Search Results (3,994)

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain. Full article

Indonesia is arguably a major player in worldwide rice production. Though white rice is the most predominantly cultivated, red, brown, and red rice are also very common. These types of rice are known to have different cooking properties that may be related to differences in their starch properties. Investigating the starch properties, especially the fine structure of their amylopectin, can help understand these differences. This study aims to investigate the starch characteristics of some Indonesian rice varieties by evaluating the starch granule morphology and size, molecular characteristics, amylopectin unit and internal chain profiles, and thermal properties. Starches were extracted from white rice (long grain (IR-64) and short grain (IR-42)), brown rice, red rice, and black rice cultivated in Java Island, Indonesia. IR-42 had the highest amylose content of 39.34% whilst the black rice had the least of 1.73%. The enthalpy of gelatinization and onset temperature of the gelatinization of starch granules were between 3.2 and 16.2 J/g and 60.1 to 73.8 °C, respectively. There were significant differences between the relative molar amounts of the internal chains of the samples. The two white rice and black rice had a significantly higher amount of A-chains, but a lower amount of B-chains and fingerprint B-chains (B_fp) than the brown and red rice. The average chain length (CL), short chain length (SCL), and external chain length (ECL) were significantly longer for the red rice and the black rice in comparison to both the white rice amylopectins. The long chain length (LCL) and internal chain length (ICL) of the sample amylopectins were similar. Rice starches were significantly different in the internal structure but not as much in their amylopectin unit chain profile. These results suggest the differences in their amylopectin clusters and building blocks. Full article

This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range. As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework. Full article

GNSS spoofing has become a significant security vulnerability threatening remote sensing systems. Hardware fingerprint-based GNSS receiver identification is one of the solutions to address this security issue. However, existing research has not provided a solution for distinguishing GNSS receivers of the same specification. This paper first theoretically proves that the CSACs (Chip-Scale Atomic Clocks) used in GNSS receivers have unique hardware noise and then proposes a fingerprinting scheme based on this hardware noise. Experiments based on the neural network method demonstrate that this fingerprint achieved an identification accuracy of 94.60% for commercial GNSS receivers of the same specification and performed excellently in anomaly detection, confirming the robustness of the fingerprinting method. This method shows a new real-time GNSS security monitoring method based on CSACs and can be easily used with any commercial GNSS receivers. Full article

Grapevines (Vitis spp.) produce several valuable polyphenol-type secondary metabolites including various stilbenoids. Although the potential application of stilbenes may offer alternative solutions to food safety or health challenges, only little information is available on their antibacterial activity against foodborne pathogens. In this work, high-performance liquid chromatography was used to analyze the stilbenoid profile of various wild Vitis species, including V. amurensis, V. davidii, V. pentagona, and V. romanetii, selected from the gene bank for grapes at the University of Pécs, Hungary. We found that the stilbene profile of cane extracts is strongly genotype-dependent, showing the predominant presence of ε-viniferin with a wide concentration range ≈ 320–3870 µg/g dry weight. A novel yet simple and efficient extraction procedure was developed and applied for the first time on grape canes, resulting in ε-viniferin-rich crude extracts that were tested against Listeria monocytogenes, an important foodborne pathogen. After 24 h exposure, V. pentagona and V. amurensis crude extracts completely eliminated the bacteria at a minimum bactericidal concentration of 42.3 µg/mL and 39.2 µg/mL of ε-viniferin, respectively. On the other hand, V. romanetii extract with 7.8 µg/mL of ε-viniferin resulted in 4 log reduction in the viable bacterial cells, while V. davidii extract with 1.4 µg/mL of ε-viniferin did not show significant antibacterial activity. These findings indicate that the ε-viniferin content was directly responsible for the antibacterial effect of cane extract. However, pure ε-viniferin (purity > 95%) required a higher concentration (188 µg/mL) to eradicate the bacteria under the same conditions, suggesting the presence of other antibacterial compounds in the cane extracts. Investigating the onset time of the bactericidal action was conducted through a kinetic experiment, and results showed that the reduction in living bacterial number started after 2 h; however, the bactericidal action demanded 24 h of exposure. Our results revealed that the canes of V. pentagona and V. amurensis species are a crucial bio-source of an important stilbene with antimicrobial activity and health benefits. Full article

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules. Full article

In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and safety reasons. Moreover, satellite-based Global Positioning System (GPS) signals are likely to be unusable in deep indoor spaces, and technologies like WiFi and Bluetooth are susceptible to signal noise and fading effects. For these reasons, a hybrid approach that employs at least two different signal typologies proved to be more effective, resilient, robust, and accurate in determining localization in indoor environments. This paper proposes an improved hybrid technique that implements fingerprinting-based indoor positioning using Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points and Wireless Sensor Network (WSN) technology. Six signals were recorded on a regular grid of anchor points covering the research surface. For optimization purposes, appropriate raw signal weighing was applied in accordance with previous research on the same data. The novel approach in this work consisted of performing a virtual tessellation of the considered indoor surface with a regular set of tiles encompassing the whole area. The optimization process was focused on varying the size of the tiles as well as their relative position concerning the signal acquisition grid, with the goal of minimizing the average distance error based on tile identification accuracy. The optimization process was conducted using a standard Quantum Particle Swarm Optimization (QPSO), while the position error estimate for each tile configuration was performed using a 3-layer Multilayer Perceptron (MLP) neural network. These experimental results showed a 16% reduction in the positioning error when a suitable tile configuration was calculated in the optimization process. Our final achieved value of 0.611 m of location incertitude shows a sensible improvement compared to our previous results. Full article

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island in the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data, to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002–2012 and 2013–2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions, and memory effects, characteristic of complex environmental processes. Full article

The pequi (Caryocar brasiliense) fruit peel, despite being frequently discarded, has a high content of bioactive compounds, and therefore has a high nutritional value. The present study aimed to explore the bioactivities in the pequi peel, particularly their potential health benefits at the level of antioxidant activity. The exploitation of this fruit could also present significant economic benefits and applications of pequi by-products would represent a reduction in waste, having a positive impact on the environment. Phenolic compounds present in the pequi exocarp and external mesocarp were identified by paper spray mass spectrometry (PS-MS) and quantified by HPLC. The total phenolic content (TPC) along with the amount of 2,2-diphenyl-1-picrylhydrazyl (DPPH), Ferric Reducing Antioxidant Power (FRAP), and the amount of 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid (ABTS) were also determined in peel extracts. Epicatechin was the most abundant phenolic compound found, followed by the caffeic, salicylic, and gallic acids. In addition, fingerprinting revealed compounds related to several beneficial health effects. In short, the results obtained were encouraging for potential applications of pequi peel in the field of functional foods. Full article

Pulmonary sclerosing pneumocytoma (PSP) is a quite rare tumor outside Eastern countries. This rarity, together with a wide histological appearance, makes its correct identification a diagnostic challenge for pathologists under the microscope. Historically, PSP was considered a vascular-derived neoplasm (sclerosing hemangioma), but its immunohistochemical profile clearly supports its epithelial origin. No specific molecular fingerprint has been detected so far. This short narrative revisits the clinical, histological, immunohistochemical, and molecular aspects of this tumor, paying special attention to some controversial points still not well clarified, i.e., clinical aggressiveness and metastatic spread, multifocality, the supposed development of sarcomatoid change in a subset of cases, and tumor associations with lung adenocarcinoma and/or well-differentiated neuroendocrine hyperplasia/tumors. The specific diagnostic difficulties on fine-needle aspiration cytology/biopsy and perioperative frozen sections are also highlighted. Finally, a teaching case of tumor concurrence of lung adenocarcinoma, neuroendocrine lesions, and PSP, paradigmatic of tumor association in this context, is also presented. Full article

Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds. Full article

The recovery of mutilated fingerprints plays an important role in improving the accuracy of fingerprint recognition and the speed of identity retrieval, so it is crucial to recover mutilated fingerprints efficiently and accurately. In this paper, we propose a method for the restoration of mutilated fingerprints based on the ridge texture and orientation field. First, the part to be restored is identified via the local quality of the fingerprint, and a mask image is generated. Second, a novel dual-stream fingerprint restoration network named IFSR is designed, which contains two branches, namely an orientation prediction branch guided by the fingerprint orientation field and a detail restoration branch guided by the high-quality fingerprint texture image, through which the damaged region of the mutilated fingerprint is restored. Finally, the method proposed in this paper is validated on a real dataset and an artificially damaged fingerprint dataset. The equal error rate (EER) achieved on the DB1, DB2, and DB4 datasets of FVC2002 is 0.10%, 0.12%, and 0.20%, respectively, while on the DB1, DB2, and DB4 datasets of FVC2004, the EER reaches 1.13%, 2.00%, and 0.27%, respectively. On the artificially corrupted fingerprint dataset, the restoration method achieves a peak signal-to-noise ratio (PSNR) of 16.6735. Full article

Neurodegenerative disorders (NDDs) such as Alzheimer’s (AD) and Parkinson’s (PD) are on the rise, robbing people of their memories and independence. While risk factors such as age and genetics play an important role, exciting studies suggest that a diet rich in foods from plant origin may offer a line of defense. These kinds of foods, namely fruits and vegetables, are packed with a plethora of powerful bioactive secondary metabolites (SBMs), including terpenoids, polyphenols, glucosinolates, phytosterols and capsaicinoids, which exhibit a wide range of biological activities including antioxidant, antidiabetic, antihypertensive, anti-Alzheimer’s, antiproliferative, and antimicrobial properties, associated with preventive effects in the development of chronic diseases mediated by oxidative stress such as type 2 diabetes mellitus, respiratory diseases, cancer, cardiovascular diseases, and NDDs. This review explores the potential of SBMs as theravention agents (metabolites with therapeutic and preventive action) against NDDs. By understanding the science behind plant-based prevention, we may be able to develop new strategies to promote brain health and prevent the rise in NDDs. The proposed review stands out by emphasizing the integration of multiple SBMs in plant-based foods and their potential in preventing NDDs. Previous research has often focused on individual compounds or specific foods, but this review aims to present a comprehensive fingerprint of how a diet rich in various SBMs can synergistically contribute to brain health. The risk factors related to NDD development and the diagnostic process, in addition to some examples of food-related products and medicinal plants that significantly reduce the inhibition of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and β-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1), are highlighted. Full article

The distinctive quality of Sichuan south-road dark tea (SSDT) is gradually disappearing with processing innovation. Here, near-infrared (NIR) spectroscopy (NIRS) and spectrofluorometric techniques were utilized to determine the spectral characteristics of dried SSDT and its brew, respectively. Combined with chemical analysis, the multidimensional quality characteristics of SSDT will be presented. Finally, the NIR spectral fingerprint of dried SSDT was observed, with Kangzhuan (KZ) and Jinjian (JJ) showing a very similar NIR spectrum. The SiPLS models effectively predicted the levels of theabrownin, caffeine, and epigallocatechin gallate, based on the NIR spectrum, with root-mean-square errors of calibration of 0.15, 0.12, and 0.02 for each chemical compound, root-mean-square errors of prediction of 0.20, 0.09, and 0.03, and both corrected and predicted correlation coefficients greater than 0.90. Meanwhile, the fluorescence characteristics of the SSDT brew were identified based on the parallel factor analysis for the fluorescence excitation–emission matrix (EEM). The KZ and JJ brews could be classified with 100% accuracy using extreme-gradient-boosting discriminant analysis. The integration of NIRS and fluorometric EEM seems to be a powerful technique for characterizing SSDTs, and the results can greatly benefit the production and quality control of SSDTs. Full article

Air pollution has been associated with several health problems. Detecting and measuring the concentration of harmful pollutants present in complex air mixtures has been a long-standing challenge, due to the intrinsic difficulty of distinguishing among these substances from interferent species and environmental conditions, both indoor and outdoor. Despite all efforts devoted by the scientific and industrial communities to tackling this challenge, the availability of suitable device technologies able to selectively discriminate these pollutants present in the air at minute, yet dangerous, concentrations and provide a quantitative measure of their concentrations is still an unmet need. Thermal conductivity detectors (TCDs) show promising characteristics that make them ideal gas sensing tools capable of recognising different gas analytes based on their physical fingerprint characteristics at the molecular level, such as their density, thermal conductivity, dynamic viscosity, and others. In this paper, the operation of TCD gas sensors is presented and explored using a finite element simulation of Joule heating in a sensing electrode placed in a gas volume. The results obtained show that the temperature, and hence, the resistance of the individual suspended microbridge sensor device, depends on the surrounding gas and its thermal conductivity, while the sensitivity and power consumption depend on the properties of the constitutive metal. Moreover, the electrode resistance is proven to be linearly dependent on the applied voltage. Full article