Model performance of the partial least squares method (PLS) alone and bagging-PLS was investigated in online near-infrared (NIR) sensor monitoring of pilot-scale extraction process in Fructus aurantii. High-performance liquid chromatography (HPLC) was used as a reference method to identify the active pharmaceutical ingredients: naringin, hesperidin and neohesperidin. Several preprocessing metho...

In this work, we develop a fast sequential lowrank tensor regression framework, namely recursive higher-order partial least squares (RHOPLS). It addresses the great challenges posed by the limited storage space and fast processing time required by dynamic environments when dealing with largescale high-speed general tensor sequences. Smartly integrating a low-rank modification strategy of Tucker...

How to relate two blocks of variables? Partial least squares Analysis two-block pls u, v u'u = v'v = 1 Low-dimensional representation of the pattern of correlations/ covariances between two blocks of variables: A second dimension can be computed as directions orthogonal to the first ones, accounting for the second most correlation/covariance. A direction in each of the two data spaces, for whic...

Partial least squares (PLS) regression on an L2-continuous stochastic process is an extension of the 2nite set case of predictor variables. The PLS components existence as eigenvectors of some operator and convergence properties of the PLS approximation are proved. The results of an application to stock-exchange data will be compared with those obtained by other methods. c © 2003 Elsevier B.V. ...

Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functio...

the potential of near infrared reflectance spectroscopy (nirs) as a fast method to predict the crude protein (cp) and moisture (m) content in fishmeal by scanning spectra between 1000 and 2500 nm using multivariate regression technique based on partial least squares (pls) was evaluated. the coefficient of determination in calibration (r2c) and standard error of calibration (sec) were 0.95 and 1...

Partial least squares (PLS) methods have recently been used for many pattern recognition problems in computer vision. Here, PLS is primarily used as a supervised dimensionality reduction tool to obtain effective feature combinations for better learning. However, application of PLS to large datasets is hindered by its higher computational cost. We propose an approach to accelerate the classical ...

This presented paper deals with application of three multivariate calibration methods methods for simultaneous spectrophotometric determination of two active substances in combined pharmaceutical formulation, composed of rutin (RU) and ascorbic acid (AA). The multivariate methods are classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS). The results...

Inhibitory activity against aldose reductase enzyme of nineteen flavonoid derivatives was subjected to classical quantitative structure activity relationship (QSAR) analysis using electrotopological state (E-state) atom parameter. For the development of the QSAR models, statistical techniques like stepwise multiple linear regression and partial least squares (PLS) were used. The best equation i...

The potential of Near Infrared Reflectance Spectroscopy (NIRS) as a fast method to predict the Crude Protein (CP) and Moisture (M) content in fishmeal by scanning spectra between 1000 and 2500 nm using multivariate regression technique based on Partial Least Squares (PLS) was evaluated. The coefficient of determination in calibration (R2C) and Standard Error of Calibra...