Selective Detection of Mixtures via a Single Nonselective Sensor—Making the Unworkable Sensor Workable by Machine Learning

Abstract

Highly selective and sensitive detection of chemical mixtures by a single sensor consistently suffers from overlapping response signals. Previous researchers have to make a detour to avoid overlapping through preparing selective sensor materials, tedious preseparation, and time‐consuming operation. Nowadays, machine learning (ML), as one remarkable branch of artificial intelligence (AI), possesses advantages to modernize pathways to approach chemical challenges and provides a novel short cut for chemical mixture detection. Herein, a challenging example of a single nonselective cataluminescence (CTL) sensor with nonequilibrium and especially nonlinear responses is taken for chemical mixture detection. Intelligent recurrent neural networks (RNNs) with gated recurrent units are applied to directly predict mixture component contents from overlapping signals and achieve 100% and 92.22% of prediction accuracy for known and unknown samples, respectively. Furthermore, the physical law of response overlapping is revealed to support the mechanism of the proposed approach. The introduction of RNN into selective sensing not only initiates a distinctive and simple pathway for selective quantitative detection of mixtures via a single nonselective sensor but also can inspire future intelligent detection approaches in analytical chemistry, biological analysis, environmental monitoring, healthcare, clinical therapy, etc.

Empowering Electrochemical Biosensors with AI: Overcoming Interference for Precise Dopamine Detection in Complex Samples

“…The solution described in this work can be categorized as the next step in the field of Computational chemistry Machine learning assisted (CompChem + ML), in which the ML would be directly installed into the electrochemical readout system itself. [ 18 ] Furthermore, ML‐based methods have been demonstrated capable of quantitative detection of chemistry mixtures via a single nonselective sensor, [ 19 ] metabolites, [ 20 ] and other biomolecules. [ 21 ]…”

Sniffing Like a Wine Taster: Multiple Overlapping Sniffs (MOSS) Strategy Enhances Electronic Nose Odor Recognition Capability

“…The luminescence of CTL reaction was collected and converted into digital signals by a photomultiplier tube (PMT), purchased from Hamamatsu Photonics Co., Ltd. It is worth mentioning that the previous study [46] has proved that the air flow inside the CTL sensor is laminar and can be simplified into a 2D model. The CTL sensor chamber is composed of a ϕ20 × 50 mm quartz tube which is represented in the simulation model as a 40 × 30 mm rectangle.…”

“…Previous researchers delved into sensing mechanisms of various gases in search of distinctive signal features. [46][47][48] This section demonstrates how adopting the multiple overlapping sniffs (MOSS) strategy provides more differentiated sensing signals, effectively enhancing VOC recognition. In this experiment, only sensor No.1 is applied to recognize three kinds of standard VOC gases, that is, butane, ethylene and acetylene (C 4 H 10 , C 2 H 4 , and C 2 H 2 ).…”