questionnaires. The target population was nurses working in public and private hospitals located in Faisalabad, Lahore, and Multan cities of the province of Punjab, Pakistan. This sector needs the attention of the policymakers if we want to enhance the performance of health sector employees and to improve their work-home enrichment as a healthy worker is a productive worker. Demographic characteristics of respondents have been identified through SPSS while Structural Equation Modeling (SEM) was used as multivariate data analysis tool for statistical analysis. Assessment of measurement and structural model was found satisfactory. The results showed that workplace interactional demands had a significant positive effect on work-family enrichment. Moreover, relatedness and personal accomplishment partially mediated the relationship between workplace interactional demands and work-family enrichment. The findings of the study revealed that workplace interactions are critical in fulfilling the basic psychological needs of individuals and as a result, they find themselves energized and achieve their full potential through the fulfillment of their need for relatedness and accomplishment. This energy is a valuable resource that can enrich their family life.

Multivariate Monitoring Workflow for Formulation, Fill and Finish Processes.

Process monitoring is a critical task in ensuring the consistent quality of the final drug product in biopharmaceutical formulation, fill, and finish (FFF) processes. Data generated during FFF monitoring includes multiple time series and high-dimensional data, which is typically investigated in a limited way and rarely examined with multivariate data analysis (MVDA) tools to optimally distinguish between normal and abnormal observations. Data alignment, data cleaning and correct feature extraction of time series of various FFF sources are resource-intensive tasks, but nonetheless they are crucial for further data analysis. Furthermore, most commercial statistical software programs offer only nonrobust MVDA, rendering the identification of multivariate outliers error-prone. To solve this issue, we aimed to develop a novel, automated, multivariate process monitoring workflow for FFF processes, which is able to robustly identify root causes in process-relevant FFF features. We demonstrate the successful implementation of algorithms capable of data alignment and cleaning of time-series data from various FFF data sources, followed by the interconnection of the time-series data with process-relevant phase settings, thus enabling the seamless extraction of process-relevant features. This workflow allows the introduction of efficient, high-dimensional monitoring in FFF for a daily work-routine as well as for continued process verification (CPV).

Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction.

Multivariate latent variable methods have become a popular and versatile toolset to analyze bioprocess data in industry and academia. This work spans such applications from the evaluation of the role of the standard process variables and metabolites to the metabolomics level, that is, to the extensive number metabolic compounds detectable in the extracellular and intracellular domains. Given the substantial effort currently required for the measurement of the latter groups, a tailored methodology is presented that is capable of providing valuable process insights as well as predicting the glycosylation profile based on only four experiments measured over 12 cell culture days. An important result of the work is the possibility to accurately predict many of the glycan variables based on the information of three experiments. An additional finding is that such predictive models can be generated from the more accessible process and extracellular information only, that is, without including the more experimentally cumbersome intracellular data. With regards to the incorporation of omics data in the standard process analytics framework in the future, this works provides a comprehensive data analysis pathway which can efficiently support numerous bioprocessing tasks.

A Front Line on Klebsiella pneumoniae Capsular Polysaccharide Knowledge: Fourier Transform Infrared Spectroscopy as an Accurate and Fast Typing Tool.

Genomics-based population analysis of multidrug-resistant (MDR) Klebsiella pneumoniae motivated a renewed interest on the capsule as an evolutionary and virulence marker of clinically relevant strains. Whole-genome sequencing (WGS)-based approaches have provided great insights into the genetic variability of the capsular locus, but genotypic-biochemical capsular (K)-type correlations are lacking, hindering the establishment of a reliable framework for K-type characterization and typing. To fill this gap, we combined molecular, comparative genomics, and multivariate data analysis tools with biochemical data on the capsular locus to support the usefulness of Fourier transform infrared (FT-IR) spectroscopy as a reliable K typing tool. To validate our approach, we used a representative collection of well-defined MDR K. pneumoniae lineages involved in local or nationwide epidemics in multiple countries. With this, we demonstrate a high accuracy and resolution of our FT-IR-based spectroscopy approach for K-type discrimination that is even higher than that provided by WGS. Moreover, the specific associations established between certain K types and specific K. pneumoniae lineages with high clinical relevance, together with the accuracy, simplicity, short time to result, and inexpensive features of the method, support the value of the developed FT-IR-based approach for an easy, fast, and cost-effective strain typing. This fulfills a still unmet need for tools to support real-time monitoring and control of K. pneumoniae infections. In addition, the genotypic-biochemical correlations established provide insights on sugar composition/structure of newly defined K. pneumoniae capsular types.IMPORTANCE Klebsiella pneumoniae is nowadays recognized as one of the most defiant human pathogens, whose infections are increasingly more challenging to treat and control. Whole-genome sequencing (WGS) has been key for clarifying the population structure of K. pneumoniae, and it is still instrumental to provide insights into potential pathogenicity and evolutionary markers, such as the capsular locus. However, this information and WGS are still far from being accessible and translated into routine clinical microbiology laboratories as quick and cost-efficient strain diagnostic tools. Here, we propose a biochemical fingerprinting approach based on Fourier transform infrared spectroscopy (FT-IR) and multivariate data analysis tools for K. pneumoniae capsular typing that, because of its high resolution, speed, and low cost, can be an asset to provide enough information to support real-time epidemiology and infection control decisions. Besides, it provides a simple framework for phenotypic/biochemical validation of K. pneumoniae capsular diversity.