On the Limit of Detection in Infrared Spectroscopic Imaging

Abstract

Infrared (IR) spectroscopic imaging instruments’ performance can be characterized and optimized by an analysis of their limit of detection (LoD). Here we report a systematic analysis of the LoD for Fourier transform IR (FT-IR) and discrete frequency IR (DFIR) imaging spectrometers. In addition to traditional measurements of sample and blank data, we propose a decision theory perspective to pose the determination of LoD as a binary classification problem under different assumptions of noise uniformity and correlation. We also examine three spectral analysis approaches, namely absorbance at a single frequency, sum of absorbance over selected frequencies and total spectral distance – to suit instruments that acquire discrete or contiguous spectral bandwidths. The analysis is validated by refining the fabrication of a bovine serum albumin protein microarray to provide eight uniform spots from 2.8 nL of solution for each concentration over a wide range (0.05 -10 mg/mL). Using scanning parameters that are typical for each instrument, we estimate a LoD of 0.16 mg/mL and 0.12 mg/mL for widefield and line scanning FT-IR imaging systems, respectively, usingthespectraldistanceapproach,and0.22mg/mLand0.15mg/mL using an optimal set of discrete frequencies. As expected, averaging and the use of post-processing techniques such as minimum noise fraction (MNF) transformation results in LoDs as low as 0.075 mg/mL that correspond to a spotted protein mass of 112 fg/pixel. We emphasize that these measurements were conducted at typical imaging parameters for each instrument and can be improved using the usual trading rules of IR spectroscopy. This systematic analysis and methodology for determining the LoD can allow for quantitative measures of confidence in imaging an analyte’s concentration and a basis for further improving IR imaging technology.

Resolution Limit in Infrared Chemical Imaging

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Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets

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Amino-Termination of Silicon Carbide Nanoparticles

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