Current challenges in second‐order calibration of hyphenated chromatographic data for analysis of highly complex samples

Abstract

Coupling of multiway and multiset modeling methods with hyphenated chromatographic data for second-order calibration purposes allows the quantification of multiple target analytes in highly complex samples, which otherwise would be impossible or at least a very hard task using univariate calibration scenario. In this regard, there are some chromatographic challenges that complicate attaining the best quantification efficiency through the highlighted advantages such as increased sensitivity and selectivity and the well-known second-order advantage. The present paper tries to overview these issues and to address the most usable strategies for their handling with a special focus on the relevant recent studies in quantitation field. KEYWORDS challenges, complex samples, hyphenated chromatography, multiway and multiset methods, quantitation 1 | INTRODUCTION One of the most important factors that enhances the quality of our life is the depth of our information about the composition of natural or synthetic materials, such as the presence of toxic or potentially dangerous substances in food products, personal care products, environmental resources, and so on. In fact, numerous similar and highly concerned examples can be noted, in addition to the cases that are recently spotlighted, like identification and quantification of new banned drugs in slimming herbal products.Chromatographic analytical systems have been widely used for analysis of complex samples, such as bioanalytical field, food analysis, and metabolomics. Hyphenated chromatographic techniques, such as high-performance liquid chromatography coupled with diode array detection or fast-scanning fluorescence detection (HPLC-DAD/FSFD), liquid chromatography mass spectrometry (LC-MS), and gas chromatography mass spectrometry (GC-MS), have found their place in most of analytical laboratories and routinely provide valuable qualitative and quantitative information. As a result, such powerful techniques enable the analyst to efficiently analyze much more complex samples of various nature. This is especially true for chromatographic systems coupled with mass spectrometric detection, because of additional selectivity and sensitivity, which is the intrinsic and advantageous property of mass spectrometers. However, parameter optimizations in chromatographic systems (especially in LC systems) is a necessary and often laborious step in the path to obtain the best performance of the instrument. In fact, success of the chromatographic separation, robustness and stability of the outcome signal, strongly depends on chromatographic equipment, such as pumping and solvent delivery system, detector performance, column type and its steadiness, and temperature control. Although some chromatographic troubles such as problematic solvent peak, a nonstable or nonuniform background offset, peak tailing or fronting, coelution of analytes with each other or matrix constituents, retention time shifts in different runs, peak shape changes, and a narrow linear response range may usually exist.Fi...

Full Workflows for the Analysis of Gas Chromatography—Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma

“…Notwithstanding these advantages, the peak deconvolution remains a challenge, once this is made into a one-dimensional spectrum and thus highly dependent on the chromatographic separation. In this case, the time drift dimensionality is lost, compromising the identification of the compounds and neglecting the second-order advantage [ 75 ]. This is the method of choice if one wishes to perform fast quality control using the IMS as a detector, but it disregards the ion mobility spectra information.…”

Implementation of Chemometric Tools To Improve Data Mining and Prioritization in LC-HRMS for Nontarget Screening of Organic Micropollutants in Complex Water Matrixes

“…Also, the importance of utilizing these methods in case of resolving low-intensity peaks, mathematically increasing the chromatographic selectivity without further LC method optimization was completely confirmed and their potential limitations were fully addressed previously. , …”

“…Also, the importance of utilizing these methods in case of resolving low-intensity peaks, mathematically increasing the chromatographic selectivity without further LC method optimization was completely confirmed and their potential limitations were fully addressed previously. 35,36 Additionally, there exist a considerable number of multivariate chemometric methods which are well-developed for exploratory data analysis such as principal component analysis (PCA) and hierarchical clustering as unsupervised methods or partial least-squares-discriminant analysis (PLS-DA) 37 and support vector machines (SVM) 38 for supervised classification purposes. Here, the information provided from the multivariate methods can be used effectively for prioritization of features occurring in different classes of environmental samples.…”

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

“…Nevertheless, multi-way methods have prerequisites to consider when applied in GC/LC-MS data processing [ 24 ]. As the most flexible algorithm, MCR-ALS provides additional benefits in NTS of water samples with complex GC- or LC-HRMS data structures (e.g., substantial RT shifts and co-elution issues) designed in response to environmental conditions.…”