Non-invasive blood glucose measurement has attracted much attention in recent years. Near-infrared (NIR) spectroscopy is currently used for frequent detection of blood glucose levels. The dynamic spectrum (DS) method, which can eliminate the individual discrepancy, is the development of ordinary NIR spectroscopy. Classic Least Squares (CLS) is usually employed to analyze the spectra. In this wo...

Visible and near-infrared hyperspectral imaging covering spectral range of 380-1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500-900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS...

In order to improve fitting and forecasting precision and solve the problem that some data with less sensitivity lead to low simulation precision of partial leastsquares regression (PLS for short) model, the new method of simulating freezing depth is presented according to ground temperature of different depths, air temperatures, surface temperatures and the like. Firstly, the PLS model, which ...

Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of...

In this paper I review covariance-based Partial Least Squares (PLS) methods, focusing on common features of their respective algorithms and optimization criteria. I then show how these algorithms can be adjusted for use as optimal scaling tools. Three new PLS-type algorithms are proposed for the analysis of one, two or several blocks of variables: the NonMetric NIPALS, the Non-Metric PLS Regres...

In this paper, we propose a generalization of Partial Least Squares Regression (PLS-R) for matrix several binary responses and set numerical predictors. We call the method Binary Logistic (PLS-BLR). That is equivalent to PLS-2 model responses. Biplot even triplot graphical representations visualizing PLS-BLR models are described, an application real data presented. Software packages calculation...