The exponential increase in the production and transportation of petroleum-derived products observed in recent years has been driven by the escalating demand for energy, textiles, plastic-based materials, and other goods derived from petroleum. Consequently, there has been a corresponding rise in spills of these petroleum derivatives, particularly in water sources utilized for transportation or, occasionally, illegally utilized for tank cleaning or industrial equipment maintenance. Numerous researchers have proposed highly effective techniques for detecting these products, aiming to facilitate their cleanup or containment and thereby minimize environmental pollution. However, many of these techniques rely on the identification of individual compounds, which presents significant drawbacks, including complexity of handling, subjectivity, lengthy analysis times, infeasibility for in situ analysis, and high costs. In response, there has been a notable surge in the utilization of sensors or generalized profiling techniques serving as sensors to generate characteristic fingerprints of these products, thereby circumventing the aforementioned disadvantages. This review comprehensively examines the evolution of techniques employed for detecting petroleum-derived products in water samples, along with their associated advantages and disadvantages. Furthermore, the review examines current perspectives on methods for the removal and/or containment of these products from water sources, to minimize their environmental impact and the associated health repercussions on living organisms and ecosystems.

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

BACKGROUND: It has been a current research hospots using fingerprinting technology for quality control of Chinese herbal medicines (CHMs), which provides a scientific basis for establishment of overall quality control in accordance with the characteristics of CHMs. The fingerprinting technology for CHMs is diverse, and the research field covers many disciplines, such as analytical chemistry, pharmacology, pharmaceutics, biochemistry, and molecular biology.
OBJECTIVE: To effectively understand the key areas and future directions of research regarding the fingerprint and adulteration of CHMs.
RESULTS: this paper analyzed 879 articles in this field in the Web of Science Core Collection from 2000 to 2023 with CiteSpace and VOSviewer, and systematically assessed the research process, hotspots, topic distribution among disciplines, etc. The most prominent contributors of fingerprint and adulteration of CHMs research are mainly from China, India, the United States, England, and Brazil. The knowledge domains of fingerprint and adulteration of CHMs research focus mainly on the topics of molecular authentication, DNA barcoding, HPLC, near-infrared spectroscopy, manage data, chemometrics, and electrochemical fingerprinting. Most countries have recognized the pharmaceutical potential of natural products, and have paid more attention to the fingerprint and adulteration of CHMs in the past decade. Future the research tends to focus more on molecular identification and authentication, and electrochemical and chromatographic fingerprinting in controlling the adulteration of CHMs.
CONCLUSIONS: This research provides a valuable reference for scholars in related fields to analyze existing research results, understand the development trend, and explore new research directions.

Magnetic resonance imaging (MRI) is used pervasively in veterinary practice for the antemortem diagnosis of intracranial tumors. Here, we provide an illustrated summary of the published MRI features of primary and secondary intracranial tumors of dogs and cats, following PRISMA scoping review guidelines. The PubMed and Web of Science databases were searched for relevant records, and input from stakeholders was solicited to select data for extraction. Sixty-seven studies of moderate to low-level evidence quality describing the MRI features of pathologically confirmed canine and feline brain tumors met inclusion criteria. Considerable variability in data inclusion and reporting, as well as low case numbers, prohibited comparative data analyses. Available data support a holistic MRI approach incorporating lesion number, location within the brain, shape, intrinsic signal appearances on multiparametric sequences, patterns of contrast enhancement, and associated secondary changes in the brain to prioritize differential imaging diagnoses, and often allows for accurate presumptive diagnosis of common intracranial tumors. Quantitative MRI techniques show promise for improving discrimination of neoplastic from non-neoplastic brain lesions, as well as differentiating brain tumor types and grades, but sample size limitations will likely remain a significant practical obstacle to the design of robustly powered radiomic studies. For many brain tumor variants, particularly in cats, there remains a need for standardized studies that correlate clinicopathologic and neuroimaging data.

Two-dimensional polymers comprise a novel class of macromolecules with promising mechanical and chemical properties that have recently become accessible under mild synthetic conditions. As nanometer-sized holes and defects are likely to influence these materials\' properties, there is a need for analytical methods to spatially and chemically characterize them down to the nanoscale. In this article, we compare tip-enhanced Raman spectroscopy to common methods in nanoanalysis for the investigation of defect sites in molecularly thin 2D-polymer sheets and briefly present measures to meet the challenges arising from tip-induced degradation processes.

This tutorial review combines the fundamentals of the design and operation of lasers with their usage in applications related to conservation and cultural heritage (CH) science - as components of analytical devices for the study of the chemical composition of materials. The development of laser instruments and their fundamental physical background, including a short explanation of their properties and parameters, are briefly summarised, and an overview of different laser-based analytical techniques is given. The analytical techniques covered in this tutorial are divided into three groups based on their technical aspects and properties: (1) vibrational spectroscopy, (2) elemental analysis, and (3) different molecular mass spectrometric techniques.

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food\'s supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food\'s safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

The present review places emphasis on a comprehensive survey of experimental techniques to probe the structural and morphological features at the nanoscale range in thin magnetic films, incorporating those available at in-house laboratories as well as those at state-of-the-art synchrotron radiation facilities. This elucidating the range of available techniques, and the information they can yield represents a step for advancing the understanding of and for unlocking new possibilities in the design and optimization of thin magnetic films across a wide range of applications.

Physical activity (PA) promotes brain health in a variety of domains including cognition, mood, and neuroplasticity. At the neurochemical level, the mechanisms underlying these effects in the brain are not fully understood. With proton Magnetic Resonance Spectroscopy (1H-MRS), it is possible to non-invasively quantify metabolite concentrations, enabling studies to obtain measures of exercise-induced neurochemical changes. This systematic review aimed to examine the existing literature on acute effects of PA on brain metabolites as measured by 1H-MRS. Four databases (Cochrane Central Register of Controlled Trials, PubMed, Embase, and PsycINFO) were searched, identifying 2965 studies, of which 9 met the inclusion criteria. Across studies, Gamma-AminoButyric Acid (GABA) and lactate tended to increase after exercise, while no significant changes in choline were reported. For glutamine/glutamate (Glx), studies were inconclusive. Conclusions were limited by the lack of consensus on 1H-MRS data processing and exercise protocols. To reduce inter-study differences, future studies are recommended to (1): apply a standardized exercise index (2), consider the onset time of MRS scans, and (3) follow standardized MRS quantification methods.