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Updated: Sep 26, 2025

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water
Published on: June 9, 2014
Paper-based multicolor sensor for on-site quantitative detection of 2,4-dichlorophenoxyacetic acid based on alkaline
Xingyan Ye1, Feng Zhang1, Lan Yang1
1State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
Abstract:
On-site quantitative analysis of pesticides is important for food safety. Colorimetric gold nanobipyramids (AuNBPs) sensors are powerful methods for on-site detection. However, a single quantitative method and the instability of AuNBPs in solution limit the practicability of those sensors. Here, a paper-based multicolor AuNBPs sensor involved a colorimeter-assisted method for quantifying color was developed for quantitative detection of 2,4-dichlorophenoxyacetic acid (2,4-D), a common herbicide. The novelty of this study lies in developing a general paper-based quantitative on-site method (PQOM) for colorimetric AuNBPs sensors. Firstly, a paper-based analytical device (PAD) consisting of a nylon membrane, absorbent cotton layers, and two acrylic plates was fabricated to deposit AuNBPs. We demonstrated the PAD could improve the stability of AuNBPs and the detection sensitivity of AuNBPs sensors. Then, a handheld colorimeter was first used to quantify the color change of AuNBPs on the PAD based on the CIELab color space. Finally, as proof of concept, the PQOM was successfully employed to quantify 2,4-D by combining with an alkaline phosphatase-mediated AuNBPs growth method. In this method, 2,4-D specifically inhibited alkaline phosphatase activity to suppress the generation of l-ascorbic acid, thereby mediating AuNBPs growth. The developed sensor exhibited seven 2,4-D concentration-related colors and detected as low as 50 ng mL-1 2,4-D by naked-eye observation and 18 ng mL-1 2,4-D by a colorimeter. It was applied to detect 2,4-D in the spiked rice and apple samples with good recovery rates (91.8-112.0%) and a relative standard deviation (n = 5) < 5%. The success of this study provides a sensing platform for quantifying 2,4-D on site.

