Search Results (261)

Hotpot dishes are widely favored by consumers for their flavor profiles developed during the cooking process. This study investigated the quality characteristics and volatile compounds (VOCs) of donkey meat slices across varying boiling durations (0–42 s) using gas chromatography–ion mobility spectrometry (GC-IMS). The results demonstrated that donkey meat boiled for 12–18 s exhibited optimal characteristics in terms of meat retention, color parameters, shear force values, and pH measurements. Forty-eight distinct VOCs were identified in the samples, with aldehydes, alcohols, ketones, acids, furans, and esters representing the predominant categories. Among these compounds, 18 were identified as characteristic aroma compounds, including 3-hexanone, 2, 3-butanedione, and oct-1-en-3-ol. Samples subjected to different boiling durations were successfully differentiated through topographic plots, fingerprint mapping, and multivariate analysis. The abundance and diversity of VOCs reached peak values in samples boiled for 12–18 s. Furthermore, 28 VOCs were identified as potential markers for distinguishing between different boiling durations, including 2-butoxyethanol D, benzaldehyde D, and (E)-2-pentenal D. This study concludes that a boiling duration of 12–18 s for donkey meat during hotpot preparation yields optimal quality characteristics and volatile flavor compound profiles and provides valuable insights for standardizing cooking parameters in hotpot preparations of other meat products. It is necessary to confirm this finding with sensory evaluations in further research. Full article

Wi-Fi fingerprinting is a widely adopted technique for indoor localization in location-based services (LBS) due to its cost-effectiveness and ease of deployment using existing infrastructure. However, the performance of these systems often suffers due to missing received signal strength indicator (RSSI) measurements, which can arise from complex indoor structures, device limitations, or user mobility, leading to incomplete and unreliable fingerprint data. To address this critical issue, we propose Feature-level Augmentation for Localization (FALoc), a novel framework that enhances Wi-Fi fingerprinting-based localization through targeted feature-level data augmentation. FALoc uniquely models the observation probabilities of RSSI signals by constructing a bipartite graph between reference points and access points, which is then processed by a variational graph auto-encoder (VGAE). Based on these learned probabilities, FALoc intelligently imputes likely missing RSSI values or removes unreliable ones, effectively enriching the training data. We evaluated FALoc using an MLP (Multi-Layer Perceptron)-based localization model on the UJIIndoorLoc and UTSIndoorLoc datasets. The experimental results demonstrate that FALoc significantly improves localization accuracy, achieving mean localization errors of 7.137 m on UJIIndoorLoc and 7.138 m on UTSIndoorLoc, which represent improvements of approximately 12.9% and 8.6% over the respective MLP baselines (8.191 m and 7.808 m), highlighting the efficacy of our approach in handling missing data. Full article

In complex indoor and outdoor scenarios, traditional GPS-based ranging technology faces limitations in availability due to signal occlusion and user privacy issues. Wireless signal ranging technology based on 5G base stations has emerged as a potential alternative. However, existing methods are limited by low efficiency in constructing static signal databases, poor environmental adaptability, and high resource overhead, restricting their practical application. This paper proposes a 5G wireless signal ranging framework that integrates mobile edge computing (MEC) and crowdsourced intelligence to systematically address the aforementioned issues. This study designs a progressive solution by (1) building a crowdsourced data collection network, using mobile terminals equipped with GPS technology to automatically collect device signal features, replacing inefficient manual drive tests; (2) developing a progressive signal update algorithm that integrates real-time crowdsourced data and historical signals to optimize the signal fingerprint database in dynamic environments; (3) establishing an edge service architecture to offload signal matching and trajectory estimation tasks to MEC nodes, using lightweight computing engines to reduce the load on the core network. Experimental results demonstrate a mean positioning error of 5 m, with 95% of devices achieving errors within 10 m, as well as building and floor prediction error rates of 0.5% and 1%, respectively. The proposed framework outperforms traditional static methods by 3× in ranging accuracy while maintaining computational efficiency, achieving significant improvements in environmental adaptability and service scalability. Full article

Gas chromatography–ion mobility spectrometry (GC-IMS) is an interesting candidate to face geographical origin declaration fraud in dehydrated apple samples. It allows the collection of the peculiar fingerprints of the analysed samples with the bi-dimensional separation of volatile molecules, based on their polarity and their dimension and shape. It represents a rapid, cost-effective, and sensitive solution for food authenticity issues. A design of experiment (DoE) led to robust sampling, taking into account different factors, such as harvesting year, the presence of peel, variety. The sample preparation was limited as it required only the milling of the dehydrated apple dices before the analysis. The GC-IMS analytical method permitted us to obtain of a 3D graph in 11 min, and the multivariate statistical analysis returned a clear separation between Italian and non-Italian (French, Chinese, Hungarian, Polish) samples, considering both unsupervised and supervised approaches. The statistical model, created employing a training set, was applied on a further test set, with a good overall performance. Thus, GC-IMS could play a relevant role as a tool to prevent/fight false origin declaration frauds and also, potentially, other kinds of food authenticity and safety frauds. Full article

The main root, reed head, and fibrous root are three different main edible medicinal parts of ginseng (Panax ginseng C. A. Meyer). When processed into ginseng products, such as ginseng powder, they exhibit similar colors and odors, easily confused in market circulation. However, there are differences in their pharmacological activity and clinical indications. Therefore, the identification of the different parts of ginseng powder is crucial for ensuring the quality, safety, and efficacy of medicinal ginseng products. In this study, we utilized gas chromatography–ion mobility spectrometry (GC–IMS) to analyze volatile organic components (VOCs) in main root, reed head, and fibrous root of ginseng. It was found that the composition of VOCs in different parts of ginseng powder was similar, but the content was different in all samples, and a total of 68 signal peaks was detected and 65 VOCs identified. In addition, combined with fingerprint analysis, principal component analysis (PCA), Euclidean distance, partial-least squares discriminant analysis (PLS-DA), and cluster analysis (CA), it clearly showed the significant differences between VOCs in different parts of ginseng powder. Our findings reveal that GC–IMS combined with chemometrics is a reliable method for distinguishing the active parts of ginseng powder, and provides essential data support for different parts of ginseng processing and functional product development. Full article

Partial fingerprint recognition has emerged as a critical method for verifying user authenticity during mobile transactions. As a result, there is a pressing need to develop techniques that effectively and accurately authenticate users, even when the scanner only captures a limited area of the finger. A key challenge in partial fingerprint matching is the inevitable loss of features when a full fingerprint image is reduced to a partial one. To address this, we propose a method that integrates deep learning with feature descriptors for partial fingerprint matching. Specifically, our approach employs a Siamese Network based on a CNN architecture for deep learning, complemented by a SIFT-based feature descriptor to extract minimal yet significant features from the partial fingerprint. The final matching score is determined by combining the outputs from both methods, using a weighted scheme. The experimental results, obtained from varying image sizes, sufficient epochs, and different datasets, indicate that our combined method achieves an Equal Error Rate (EER) of approximately 4% for databases DB1 and DB3 in the FVC2002 dataset. Additionally, validation at FRR@FAR 1/50,000 yields results of about 6.36% and 8.11% for DB1 and DB2, respectively. These findings demonstrate the efficacy of our approach in partial fingerprint recognition. Future work could involve utilizing higher-resolution datasets to capture more detailed fingerprint features, such as pore structures, and exploring alternative deep learning techniques to further streamline the training process. Full article

To investigate the impact of varying steaming durations on the flavor characteristics of mutton and carrot stuffing, dynamic changes in volatile organic compounds (VOCs) and fatty acids were analyzed using solid-phase micro-extraction gas chromatography–mass spectrometry (SPME-GC-MS) and gas chromatography–ion mobility spectrometry (GC-IMS). The results revealed a total of 116 VOCs identified throughout the steaming process, with 73 detected by GC-MS and 44 by GC-IMS. Notably, VOC concentrations were significantly higher at 18–24 min compared to 8–16 min. Additionally, a GC-IMS fingerprint was developed to assess the distribution of VOCs during steaming. Orthogonal partial least squares discriminant analysis (OPLS-DA) indicated that 11 compounds, such as ethyl caprylate (B3), linalyl acetate (B6), and 1-nonanal (C1), significantly influenced the flavor characteristics of the mutton and carrot filling. Further analysis demonstrated that stearic acid content reached its lowest point at 20–22 min of steaming, while n-6 and n-3 series polyunsaturated fatty acids (PUFAs) and the ratio of polyunsaturated fatty acids to saturated fatty acids (P/S) peaked at this time. Full article

This study investigated the impact of various thermal processing methods—steaming, boiling, frying, and baking—on the volatile organic compounds (VOCs) in sweet potato (Ipomoea batatas L.) brandy using gas chromatography–ion mobility spectrometry (GC-IMS). Yanshu No. 25 sweet potatoes, recognized for their high levels of mucin protein and soluble sugars, were employed for the fermentation of the brandy. GC-IMS analysis generated three-dimensional spectrograms, which revealed distinct VOC profiles depending on the processing method used. Notably, steaming, frying, boiling, and baking significantly altered the VOC composition, imparting unique flavor characteristics. A total of 37 VOCs were identified, with esters being the predominant class, contributing to fruity and floral notes in the brandy. Principal component analysis (PCA) and Euclidean distance-based fingerprint similarity analysis further differentiated the VOC profiles, highlighting the essential role of processing techniques in flavor development. These findings provide a foundation for future research aimed at optimizing processing methods to create specific aromatic profiles in sweet potato brandy. Full article

Background: This study aims to develop a fingerprint analysis method using ultra-high performance liquid chromatography (UPLC) for Moutan Cortex sourced from different regions. The objective is to establish quality control standards validated through the integration of chemometric methods and component structure theory. Methods: The mobile phase for UPLC consisted of acetonitrile (A) and a 0.1% aqueous formic acid solution (B), with gradient elution set as follows: 0–1 min, 8% A → 15% A; 1–8 min, 15% A → 18% A; 8–10 min, 18% A → 30% A; 10–15 min, 30% A → 35% A; 15–20 min, 35% A → 85% A; 20–21 min, 85% A → 8% A; and 21–26 min, 8% A → 8% A. Chemical markers significantly affecting Moutan Cortex from various regions were screened, and their identification was based on comparison with reference materials and content determination. Results: A total of 15 chemical markers were identified, including gallic acid, oxypaeoniflorin, catechin, methyl gallate, paeonolide, apiopaeonoside, albiflorin, paeoniflorin, benzoic acid, 1,2,3,6-tetra-O-galloyl-D-glucose, 1,2,3,4,6-pentagalloylglucose, mudanpioside C, benzoyloxypaeoniflorin, benzoylpaeoniflorin, and paeonol. These markers align with component structure theory, allowing for an analysis of the structural characteristics of Moutan Cortex from different regions. Conclusions: The findings provide a valuable reference for the future quality evaluation of traditional Chinese medicine preparations, enhancing the understanding of the material basis components in Moutan Cortex from diverse sources. Full article

In this study, a quantitative profiling method for detecting free fatty acids in crude lanolin based on the Quality by Design (QbD) concept was developed. High-performance liquid chromatography (HPLC) equipped with a charged aerosol detector (CAD) and a Proshell 120 EC C18 column was employed for the separation of crude lanolin components. Initially, the analytical target profile and critical method attributes were defined. Potential critical method parameters, including column temperature, flow rate, isocratic run time, gradient end organic phase ratio, and gradient time, were identified using fishbone diagrams and single-factor experiments. The definitive screening design (DSD) was then utilized to screen and optimize these parameters. Stepwise regression was applied to establish quantitative models between the critical method attributes and the method parameters. Subsequently, the method operable design region (MODR) was calculated and was successfully verified. The analytical conditions established were configured with 0.1% formic acid in water and 0.1% formic acid in acetonitrile serving as the mobile phases. The flow rate was set at 0.8 mL/min, and the column temperature was maintained at 35 °C with the evaporation tube temperature also set at 35 °C. An injection volume of 10 μL was used for each analysis. The gradient elution conditions were as follows: from 0 to 30 min, 75% of solvent B was used, and from 30 to 60 min, the proportion of solvent B was increased from 75% to 79%. Ten components, including 12-hydroxystearic acid, 2-hexyldecanoic acid, and palmitic acid, were identified by mass spectrometry, and seven common peaks were found in the fingerprints. The contents of palmitic acid, oleic acid, and stearic acid in the crude lanolin were quantitatively determined. Both the fingerprint and quantitative analysis methods were validated. The method was applied to analyze 15 batches of crude lanolin from different sources. The new established quantitative profiling method for free fatty acids can be potentially used for industrial applications to enhance the quality control of crude lanolin. Full article

The production of high-quality specialty meats has emerged as a prominent research focus within the livestock industry, under the broader concept of big food. However, the composition and variances of volatile compounds (VOCs) in donkey meat (DM) and horse meat (HM) remain unclear, which complicates their effective identification. In the present study, the VOCs of DM and HM were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS) in combination with a multivariate analysis. Our results indicate that a total of 39 VOCs were identified in both DM and HM. These VOCs were categorized into five groups: aldehydes (39.53%), ketones (28.89%), alcohols (28.89%), acids (6.98%), and furans (2.33%). Compared with HM, the concentration of aldehydes, ketones, and alcohols in DM is significantly higher. (p < 0.001). Additionally, 16 characteristic-flavor VOCs were identified in both types of meat, with notable compounds including oct-1-en-3-ol, 3-hexanone, and heptanol. Topography, fingerprinting, and multivariate analysis effectively differentiated the VOC profiles of DM and HM. Furthermore, the 28 differential VOCs identified in DM and HM were all significantly higher in DM than in HM. In summary, this study conducted a comprehensive analysis of the VOC composition and characteristic flavor compounds in DM and HM, highlighting key differential VOCs. These findings contribute valuable data for flavor regulation and offer technical support for detecting the adulteration of DM with HM. The difference in sensory quality between DM and HM needs further research. Full article

Surface-enhanced Raman spectroscopy (SERS) is widely employed due to its high sensitivity and distinctive fingerprinting capabilities. Colloidal nanoaggregates are commonly used as SERS substrates because of their mobility and the abundance of “hotspots”. Although the reagent-free “freeze-thaw-ultrasonication” method for preparing Ag nanoaggregates (AgNAs) does not introduce additional background interference and maintains the original interfacial properties of AgNAs, their unstable physical nanostructure limits SERS detection to just 7 days. Herein, we demonstrate mesoporous silica-encapsulated colloidal Ag nanoaggregates (AgNAs@m-SiO₂) by combining a freeze-thaw-ultrasonication method and a cetyltrimethylammonium bromide (CTAB)-assisted silanization reaction, achieving long-term SERS stability of more than two months. The prepared AgNAs@m-SiO₂ serve a dual capability: (1) preserving electromagnetic “hotspots” for ultra-sensitive detection (e.g., malachite green detection limit: 3.60 × 10⁻⁸ M), and (2) maintaining structural stability under harsh conditions. The AgNAs@m-SiO₂ substrate exhibited superior structural stability after 50 min of ultrasonic treatment, with an initial SERS signal retention of 91.8%, which is twice that of the bare AgNAs (retention of 45%). The long-term performance further highlighted its superiority: after 70 days of storage, the composite maintained 84.3% of its original signal strength, outperforming the uncoated controls by over ten times (which retained only 8%). Crucially, the substrate’s robust design enables the direct detection of contaminants in real environmental matrices (river and seawater) for qualitative analyses and water quality assessments, thus validating its suitability for environmental sensing applications in the field. Full article

The flavour of foxtail millet (Setaria italica (L.) P. Beauv.) is an important indicator for evaluating the quality of the millet. The volatile components in steamed millet porridge samples were analysed using electronic nose (E-Nose) and gas chromatography–ion mobility spectrometry (GC-IMS) techniques, and characteristic volatile fingerprints were constructed to clarify the differences in the main flavour substances in different foxtail millet varieties (two hybrids and two conventional foxtail millets). After sensory evaluation by judges, Jingu 21 (JG) scored significantly higher than the other varieties, and the others were, in order, Jinmiao K1 (JM), Changzagu 466 (CZ) and Zhangzagu 3 (ZZ). E-Nose analysis showed differences in sulphides and terpenoids, nitrogen oxides, organosulphides and aromatic compounds in different varieties of millet porridge. A total of 59 volatile components were determined by GC-IMS in the four varieties of millet porridge, including 23 aldehydes, 17 alcohols, 9 ketones, 4 esters, 2 acids, 3 furans and 1 pyrazine. Comparative analyses of the volatile components in JG, JM, ZZ and CZ revealed that the contents of octanal, nonanal and 3-methyl-2-butenal were higher in JG; the contents of trans-2-butenal, 2-methyl-1-propanol, trans-2-heptenal and trans-2-pentenal were higher in JM; and the contents of 2-octanone, hexanol, 1-octen-3-ol, 2-pentanone and butyraldehyde were higher in ZZ. The contents of 2-butanol, propionic acid and acetic acid were higher in CZ. A prediction model with good stability was established by orthogonal partial least squares discriminant analysis (OPLS-DA), and 25 potential characteristic markers (VIP > 1) were screened out from 59 volatile organic compounds (VOCs). These volatile components can be used to distinguish the different varieties of millet porridge samples. Moreover, we found conventional foxtail millet contained more aldehydes than the hybridised foxtail millet; especially decanal, 1-nonanal-D, heptanal-D, 1-octanal-M, 1-octanal-D and 1-nonanal-M were significantly higher in JG than in the other varieties. These results indicate that the E-Nose combined with GC-IMS can be used to characterise the flavour volatiles of different foxtail millet, and the results of this study may provide some information for future understanding of the aroma characteristics of foxtail millet and the genetic improvement of hybrid grains. Full article

To investigate the effects of lemongrass essential oil on the key volatile aroma compounds of pickled radish (PR) during storage, this study used headspace–gas chromatography–ion mobility spectrometry, fingerprint analysis, multivariate statistical analysis, and molecular docking to study different PR samples. The results indicated that a total of 48 volatile aromatic compounds were identified. Fingerprint analysis revealed that the aroma profiles of samples at different storage stages were different. Using the screening criteria of p < 0.05 and variable importance for the projection > 1 in multivariate statistical analysis, and relative odor activity value > 1, six potential key aroma compounds were selected. Furthermore, phenylethyl acetate, β-ocimene, 4-heptanone, and limonene were determined as the key aroma compounds that affect the PR aroma profile after adding lemongrass essential oil. Moreover, the addition of lemongrass essential oil increased the fruit and sweet aroma of PR samples during storage. The results of molecular docking indicated that the recognition of these four odors was mainly accomplished through hydrophobic interactions and hydrogen bond interactions by docking OR1A1 and OR5M3 receptors. This study can offer a preliminary foundation and theoretical support for the in-depth exploration of the paocai industry. Full article

The advancement of Internet of Things (IoT), robots, drones, and vehicles signifies ongoing progress, accompanied by increasing complexities and challenges in forensic investigations. Globally, investigators encounter obstacles when extracting evidence from these vast landscapes, which include diverse devices, networks, and cloud environments. Of particular concern is the process of evidence collection, especially regarding fingerprints and facial recognition within the realm of vehicle forensics. Moreover, ensuring the integrity of forensic evidence is a critical issue, as it is vulnerable to attacks targeting data centres and server farms. Mitigating these challenges, along with addressing evidence mobility, presents additional complexities. This paper introduces a groundbreaking infrastructure known as SpiderNet, which is based on cloud computing principles. We will illustrate how this architecture facilitates the identification of devices, secures the integrity of evidence both at its source and during transit, and enables investigations into individuals involved in criminal activities. Through case studies, we will demonstrate the potential of SpiderNet to assist law enforcement agencies in addressing crimes perpetrated within IoT environments. Full article