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

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.