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. (30th March 2022) - Huang, Zhao-Jing; Luo, Jia-yi; Zheng, Feng-Ying; Li, Shun-Xing; Liu, Feng-Jiao; Lin, Lu-Xiu; Huang, Yong-Jun; Man, Shan; Cao, Gong-Xun; Huang, Xu-Guang
Graphical abstract: Highlights: In-field measurement of nitrite as carcinogen is important for food safety. A fully integrated colorimetric detection system for nitrite is offered. APP is combined with DCNN as visual monitoring platform. Validity period of test paper is prolonged from 7 d to more than 30 d. The accuracy of food classification is high as 91.33–100% Abstract: 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.