Whisky is one of the most popular spirit drinks in the world. Unfortunately, this highly valued commodity is vulnerable to fraud. To detect fraudulent practices and document quality parameters, a number of laboratory tests based on various principles including chromatography and spectroscopy have been developed. In most cases, the analytical methods are based on targeted screening strategies. Non-targeted screening (metabolomics fingerprinting) of (semi)volatile substances was used in our study. Following the pre-concentration of these compounds, either by solid phase microextraction (SPME) or by ethyl acetate extraction, gas chromatography (GC) coupled to tandem mass spectrometry (Q-TOF mass analyser) was employed. Unsupervised principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) were used for evaluation of data obtained by analysis of a unique set of 171 authentic whisky samples provided by the Scotch Whisky Research Institute. Very good separation of malt whiskies according to the type of cask in which they were matured (bourbon versus bourbon and wine) was achieved, and significant ´markers' for bourbon and wine cask maturation, such as N-(3-methylbutyl) acetamide and 5-oxooxolane-2-carboxylic acid, were identified. Subsequently, the unique sample set was used to construct a statistical model for distinguishing malt and blended whiskies. In the final phase, 20 fake samples were analysed and the data processed in the same way. Some differences could be observed in the (semi)volatile profiles of authentic and fake samples. Employing the statistical model developed by PLS-DA for this purpose, marker compounds that positively distinguish fake samples were identified.
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http://dx.doi.org/10.1016/j.aca.2018.09.017 | DOI Listing |
Ann Ig
January 2025
Department of Experimental Medicine, University of Salento, Lecce, Complesso Ecotekne, Lecce, Italy.
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View Article and Find Full Text PDFPhysiol Meas
January 2025
Harbin Institute of Technology, Harbin Institute of Technology, Harbin, 150001, CHINA.
Objective: The demand for ECG datasets, particularly those containing rare classes, poses a significant challenge as deep learning becomes increasingly prevalent in ECG signal research. While Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are widely adopted, they encounter difficulties in effectively generating samples for classes with limited instances.
Approach: To address this issue, we propose a novel Feature Disentanglement Auto-Encoder (FDAE) designed to dissect various generative factors under a contrastive learning framework within ECG data to facilitate the generation of new ECG samples.
Sci Rep
January 2025
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
Insider threats pose a significant challenge to IT security, particularly with the rise of generative AI technologies, which can create convincing fake user profiles and mimic legitimate behaviors. Traditional intrusion detection systems struggle to differentiate between real and AI-generated activities, creating vulnerabilities in detecting malicious insiders. To address this challenge, this paper introduces a novel Deep Synthesis Insider Intrusion Detection (DS-IID) model.
View Article and Find Full Text PDFBMC Health Serv Res
December 2024
Environmental and Occupational Hazards Control Research Center, Research Institute for Health Sciences and Environment, Shahid Beheshti University of Medical Sciences, Tabnak Ave., Daneshjou Blvd., Velenjak, P.O. Box 19835-35511, Tehran, I.R, Iran.
Background: Toward delivering appropriately safe, high quality and effective health care, healthcare organization should be health literate. This paper presents the development and psychometrics of an instrument for assessing the attributes of a health literate hospital which is called MAHLO-76 (Measure to Assess Health Literate Organization) here by authors.
Methods: The current study is methodological research which is involved two phases of tool development and psychometric evaluation.
J Med Internet Res
December 2024
Institute for Global Tobacco Control, Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, MD, United States.
In 2019, we launched a web-based longitudinal survey of adults who frequently use e-cigarettes, called the Vaping and Patterns of E-cigarette Use Research (VAPER) Study. The initial attempt to collect survey data failed due to fraudulent survey submissions, likely submitted by survey bots and other survey takers. This paper chronicles the journey from that setback to the successful completion of 5 waves of data collection.
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