Agricultural & Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, USA.
Analyst. 2018 Apr 30;143(9):2066-2075. doi: 10.1039/c8an00065d.
Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Protein-ligand binding sensors have enormous potential for biosensing, but achieving accuracy in complex solutions is a major challenge. There is a need for simple post hoc analytical tools that are not computationally expensive, yet provide near real time feedback on data derived from impedance spectra. Here, we show the use of a simple, open source support vector machine learning algorithm for analyzing impedimetric data in lieu of using equivalent circuit analysis. We demonstrate two different protein-based biosensors to show that the tool can be used for various applications. We conclude with a mobile phone-based demonstration focused on the measurement of acetone, an important biomarker related to the onset of diabetic ketoacidosis. In all conditions tested, the open source classifier was capable of performing as well as, or better, than the equivalent circuit analysis for characterizing weak/transient interactions between a model ligand (acetone) and a small chemosensory protein derived from the tsetse fly. In addition, the tool has a low computational requirement, facilitating use for mobile acquisition systems such as mobile phones. The protocol is deployed through Jupyter notebook (an open source computing environment available for mobile phone, tablet or computer use) and the code was written in Python. For each of the applications, we provide step-by-step instructions in English, Spanish, Mandarin and Portuguese to facilitate widespread use. All codes were based on scikit-learn, an open source software machine learning library in the Python language, and were processed in Jupyter notebook, an open-source web application for Python. The tool can easily be integrated with the mobile biosensor equipment for rapid detection, facilitating use by a broad range of impedimetric biosensor users. This post hoc analysis tool can serve as a launchpad for the convergence of nanobiosensors in planetary health monitoring applications based on mobile phone hardware.
基于生物受体与目标分析物之间弱/瞬时相互作用的小分子测量的阻抗生物传感器对检测电子学来说是一个挑战，尤其是在现场研究或复杂基质分析中。蛋白质-配体结合传感器在生物传感方面具有巨大的潜力，但在复杂溶液中实现准确性是一个主要挑战。需要简单的事后分析工具，这些工具不计算昂贵，但能提供阻抗谱数据的近实时反馈。在这里，我们展示了使用简单的、开源的支持向量机学习算法来分析阻抗数据，而不是使用等效电路分析。我们展示了两种不同的基于蛋白质的生物传感器，以表明该工具可用于各种应用。我们最后展示了一个基于手机的演示，重点是测量丙酮，这是与糖尿病酮症酸中毒发作有关的重要生物标志物。在所有测试的条件下，开源分类器都能够像等效电路分析一样，或者更好地，用于表征模型配体（丙酮）和一种源自采采蝇的小型化学感觉蛋白之间的弱/瞬时相互作用。此外，该工具的计算要求低，便于移动采集系统（如手机）使用。该协议通过 Jupyter 笔记本（一种可用于手机、平板电脑或计算机使用的开源计算环境）部署，代码用 Python 编写。对于每个应用程序，我们提供了英语、西班牙语、普通话和葡萄牙语的分步说明，以促进广泛使用。所有代码都基于 scikit-learn，这是一个用 Python 语言编写的开源软件机器学习库，并在 Jupyter 笔记本中处理，这是一个用于 Python 的开源网络应用程序。该工具可以很容易地与移动生物传感器设备集成，用于快速检测，便于广泛的阻抗生物传感器用户使用。这种事后分析工具可以作为纳米生物传感器在基于手机硬件的行星健康监测应用中的融合的起点。
