Showing papers by "Svante Wold published in 2006"
01 Jan 2006
TL;DR: This second volume has two parts, the first with specialized applications of multi- and mega-variate analysis, namely: QSAR (quantitative structure-activity relationships) describes how series of mo ...
Abstract: This second volume has two parts, the first with specialized applications of multi- and mega-variate analysis, namely:QSAR (quantitative structure-activity relationships) describes how series of mo ...
152 citations
TL;DR: The problems of constructing and implementing a well working checking system are discussed in relation to its different parts — analytical and process data, chemometrical and other methods for their modeling and analysis, and various forms of data management to handle the data flow and synchronization, as well as storage and retrieval.
47 citations
TL;DR: A new approach for experimental design in 96-well plates is introduced that minimizes the manual workload without compromising the quality of the experimental design and is scalable to larger rectangular formats such as 384- and 1536- well plates.
23 citations
15 Sep 2006
TL;DR: Multivariate calibration (MVC) is a methodology for using multiple signals, for instance a digitized spectrum, to determine the levels of concentrations of chemical compounds in analytical samples to determine other properties of interest.
Abstract: Multivariate calibration (MVC) is a methodology for using multiple signals, for instance a digitized spectrum, to determine the levels of concentrations of chemical compounds in analytical samples. MVC can also be used to determine other properties of interest, for instance viscosity, particle size distribution, energy content, or taste. MVC is made in two phases. In the first, the “training” or “calibration” phase, samples with known concentration (property) values and their signal profiles are used to develop a model of their relationship, a multivariate standard curve. In the second phase, this model is used with new samples to determine their concentration (property) values from their signal profiles.
19 citations
TL;DR: In this paper, an approach based on a combination of hierarchical modeling and orthogonal partial least squares (OPLS) is proposed to resolve the sources of variation in spectral data.
Abstract: Spectral data (X) may contain (a) variation that is correlated to concentrations or properties (Y) of samples and (b) variation that is unrelated to the same Y. This paper outlines an approach by which both such sources of variation may be resolved. The approach is based on a combination of hierarchical modelling and orthogonal partial least squares (OPLS). OPLS is first used at the base hierarchical level. The output is a labelling of the resulting score vectors as representing Y-predictive or Y-orthogonal variation. OPLS is then also used at the top hierarchical level together with principal components analysis (PCA). With PCA the Y-orthogonal X-variation is analysed and interpreted. With OPLS the Y-predictive X-variation is examined. The applicability of the proposed strategy is illustrated using one multi-block spectral data set.
19 citations