分析物
传感器阵列
计算机科学
人工智能
线性判别分析
模式识别(心理学)
机器学习
化学
物理化学
作者
Subrata Pandit,Tuseeta Banerjee,Indrajit Srivastava,Shuming Nie,Dipanjan Pan
出处
期刊:ACS Sensors
[American Chemical Society]
日期:2019-09-18
卷期号:4 (10): 2730-2737
被引量:80
标识
DOI:10.1021/acssensors.9b01227
摘要
Fluorescent array-based sensing is an emerging differential sensing platform for sensitive detection of analytes in a complex environment without involving a conventional "lock and key" type-specific interaction. These sensing techniques mainly rely on different optical pattern generation from a sensor array and their pattern recognition to differentiate analytes. Currently emerging, compelling pattern-recognition method, Machine Learning (ML), enables a machine to "learn" a pattern by training without having the recognition method explicitly programmed into it. Thus, ML has an enormous potential to analyze these sensing data better than widely used statistical pattern-recognition methods. Here, an array-based sensor using easy-to-synthesize carbon dots with varied surface functionality is reported, which can differentiate between eight different proteins at 100 nM concentration. The utility of using machine learning algorithms in pattern recognition of fluorescence signals from the array has also been demonstrated. In analyzing the array-based sensing data, Machine Learning algorithms like "Gradient-Boosted Trees" have achieved a 100% prediction efficiency compared to inferior-performing classical statistical method "Linear Discriminant Analysis".
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