Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, USA.
Universidad Nacional Colombia-Palmira, Palmira Colombia.
Sci Rep. 2017 Jan 9;7:40203. doi: 10.1038/srep40203.
We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation by smartphone (BAQS), provides an opportunity for onsite analysis and quantitation of luminescent signals from biological and non-biological sensing elements which emit photons in response to an analyte. A simple cradle that houses the smartphone, sample tube, and collection lens supports the measuring platform, while noise reduction by ensemble averaging simultaneously lowers the background and enhances the signal from emitted photons. Five different types of smartphones, both Android and iOS devices, were tested, and the top two candidates were used to evaluate luminescence from the bioluminescent reporter Pseudomonas fluorescens M3A. The best results were achieved by OnePlus One (android), which was able to detect luminescence from ~10 CFU/mL of the bio-reporter, which corresponds to ~10 photons/s with 180 seconds of integration time.
我们报告了一种基于智能手机的设备和相关成像处理算法，可最大限度地提高标准智能手机相机的灵敏度，以检测出个位数 pW 的辐射通量强度。该硬件和软件被称为基于生物发光的智能手机分析物定量检测（BAQS），为现场分析和定量检测生物和非生物传感元件的发光信号提供了机会，这些元件在响应分析物时会发射光子。一个简单的支架，用于容纳智能手机、样品管和收集透镜，支持测量平台，而通过集合平均进行的降噪同时降低了背景并增强了发射光子的信号。我们测试了五种不同类型的智能手机，包括 Android 和 iOS 设备，然后选择前两名候选者来评估荧光假单胞菌 M3A 的生物发光报告基因的发光情况。结果OnePlus One（安卓系统）表现最佳，它能够检测到约 10 CFU/mL 的生物报告基因的发光，对应于 180 秒积分时间内约 10 个光子/s。
