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ABSTRACT A robust method for multiplicative scatter correction (MSC) in infrared spectroscopy is presented. Using quantile regression, the outlier wavelengths (concentration‐dependent wavelengths) that are irrelevant to the regression are identified and therefore excluded from the regression model. This new MCS method, which could be implemented in its simple or extended form, is much simpler than the recently proposed methods and has only one hyperparameter (the quantile value) to be adjusted. To achieve this, a scoring function based on residual analysis can automatically determine the correct quantile value. The method is first explained using simulation data sets and then its validation is explained by analysing some experimental data sets. It was found that our new method can perform well in the presence of strong outlying variables. On the other hand, when the data sets are not associated outlying wavelengths, this method behaves similarly to the conventional MSC method. [ABSTRACT FROM AUTHOR]

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton ( 1 H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1 H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1 H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1 H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows ( n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using 1 H nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the 1 H NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q 2 of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized 1 H NMR spectral recordings.

Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1 H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)