Multi-layer perceptron for detection of different class antibiotics from visual fluorescence response of a carbon nanoparticle-based multichannel array sensor

人工智能 计算机科学 荧光 碳纳米颗粒 班级(哲学) 感知器 图层(电子) 纳米颗粒 碳纤维 模式识别(心理学) 人工神经网络 纳米技术 材料科学 光学 算法 物理 复合数
作者
Saptarshi Mandal,Dipanjyoti Paul,Sriparna Saha,Prolay Das
出处
期刊:Sensors and Actuators B-chemical [Elsevier BV]
卷期号:360: 131660-131660 被引量:18
标识
DOI:10.1016/j.snb.2022.131660
摘要

Lack of automated accurate decision-making along with an on-site detection system impedes the identification of substances of environmental concern. In pursuit of making this feasible, we interconnected our optical fluorescence array sensing strategy with the predictive analytics of artificial intelligence. Herein, we developed a Carbon Nanoparticle-based nine-channel fluorescence array sensing method for the detection of six antibiotics of different classes that are precariously dumped in the environment from various industrial and animal husbandry sources. The fluorescence responses of the arrays in the presence or absence of six antibiotics were captured digitally and these were utilized as feature values for the identification of classes using machine learning and deep learning algorithms. Among the seven tested multi-class classification algorithms, Multi-layer Perceptron (MLP) with Generative Adversarial Nets stimulated augmented data set (Aug-MLP) outdid the others in recognizing the antibiotics. Most importantly, the performance of Aug-MLP is comparable to fluorescence spectroscopic discrimination that outclasses human visual judgment. The whole methodology was found to adapt well in real samples like extracts of poultry feeds. In a nutshell, a nanotechnology-deep learning interfaced semi-automated on-site multi-class antibiotic detection strategy has been developed that could be extended for inexpensive and expedited detection of other chemical entities. • Generative Adversarial Nets (GANs) have been used for the first time in visual fluorescence-based sensing. • Carbon nanoparticle-based visual fluorescence array sensor assimilated to predictive analytics of artificial intelligence. • Seven multi-class supervised algorithms employed to recognise various antibiotics through CMYK extraction of digital images. • Generative Adversarial Nets (GANs) aided Multi-Layer Perceptron (MLP) surpassed visual judgement to detect antibiotics. • Nanotechnology-deep learning interfaced semi-automated, inexpensive, point-of-care, antibiotic detection strategy developed.
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