The oil crisis during the 1970s turned interest towards the utilization of renewable resources and towards lignocellulosics in particular. The 1970s were also the cradle period of biotechnology, and the years when biotechnical utilization of lignocellulosic waste from agriculture and forestry gained priori ty. This was a logical conclusion since one of nature's most important biologi cal reactions is the conversion of wood and other lignocellulosic materials to carbon dioxide, water and humic substances. However, while biotechnology in other areas like medicine and pharmacology concerned production of expen sive products on a small scale, biotechnical utilization and conversion of ligno cellulosics meant production of inexpensive products on a large scale. Biotechnical utilization of lignocellulosic materials is therefore a very difficult task, and the commercial utilization of this technology has not progressed as rapidly as one would have desired. One reason for this was the lack of basic knowledge of enzyme mechanisms involved in the degradation and conversion of wood, other lignocellulosics and their individual components. There are also risks associated with initiating a technical development before a stable platform of knowledge is available. Several of the projects started with en thusiasm have therefore suffered some loss of interest. Also contributing to this failing interest is the fact that the oil crisis at the time was not a real one. At present, nobody predicts a rapid exhaustion of the oil resources and fuel production from lignocellulosics is no longer a high priority.
To understand the world around us, as well as ourselves, we need to measure many things, many variables, many properties of the systems and processes we investigate. Hence, data collected in science, technology, and almost everywhere else are multivariate, a data table with multiple variables measured on multiple observations (cases, samples, items, process time points, experiments). This book describes a remarkably simple minimalistic and practical approach to the analysis of data tables (multivariate data). The approach is based on projection methods, which are PCA (principal components analysis), and PLS (projection to latent structures) and the book shows how this works in science and technology for a wide variety of applications. In particular, it is shown how the great information content in well collected multivariate data can be expressed in terms of simple but illuminating plots, facilitating the understanding and interpretation of the data. The projection approach applies to a variety of data-analytical objectives, i.e., (i) summarizing and visualizing a data set, (ii) multivariate classification and discriminant analysis, and (iii) finding quantitative relationships among the variables. This works with any shape of data table, with many or few variables (columns), many or few observations (rows), and complete or incomplete data tables (missing data). In particular, projections handle data matrices with more variables than observations very well, and the data can be noisy and highly collinear. Authors: The five authors are all connected to the Umetrics company (www.umetrics.com) which has developed and sold software for multivariate analysis since 1987, as well as supports customers with training and consultations. Umetrics' customers include most large and medium sized companies in the pharmaceutical, biopharm, chemical, and semiconductor sectors.