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Multivariate Data Analysis (multivariate + data_analysis)

Distribution by Scientific Domains

Chemistry	50%
Life Sciences	29%
Polymers and Materials Science	8%
Medical Sciences	8%

Selected Abstracts

Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2008
Venkata Putcha
No abstract is available for this article. [source]

Application of near-infrared (NIR) spectroscopy for screening of raw materials used in the cell culture medium for the production of a recombinant therapeutic protein

BIOTECHNOLOGY PROGRESS, Issue 2 2010
Alime Ozlem Kirdar
Abstract Control of raw materials based on an understanding of their impact on product attributes has been identified as a key aspect of developing a control strategy in the Quality by Design (QbD) paradigm. This article presents a case study involving use of a combined approach of Near-infrared (NIR) spectroscopy and Multivariate Data Analysis (MVDA) for screening of lots of basal medium powders based on their impact on process performance and product attributes. These lots had identical composition as per the supplier and were manufactured at different scales using an identical process. The NIR/MVDA analysis, combined with further investigation at the supplier site, concluded that grouping of medium components during the milling and blending process varied with the scale of production and media type. As a result, uniformity of blending, impurity levels, chemical compatibility, and/or heat sensitivity during the milling process for batches of large-scale media powder were deemed to be the source of variation as detected by NIR spectra. This variability in the raw materials was enough to cause unacceptably large variability in the performance of the cell culture step and impact the attributes of the resulting product. A combined NIR/MVDA approach made it possible to finger print the raw materials and distinguish between good and poor performing media lots. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010 [source]

Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing Application

BIOTECHNOLOGY PROGRESS, Issue 3 2008
Alime Ozlem Kirdar
Multivariate Data Analysis (MVDA) can be used for supporting key activities required for successful bioprocessing. These activities include process characterization, process scale-up, process monitoring, fault diagnosis and root cause analysis. This paper examines an application of MVDA towards root cause analysis for identifying scale-up differences and parameter interactions that adversely impact cell culture process performance. Multivariate data analysis and modeling were performed using data from small-scale (2 L), pilot-scale (2,000 L) and commercial-scale (15,000 L) batches. The input parameters examined included bioreactor pCO2, glucose, lactate, ammonium, raw materials and seed inocula. The output parameters included product attributes, product titer, viable cell density, cell viability and osmolality. Time course performance variables (daily, initial, peak and end point) were also evaluated. Application of MVDA as a diagnostic tool was successful in identifying the root cause and designing experimental conditions to demonstrate and correct it. Process parameters and their interactions that adversely impact cell culture performance and product attributes were successfully identified. MVDA was successfully used as an effective tool for collating process knowledge and increasing process understanding. [source]

SENSORY QUALITY CRITERIA FOR FIVE FISH SPECIES PREDICTED FROM NEAR-INFRARED (NIR) REFLECTANCE MEASUREMENT

JOURNAL OF FOOD QUALITY, Issue 5 2001
KARIN WARM
ABSTRACT Sensory profiling and Near-Infrared (NIR) reflectance analysis were carried out on cod (Gadus morhua), saithe (Pollachius virens), rainbow trout (Salmo gardineri), herring (Clupea harengus) and flounder (Platichthys flessus). A nine-member trained panel performed the profiling on cooked fillet samples and NIR was measured on the same material as whole, raw fish and raw fillet. For each species, samples varied in storage time (1,11 days in ice at OC) and season (spring, autumn and winter). One descriptive vocabulary was developed, containing 46 descriptive words altogether: 7 for appearance, 15, odor, 16, taste and 8 texture words. Multivariate data analysis was used to reduce the 46 words to 18, covering the main systematic variations in appearance, odor, taste and texture in conformance with a previous study. The same 18 sensory attributes were modeled by NIR measurements on whole, new fish and fillet. The predictive results showed explained variances to be higher for appearance and texture rhan for odor, and lowest for taste. The results indicate that NIR spectroscopy of raw fish as a supplement to sensory analysis might be useful as a rapid tool in Quality Monitoring for measuring the sensory parameters of appearance and texture of cooked fish. [source]

The importance of gel properties for mucoadhesion measurements: a multivariate data analysis approach

JOURNAL OF PHARMACY AND PHARMACOLOGY: AN INTERNATI ONAL JOURNAL OF PHARMACEUTICAL SCIENCE, Issue 2 2004
Helene Hägerström
ABSTRACT In this study we used tensile strength measurements and a recently developed interpretation procedure to evaluate the mucoadhesive properties of a large set of gel preparations with diverse rheological properties. Multivariate data analysis in the form of principal component analysis (PCA) and partial least square projection to latent structures (PLS) was applied to extract useful information from the rather large quantities of data obtained. PCA showed that the selected series of gels was heterogeneous. Some groupings could be detected but none of the gels was identified as an outlier. By using PLS we investigated the relations between the rheological properties of a gel and the parameters defining the cohesiveness, as measured with the texture analyser used for the mucoadhesion measurements. The rheological properties proved to be important for the results of both the mucoadhesion and the cohesiveness measurements. Furthermore, by using PLS two different measurement configurations were evaluated and it was concluded that the combination of a relatively small volume of gel and two pieces of mucosa seems to be more appropriate than a large volume of gel in combination with one piece of mucosa. [source]

INFLUENCE OF UNIAXIAL COMPRESSION RATE ON RHEOLOGICAL PARAMETERS AND SENSORY TEXTURE PREDICTION OF COOKED POTATOES

JOURNAL OF TEXTURE STUDIES, Issue 1 2000
ANETTE KISTRUP THYBO
ABSTRACT The effect of uniaxial compression rate (20,1000 mm/min) on the parameters: Stress (,ftrue), strain (,fHencky) and work to fracture (Wf), modulus of deformability (Ed), maximum slope before fracture (Emax) and work during 75% compression (Wtotal) was investigated for ten potato varieties. Multivariate data analysis was used to study the correlation between and within the sensory and nonsensory measurements by Principal Component Analysis (PCA) which showed ,ftrue, Emax, Wf, and Wtotal to explain the same type of information in the data, and ,fHencky versus Ed another type of information in the data. The deformation rate had a large effect on ,fHencky. Nine sensory texture attributes covering the mechanical, geometrical and moistness attributes were evaluated. Relationships between uniaxial compression data at various deformation rates and the sensory texture attributes were studied by Partial Least Squares Regression (PLSR). A minor effect of deformation rate on the correlation with the sensory texture properties was obtained. Mechanical properties were predicted to a higher extent than the geometrical attributes and moistness. The prediction of the mechanical, geometrical and moistness attributes increased largely by using uniaxial compression supplemented by chemical measures such as dry matter and pectin methylesterase, but here no relevant effect of deformation rate was obtained. [source]

Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 5 2006
Inger V. H. Kjærsgård Dr.
Abstract Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere packing did not lead to distinct changes in protein pattern. Applying DPLSR to the 2-DE data enabled the selection of protein spots critical for differentiation between 3 and 6,months frozen storage with 12,months frozen storage. Some of these protein spots have been identified by MS/MS, revealing myosin light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ,-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse protein changes in cod muscle proteins during storage has revealed new knowledge on the issue and enables a better understanding of biochemical processes occurring. [source]

Multivariate data analysis on historical IPV production data for better process understanding and future improvements

BIOTECHNOLOGY & BIOENGINEERING, Issue 1 2010
Yvonne E. Thomassen
Abstract Historical manufacturing data can potentially harbor a wealth of information for process optimization and enhancement of efficiency and robustness. To extract useful data multivariate data analysis (MVDA) using projection methods is often applied. In this contribution, the results obtained from applying MVDA on data from inactivated polio vaccine (IPV) production runs are described. Data from over 50 batches at two different production scales (700-L and 1,500-L) were available. The explorative analysis performed on single unit operations indicated consistent manufacturing. Known outliers (e.g., rejected batches) were identified using principal component analysis (PCA). The source of operational variation was pinpointed to variation of input such as media. Other relevant process parameters were in control and, using this manufacturing data, could not be correlated to product quality attributes. The gained knowledge of the IPV production process, not only from the MVDA, but also from digitalizing the available historical data, has proven to be useful for troubleshooting, understanding limitations of available data and seeing the opportunity for improvements. Biotechnol. Bioeng. 2010;107: 96,104. © 2010 Wiley Periodicals, Inc. [source]

Application of Multivariate Data Analysis for Identification and Successful Resolution of a Root Cause for a Bioprocessing Application

Data processing in metabolic fingerprinting by CE-UV: Application to urine samples from autistic children

ELECTROPHORESIS, Issue 6 2007
Ana C. Soria
Abstract Metabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV,Vis detection. [source]

Multivariate analysis of congruent images (MACI)

JOURNAL OF CHEMOMETRICS, Issue 5-7 2005
Lennart Eriksson
Abstract The multivariate analysis of congruent images (MACI) is discussed. Here, each image represents one observation and the data set contains a set of congruent images. With ,congruent images' we mean a set of images, properly pre-processed, oriented and aligned, so that each data element (,feature', pixel) corresponds to the same element across all images. An example may be a set of frames from a fixed video camera looking at a stable process. The purpose of a MACI is to find and express patterns over a set of images for the purpose of classification or quantitative regression-like relationships. This is in contrast to standard image analysis, which is usually concerned with a single image and the identification of parts of the image, for example tumour tissue versus normal. We also extend MACI to the case with a set of images that initially are not fully congruent, but are made so by the use of wavelet analysis and the distributions of the wavelet coefficients. Thus, the resulting description forms a set of congruent vectors amenable to multivariate data analysis. The MACI approach will be illustrated by four data sets, three easy-to-understand tutorial image data sets and one industrial image data set relating to quality control of steel rolls. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Multivariate methods in pharmaceutical applications

JOURNAL OF CHEMOMETRICS, Issue 3 2002
Jon Gabrielsson
Abstract This review covers material published within the field of pharmacy in the last five years. Articles concerning experimental design, optimization and applications of multivariate techniques have been published, from factorial designs to multivariate data analysis, and the combination of the two in multivariate design. The number of publications on this topic testifies to the good results obtained in the studies. Much of the published material highlights the usefulness of experimental design, with many articles dealing with optimization, where much effort is spent on getting useful results. Examples of multivariate data analysis are comparatively few, but these methods are gaining in use. The employment of multivariate techniques in different applications has been reviewed. The examples in this review represent just a few of the possible applications with different aims within pharmaceutical applications. A number of companies are using experimental design as a standard tool in preformulation and in combination with response surface modeling. The properties of e.g. a tablet can be optimized to fulfill a well-specified aim such as a specific release profile, hardness, disintegration time etc. However, none of the companies apply multivariate methods in all steps of the drug development process. As this is still very much a growing field, it is only a question of time before experimental design, optimization and multivariate data analysis are implemented throughout the entire formulation process, from performulation to multivariate process control. Copyright ©,2002 John Wiley & Sons, Ltd. [source]

Importance of instrumental and sensory analysis in the assessment of oxidative deterioration of omega-3 long-chain polyunsaturated fatty acid-rich foods

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 2 2007
Wojciech Kolanowski
Abstract Omega-3 long-chain polyunsaturated fatty acids (LC PUFA) positively influence human health. Their main dietary source is fish, especially fish oil. Owing to low fish consumption in many Western countries the average intake of omega-3 LC PUFA is below the recommended level. This raises interest in diet supplementation and food enrichment with fish oil. However, due to a high degree of unsaturation fish oil is extremely susceptible to oxidation. Oxidation of fish oil increases when added to food products, which may be enhanced by some antioxidants, under certain conditions. For quality control of omega-3 LC PUFA-containing foods adequate and combined methods of oxidation assessment should be used, beginning from the raw material and continuing during processing, storage and distribution. To achieve this goal correlation of instrumental and sensory methods with multivariate data analysis may give the best results. In this paper problems of oxidation of fish oil and fish oil-containing foods, as well as methods for its assessment, are reviewed. Copyright © 2006 Society of Chemical Industry [source]

Automated Raman Spectroscopy as a Tool for the High-Throughput Characterization of Molecular Structure and Mechanical Properties of Polyethylenes

MACROMOLECULAR RAPID COMMUNICATIONS, Issue 1 2003
Claus Gabriel
Abstract Raman spectroscopy, which does not require a time-consuming sample preparation, is described as an analytical tool for the high-throughput characterization of polyethylenes. The content of comonomer and the amount of methyl groups per 1,000 carbon atoms of polyethylenes can be predicted from Raman spectra using multivariate data analysis. In addition, macroscopic properties, such as density and elastic modulus as well as yield stress, can be derived from Raman spectra. Raman spectra of selected metallocene-catalyzed polyethylenes of different comonomer content. [source]

Proteome analysis of non-model plants: A challenging but powerful approach

MASS SPECTROMETRY REVIEWS, Issue 4 2008
Sebastien Christian Carpentier
Abstract Biological research has focused in the past on model organisms and most of the functional genomics studies in the field of plant sciences are still performed on model species or species that are characterized to a great extent. However, numerous non-model plants are essential as food, feed, or energy resource. Some features and processes are unique to these plant species or families and cannot be approached via a model plant. The power of all proteomic and transcriptomic methods, that is, high-throughput identification of candidate gene products, tends to be lost in non-model species due to the lack of genomic information or due to the sequence divergence to a related model organism. Nevertheless, a proteomics approach has a great potential to study non-model species. This work reviews non-model plants from a proteomic angle and provides an outline of the problems encountered when initiating the proteome analysis of a non-model organism. The review tackles problems associated with (i) sample preparation, (ii) the analysis and interpretation of a complex data set, (iii) the protein identification via MS, and (iv) data management and integration. We will illustrate the power of 2DE for non-model plants in combination with multivariate data analysis and MS/MS identification and will evaluate possible alternatives. © 2008 Wiley Periodicals, Inc., Mass Spec Rev 27: 354,377, 2008 [source]

Parents' safety beliefs and childhood agricultural injury

AMERICAN JOURNAL OF INDUSTRIAL MEDICINE, Issue 9 2009
Muree Larson-Bright PhD
Abstract Background This study examined potential associations between parental safety beliefs and children's chore assignments or risk of agricultural injury. Methods Analyses were based on nested case,control data collected by the 1999 and 2001 Regional Rural Injury Study-II (RRIS-II) surveillance efforts. Cases (n,=,425, reporting injuries) and controls (n,=,1,886, no injuries; selected using incidence density sampling) were persons younger than 20 years of age from Midwestern agricultural households. A causal model served as the basis for multivariate data analysis. Results Decreased risks of injury (odds ratio [OR] and 95% confidence intervals [CI]) were observed for working-aged children with "moderate," compared to "very strict" parental monitoring (0.60; 0.40,0.90), and with parents believing in the importance of physical (0.80; 0.60,0.95) and cognitive readiness (0.70, 0.50,0.90, all children; 0.30, 0.20,0.50, females) when assigning new tasks. Parents' safety beliefs were not associated with chore assignments. Conclusions Parents' safety beliefs were associated with reduced risk of childhood agricultural injury; the association was not mediated by chore assignments. Am. J. Ind. Med. Am. J. Ind. Med. 52:724,733, 2009. © 2009 Wiley-Liss, Inc. [source]

NIR, DSC, and FTIR as quantitative methods for compositional analysis of blends of polymers obtained from recycled mixed plastic waste

POLYMER ENGINEERING & SCIENCE, Issue 9 2001
Walker Camacho
Methods for the determination of the composition of two binary blends in mixtures of recycled polymeric materials were analyzed and compared. Recycled polypropylene/polyethylene (PP/HDPE) and recycled poly(acryl-butadiene-styrene) and polypropylene(ABS/PP) were used to develop and validate the methods. Diffuse reflectance near infrared spectroscopy (NIRS) offers high sensitivity and ease of operation and a possibility to perform multivariate data analysis. In comparison, differential scanning calorimetry (DSC) and Mid-IR, which are commonly used for this purpose require certain sample preparation and are indeed time consuming. In addition, the low sensitivity of these two methods to concentrations lower than 1% wt makes their application in quality control of recycled polymers inappropriate. NIR can be used for estimating the composition of the recyclate on-line in only a few seconds, no sample preparation is required, and high precision is achieved. We obtained a root mean square error of prediction (RMSEP) equal to 0.21% wt in the interval from 0-15% wt of PP in HDPE and a RMSEP equal to 0.91% wt in the interval 0-100%. For blends of PP/ABS a RMSEP of 0.74% wt in the range 0-100% and 0.32% wt in the range 0-15% wt PP was calculated. Most of the variation in the spectral data with respect to the polymer blend composition for all the studied blends were explained by two principal components (PC). The optimal number of factors (PC) was determined by cross-validation analysis. [source]

Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

Comprehensive analysis of short peptides in sera from patients with IgA nephropathy

RAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 23 2009
Nagayuki Kaneshiro
We analyzed serum short peptides comprehensively to know whether they were useful to characterize IgA nephropathy (IgAN). Serum samples from 26 patients with untreated IgAN and 25 healthy donors were tested. Short peptides with molecular weights of ,7,kDa, purified from the serum samples by magnetic-beads-based weak cation exchange, were detected by mass spectrometry. Then the peptide peaks detected were subjected to the multivariate data analysis by SIMCA-P+® containing principal component analysis (PCA) and orthogonal partial-least-squares-discriminate analysis (OPLS-DA). A total of 92 peptide peaks were detected in the tested serum samples. The OPLS-DA analysis revealed that the profile of all the peptide peak intensities discriminated the IgAN group and the healthy group completely with a high R2 value (0.919) and a high Q2 value (0.861). Further, the profile of only five peptide peaks was found to discriminate the two groups. By tandem mass spectrometry and database searching, three of the five peptides which increased in the IgAN group were identified as fragments of fibrinogen alpha chain, and the two peptides which increased in the healthy group were identified as fragments of complement C3f and kininogen-1 light chain. Taken together, the profile of the serum short peptides would be useful to discriminate IgAN and healthy conditions. Further, the five peptides may be candidate serum markers for IgAN and may be related to pathogenesis of IgA. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Liquid chromatography/mass spectrometry for metabonomics investigation of the biochemical effects induced by aristolochic acid in rats: the use of information-dependent acquisition for biomarker identification

RAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 6 2008
Wan Chan
The toxic effects of oral administrations of nephrotoxic and carcinogenic aristolochic acid (AA) to male Sprague-Dawley rats were investigated by using high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer. Analysis of the urine and plasma samples revealed distinct changes in the biochemical patterns in the AA-dosed rats. After peak finding and alignment, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used for multivariate data analysis. Potential biomarkers were studied by high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) analyses. The MS/MS spectra of all endogenous metabolites satisfying the pre-defined criteria were acquired in a single information-dependent acquisition (IDA) analysis, demonstrating that IDA was an efficient approach for structural elucidation in metabonomic studies. Citric acid and a glucuronide-containing metabolite were observed as potential biomarkers in rat urine. A significant increase in plasma creatinine concentration was also observed in the AA-dosed rats, which indicated that AA induced an adverse effect on the renal clearance function. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Determination of wheat quality by mass spectrometry and multivariate data analysis

RAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 21 2002
David Mark Gottlieb
Multivariate analysis has been applied as support to proteome analysis in order to implement an easier and faster way of data handling based on separation by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The characterisation phase in proteome analysis by means of simple visual inspection is a demanding process and also insecure because subjectivity is the controlling element. Multivariate analysis offers, to a considerable extent, objectivity and must therefore be regarded as a neutral way to evaluate results obtained by proteome analysis. Proteome analysis of storage proteins from the wheat gluten complex based on two-dimensional electrophoresis and analysis of the N-terminal sequence has revealed a protein homologous to ,-gliadins, tentatively associated with quality and within the molecular weight range 27,35,kDa. Further examinations of gliadin data based on mass spectrometry revealed that quality among wheat varieties could be determined by means of principal component analysis. Further examinations by interval partial least squares made it possible to encircle an overall optimal molecular weight interval from 31.5 to 33.7,kDa. The use of multivariate analysis on data from mass spectrometry has thus shown to be a promising technique to minimize the number of two-dimensional gels within the field of proteome analysis. Copyright © 2002 John Wiley & Sons, Ltd. [source]