Fused adjacency matrices to enhance information extraction: The beer benchmark.

[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.