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Acephate is an organophosphorus pesticide (OP) that is widely used to control insects in agricultural fields such as in vegetables and fruits. Toxic OPs can enter human and animal bodies and eventually lead to chronic or acute poisoning. However, traditional enzyme inhibition and colorimetric methods for OPs detection usually require complicated detection procedures and prolonged time and have low detection sensitivity. High-sensitivity monitoring of trace levels of acephate residues is of great significance to food safety and human health. Here, we developed a simple method for ultrasensitive quantitative detection of acephate based on the carbon quantum dot (CQD)-mediated fluorescence inner filter effect (IFE). In this method, the fluorescence from CQDs at 460 nm is quenched by 2,3-diaminophenazine (DAP) and the resulting fluorescence from DAP at 558 nm is through an IFE mechanism between CQDs and DAP, producing ratiometric responses. The ratiometric signal I 558 / I 460 was found to exhibit a linear relationship with the concentration of acephate. The detection limit of this method was 0.052 ppb, which is far lower than the standards for acephate from China and EU in food safety administration. The ratiometric fluorescence sensor was further validated by testing spiked samples of tap water and pear, indicating its great potential for sensitive detection of trace OPs in complex matrixes of real samples.
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This content will become publicly available on June 1, 2026
A dual-functional needle-based VOC sensing platform for rapid vegetable phenotypic classification
Volatile organic compounds (VOCs) are common constituents of fruits, vegetables, and crops, and are closely associated with their quality attributes, such as firmness, sugar level, ripeness, translucency, and pungency levels. While VOCs are vital for assessing vegetable quality and phenotypic classification, traditional detection methods, such as Gas Chromatography-Mass Spectrometry (GC-MS) and Proton Transfer Reaction Mass Spectrometry (PTR-MS) are limited by expensive equipment, complex sample reparation, and slow turnaround time. Additionally, the transient nature of VOCs complicates their detection using these methods. Here, we developed a paper-based colorimetric sensor array combined with needles that could: 1) induce vegetable VOC release in a minimally invasive fashion, and 2) analyze VOCs in situ with a smartphone reader device. The needle sampling device helped release specific VOCs from the studied vegetables that usually require mechanic stimulation, while maintaining the vegetable viability. On the other hand, the colorimetric sensor array was optimized for sulfur compound-based VOCs with a limit of detection (LOD) in the 1–25 ppm range, and classified fourteen different vegetable VOCs, including sulfoxides, sulfides, mercaptans, thiophenes, and aldehydes. By combining principal components analysis (PCA) nalysis, the integrated sensor platform proficiently discriminated between four vegetable subtypes originating from two major categories within 2 min of testing time. Additionally, the sensor demomstrated the capability to distinguish between different types of tested fruits and vegetables, including garlic, green pepper, and nectarine. This rapid and minimally invasive sensing technology holds great promise for conducting field-based vegetable quality monitoring.
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- Award ID(s):
- 2200038
- PAR ID:
- 10578872
- Publisher / Repository:
- Elsevior
- Date Published:
- Journal Name:
- Biosensors and Bioelectronics
- Volume:
- 278
- Issue:
- C
- ISSN:
- 0956-5663
- Page Range / eLocation ID:
- 117341
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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