Application of Machine Learning for the Determination of Damaged Starch Ratio as an Alternative to Medcalf and Gilles Principle

[1]  Wei Zhang,et al.  Electrochemically Generated Iodine Cations from a Glassy Carbon Electrode for Highly Selective Iodination of Anisole , 2022, Transactions of Tianjin University.

[2]  Peng Liu,et al.  EIS equivalent circuit model prediction using interpretable machine learning and parameter identification using global optimization algorithms , 2022, Electrochimica Acta.

[3]  Muhammet Fatih Aslan,et al.  A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest–Damaged Wheat Grain Detection , 2022, Food Analytical Methods.

[4]  Suyong Lee,et al.  Establishment of machine learning hyperparameters for predicting the extensional properties of noodles from the thermo-mechanical properties of wheat flour , 2022, Journal of Food Engineering.

[5]  A. Tapan,et al.  Data Mining and Application of Decision Tree Modelling on Electrochemical Data Used for Damaged Starch Detection , 2021, Gazi University Journal of Science Part A: Engineering and Innovation.

[6]  Hugo Thienpont,et al.  Combining optical spectroscopy and machine learning to improve food classification , 2021 .

[7]  M. E. Günay,et al.  Analysis and modeling of high-performance polymer electrolyte membrane electrolyzers by machine learning , 2021, International Journal of Hydrogen Energy.

[8]  M. Kim,et al.  Machine learning to electrochemistry: analysis of polymers and halide ions in a copper electrolyte , 2021, Electrochimica Acta.

[9]  Weimin Huang,et al.  Accelerating Optimizing the Design of Carbon‐based Electrocatalyst Via Machine Learning , 2021, Electroanalysis.

[10]  M. Kitagawa,et al.  Anodic and cathodic modification of glassy-carbon electrodes affect iodine electrochemistry , 2021 .

[11]  A. Manickavasagan,et al.  Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review , 2021, Current research in food science.

[12]  Jennifer L. M. Rupp,et al.  Text mining for processing conditions of solid-state battery electrolytes , 2020 .

[13]  Tong-Yi Zhang,et al.  Analytic formulas of peak current in cyclic voltammogram: Machine learning as an alternative way? , 2020, Journal of Chemometrics.

[14]  Xingjiu Huang,et al.  Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak , 2020 .

[15]  R. Compton,et al.  Iodine‐mediated Oxidation of Resveratrol. An Electroanalytical Study Using Platinum and Glassy Carbon Electrodes , 2019, Electroanalysis.

[16]  M. Erdem Günay,et al.  Decision tree analysis for efficient CO2 utilization in electrochemical systems , 2018, Journal of CO2 Utilization.

[17]  Costel Mironeasa,et al.  Multivariate analysis of wheat flour dough sugars, gas production, and dough development at different fermentation times , 2018 .

[18]  Fengwei Xie,et al.  Shear degradation of corn starches with different amylose contents , 2017 .

[19]  Ethem Alpaydin,et al.  Machine Learning: The New AI , 2016 .

[20]  R. Yıldırım,et al.  Analysis of past experimental data in literature to determine conditions for high performance in biodiesel production , 2016 .

[21]  F. Zhu.  Buckwheat starch: Structures, properties, and applications , 2016 .

[22]  M. Erdem Günay,et al.  Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells , 2016 .

[23]  Bilal I. Sowan,et al.  Using the interestingness measure lift to generate association rules , 2015 .

[24]  P. Halley,et al.  Shear degradation of molecular, crystalline, and granular structures of starch during extrusion , 2014 .

[25]  Charles Loubersac. Dhotel.  Damaged starch in the flour mill: how to reduce the electricity bill , 2012 .

[26]  M. Gidley,et al.  Cryo-milling of starch granules leads to differential effects on molecular size and conformation , 2011 .

[27]  Anders Hagfeldt,et al.  Characteristics of the iodide/triiodide redox mediator in dye-sensitized solar cells. , 2009, Accounts of chemical research.

[28]  C. Janiak,et al.  An inorganic starch-iodine model: the inorganic-organic hybrid compound {(C4H12N2)2[Cu(I)I4](I2)}n. , 2009, Chemical communications.

[29]  M. Orlik.  Self-organization in nonlinear dynamical systems and its relation to the materials science , 2009 .

[30]  A. Niazi,et al.  Least-squares support vector machines for simultaneous voltammetric determination of lead and tin: A comparison between LS-SVM and PLS in voltammetric data , 2008 .

[31]  Dirk Henrici,et al.  Analysis and Modeling , 2008 .

[32]  A. Dubat.  Collaborative Study Concerned with Measuring Damaged Starch Using an Amperometric Method , 2007 .

[33]  B. Grgur,et al.  Electrochemical oxidation of iodide in aqueous solution , 2006 .

[34]  Kurt Hornik,et al.  Introduction to arules – A computational environment for mining association rules and frequent item sets , 2009 .

[35]  C. Bessant,et al.  Multivariate Data Analysis in Electroanalytical Chemistry , 2002 .

[36]  K. Krischer,et al.  A Theoretical Study on Turing Patterns in Electrochemical Systems , 2000 .

[37]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[38]  E. Hamori,et al.  Kinetic investigation of the amylose-iodine reaction , 1971 .

[39]  P. Elving,et al.  Electrooxidation of halides at pyrolytic graphite electrode in aqueous and acetonitrile solutions , 1967 .

[40]  Alice D. Awtrey,et al.  The Rate Law and Mechanism of the Reaction of Iodine with Thiosulfate Ion: The Formation of the Intermediate S2O3I- , 1951 .