Fused adjacency matrices to enhance information extraction: The beer benchmark.
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[1] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[2] Guillaume L. Erny,et al. Quantification of organic acids in beer by nuclear magnetic resonance (NMR)-based methods. , 2010, Analytica chimica acta.
[3] P. Alam. ‘W’ , 2021, Composites Engineering.
[4] Federico Marini,et al. Data Fusion for Food Authentication. Combining near and Mid Infrared to Trace the Origin of Extra Virgin Olive Oils , 2013 .
[5] Giorgio Tomasi,et al. Alignment of 1D NMR Data using the iCoshift Tool: A Tutorial , 2013 .
[6] Marina Cocchi,et al. Exploratory Data Analysis , 2013 .
[7] Chuanyi Ji,et al. Combinations of Weak Classifiers , 1996, NIPS.
[8] Peter Christen,et al. Data Pre-Processing , 2012 .
[9] Desire L. Massart,et al. Looking for Natural Patterns in Analytical Data, 2. Tracing Local Density with OPTICS , 2002, J. Chem. Inf. Comput. Sci..
[10] A. M. Gil,et al. Composition of beer by 1H NMR spectroscopy: effects of brewing site and date of production. , 2006, Journal of agricultural and food chemistry.
[11] Age K. Smilde,et al. Principal Component Analysis , 2003, Encyclopedia of Machine Learning.
[12] P. Alam. ‘U’ , 2021, Composites Engineering: An A–Z Guide.
[13] John H Kalivas,et al. Consensus Classification Using Non-Optimized Classifiers. , 2018, Analytical chemistry.
[14] J. Duus,et al. Quantification of organic and amino acids in beer by 1H NMR spectroscopy. , 2004, Analytical chemistry.
[15] Romà Tauler,et al. Multivariate Curve Resolution (MCR) from 2000: Progress in Concepts and Applications , 2006 .
[16] A. Tárrega,et al. The impact of hop bitter acid and polyphenol profiles on the perceived bitterness of beer. , 2016, Food chemistry.
[17] José Manuel Andrade,et al. Procrustes rotation in analytical chemistry, a tutorial , 2004 .
[18] Federico Marini,et al. Artificial neural networks in foodstuff analyses: Trends and perspectives A review. , 2009, Analytica chimica acta.
[19] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[20] I. Jolliffe. Principal Component Analysis , 2005 .
[21] Rasmus Bro,et al. Data Pre-processing , 2009 .
[22] B. Walczak,et al. Relating gas chromatographic profiles to sensory measurements describing the end products of the Maillard reaction. , 2011, Talanta.
[23] Seungjin Choi,et al. Independent Component Analysis , 2009, Handbook of Natural Computing.
[24] Teuvo Kohonen,et al. Essentials of the self-organizing map , 2013, Neural Networks.
[25] W. Mäntele,et al. The Good Vibrations of Beer. The Use of Infrared and UV/Vis Spectroscopy and Chemometry for the Quantitative Analysis of Beverages , 2012 .
[26] S. M. Smedley. COLOUR DETERMINATION OF BEER USING TRISTIMULUS VALUES , 1992 .
[27] P. Alam. ‘T’ , 2021, Composites Engineering: An A–Z Guide.
[28] Josef Kittler,et al. Combining classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[29] Yan-Yong Xu,et al. Weak learning algorithm for multi-label multiclass text categorization , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.
[30] Peter Filzmoser,et al. Introduction to Multivariate Statistical Analysis in Chemometrics , 2009 .
[31] Francesco Savorani,et al. Interval-Based Chemometric Methods in NMR Foodomics , 2013 .
[32] Andrea D. Magrì,et al. Artificial neural networks in chemometrics: History, examples and perspectives , 2008 .
[33] A. M. Gil,et al. High-resolution nuclear magnetic resonance spectroscopy and multivariate analysis for the characterization of beer. , 2002, Journal of agricultural and food chemistry.
[34] Andreas Stephan,et al. Comprehensive sensomics analysis of hop-derived bitter compounds during storage of beer. , 2011, Journal of agricultural and food chemistry.
[35] Andrea Marchetti,et al. A mid level data fusion strategy for the Varietal Classification of Lambrusco PDO wines , 2014 .
[36] Subhash C. Bagui,et al. Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.
[37] Roberto Beghi,et al. Rapid evaluation of craft beer quality during fermentation process by vis/NIR spectroscopy , 2014 .
[38] Hans-Peter Kriegel,et al. OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.
[39] Dirk W. Lachenmeier,et al. Rapid quality control of spirit drinks and beer using multivariate data analysis of Fourier transform infrared spectra , 2007 .
[40] Israel Spiegler,et al. Investigating diversity of clustering methods: An empirical comparison , 2007, Data Knowl. Eng..
[41] C. Blanco,et al. Mass spectrometry-based metabolomics approach to determine differential metabolites between regular and non-alcohol beers. , 2014, Food chemistry.
[42] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[43] Alberto Ferrer,et al. A kernel‐based approach for fault diagnosis in batch processes , 2014 .
[44] Hans-Gerd Löhmannsröben,et al. Quantifying Ethanol Content of Beer Using Interpretive Near-Infrared Spectroscopy , 2004, Applied spectroscopy.
[45] Michael C. Hout,et al. Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.
[46] A. M. Gil,et al. Characterization of the aromatic composition of some liquid foods by nuclear magnetic resonance spectrometry and liquid chromatography with nuclear magnetic resonance and mass spectrometric detection , 2003 .
[47] Maria Fernanda Pimentel,et al. Projection pursuit and PCA associated with near and middle infrared hyperspectral images to investigate forensic cases of fraudulent documents , 2017 .
[48] P. D. Wentzell,et al. Other Topics in Soft-Modeling: Maximum Likelihood-Based Soft-Modeling Methods-2.25 , 2009 .
[49] O. Busto,et al. Discrimination and sensory description of beers through data fusion. , 2011, Talanta.
[50] J. Izaac,et al. The eigenvalue problem , 2021, Practical Numerical Mathematics with MATLAB.
[51] G. Perretti,et al. Determination of free fatty acids in beer. , 2017, Food chemistry.
[52] Abdel-Ouahab Boudraa,et al. Dempster-Shafer's Basic Probability Assignment Based on fuzzy Membership Functions , 2009, Progress in Computer Vision and Image Analysis.
[53] Roberto Todeschini,et al. Distances and Other Dissimilarity Measures in Chemometrics , 2015 .
[54] António S. Barros,et al. Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis. , 2011, Analytica chimica acta.
[55] Tsuyoshi Murata,et al. {m , 1934, ACML.
[56] A. M. Gil,et al. Application of NMR spectroscopy and LC-NMR/MS to the identification of carbohydrates in beer. , 2003, Journal of agricultural and food chemistry.
[57] Dirk W Lachenmeier,et al. Quality control of beer using high-resolution nuclear magnetic resonance spectroscopy and multivariate analysis , 2005 .
[58] G. Perretti,et al. Characterization of the volatile profiles of beer using headspace solid-phase microextraction and gas chromatography-mass spectrometry. , 2014, Journal of the science of food and agriculture.
[59] Andrea D. Magrì,et al. Data-fusion for multiplatform characterization of an Italian craft beer aimed at its authentication. , 2014, Analytica chimica acta.
[60] Carolina S. Silva,et al. Procrustes rotation as a diagnostic tool for projection pursuit analysis. , 2015, Analytica Chimica Acta.
[61] José Manuel Amigo,et al. Assessment of the sugars and ethanol development in beer fermentation with FT-IR and multivariate curve resolution models , 2014 .
[62] Ickjai Lee,et al. Common Clustering Algorithms , 2020, Comprehensive Chemometrics.
[63] Ricard Boqué,et al. Data fusion methodologies for food and beverage authentication and quality assessment - a review. , 2015, Analytica chimica acta.
[64] Romà Tauler,et al. MCR-ALS GUI 2.0: New features and applications , 2015 .
[65] John W. Tukey,et al. A Projection Pursuit Algorithm for Exploratory Data Analysis , 1974, IEEE Transactions on Computers.
[66] Vili Podgorelec,et al. Decision trees , 2018, Encyclopedia of Database Systems.
[67] Olivier Debeir,et al. Combining Different Methods and Numbers of Weak Decision Trees , 2002, Pattern Analysis & Applications.
[68] P. Alam. ‘A’ , 2021, Composites Engineering: An A–Z Guide.
[69] A. M. Gil,et al. Multivariate analysis of NMR and FTIR data as a potential tool for the quality control of beer. , 2004, Journal of agricultural and food chemistry.
[70] P. Alam. ‘E’ , 2021, Composites Engineering: An A–Z Guide.
[71] Beata Walczak,et al. Density-Based Clustering Methods , 2009 .
[72] Aapo Hyvärinen,et al. Independent component analysis: recent advances , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[73] F Savorani,et al. icoshift: A versatile tool for the rapid alignment of 1D NMR spectra. , 2010, Journal of magnetic resonance.