Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Miguela Martin, David Perez-Guaita, Dean W Andrew, Jack S. Richards, Bayden R. Wood, Philip Heraud

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Abstract

We demonstrate a method of quantification and detection of parasites in aqueous red blood cells (RBCs) by using a simple benchtop Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectrometer in conjunction with Multivariate Data Analysis (MVDA). 3D7 P. falciparum were cultured to 10% parasitemia ring stage parasites and used to spike fresh donor isolated RBCs to create a dilution series between 0-1%. 10 µL of each sample were placed onto the center of the ATR diamond window to acquire the spectrum. The sample data was treated to improve the signal to noise ratio and to remove the contribution of water, and then the second derivative was applied to resolve spectral features. The data were then analyzed using two types of MVDA: first Principal Component Analysis (PCA) to determine any outliers and then Partial Least Squares Regression (PLS-R) to build the quantification model.

Original languageEnglish
Article numbere56797
JournalJournal of visualized experiments : JoVE
Issue number141
DOIs
Publication statusPublished - 2 Nov 2018

Cite this

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title = "Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis",
abstract = "We demonstrate a method of quantification and detection of parasites in aqueous red blood cells (RBCs) by using a simple benchtop Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectrometer in conjunction with Multivariate Data Analysis (MVDA). 3D7 P. falciparum were cultured to 10{\%} parasitemia ring stage parasites and used to spike fresh donor isolated RBCs to create a dilution series between 0-1{\%}. 10 µL of each sample were placed onto the center of the ATR diamond window to acquire the spectrum. The sample data was treated to improve the signal to noise ratio and to remove the contribution of water, and then the second derivative was applied to resolve spectral features. The data were then analyzed using two types of MVDA: first Principal Component Analysis (PCA) to determine any outliers and then Partial Least Squares Regression (PLS-R) to build the quantification model.",
author = "Miguela Martin and David Perez-Guaita and Andrew, {Dean W} and Richards, {Jack S.} and Wood, {Bayden R.} and Philip Heraud",
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AU - Martin, Miguela

AU - Perez-Guaita, David

AU - Andrew, Dean W

AU - Richards, Jack S.

AU - Wood, Bayden R.

AU - Heraud, Philip

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