Susan J. Lister

Bio: Susan J. Lister is an academic researcher. The author has contributed to research in topics: Diffuse reflectance infrared fourier transform. The author has an hindex of 1, co-authored 1 publications receiving 2707 citations.

Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra

Abstract: Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.

Orthogonal projections to latent structures (O-PLS)

Abstract: A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described. O-PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y. In an earlier paper, Wold et al. (Chemometrics Intell. Lab. Syst. 1998; 44: 175-185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O-PLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non-correlated variation itself can be analyzed further. As an example, near-infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O-PLS resulted in reduced model complexity with preserved prediction ability, effective removal of non-correlated variation in X and, not least, improved interpretational ability of both correlated and non-correlated variation in the NIR spectra.

Review of the most common pre-processing techniques for near-infrared spectra

Abstract: Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.

Visible and Near Infrared Spectroscopy in Soil Science

Abstract: This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy Our intention is for the review to serve as a source of up-to-date information on the past and current role of vis–NIR spectroscopy in soil science It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pretratments, covariations in data sets, and mathematical data preprocessing Field analyses and strategies for the practical use of vis–NIR are considered We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function To do this, research in soil spectroscopy needs to be more collaborative and strategic The development of the Global Soil Spectral Library might be a step in the right direction

Theory and application of near infrared reflectance spectroscopy in determination of food quality

Abstract: Near infrared reflectance spectroscopy (NIRS) is a non-destructive and rapid technique applied increasingly for food quality evaluation in recent years. It provides us more information to research the quality of food products. This review intends to give an overview of the type of information that can be obtained based on some developed theory and food research of NIRS. It includes the principle of NIRS technique, the specific techniques with chemometrics for data pre-processing methods, qualitative and quantitative analysis and model transfer, and the wide applications of NIRS in food science. In addition, the promise of NIRS technique for food quality evaluation is demonstrated, and some problems which need to be solved or investigated further are also discussed.