Gonzalez-Navarro Felix F, Stilianova-Stoytcheva Margarita, Renteria-Gutierrez Livier, Belanche-Muñoz Lluís A, Flores-Rios Brenda L, Ibarra-Esquer Jorge E
Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali, B.C. 21290, Mexico.
Computer Science Department, Universitat Politecnica de Catalunya, Barcelona 08034, Spain.
Sensors (Basel). 2016 Oct 26;16(11):1483. doi: 10.3390/s16111483.
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.
生物传感器是一种小型分析设备，它集成了生物识别元件和物理化学换能器，用于将生物信号转换为电读数。如今，它们的技术吸引力在于其快速性能、高灵敏度和连续测量能力；然而，仍在进行全面的研究。本文旨在为这一不断发展的生物技术领域做出贡献，重点是从回归角度通过统计学习方法对葡萄糖氧化酶生物传感器（GOB）进行建模。我们借助几种机器学习算法，对在不同条件下（如温度、苯醌、pH值和葡萄糖浓度）具有因变量的GOB的安培响应进行建模。由于GOB响应的灵敏度与这些因变量密切相关，因此应优化它们之间的相互作用以最大化输出信号，为此使用了遗传算法和模拟退火算法。我们报告了一个模型，该模型显示出良好的泛化误差并且与优化结果一致。
