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

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.

Long-term stable, high accuracy, and visual detection platform for In-field analysis of nitrite in food based on colorimetric test paper and deep convolutional neural networks.

Nitrite is one of the most common carcinogens in daily food. Its simple, rapid, inexpensive, and in-field measurement is important for food safety, based on the requirements of the standard from Codex Alimentarius Commission and China. Using polyacrylonitrile (PAN) and thin layer silica gel (SG), p-aminophenylcyclic acid (SA) and naphthalene ethylenediamine hydrochloride (NEH), as carriers and chromogenic agents, respectively, PAN-NSS as nitrite color sensor is proposed. After fixing and protecting of SA and NEH with layer-upon-layer PAN, the validity period of the test paper can be prolonged from 7 days to more than 30 days. The reproducibility of PAN-NSS preparation is ensured by electrospinning. Combined with PAN-NSS, deep convolutional neural network (DCNN) and APP as a visual monitoring platform, which has the functions of rapid sampling, data processing and transmission, intuitive feedback, etc., and provides a fully integrated detection system for field detection.