ff7bd7f5b4cc40187a9e59ce38163954 pmc_27792165.pdf 4cf3b040477489d19f76296546866a9370221b60 pmc_27792165.pdf 42631d0870053cfbcfa46ce60b8b0768dfdb7a7ba2c8344c19465cd831b6cddc pmc_27792165.pdf Title: Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods Subject: Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization. Keywords: machine learning; biosensors; glucose-oxidase; neural networks; support vector machines; PLS; multivariate polynomial regression; optimization Author: Felix F. Gonzalez-Navarro, Margarita Stilianova-Stoytcheva, Livier Renteria-Gutierrez, Lluís A. Belanche-Muñoz, Brenda L. Flores-Rios and Jorge E. Ibarra-Esquer Creator: LaTeX with hyperref package Producer: pdfTeX-1.40.15 CreationDate: Wed Oct 26 06:22:27 2016 CEST ModDate: Wed Oct 26 06:22:27 2016 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 13 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1748044 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- NCFCLR+URWPalladioL-Ital Type 1 Custom yes yes no 77 0 KWMMHS+URWPalladioL-Bold Type 1 Custom yes yes no 78 0 LSBWRB+URWPalladioL-Roma Type 1 Custom yes yes no 79 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 83 0 DZMKED+PazoMath-Italic Type 1 Builtin yes yes no 112 0 GSBQOU+CMSY10 Type 1 Builtin yes yes no 113 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 134 0 JLYSBA+PazoMath Type 1 Builtin yes yes no 136 0 FORVKJ+CMEX10 Type 1 Builtin yes yes no 142 0 GLYKSY+URWPalladioL-BoldItal Type 1 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