Analytical science advances最新文献

Spectrometry is a fascinating field of analytical science that encompasses a range of techniques used to study the interaction between electromagnetic radiation and matter. Through the measurement and analysis of various radiations, spectrometry provides valuable insights into the composition, structure and properties of different substances. Spectrometry allows for the identification and quantification of proteins based on their characteristics and abundance. By comparing the mass spectrometry data obtained from the pulldown assay with databases of known proteins, it is possible to identify the interacting proteins with high confidence. Long non-coding RNAs (lncRNAs), as one of the most important RNA-binding proteins, have emerged as key players in gene regulation, with nearly 80% of transcripts in human cells being lncRNA species. These nonprotein-coding transcripts, longer than 200 nucleotides, have shown great potential in various biological processes and diseases. However, their functional characterization remains a challenge due to their lower expression levels and the limitations of current techniques. Therefore, in this study, we aim to review spectrometry and its diverse types for application in the determination of general properties of RNA-binding proteins.

A steelmaking method using electric furnaces is attracting attention in the iron and steel industry, and a carbon material called needle coke is used as an aggregate for the electrode in electric steelmaking. The performance of needle coke as an aggregate for electrodes in steelmaking is greatly affected by the quality of the needle coke, which depends on the ingredients of the raw materials and the process conditions. Because the raw material ingredients are not always constant and depend on the place and time they are produced, the quality of the needle coke is not stable under the same process conditions. Therefore, it is necessary to optimise the process conditions. In this study, to optimise the process conditions using machine learning, a model was constructed to predict the thermophysical properties of needle coke from the raw material ingredients and process conditions based on previous data. Because the subject plant is operated in a dynamic process and there is a time delay in the previous data, the genetic-algorithm-based process variables and dynamics selection method, which selects the time delays and process variable regionally, was studied. Furthermore, inverse analysis was performed on a sample whose quality was considered to be outside the specifications based on the previous data, with the aim of controlling the quality within the product specifications by changing only the process conditions.

In recent years there has been a growing interest in cocoa and their sub products in the world, given the beneficial properties of these products. This interest has led to increased research in the study of the composition of cocoa and its relationship with its varieties, mainly the principal alkaloids, theobromine and caffeine. Venezuela, although a small-scale producer, is recognised worldwide for the quality of its cocoa. This work presents a robust, unambiguous and cost-effective methodology for the rapid and accurate quantification of theobromine and caffeine in cocoa beans extracts from Venezuelan cocoa. Thin layer chromatography (TLC) is used for separation, and alkaloids are identified by their Rf values and by their Raman spectra obtained by surface-enhanced Raman spectroscopy (SERS). In the SERS technique, the spots of separated compounds by TLC were impregnated with a solution of silver nanoparticles and the SERS spectra record. Given the great structural similarity of these alkaloids, principal component analysis (PCA) was used to show that despite the similarities of the Raman spectra, they are perfectly distinguishable. Theoretical calculations were performed using Orca software, obtaining Raman and FTIR spectra, and similarities were found between the theoretical and experimental responses, validating the computational approach. The synergistic integration of TLC for separation, SERS for sensitive detection, PCA for robust differentiation and DFT for theoretical validation offers a cost-effective, rapid and robust analytical platform for the unambiguous identification of theobromine and caffeine in complex matrices. This methodology lays the foundation for future quantitative applications in the evaluation of cocoa quality and origin.

Amomi Fructus (SR) is an important edible herb widely used as a spice and traditional Chinese medicine. To comprehensively solve the serious practical problems of origins and species confusion in SR, the systematic characterization methods were established by liquid chromatography–mass spectrometer, gas chromatography–mass spectrometer, nuclear magnetic resonance and infrared spectroscopy. A total of 286 compounds and functional group information were detected. The classification of SR from different origins was performed by data fusion models built using random forest (RF) and other algorithms. A mid-level data fusion model (an RF model established after combining the features selected by RF and RF–RF) performed the best classification. Then 27 differential compounds (including flavonoids, polyphenols and terpenoids) and their functional group information were screened for external verification and could significantly improve the groups’ separation effect just by simple principal component analysis. A more comprehensive and accurate means of analysis was found.

In the design of molecules, materials and processes, outliers or outlier samples can be included in a dataset when performing machine learning or regression analysis. Although outlier samples with high prediction errors in regression analysis have been divided into bad leverage points and vertical outliers (good leverage points have low prediction errors), this study classifies the outlier samples into consistent outliers (CO) and inconsistent outliers (ICO) for a detailed discussion of outlier samples and their effective utilisation. The relationship between the explanatory variables (x) and dependent variables (y) is consistent with the other samples for CO but not for ICO. Furthermore, an index of ICO-likeness based on triple cross-validation and the mean absolute error is proposed, and a method to determine whether an outlier sample is an ICO or a CO is developed. Data analysis using numerical simulation datasets and a compound dataset with boiling points confirms that the proposed method can appropriately discriminate between ICO and CO. When an outlier sample is determined to be an ICO, the errors in x and y should be checked first for the sample. If no errors exist in x and y, a new x should be added to explain y of the ICO. When an outlier sample is determined to be CO, it is expected that exploring the extrapolation from CO in x will further improve the y values using a model that includes CO.

The pharmaceutical industry has recently introduced a new combination of eye drop containing alcaftadine (ALF) and ketorolac tromethamine (KTC) utilized in managing allergic conjunctivitis. This new formulation elevates the necessity for developing sensitive, reliable, rapid and simple spectrophotometric methods to accurately quantify ALF and KTC in their dosage form with full consideration of its preservative benzalkonium chloride. In this study, three simple green spectrophotometric methods were developed to simultaneously determine ALF, KTC, and benzalkonium chloride in their ternary combinations without requiring preliminary separation steps. The expected methods employed unique spectral properties of the mixture including the extension of KTC's spectrum beyond that of ALF. The suggested methods were direct spectrophotometric, absorbance resolution, and factorized zero-order methods. The proposed methods were assessed for validity in accordance with ICH guidelines and were determined to be linear within concentration ranges of 1.0–14.0 µg/mL for ALF and 3.0–30.0 µg/mL for KTC. A statistical comparison between the developed methods versus the reported and official methods revealed lack of any difference in terms of accuracy and precision. The work was also considered a successful attempt to stick to Green Analytical Chemistry principles and preserve our environment via using water as the main solvent. This was reflected by the highest greenness score of the proposed methods upon adopting state-of-the art assessment metric tools including Complementary Green Analytical Procedure Index (ComplexGAPI) and analytical GREEnness (AGREE). Considering these benefits, our developed methods were demonstrated to be green, sensitive, and accurate making it suitable for routine drug analysis.

Severe haze episodes in Southeast Asia, largely attributed to transboundary pollution from neighbouring countries, have led to substantial environmental degradation and adverse health effects. This study presents the development of a novel air quality forecasting model specifically developed to predict particulate matter with a diameter of less than 10 µm (PM₁₀) concentrations 1 to 3 days in advance during transboundary haze events in Malaysia. The innovation lies in the integration of quantile regression (QR) with advanced feature selection techniques—namely, Relief-based ranking, correlation-based selection and principal component analysis (PCA)—to form modified predictive models. These hybrid models, referred to as QR-Relief, QR-correlation and QR-PCA, demonstrated superior performance over traditional QR and multiple linear regression models across four urban locations: Klang, Melaka, Pasir Gudang and Petaling Jaya. Model accuracy was evaluated using selected performance metrics, including mean absolute error, normalized absolute error and root mean square error. The results indicate that reducing the dimensionality of input variables through analytical feature selection significantly improves predictive reliability. Furthermore, model validation using an independent dataset from 2019 confirmed their real-world applicability. This methodological advancement provides a robust analytical framework for developing early warning systems during haze events, offering valuable decision-support tools for environmental and public health management.

Acute kidney injury is a common complication in patients undergoing orthotopic liver transplantation, being associated with increased mortality and prolonged hospitalization. This study aimed to develop a novel analytical strategy for the early diagnosis of acute kidney injury in liver transplant recipients by combining an untargeted metabolomic approach with advanced chemometric techniques. Using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry coupled with partial least squares discriminant analysis, we successfully classified patient samples according to the severity of renal dysfunction. This integration of high-resolution mass spectrometry with multivariate analysis offers a minimally invasive, cost-effective and precise diagnostic method, requiring only a single serum sample per analysis. The study analysed 132 serum samples from 59 patients at three postoperative time points (0–6 h, 24 h and 48 h). The mass spectra revealed distinct metabolic profiles across different stages of acute kidney injury, highlighting biochemical shifts related to oxidative stress, inflammation and impaired renal function. The discriminant models demonstrated high sensitivity (80%–98%) and specificity (80%–97%), especially in distinguishing advanced stages of kidney injury, where metabolic alterations were most evident. These results support the use of this analytical workflow as a promising tool for early detection and monitoring of acute kidney injury, representing a significant advance in the application of untargeted metabolomics to clinical diagnostics. This work lays the foundation for future clinical translation of metabolomics-based diagnostics in liver transplantation, enabling more effective and timely therapeutic interventions.

Liquid chromatography–mass spectrometry (LC–MS) is a highly sophisticated analytical technique that has become indispensable for sample analysis across various scientific domains. In recent years, LC–MS has emerged as a cornerstone technology in comparative replicate sample analysis, particularly in the fields of proteomics, lipidomics and metabolomics. This review provides a comprehensive and in-depth exploration of LC–MS applications in biological, and applied sciences from the key aspects include (1) a historical perspective on LC–MS development and advancements; (2) its critical role in forensic investigations and environmental science; (3) its applications in food safety, quality control and pharmaceutical analysis and (4) its significance in the examination of pharmaceuticals and biological specimens. By presenting a broad and integrated understanding of LC–MS, this review underscores its transformative impact on life sciences and its expanding role in scientific research and innovation.

The proportion of older people in the world's total population is expected to increase. Bone diseases are more prevalent in older people; therefore, the number of patients with such diseases is expected to increase worldwide. Artificial bone is a biomaterial used in the treatment of bone diseases. Artificial bones with high bone formation rates are desired; however, the results of artificial bone implantation vary. There are also ethical issues associated with animal experiments. Our purpose in this study is to predict the variation in bone formation rates. We created multiple sub-datasets and constructed a machine learning model to predict the variation in bone formation rates by considering the results of multiple measurements. We also propose a metric, Jensen–Shannon (JS) divergence, to evaluate the accuracy of the model for predicting variation. We tested the validity of JS divergence by comparing combinations of explanatory variables. Additionally, we found an optimal combination of explanatory variables to construct a model with high predictive accuracy. We expect that the prediction of variation will be useful for improving the practical development of materials and medicines, such as artificial bones, for which stable effects are required, regardless of the individual.