J. Kalivas

发表

Two data sets of near infrared spectra

J. Kalivas, 1997 .

Partial least squares solutions for multicomponent analysis

B. Kowalski, I. E. Frank, J. Kalivas, 1983 .

Local prediction models by principal component regression

J. Kalivas, Yu-Long Xie, 1997 .

Adaption of simulated annealing to chemical optimization problems

John H. Kalivas, J. Kalivas, 1995 .

Comparison of Forward Selection, Backward Elimination, and Generalized Simulated Annealing for Variable Selection

John H. Kalivas, J. Kalivas, J. Sutter, 1993 .

Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods.

Károly Héberger, Erik Andries, John H Kalivas, 2015, Analytica chimica acta.

Characterizing multivariate calibration tradeoffs (bias, variance, selectivity, and sensitivity) to select model tuning parameters

J. Kalivas, J. Palmer, 2014 .

Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)

N. M. Faber, J. Kalivas, R. Boqué, 2006 .

Second-order bilinear calibration : the effects of vectorising the data matrices of the calibration set

N. M. Faber, J. Kalivas, R. Boqué, 2002 .

Convergence of generalized simulated annealing with variable step size with application towards parameter estimations of linear and nonlinear models

John H. Kalivas, Jon M. Sutter, J. Kalivas, 1991 .

Correction to Consensus Outlier Detection Using Sum of Ranking Differences of Common and New Outlier Measures Without Tuning Parameter Selections.

John H Kalivas, J. Kalivas, B. Brownfield, 2017, Analytical chemistry.

Effective rank for multivariate calibration methods

John H. Kalivas, Heather A. Seipel, J. Kalivas, 2004 .

A consensus modeling approach to update a spectroscopic calibration

Parviz Shahbazikhah, John H. Kalivas, J. Kalivas, 2013 .

Calibration Maintenance and Transfer Using Tikhonov Regularization Approaches

Erik Andries, John H Kalivas, J. Kalivas, 2009, Applied spectroscopy.

Learning from Procrustes analysis to improve multivariate calibration

John H. Kalivas, J. Kalivas, 2008 .

Evaluation of Multivariate Calibration Using a Tikhonov Regularization Approach and the Generalized Pair‐Correlation Method with Nonlinear Data

John H. Kalivas, J. Kalivas, Forrest Stout, 2007 .

Application of a Hybrid Fusion Classification Process for Identification of Microplastics Based on Fourier Transform Infrared Spectroscopy

J. Kalivas, Beauty K Chabuka, Beauty K. Chabuka, 2020, Applied spectroscopy.

Which principal components to utilize for principal component regression

John H. Kalivas, Jon M. Sutter, J. Kalivas, 1992 .

Multivariate calibration leverages and spectral F‐ratios via the filter factor representation

J. Kalivas, E. Andries, 2010 .

Evaluation of target factor analysis and net analyte signal as processes for classification purposes with application to benchmark data sets and extra virgin olive oil adulterant identification

Kevin M. Higgins, J. Kalivas, E. Andries, 2012 .

Restoration of defaced serial numbers using lock-in infrared thermography (Part I)

J. Kalivas, R. Rodriguez, L. Lau, 2019, Journal of Spectral Imaging.

Wavelength Selection Characterization for NIR Spectra

John H. Kalivas, Uwe Hörchner, J. Kalivas, 1997 .

Model selection challenges with application to multivariate calibration updating methods

J. Kalivas, Anit Gurung, 2020 .

Using the L1 norm to select basis set vectors for multivariate calibration and calibration updating

J. Kalivas, E. Andries, P. Shahbazikhah, 2016 .

Condition numbers, iterative refinement and error bounds

J. Kalivas, P. Lang, 1989 .

Penalty processes for combining roughness and smoothness in spectral multivariate calibration

J. Kalivas, E. Andries, A. Tencate, 2016 .

Pareto Optimal Multivariate Calibration for Spectroscopic Data

J. Kalivas, R. L. Green, R. Green, 2001 .

Food adulteration analysis without laboratory prepared or determined reference food adulterant values.

J. Kalivas, G. Mousdis, C. Georgiou, 2014, Food chemistry.

Interrelationships between generalized Tikhonov regularization, generalized net analyte signal, and generalized least squares for desensitizing a multivariate calibration to interferences

J. Kalivas, E. Andries, 2013 .

Feasibility Assessment of Synchronous Fluorescence Spectral Fusion by Application to Argan Oil for Adulteration Analysis

J. Kalivas, A. Amine, G. Mousdis, 2018, Applied spectroscopy.

PCR eigenvector selection based on correlation relative standard deviations

J. Kalivas, S. Z. Fairchild, 2001 .

Wavelength Selection for Multivariate Calibration Using Tikhonov Regularization

Károly Héberger, John H Kalivas, J. Kalivas, 2007, Applied spectroscopy.

Erratum to Seipel HA, Kalivas JH. Effective rank for multivariate calibration methods. J. Chemometrics 2004; 18: 306–311

J. Kalivas, H. Seipel, 2005 .

Orthogonality considerations for library searching Nth-order data

Chad E. Anderson, J. Kalivas, R. Nieves, 1998 .

A Service-Learning Project Based on a Research Supportive Curriculum Format in the General Chemistry Laboratory

J. Kalivas, 2008 .

Data fusion for food authentication. Combining rare earth elements and trace metals to discriminate "Fava Santorinis" from other yellow split peas using chemometric tools.

Kevin M. Higgins, J. Kalivas, S. Haroutounian, 2014, Food chemistry.

Computer‐generated multicomponent calibration designs for optimal analysis sample predictions

J. Kalivas, J. Sutter, K. Hitchcock, 1992 .

Spectral Multivariate Calibration with Wavelength Selection Using Variants of Tikhonov Regularization

Erik Andries, John H. Kalivas, Joshua Ottaway, 2010, Applied spectroscopy.

Selectivity‐relaxed classical and inverse least squares calibration and selectivity measures with a unified selectivity coefficient

J. Kalivas, J. Ferré, A. Tencate, 2017 .

Ultrasonic-assisted catalytic transfer hydrogenation for upgrading pyrolysis-oil

Robert C. Spiers, M. M. Ramirez-Corredores, Samuel N. Hansen, 2021, Ultrasonics sonochemistry.

Automated multicomponent analysis with correction for interferences and matrix effects

B. Kowalski, J. Kalivas, 1983 .

Fundamentals of condition index evolving profiles for qualitative analysis of unresolved chromatographic peaks

John H. Kalivas, J. Kalivas, Taura D. Jarvis, 1992 .

Observations of trends for singular values and eigenvectors from library searching ultraviolet-visible spectra with spectral subtraction

J. Kalivas, J. Sutter, L. Juhl, 1990 .

Generalized standard addition method for multicomponent instrument characterization and elimination of interferences in inductively coupled plasma spectroscopy

B. Kowalski, J. Kalivas, 1981 .

Updating a synchronous fluorescence spectroscopic virgin olive oil adulteration calibration to a new geographical region.

Joshua Ottaway, John H Kalivas, Matthew Ross Kunz, 2011, Journal of agricultural and food chemistry.

Assessing Spectral Orthogonality

J. Kalivas, 1989 .

Post-Consumer Plastic Identification Using Raman Spectroscopy

John H. Kalivas, J. Kalivas, R. Rodriguez, 1999 .

Quantifying selectivity in spectrophotometric multicomponent analysis

John H. Kalivas, Nicolaas M. Faber, N. M. Faber, 2003 .

3 Sampling Theory, Distribution Functions, and the Multivariate Normal Distribution

Paul J. Gemperline, John H. Kalivas, J. Kalivas, 2006 .

Evaluation of principal component selection methods to form a global prediction model by principal component regression

Yu-Long Xie, John H. Kalivas, J. Kalivas, 1997 .

EXCHANGE OF COMMENTS ON CONVERGENCE OF GENERALIZED SIMULATED ANNEALING WITH VARIABLE STEP SIZE WITH APPLICATION TOWARD PARAMETER ESTIMATIONS OF LINEAR AND NONLINEAR MODELS : AUTHOR'S REPLY

J. Kalivas, J. Sutter, J. M. Sutter, 1992 .

Generalized simulated annealing for calibration sample selection from an existing set and orthogonalization of undesigned experiments

John H. Kalivas, J. Kalivas, 1991 .

Compensation for Drift and Interferences in Multicomponent Analysis.

B. Kowalski, J. Kalivas, 1982 .

Precision and stability in the generalized standard addition method

J. H. Kalivas, J. Kalivas, 1983 .

Comparison of extreme learning machine models for gasoline octane number forecasting by near-infrared spectra analysis

Xiaoyu Wang, Kan Yang, John H. Kalivas, 2020 .

Fusion strategies for selecting multiple tuning parameters for multivariate calibration and other penalty based processes: A model updating application for pharmaceutical analysis.

John H. Kalivas, Alexander J. White, J. Kalivas, 2016, Analytica chimica acta.

Pareto calibration with built-in wavelength selection

John H. Kalivas, J. Kalivas, 2004 .

Assessment of Pareto Calibration, Stability, and Wavelength Selection

John H Kalivas, J. Kalivas, Kelly J Anderson, 2003, Applied spectroscopy.

Cyclic subspace regression with analysis of the hat matrix

J. Kalivas, 1999 .

Feasibility Study for Transforming Spectral and Instrumental Artifacts for Multivariate Calibration Maintenance

Joshua Ottaway, John H Kalivas, J. Kalivas, 2015, Applied spectroscopy.

Updating a near-infrared multivariate calibration model formed with lab-prepared pharmaceutical tablet types to new tablet types in full production.

Kevin M. Higgins, John H Kalivas, J. Kalivas, 2012, Journal of pharmaceutical and biomedical analysis.

Automatic food and beverage authentication and adulteration detection by classification hybrid fusion

John H. Kalivas, Tony Lemos, J. Kalivas, 2021, Journal of Chemometrics.

Self-Optimized One-Class Classification Using Sum of Ranking Differences Combined with a Receiver Operator Characteristic Curve.

J. Kalivas, Tony Lemos, 2020, Analytical chemistry.

Calibration Methodologies

J. Kalivas, Steven D. Brown, 2020, Comprehensive Chemometrics.

Fundamentals of Calibration Transfer through Procrustes Analysis

John H. Kalivas, Chad E. Anderson, J. Kalivas, 1999 .

Demonstrated Potential of Ion Mobility Spectrometry for Detection of Adulterated Perfumes and Plant Speciation

J. Kalivas, K. Daum, Jared M. Clark, 2003 .

Realizing Workplace Skills in Instrumental Analysis

J. Kalivas, 2005 .

Identifying Chemical, Physical, and Instrumental Matrix Matched Samples by Leveraging Spectral Model Regression Vectors.

J. Kalivas, Rachel M. Emerson, Tony Lemos, 2019, Analytical chemistry.

Net Analyte Signal (NAS) for Selection of Multivariate Calibration Models and Development of NAS Sample-Wise Target Calibration Model Attributes

J. Kalivas, J. Palmer, 2015 .

3.01 – Calibration Methodologies

J. H. Kalivas, J. Kalivas, 2009 .

Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry

John H. Kalivas, Jon M. Sutter, J. Kalivas, 1989 .

Further investigation on a comparative study of simulated annealing and genetic algorithm for wavelength selection

John H. Kalivas, Uwe Hörchner, J. Kalivas, 1995 .

Selectivity and Related Measures for nth-Order Data.

J. Kalivas, P. Lang, N J Messick, 1996, Analytical chemistry.

Variance‐decomposition of pure‐component spectra as a measure of selectivity

John H. Kalivas, J. Kalivas, 1989 .

Sample‐wise spectral multivariate calibration desensitized to new artifacts relative to the calibration data using a residual penalty

J. Kalivas, B. Brownfield, B. Karki, 2017 .

Impact of standardization sample design on Tikhonov regularization variants for spectroscopic calibration maintenance and transfer

Erik Andries, John H. Kalivas, Joshua Ottaway, 2010 .

Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data.

S. Wold, Jian-hui Jiang, W. Lindberg, 2002, Analytical chemistry.

Overview of two‐norm (L2) and one‐norm (L1) Tikhonov regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance

John H. Kalivas, J. Kalivas, John H. Kalivas, 2012 .

Cyclic subspace regression with analysis of wavelength-selection criteria

John H. Kalivas, Tracy P. Houghton, Gregory A. Bakken, 1999 .

Simulated‐annealing‐based optimization algorithms: Fundamentals and wavelength selection applications

John H. Kalivas, Uwe Hörchner, J. Kalivas, 1995 .

Restoration of defaced serial numbers using lock-in infrared thermography (Part I)

J. Kalivas, R. Rodriguez, L. Lau, 2019, Journal of Spectral Imaging.

Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra.

Robert C. Spiers, J. Kalivas, C. Norby, 2023, Analytical chemistry.

Interrelationships of multivariate regression methods using eigenvector basis sets

J. Kalivas, John H. Kalivas, 1999 .

Graphical diagnostics for regression model determinations with consideration of the bias/variance trade-off

John H. Kalivas, Robert L. Green, J. Kalivas, 2002 .

Impartial graphical comparison of multivariate calibration methods and the harmony/parsimony tradeoff

John H. Kalivas, J. Kalivas, Forrest Stout, 2006 .

Multivariate Calibration, an Overview

John H. Kalivas, J. Kalivas, 2005 .

Ridge regression optimization using a harmonious approach

John H. Kalivas, Joel B. Forrester, J. Kalivas, 2004 .

Prediction of retention indices for identification of fatty acid methyl esters.

J. Kalivas, K. Héberger, I. Zenkevich, 2008, Journal of chromatography. A.

Sample and feature augmentation strategies for calibration updating

Erik Andries, John H. Kalivas, Anit Gurung, 2018, Journal of Chemometrics.

Evaluation of experimental designs for multicomponent determinations by spectrophotometry

J. Kalivas, L. Juhl, 1988 .

Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods.

J. Kalivas, K. Héberger, E. Andries, 2015, Analytica chimica acta.

Chemometrics with undergraduates

John H. Kalivas, J. Kalivas, 1992 .

A Simplex Optimized Inductively Coupled Plasma Spectrometer with Minimization of Interferences

J. Kalivas, 1987 .

Evaluation of the calibration matrix condition number as a criterion for optimal derivative-spectrophotometric multicomponent quantitation

John H. Kalivas, J. Kalivas, L. L. Juhl, 1986 .

Assessing chromatographic peak purity using condition index and singular value evolving profiles

Gregory A. Bakken, J. Kalivas, 1995 .

Use of Matrix Orthogonal Projection for Peak Purity Assessment

J. Kalivas, Yu-Long Xie, 1997 .

Determination of componentwise chromatographic elution regions using singular value evolving profiles

Gregory A. Bakken, J. Kalivas, N. Messick, 1996 .

Leveraging Multiple Linear Regression for Wavelength Selection

John H. Kalivas, Tony Lemos, J. Kalivas, 2017 .

Spectral multivariate calibration without laboratory prepared or determined reference analyte values.

J. Kalivas, J. Ottaway, J. Farrell, 2013, Analytical chemistry.

Response to “comments on interrelationships between sensitivity and selectivity measures for spectroscopic analysis” by K. Faber et al.

J. Kalivas, P. Lang, 1997 .

Interrelationships between sensitivity and selectivity measures for spectroscopic analysis

John H. Kalivas, J. Kalivas, P. Lang, 1996 .

Basis sets for multivariate regression

J. Kalivas, 2001 .

QSAR modeling based on the bias/variance compromise: a harmonious

John H. Kalivas, Joel B. Forrester, Heather A. Seipel, 2004, J. Comput. Aided Mol. Des..

Cyclic Subspace Regression

John H. Kalivas, Reinaldo G. Nieves, J. Kalivas, 1998 .

Feasibility Assessment of Synchronous Fluorescence Spectral Fusion by Application to Argan Oil for Adulteration Analysis

J. Kalivas, A. Amine, G. Mousdis, 2018, Applied spectroscopy.

Data Fusion of Nonoptimized Models

John H. Kalivas, J. Kalivas, 2019, Data Handling in Science and Technology.

Tikhonov regularization in standardized and general form for multivariate calibration with application towards removing unwanted spectral artifacts

John H. Kalivas, J. Kalivas, Forrest Stout, 2006 .

Evaluation of volume and matrix effects for the generalized standard addition method.

J. Kalivas, 1987, Talanta.

Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity

John H. Kalivas, Robert C. Spiers, J. Kalivas, 2021, J. Chem. Inf. Model..