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Generalized Classical Least Squares: Toward Unifying Classical and Inverse Calibrations

https://doi.org/10.2139/ssrn.4870259
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A general rule for uniqueness in self‐modeling curve resolution methods
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Principal component analysis
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Calibration model transfer for near-infrared spectra based on canonical correlation analysis
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Improvement of Direct Calibration in spectroscopy
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Efficient use of pure component and interferent spectra in multivariate calibration
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Non-linear calibration models for near infrared spectroscopy
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On the area of feasible solutions and its reduction by the complementarity theorem
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Interference-free calibration with first-order instrumental data and multivariate curve resolution. When and why?
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Linear or non-linear multivariate calibration models? That is the question
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A simple, projection-based geometric model for several linear pretreatment and calibration methods
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Quantitative analysis of NMR spectra with chemometrics
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Multivariate calibration
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Monte Carlo cross validation
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Two data sets of near infrared spectra
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New augmented classical least squares methods for improved quantitative spectral analyses
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An Introduction to Multivariate Calibration and Analysis
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Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information
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Constrained Regularization: Hybrid Method for Multivariate Calibration
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Increasing Robustness against Changes in the Interferent Structure by Incorporating Prior Information in the Augmented Classical Least-Squares Framework
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Net Analyte Signal Calculation in Multivariate Calibration
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Jackknife-Based Selection of Gram−Schmidt Orthogonalized Descriptors in QSAR
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ANALYSIS OF ABSORPTION SPECTRA OF MULTICOMPONENT SYSTEMS1
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Computer Aided Design of Experiments
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Self Modeling Curve Resolution
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Direct Spectrophotometric Determination of Fat and Moisture in Meat Products
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A New Method for Multivariate Calibration
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New Prediction-Augmented Classical Least-Squares (PACLS) Methods: Application to Unmodeled Interferents
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Concentration Residual Augmented Classical Least Squares (CRACLS): A Multivariate Calibration Method with Advantages over Partial Least Squares
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Effects of Nonlinearities and Uncorrelated or Correlated Errors in Realistic Simulated Data on the Prediction Abilities of Augmented Classical Least Squares and Partial Least Squares
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Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra
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An Introduction to Multivariate Calibration and Analysis
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