A dual-functional needle-based VOC sensing platform for rapid vegetable phenotypic classification.

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Tác giả: Oindrila Hossain, Sina Jamalzadegan, Mingzhuo Li, Belinda Mativenga, Noor Mohammad, Aditi Dey Poonam, Alireza Velayati, Yan Wang, Qingshan Wei

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: England : Biosensors & bioelectronics , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 685263

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 preparation, 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) analysis, the integrated sensor platform proficiently discriminated between four vegetable subtypes originating from two major categories within 2 min of testing time. Additionally, the sensor demonstrates 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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