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Improving interpretation by orthogonal variation: Multivariate analysis of spectroscopic data
Umeå University, Faculty of Science and Technology, Department of Chemistry.
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The desire to use the tools and concepts of chemometrics when studying problems in the life sciences, especially biology and medicine, has prompted chemometricians to shift their focus away from their field‘s traditional emphasis on model predictivity and towards the more contemporary objective of optimizing information exchange via model interpretation. The complex data structures that are captured by modern advanced analytical instruments open up new possibilities for extracting information from complex data sets. This in turn imposes higher demands on the quality of data and the modeling techniques used.

The introduction of the concept of orthogonal variation in the late 1990‘s led to a shift of focus within chemometrics; the information gained from analysis of orthogonal structures complements that obtained from the predictive structures that were the discipline‘s previous focus. OPLS, which was introduced in the beginning of 2000‘s, refined this view by formalizing the model structure and the separation of orthogonal variations. Orthogonal variation stems from experimental/analytical issues such as time trends, process drift, storage, sample handling, and instrumental differences, or from inherent properties of the sample such as age, gender, genetics, and environmental influence.

The usefulness and versatility of OPLS has been demonstrated in over 500 citations, mainly in the fields of metabolomics and transcriptomics but also in NIR, UV and FTIR spectroscopy. In all cases, the predictive precision of OPLS is identical to that of PLS, but OPLS is superior when it comes to the interpretation of both predictive and orthogonal variation. Thus, OPLS models the same data structures but provides increased scope for interpretation, making it more suitable for contemporary applications in the life sciences.

This thesis discusses four different research projects, including analyses of NIR, FTIR and NMR spectroscopic data. The discussion includes comparisons of OPLS and PLS models of complex datasets in which experimental variation conceals and confounds relevant information. The PLS and OPLS methods are discussed in detail. In addition, the thesis describes new OPLS-based methods developed to accommodate hyperspectral images for supervised modeling. Proper handling of orthogonal structures revealed the weaknesses in the analytical chains examined. In all of the studies described, the orthogonal structures were used to validate the quality of the generated models as well as gaining new knowledge. These aspects are crucial in order to enhance the information exchange from both past and future studies.

Place, publisher, year, edition, pages
Umeå: Kemiska institutionen, Umeå universitet , 2011. , 62 p.
Keyword [en]
OPLS, PLS, Multivariate Analysis, Orthogonal variation, Chemometrics, hyperspectral images, FTIR, NIR, spectroscopy
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-43476ISBN: 978-91-7459-207-8OAI: oai:DiVA.org:umu-43476DiVA: diva2:414209
Public defence
2011-06-01, KBC-huset, KB3A9, Umeå universitet, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2011-05-11 Created: 2011-05-02 Last updated: 2011-05-30Bibliographically approved
List of papers
1. Unlocking Interpretation in Near Infrared Multivariate Calibrations by Orthogonal Partial Least Squares
Open this publication in new window or tab >>Unlocking Interpretation in Near Infrared Multivariate Calibrations by Orthogonal Partial Least Squares
2009 (English)In: Analytical Chemistry, Vol. 81, no 1, 203-9 p.Article in journal (Refereed) Published
Abstract [en]

Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood’s anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in addition, the Food and Drug Administration (FDA) demands “process understanding” in PAT. Both these issues make OPLS the ideal tool for a multitude of NIR calibrations. In conclusion, OPLS leads to better interpretation of spectrometry data (e.g., NIR) and improved understanding facilitates cross-scientific communication. Such improved knowledge will decrease risk, with respect to both accuracy and precision, when using NIR for PAT applications.

National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-11188 (URN)
Available from: 2009-01-08 Created: 2009-01-08 Last updated: 2012-09-05
2. Monitoring kidney-transplant patients using metabolomics and dynamic modeling
Open this publication in new window or tab >>Monitoring kidney-transplant patients using metabolomics and dynamic modeling
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2009 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 98, no 1, 45-50 p.Article in journal (Refereed) Published
Abstract [en]

A kidney transplant provides the only hope for a normal life for patients with end-stage renal disease, i.e., kidney failure. Unfortunately, the lack of available organs leaves some patients on the waiting list for years. In addition, the post-transplant treatment is extremely important for the final outcome of the surgery, since immune responses, drug toxicity and other complications pose a real and present threat to the patient. In this article, we describe a novel strategy for monitoring kidney transplanted patients for immune responses and adverse drug effects in their early recovery. Nineteen patients were followed for two weeks after renal transplantation, two of them experienced problems related to kidney function, both of whom were correctly identified by means of nuclear magnetic resonance spectroscopic analysis of urine samples and multivariate data analysis.

Place, publisher, year, edition, pages
Elsevier B.V., 2009
Keyword
Nuclear Magnetic Resonance (NMR) Spectroscopy, Chemometrics; Metabolomics, Kidney transplant, Dynamic modeling, Orthogonal Projections to Latent Structures (OPLS)
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-26561 (URN)10.1016/j.chemolab.2009.04.013 (DOI)
Available from: 2009-10-15 Created: 2009-10-15 Last updated: 2012-09-05
3. Orthogonal Projections to Latent Structures Discriminant Analysis Modeling on in Situ FT-IR Spectral Imaging of Liver Tissue for Identifying Sources of Variability.
Open this publication in new window or tab >>Orthogonal Projections to Latent Structures Discriminant Analysis Modeling on in Situ FT-IR Spectral Imaging of Liver Tissue for Identifying Sources of Variability.
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2008 (English)In: Analytical Chemistry, ISSN 1520-6882, Vol. 80, no 18, 6898-906 p.Article in journal (Refereed) Published
Abstract [en]

In this study, the orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to assess the in situ chemical composition of two different cell types in mouse liver samples, hepatocytes and erythrocytes. High spatial resolution FT-IR microspectroscopy equipped with a focal plan array (FPA) detector is capable of simultaneously recording over 4000 spectra from 64 x 64 pixels with a maximum spatial resolution of about 5 mum x 5 mum, which allows for the differentiation of individual cells. The main benefit with OPLS-DA lies in the ability to separate predictive variation (between cell type) from variation that is uncorrelated to cell type in order to facilitate understanding of different sources of variation. OPLS-DA was able to differentiate between chemical properties and physical properties (e.g., edge effects). OPLS-DA model interpretation of the chemical features that separated the two cell types clearly highlighted proteins and lipids/bile acids. The modeled variation that was uncorrelated to cell type made up a larger portion of the total variation and displayed strong variability in the amide I region. This could be traced back to a gradient in the high intensity (high-density) areas vs the low intensity areas (close to empty areas) that as a result of normalization had an adverse effect on FT-IR spectral profiles. This highlights that OPLS-DA provides an effective solution to identify different sources of variability, both predictive and uncorrelated, and also facilitates understanding of any sampling, experimental, or preprocessing issues.

National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-10361 (URN)10.1021/ac8005318 (DOI)18714965 (PubMedID)
Available from: 2008-10-13 Created: 2008-10-13 Last updated: 2013-02-14
4. Cell specific chemotyping and multivariate imaging by combined FT-IR microspectroscopy and OPLS analysis reveals the chemical landscape of secondary xylem
Open this publication in new window or tab >>Cell specific chemotyping and multivariate imaging by combined FT-IR microspectroscopy and OPLS analysis reveals the chemical landscape of secondary xylem
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2011 (English)In: The Plant Journal, ISSN 0960-7412, E-ISSN 1365-313X, Vol. 66, no 5, 903-914 p.Article in journal (Refereed) Published
Abstract [en]

Fourier-transform infrared (FT-IR) spectroscopy combined with microscopy enables acquiring chemical information from native plant cell walls with high spatial resolution. Combined with a 64 x 64 focal plane array (FPA) detector 4096 spectra from a 0.3 x 0.3 mm image can be simultaneously obtained, where each spectrum represents a compositional and structural "fingerprint" of all cell wall components. For optimal use and analysis of such large amount of information, multivariate approaches are preferred. Here, FT-IR microspectroscopy with FPA detection is combined with orthogonal projections to latent structures discriminant analysis (OPLS-DA). This allows for 1) the extraction of spectra from specific cell types, 2) identification and characterization of different chemotypes using the full spectral information, and 3) further visualising the pattern of identified chemotypes by multivariate imaging. As proof of concept, the chemotypes of Populus tremula xylem cell types are described. The approach revealed unknown features about chemical plasticity and patterns of lignin composition in wood fibers that would have remained hidden in the dataset with traditional data analysis. The applicability of the method on Arabidopsis xylem, and its usefulness in mutant chemotyping is also demonstrated. The methodological approach is not limited to xylem tissues but can be applied to any plant organ/tissue also using other microspectroscopy techniques such as Raman- and UV-microspectroscopy.

Place, publisher, year, edition, pages
Blackwell Publishing Ltd, 2011
Keyword
FT-IR microspectroscopy, cell wall, lignin composition, wood, poplar, Arabidopsis
Identifiers
urn:nbn:se:umu:diva-41255 (URN)10.1111/j.1365-313X.2011.04542.x (DOI)21332846 (PubMedID)
Available from: 2011-03-22 Created: 2011-03-22 Last updated: 2013-02-14Bibliographically approved

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