Remote monitoring of volatiles by ion mobility spectrometry with wireless data transmission and centralized data analysis

Abstract

Volatile organic compounds (VOCs) are omnipresent in daily life, industry, and science. Among many VOCs, some can affect the food quality while others are environmental pollutants. We previously developed a portable pen-probe ion-mobility spectrometry (IMS)-based analyzer for in situ detection of VOCs emanating from surfaces and wireless transmission of the acquired spectra. In the current work, we have improved the platform with regard to its analytical performance and connectivity. The acquired data can be transmitted to a central computer via a long-range wide-area network, in the absence of a mobile phone network. When the data are deposited on the cloud, a program written in Python performs data treatment in order to spot abnormal spectral patterns. The ion-mobility spectra are binned and subjected to principal component analysis (PCA). The PCA scoring plots are updated and available for viewing on a dedicated website as soon as new data are transmitted from the field. Field analysis, relying on a distributed network of such IMS-based sensing devices and centralized data treatment, can potentially shorten the response time to emerging events. The limits of detection for pyrrolidine, trimethylamine, 1,4-diaminobutane, 1,5-diaminopentane, 2,4-lutidine, and (−)-nicotine are in the range of 0.18–21.71 nmol. The resolving power values are in the range of 38.8–69.1. The platform was tested by following a food degradation process.

Graphical abstract: Remote monitoring of volatiles by ion mobility spectrometry with wireless data transmission and centralized data analysis

Supplementary files

Article information

Article type
Paper
Submitted
02 Aug 2022
Accepted
12 Sep 2022
First published
12 Sep 2022
This article is Open Access
Creative Commons BY license

Digital Discovery, 2022, Advance Article

Remote monitoring of volatiles by ion mobility spectrometry with wireless data transmission and centralized data analysis

H. Ou, K. Buchowiecki and P. L. Urban, Digital Discovery, 2022, Advance Article , DOI: 10.1039/D2DD00080F

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements