Few-Shot Learning-Based, Long-Term Stable, Sensitive Chemosensor for On-Site Colorimetric Detection of Cr(VI)

Abstract

The rapid emergence of deep learning, e.g., deep convolutional neural networks (DCNNs) as one-click image analysis with super-resolution, has already revolutionized colorimetric determination. But it is severely limited by its data-hungry nature, which is overcome by combining the generative adversarial network (GAN), i.e., few-shot learning (FSL). Using the same amount of real sample data, i.e., 414 and 447 samples as training and test sets, respectively, the accuracy could be increased from 51.26 to 85.00% because 13,500 antagonistic samples are created and used by GAN as the training set. Meanwhile, the generated image quality with GAN is better than that with the commonly used convolution self-encoder method. The simple and rapid on-site determination of Cr(VI) with 1,5-diphenylcarbazide (DPC)-based test paper is a favorite for environment monitoring but is limited by unstable DPC, poor sensitivity, and narrow linear range. The chromogenic agent of DPC is protected by the blending of polyacrylonitrile (PAN) and then loaded onto thin chromatographic silica gel (SG) as a Cr(VI) colorimetric sensor (DPC/PAN/SG); its stability could be prolonged from 18 h to more than 30 days, and its repeatable reproducibility is realized via facile electrospinning. By replacing the traditional Ed method with DCNN, the detection limit is greatly improved from 1.571 mg/L to 50.00 μg/L, and the detection range is prolonged from 1.571–8.000 to 0.0500–20.00 mg/L. The complete test time is shortened to 3 min. Even without time-consuming and easily stained enrichment processing, its detection limit of Cr(VI) in the drinking water can meet on-site detection requirements by USEPA, WHO, and China.

Cr-Detector: A simple chemosensing system for onsite Cr (VI) detection in water

“…The DPC-based sensor is one the favourites among several other chromogens for environment monitoring. Despite its advantages, the use of DPC is limited due to its unstable nature, reduced sensitivity, and narrow linear range [ 29 ]. Research works have been conducted to increase the stability of DPC by coating various polymers, which, moreover, increases the sensor fabrication cost.…”

“…Moreover, the advancement in soft computing has revolutionized data analytics for sensor development. The rapid emergence of deep learning has been successfully applied to improve the analytical performance of colorimetric determination (e.g., UV-Vis spectrophotometer and colorimetric test paper), including denoising, recognition, and summary of every small characteristic change from each image [49][50][51][52]. One example is Deep Convolutional Neural Networks (DCNNs) [51].…”

“…The rapid emergence of deep learning has been successfully applied to improve the analytical performance of colorimetric determination (e.g., UV-Vis spectrophotometer and colorimetric test paper), including denoising, recognition, and summary of every small characteristic change from each image [49][50][51][52]. One example is Deep Convolutional Neural Networks (DCNNs) [51]. DCNNs have excellent potential to increase the sensitivity and selectivity of any biosensor by rapid and effective decision generation.…”

Nitrogen‐Containing Functional Groups Dominate the Molecular Absorption of Water‐Soluble Humic‐Like Substances in Air From Nanjing, China Revealed by the Machine Learning Combined FT‐ICR‐MS Technique

“…As the workflow shown in Figure 1, then, an approach using the few‐shot learning (FSL) method (FSL Abs ) was built to decipher the relationship from Abs value to the relative intensity of molecular markers in 12 samples determined by the ESI‐FT‐ICR‐MS (see Text S6 in Supporting Information ) (Wright & Ziegler, 2017). FSL is an algorithm of the ML that is aimed to learn the underlying pattern from a few samples (Parnami & Lee, 2022), and has been widely used in previous object detection (Kisantal et al., 2019), cheminformatics (Chen et al., 2023), and environmental studies (Huang et al., 2023). Here, we used the synthetic minority over‐sampling technique (SMOTE) (Chawla et al., 2002) combined with the random forest (RF) algorithm provided by Ranger package (Fan et al., 2023; Hong, Cao, Fan, Lin, Bao, et al., 2022; Wright & Ziegler, 2017) to successfully build an FSL model without the risk of overfitting (has been proved in a 55 × 35799 ESI‐FT‐ICR‐MS data set, see details in Text S8 of the Supporting Information ) (Belgiu & Drăguţ, 2016; Cortes‐Ciriano & Bender, 2015; Jablonka et al., 2020), and then proved by the validation data set and model outputs (Text S9 in Supporting Information ) (Arulkumaran et al., 2017).…”

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

“…Huang et al developed a new ML model to support a nanofiber-based colorimetric sensor for environmental Cr(VI) monitoring. [168] The authors developed a novel few-shot learning ML model, based on a combination of a deep CNN and a generative adversarial network, to extract color features from smartphone images of the colorimetric sensor, leading to an increase in accuracy from 51% to 85%. Furthermore, the implementation of the few-shot learning improved the Cr(VI) detection limit from 1.571 to 0.05 mg L −1 , and allowed for accurate Cr(VI) detection in water samples in just 3 min.…”