A Combined Near-Infrared and Mid-Infrared Spectroscopic Approach for the Detection and Quantification of Glycine in Human Serum

Abstract

Serum is an important candidate in proteomics analysis as it potentially carries key markers on health status and disease progression. However, several important diagnostic markers found in the circulatory proteome and the low-molecular-weight (LMW) peptidome have become analytically challenging due to the high dynamic concentration range of the constituent protein/peptide species in serum. Herein, we propose a novel approach to improve the limit of detection (LoD) of LMW amino acids by combining mid-IR (MIR) and near-IR spectroscopic data using glycine as a model LMW analyte. This is the first example of near-IR spectroscopy applied to elucidate the detection limit of LMW components in serum; moreover, it is the first study of its kind to combine mid-infrared (25–2.5 μm) and near-infrared (2500–800 nm) to detect an analyte in serum. First, we evaluated the prediction model performance individually with MIR (ATR-FTIR) and NIR spectroscopic methods using partial least squares regression (PLS-R) analysis. The LoD was found to be 0.26 mg/mL with ATR spectroscopy and 0.22 mg/mL with NIR spectroscopy. Secondly, we examined the ability of combined spectral regions to enhance the detection limit of serum-based LMW amino acids. Supervised extended wavelength PLS-R resulted in a root mean square error of prediction (RMSEP) value of 0.303 mg/mL and R2 value of 0.999 over a concentration range of 0–50 mg/mL for glycine spiked in whole serum. The LoD improved to 0.17 mg/mL from 0.26 mg/mL. Thus, the combination of NIR and mid-IR spectroscopy can improve the limit of detection for an LMW compound in a complex serum matrix.

Application of Serum Mid-Infrared Spectroscopy Combined with Machine Learning in Rapid Screening of Breast Cancer and Lung Cancer

“…Te sample drying process is cumbersome and does not meet the practical needs of clinical practice. Related research reports in the past year have also shown that satisfactory results can be obtained from serum samples [15,[17][18][19]. It should be noted that although biochemical components require relatively high concentrations to prevent their biochemical characteristics from being masked, this study's results show that the differences between spectra are mainly concentrated in the wavenumber range (1700 cm −1 ∼1500 cm −1 ) that refects key protein structures.…”

“…Tulya et al improved the detection limit of low molecular weight amino acids by using glycine as a model analyte combined with mid-infrared (MIR) and near-infrared spectroscopy data, with a linear regression coefcient of determination R 2 reaching 0.997. Tis study successfully expanded the application limits of infrared spectroscopy technology [15]. Te potential of using biological specimen analysis as a diagnostic tool for cancer has been recognized for a long time [16].…”

Single-path supercontinuum laser spectroscopy combining MIR-ATR and NIR transmission

“…Considering the small sized dataset used for this study, the models were validated using a full cross validation (leave-one-out method). PLSR is commonly used for developing multivariate calibration models for infrared spectroscopy [7] with its ability to estimate physical and chemical properties from the spectra [8,9]. The model performance was assessed by the coefficient of determination (R 2 ) and the root mean square error (RMSE) of the cross-validation (CV) [10].…”