Typically, lignins are characterized by IR spectroscopy in the solid state. The examination of lignins in solution has long been a domain of UV and NMR spectroscopy. However, with the advent of the FTIR technique, the IR spectra of liquid samples now can be readily obtained. The need to measure IR spectra in solution exists when there is not enough time to isolate lignin from solution as, for example, is the case when spent pulping liquors have to be analyzed rapidly during a pulping process for process control. This section describes briefly the common liquid sampling accessories and applications of solution state FTIR spectroscopy to lignins. For more details on FTIR spectroscopy, the reader is referred to Chapter 4.1.

Prediction of Product Quality from Spectral Data Using the Partial Least-Squares Method

Fourier Transform Infrared Spectroscopy

The process chemometrics approach to process monitoring and fault detection

Partial least squares (PLS) modeling is an algorithm for relating one or more dependent variables to two or more independent variables. As a regression procedure it apparently evolved from the method of principal components regression (PCR) using the NIPALS algorithm, which is similar to the power method for determining the eigenvectors and eigenvalues of a matrix. This paper presents a theoretical explanation of the PLS algorithm using singular value decomposition and the power method. The relation of PLS to PCR is demonstrated, and PLS is shown to be one of a continuum of possible solutions of a similar type. These other solutions may give better prediction than either PLS or PCR under appropriate conditions.

A theoretical foundation for the PLS algorithm

Pattern recognition methods and applications in biomedical magnetic resonance

The growing awareness of the need for non-deterministic and distribution-free (soft) models combined with an iterative algorithm for finding latent variables led to the construction of partial least squares models. There have been separate developments in the humanistic and the natural sciences, with stress on different aspects and a different terminology. The historical development is described and some key topics are explained.

Prediction of Product Quality from Spectral Data Using the Partial Least-Squares Method

Citations

Fourier Transform Infrared Spectroscopy

The process chemometrics approach to process monitoring and fault detection

A theoretical foundation for the PLS algorithm

Pattern recognition methods and applications in biomedical magnetic resonance

Notes on the history and nature of partial least squares (PLS) modelling

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