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Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time

[Image: see text] The modern tools of chemistry excel at identifying a sample, but the cost, size, complexity, and power consumption of these instruments often preclude their use in resource-limited settings. In this work, we demonstrate a simple and low-cost method for identifying a sample based on...

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Autores principales: McKenzie, Brittney A., Robles-Najar, Jessica, Duong, Eric, Brisk, Philip, Grover, William H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487450/
https://www.ncbi.nlm.nih.gov/pubmed/31041378
http://dx.doi.org/10.1021/acscentsci.8b00860
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author McKenzie, Brittney A.
Robles-Najar, Jessica
Duong, Eric
Brisk, Philip
Grover, William H.
author_facet McKenzie, Brittney A.
Robles-Najar, Jessica
Duong, Eric
Brisk, Philip
Grover, William H.
author_sort McKenzie, Brittney A.
collection PubMed
description [Image: see text] The modern tools of chemistry excel at identifying a sample, but the cost, size, complexity, and power consumption of these instruments often preclude their use in resource-limited settings. In this work, we demonstrate a simple and low-cost method for identifying a sample based on visualizing how the sample changes over space and time in response to a perturbation. Different types of perturbations could be used, and in this proof-of-concept we use a dynamic temperature gradient that rapidly cools different parts of the sample at different rates. We accomplish this by first loading several samples into long parallel channels on a “microfluidic thermometer chip.” We then immerse one end of the chip in liquid nitrogen to create a dynamic temperature gradient along the channels, and we use an inexpensive USB microscope to record a video of how the samples respond to the changing temperature gradient. The video is then converted into several bitmap images (one per sample) that capture each sample’s response to the perturbation in both space (the y-axis; the distance along the dynamic temperature gradient) and time (the x-axis); we call these images “chronological fingerprints” or “chronoprints” of each sample. If two samples’ chronoprints are similar, this suggests that the samples are the same chemical substance or mixture, but if two samples’ chronoprints are significantly different, this proves that the samples are chemically different. Since chronoprints are just bitmap images, they can be compared using a variety of techniques from computer science, and in this work we use three different image comparison algorithms to quantify chronoprint similarity. As a demonstration of the versatility of chronoprints, we use them in three different applications: distinguishing authentic olive oil from adulterated oil (an example of the over $10 billion global problem of food fraud), identifying adulterated or counterfeit medication (which represents around 10% of all medication in low- and middle-income countries), and distinguishing the occasionally confused pharmaceutical ingredients glycerol and diethylene glycol (whose accidental or intentional substitution has led to hundreds of deaths). The simplicity and versatility of chronoprints should make them valuable analytical tools in a variety of different fields.
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spelling pubmed-64874502019-04-30 Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time McKenzie, Brittney A. Robles-Najar, Jessica Duong, Eric Brisk, Philip Grover, William H. ACS Cent Sci [Image: see text] The modern tools of chemistry excel at identifying a sample, but the cost, size, complexity, and power consumption of these instruments often preclude their use in resource-limited settings. In this work, we demonstrate a simple and low-cost method for identifying a sample based on visualizing how the sample changes over space and time in response to a perturbation. Different types of perturbations could be used, and in this proof-of-concept we use a dynamic temperature gradient that rapidly cools different parts of the sample at different rates. We accomplish this by first loading several samples into long parallel channels on a “microfluidic thermometer chip.” We then immerse one end of the chip in liquid nitrogen to create a dynamic temperature gradient along the channels, and we use an inexpensive USB microscope to record a video of how the samples respond to the changing temperature gradient. The video is then converted into several bitmap images (one per sample) that capture each sample’s response to the perturbation in both space (the y-axis; the distance along the dynamic temperature gradient) and time (the x-axis); we call these images “chronological fingerprints” or “chronoprints” of each sample. If two samples’ chronoprints are similar, this suggests that the samples are the same chemical substance or mixture, but if two samples’ chronoprints are significantly different, this proves that the samples are chemically different. Since chronoprints are just bitmap images, they can be compared using a variety of techniques from computer science, and in this work we use three different image comparison algorithms to quantify chronoprint similarity. As a demonstration of the versatility of chronoprints, we use them in three different applications: distinguishing authentic olive oil from adulterated oil (an example of the over $10 billion global problem of food fraud), identifying adulterated or counterfeit medication (which represents around 10% of all medication in low- and middle-income countries), and distinguishing the occasionally confused pharmaceutical ingredients glycerol and diethylene glycol (whose accidental or intentional substitution has led to hundreds of deaths). The simplicity and versatility of chronoprints should make them valuable analytical tools in a variety of different fields. American Chemical Society 2019-03-20 2019-04-24 /pmc/articles/PMC6487450/ /pubmed/31041378 http://dx.doi.org/10.1021/acscentsci.8b00860 Text en Copyright © 2019 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle McKenzie, Brittney A.
Robles-Najar, Jessica
Duong, Eric
Brisk, Philip
Grover, William H.
Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time
title Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time
title_full Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time
title_fullStr Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time
title_full_unstemmed Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time
title_short Chronoprints: Identifying Samples by Visualizing How They Change over Space and Time
title_sort chronoprints: identifying samples by visualizing how they change over space and time
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487450/
https://www.ncbi.nlm.nih.gov/pubmed/31041378
http://dx.doi.org/10.1021/acscentsci.8b00860
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