Electrochemical sensor arrays have shown potential for fast and untargeted chemical analysis of multi-component media, enabling simultaneous quantification of multiple analytes and estimation of liquid properties that could correlate with human sensory perception. In particular, potentiometric electronic tongues (e-tongues) have been explored as promising alternative tools for chemical analysis in various applications, ranging from traceability of goods to characterization of food products for quality control and innovation processes. The reduced complexity of non-selective sensor fabrication and the ease of potentiometric transduction make these devices suitable for portable and decentralized chemical analysis. However, the interpretation of the sensor array response has always been a challenge due to the inherent cross-sensitivity of the sensing materials and the corresponding combinatorial signals arising upon sensor interaction with a liquid. Machine learning can help recognizing and mapping signal patterns and various data processing, unsupervised and supervised techniques have been proposed to calibrate e-tongue devices. These methods usually require exposing the sensor array to a set of liquids with known properties that serve as training base to build a calibration model. Therefore, the quality and quantity of tests is crucial to boost sensor performances. Nevertheless, performing an extensive number of measurements could be extremely time-consuming and results to be tedious to achieve in practice for certain use-cases. In this context, advances in deep learning and machine learning models have shown potential to accelerate chemical and materials discovery when combined with high-throughput experimentation, highlighting the benefits of AI-assisted research practices. Moreover, the recent advent of multi-domain and multi-task models trained by self-supervision, so-called foundation models, bears also promises for extending learnt representations across multiple fields, thus counteracting the reduced data availability in certain applications, and benefiting from information exchange across domains. Thus, in the present contribution we propose extending this approach to data-driven chemical sensors. More specifically, we leverage transfer learning based on fingerprints pretrained in other domains to model new instrument/sensor data representations. Herein, we demonstrate how the output of a model system comprising an integrated electrochemical sensor array for analysis of multi-component liquids can be encoded as image representations to leverage existing deep learning computer vision models pretrained on large collections of image data. The models effectively extract features from these representations and feed specific model heads to perform downstream tasks. Firstly, an integrated sensor array comprising 16 polymeric sensors was fabricated through electrodeposition on conventional electroless nickel immersion gold (ENIG) electrodes. The conductive polymers (PEDOT, PPy, PANI and PAPBA) were synthesized by chronoamperometry or cyclic voltammetry in a three-electrode configuration and were enriched with doping agents for enhanced sensitivity. 15 linearly independent differential voltages between these polymeric sensors were measured during the transition of the sensor array from a reference solution to a test solution, thereby obviating the need for a conventional reference electrode. Indeed, the use of low-selective polymeric sensors for potentiometric measurements does not necessarily require integration of reference electrodes, which are known to be unpractical for remote sensing applications. Training data were obtained by alternatively immersing the sensor array in reference (120 s) and test (60 s) solutions continuously using an automated test rig. The 15 raw time-series data from the sensor array were processed and concatenated to yield a spectral response, which was smoothed by means of the moving average technique and standardized using Standard Normal Variate (SNV). The obtained spectra were encoded into image representations using the Gramian Angular Summation Field transformation. Off-the-shelf features were generated leveraging pretrained neural networks developed to classify natural images and applied to the “sensor images”. Dimensionality reduction through Principal Component Analysis (PCA) yielded a set of features that could then be used to train machine learning classifiers and regressors. The pipeline was applied to generate visual fingerprints of multiple beverages, proving full discrimination of liquid types (mineral waters, coffees, fruit juices, soft beverages and wines). On a model dataset comprising 11 Italian red wines, it was demonstrated that image fingerprints of samples enabled class identification with a mean accuracy ~95%. The results demonstrate the successful creation of a new representation of the chemical sensing space which achieves comparable performance as domain-specific hand-crafted feature selection. The present contribution represents an example of integration of data processing techniques and publicly available libraries/models to support transfer of methodologies across domains. We believe this approach could be disruptive in the field of electrochemical sensor arrays, especially for processing e-tongues and e-noses response and enhancing capabilities of data-driven chemical sensors. Figure 1
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Aug 3, 2024
Zihan Ning + 10
Open Access