Feasible band boundaries computation in bilinear matrix decomposition using essential data.

Background: Multivariate curve resolution methods are usually confronted with non-unique pure component factors. This rotational ambiguity can be represented by ranges of feasible profiles, which are equally compatible with the imposed constraints. Sensor-wise N-BANDS is an effective algorithm for the calculation of the bounds of feasible profiles in the presence of noise, but suffers from high computational cost.

Metabolomic and sphingolipidomic profiling of human hepatoma cells exposed to widely used pharmaceuticals.

Pharmaceutical compounds have become one of the main contaminants of emerging concern (CECs) due to their high usage and increased release into the environment. This study aims to assess the effects caused by three widely consumed hepatotoxic pharmaceutical compounds: an antibiotic (amoxicillin), an antiepileptic (carbamazepine), and an antidepressant (trazodone), on human health when indirectly exposed to toxicologically relevant concentrations (30, 15, and 7.5 μM for amoxicillin and carbamazepine, and 4, 2, and 1 μM for trazodone).

Multivariate curve resolution-based data fusion approaches applied in H NMR metabolomic analysis of healthy cohorts.

Background: Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach.

Data-independent acquisition (DIA) mode in liquid chromatography (LC) high-resolution mass spectrometry (HRMS) has emerged as a powerful strategy in untargeted metabolomics for detecting a broad range of metabolites. However, the use of this approach also represents a challenge in the analysis of the large datasets generated. The regions of interest (ROI) multivariate curve resolution (MCR) approach can help in the identification and characterization of unknown metabolites in their mixtures by linking their MS1 and MS2 DIA spectral signals.