A Rapid and Simplified Approach to Correct Atmospheric Absorptions in Infrared Spectra.

Infrared (IR) spectroscopy is a powerful analytical technique used to identify and quantify different components within a sample. However, spectral interference from fluctuating concentrations of water vapor and CO2 in the measurement chamber can significantly impede the extraction of quantitative information. These temporal fluctuations cause absorption variations that interfere with the sample's spectrum, making accurate analysis challenging. While several techniques to overcome this problem exist in the literature, many are time-consuming or ineffective. We present a simple method utilizing just two sample spectra taken sequentially. The difference of these spectra, multiplied by a scaling factor, determined by minimization of the point-to-point spectral length, provides a correction spectrum. Subtracting this from the spectrum to be corrected results in a fully corrected spectrum. We demonstrate the effectiveness of this method via the improved ability to determine analyte concentration from corrected spectra over uncorrected spectra using a partial least square regression (PLSR) model. This technique therefore offers rapid, effective, and automated spectral correction, which is ideal for a nonexpert user in a clinical or industrial setting.

Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.

The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with machine learning as a point-of-care (POC) diagnostic platform, considering neonatal respiratory distress syndrome (nRDS), for which no POC currently exists, as an example. nRDS can be diagnosed by a ratio of less than 2.2 of two nRDS biomarkers, lecithin and sphingomyelin (L/S ratio), and in this study, ATR-FTIR spectra were recorded from L/S ratios of between 1.0 and 3.4, which were generated using purified reagents. The calibration of principal component (PCR) and partial least squares (PLSR) regression models was performed using 155 raw baselined and second derivative spectra prior to predicting the concentration of a further 104 spectra. A three-factor PLSR model of second derivative spectra best predicted L/S ratios across the full range (R2: 0.967; MSE: 0.014). The L/S ratios from 1.0 to 3.4 were predicted with a prediction interval of +0.29, -0.37 when using a second derivative spectra PLSR model and had a mean prediction interval of +0.26, -0.34 around the L/S 2.2 region. These results support the validity of combining ATR-FTIR with machine learning to develop a point-of-care device for detecting and quantifying any biomarker with an interpretable mid-infrared spectrum.

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomarkers, lecithin (L) and sphingomyelin (S) with the potential for point of care (POC) diagnosis and monitoring. The effects of varying other lipid species present in lung surfactant on the mid IR spectra used to train machine learning models are explored. This study presents a lung lipid model of five lipids present in lung surfactant and varies each in a systematic approach to evaluate the ability of machine learning models to predict the lipid concentrations, the L/S ratio and to quantify the uncertainty in the predictions using the jackknife + -after-bootstrap and variant bootstrap methods. We establish the L/S ratio can be determined with an uncertainty of approximately ±0.3 mol/mol and we further identify the 5 most prominent wavenumbers associated with each machine learning model.