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- Lundstedt-Enkel, Katrin, et al.
(författare)
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Different multivariate approaches to material discovery, process development, PAT and environmental process monitoring
- 2006
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 201-207
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Tidskriftsartikel (refereegranskat)abstract
- The aim with the present paper is to illustrate the use of multivariate strategies (i.e. integration of different multivariate methods) with five examples, four from the pharmaceutical industry and one from environmental research. In the first part, two examples wherein hierarchical models are applied to quality control (QC) and process control are discussed. In the second part a more complex problem and a strategy for material discovery/development are presented wherein a combination of multivariate calibration, multivariate analysis and multivariate design is needed. In the third part, a process analytical/optimization problem is illustrated with a two-step process, demanding that different multivariate tools are combined in a sequential way so that a useful model can be established and the process can be understood. In the final part the usefulness of principal component analysis followed by soft independent modelling of class analogy is illustrated with an example from environmental process monitoring. The five examples from quite different areas show that the chemometric tools are even more powerful if used integrated. However, different strategies and combinations of the tools have to be applied, depending on the problem and the aim.
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- Lundstedt-Enkel, Katrin, et al.
(författare)
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QSBMR - Quantitative Structure Biomagnification Relationships : Physicochemical and Structural Descriptors Important for the Biomagnification of Organochlorines and Brominated Flame Retardants
- 2006
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 392-401
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this project is to establish models to predict the biomagnification of contaminants present in Baltic Sea biota. In this paper a quantitative model that we term QSBMR-Quantitative Structure Biomagnification Relationships is presented. This model describes the relationship between the biomagnification factors (BMFs) for several organochlorines (OCs) and brominated flame retardants (BFRs), for example, polychlorinated biphenyls (PCBs), polybrominated diphenylethers (PBDEs) and hexabromocyclododecane (HBCD), and their descriptors, for example, physico-chemical properties and structural descriptors. The concentrations of contaminants in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg from the Baltic Sea were used. The BMFs were calculated with the randomly sampled ratios (RSR) method that denotes the BMFs with a measure of the variation. In order to describe the physico-chemical properties and chemical structures, approximately 100 descriptors for the contaminants were generated: (a), by using the software (TSAR); (b) finding log Kow values from the literature, and (c) creating binary fingerprint variables that described the position of the chlorine and bromine for the respective PCB and PBDE molecules. Partial least squares (PLS) regression was used to model the relationship between the contaminants' BMF and the descriptors and the resulting QSBMR revealed that more than 20 descriptors in combination were important for the biomagnification of OCs and BFRs between herring and guillemot. The model including all contaminants (R2X=0.73, R2Y=0.87 and Q2=0.63, three components) explained approximately as much of the variation as the model with the PCBs alone (R2X=0.83, R2Y=0.87 and Q2=0.58, two components). The model with the BFRs alone (R2X=0.68, R2Y=0.88 and Q2 = 0.41, two components) had a slightly lower Q2 than the model including all contaminants. For validation, a training set of seven contaminants was selected by multivariate design (MVD) and a model was established. This model was then used to predict the BMFs of the test set (seven contaminants not included in the model). The resulting R2 for the regression Observed BMF versus Predicted BMF was high (0.65). The good models showed that descriptors important for the biomagnification of OCs and BFRs had been used. These types of models will be useful for in silico predictions of the biomagnification of new, not yet investigated, compounds as an aid in risk assessments.
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