Analytical science advances最新文献

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called “lipidomics” and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

Many defects occur during the mass production of precision electrical components. To control and manage them, process variables (PVs), such as the temperature, pressure, flow rate, and liquid level, are measured and time-series data analyzed. However, identification of point of defects is difficult as any operation can cause defects and multiple equipment units are used in parallel for some operations. This study considers the combination of unfavourable conditions between operations to predict the defect rate (DR) of products. A dataset measured in an actual mass-production process for precision electrical components is analysed to predict the DR of the products. Data analysis is performed on a dataset generated from an actual mass-production process for precision electrical components, and machine learning models. are constructed using ensemble learning methods, such as random forests, the gradient boosting decision tree, XGBoost, and LightGBM. Conventional univariate analyses only show a maximum correlation coefficient of 0.17 with a DR and process variables (PVs). In this study, we improved the correlation coefficient to 0.73 using a multivariate analysis, including the data of PVs that are not considered important in the process, and appropriately transformed PVs based on the domain knowledge of the process. Furthermore, PVs that were closely related to the DR could be diagnosed based on the feature importance of the constructed machine-learning models. This study confirms the importance of using domain knowledge to improve the prediction ability of machine learning models and the interpretation of constructed models.

The development of ambient ionization mass spectrometry (AIMS) has transformed analytical science, providing the means of performing rapid analysis of samples in their native state, both in and out of the laboratory. The capacity to eliminate sample preparation and pre-MS separation techniques, leading to true real-time analysis, has led to AIMS naturally gaining a broad interest across the scientific community. Since the introduction of the first AIMS techniques in the mid-2000s, the field has exploded with dozens of novel ion sources, an array of intriguing applications, and an evident growing interest across diverse areas of study. As the field continues to surge forward each year, ambient ionization techniques are increasingly becoming commonplace in laboratories around the world. This annual review provides an overview of AIMS techniques and applications throughout 2022, with a specific focus on some of the major fields of research, including forensic science, disease diagnostics, pharmaceuticals and food sciences. New techniques and methods are introduced, demonstrating the unwavering drive of the analytical community to further advance this exciting field and push the boundaries of what analytical chemistry can achieve.

Copper(II)/polyimide-linked covalent organic frameworks under solvent-free and microwave-assisted conditions have been used in an efficient one-pot protocol for the preparation of 2,4,5-trisubstituted imidazoles via benzil, aromatic aldehydes and ammonium acetate. By applying solvent-free conditions and microwave irradiation, three-component condensation provides safe operations, low pollution, quick access to products, and an easy set-up. As a result of its reusability, the catalyst can also be reutilized for many runs without missing any activity.

Systematic toxicological analysis (STA) is the process of using an adequate analytical methodology to detect and identify as many potentially toxicologically relevant compounds as possible in biological samples. STA is an important part of everyday routine work within forensic toxicology, and several methods for STA have frequently been published and reviewed independently. However, the many drugs and other substances involved, as well as the constant emergence of new ones, may pose a major challenge in STA, which often demands a strategy involving multiple analytical methods in parallel. Such strategies have been published and evaluated less frequently despite their relevance in forensic toxicology. This mini-review briefly summarizes commonly applied methods for STA in forensic toxicology, including gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–MS (LC–MS) methods, and highlights some of their potential pitfalls. Second, it provides an overview of previously reported strategies to conduct STA, including a presentation of the STA strategy applied in the authors’ laboratory. This involves broad drug screening by LC–high-resolution MS, supported by targeted screening and quantification using LC–tandem MS, headspace (HS)-GC–MS, HS-GC–flame ionization detector and other complementary methods. The STA strategy aims to cover as many potentially relevant drugs as possible and seeks to reduce potential pitfalls arising in forensic casework. The review underlines that not every substance can be identified in all circumstances even with a comprehensive STA strategy.

Research in sport and exercise science (SES) is reliant on robust analyses of biomarker measurements to assist with the interpretation of physiological outcomes. Mass spectrometry (MS) is an analytical approach capable of highly sensitive, specific, precise, and accurate analyses of a range of biomolecules, many of which are of interest in SES including, but not limited to, endogenous metabolites, exogenously administered compounds (e.g. supplements), mineral ions, and circulating/tissue proteins. This annual review provides a summary of the applications of MS across studies investigating aspects related to sport or exercise in manuscripts published, or currently in press, in 2022. In total, 93 publications are included and categorized according to their methodologies including targeted analyses, metabolomics, lipidomics, proteomics, and isotope ratio/elemental MS. The advantageous analytical opportunities afforded by MS technologies are discussed across a selection of relevant articles. In addition, considerations for the future of MS in SES, including the need to improve the reporting of assay characteristics and validation data, are discussed, alongside the recommendation for selected current methods to be superseded by MS-based approaches where appropriate. The review identifies that a targeted, mostly quantitative, approach is the most commonly applied MS approach within SES, although there has also been a keen interest in the use of ‘omics’ to perform hypothesis-generating research studies. Nonetheless, MS is not commonplace in SES at this time, but its use to expand, and possibly improve, the analytical options should be continually considered to exploit the benefits of analytical chemistry in exercise/sports-based research. Overall, it is exciting to see the gradually increasing adoption of MS in SES and it is expected that the number, and quality, of MS-based assays in SES will increase over time, with the potential for 2023 to further establish this technique within the field.

Rapid and sensitive bacteria detection and identification are becoming increasingly important for a wide range of areas including the control of food safety, the prevention of infectious diseases, and environmental monitoring. Raman spectroscopy is an emerging technology which provides comprehensive information for the analysis of bacteria in a short time and with high sensitivity. Raman spectroscopy offers many advantages including relatively simple operation, non-destructive analysis, and information on molecular differences between bacteria species and strains. A variety of biochemical properties can be measured in a single spectrum. This short review covers the recent advancements and applications of Raman spectroscopy for bacteria analysis with specific focuses on bacteria detection, bacteria identification and discrimination, as well as bacteria antibiotic susceptibility testing in 2022. The development of novel substrates, the combination with other techniques, and the utilization of advanced data processing tools for the improvement of Raman spectroscopy and future directions are discussed.

Since the late 1970s, many ‘omics-style investigations have advanced our understanding of systems at all levels, from community level, through organismal, to individual cellular processes. Beginning with genomics and progressing through transcriptomics, proteomics and finally to metabolomics, the scope of interest shifts significantly from what is genetically possible to what is currently expressed, produced and measurable in a system. While the ideal goal of any ‘omics investigation is to fully describe a system, loss of information occurs at each decision-making juncture. These losses are often not considered in the experimental planning stage but, when combined, they drastically affect the power of an investigation and the conclusions that can be drawn from it. Herein we discuss through the analogy of photography many of the decision-making junctures of metabolomics investigations and the resultant losses of information occurring at each.

Triglycerides (TGs) are one of the main components of the glycerolipid family. Their main task in cells is to store excess fatty acids. TG energy storage is mainly concentrated in adipocytes. TGs and free fatty acids constitute the majority (57.5%) of the skin surface lipids (SSLs). TGs are essential for the formation of the skin water barrier. This work is the second part of a global study that aims to evaluate the effect of solar radiations on SSLs using vibrational spectroscopy. In the first part of this work, a stepwise characterization of free fatty acids was performed, and different spectral descriptors were used to follow the different structural modifications during the photo-oxidation process, that is hydrogen abstraction, formation of hydroperoxides and peroxyl radicals as primary oxidation products and the formation of aldehydes, ketones, alcohol as secondary products. In this second part, the photo-oxidation of TGs was evaluated using Raman spectroscopy. A decrease in the CH₂/CH₃ stretching bands ratio that confirmed the hydrogen abstraction, an increase in the 1165/1740 cm⁻¹ ((δ(OH) and υ(C–O))/ν(C=O) (ester)) ratio indicated the formation of secondary oxidation products such as hydroperoxides. And finally, an increase in the 1725/1740 cm⁻¹ (υ(C=O) (ald.)/υ(C=O) (ester)) ratio and the trans ν(C=C)/cis ν(C=C) ratio highlighted the formation of aldehydes, alcohols, ketone, trans secondary products and others.