Fingerprinting Approach Research Articles

Innovative non-targeted liquid chromatography fingerprinting approach for authenticating tigernuts under Protected Designation of Origin quality seal.

Tigernut is a typical foodstuff from a specific region of Valencia (Spain) called 'L'Horta Nord', where it is commercialized under a Protected Designation of Origin (PDO) as Chufa de Valencia ('Valencia's tigernut'). PDO-recognized tigernuts present unique characteristics associated with their particular production region. Increasing demand and the associated expansion of its cultivation area has made necessary an exhaustive quality control to check the geographical origin and quality seal. In this work, a new multivariate analytical method capable of authenticating the PDO quality seal of tigernut samples was developed. Tigernut fat fraction was extracted under optimal conditions, applying the methodology of design of experiments. The analytical method combined fingerprinting methodology and chemometric tools to observe the natural grouping of samples using the exploratory analysis method and to develop classification models (partial least squares-discriminatory analysis; PLS-DA) to discriminate between two sample categories: (i) PDO tigernuts; and (ii) NON-PDO tigernuts. The built PLS-DA model demonstrated 100% accuracy, high sensitivity and specificity, revealing that the tigernut fat fraction can be applied to authenticate the PDO quality seal. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Geochemical fingerprinting to determine sediment source contribution and improve contamination assessment in mining-impacted floodplains in the Philippines

Constraining the main sediment sources and pathways across landscapes impacted by anthropogenic activity is essential to limit the dispersal of sediment-borne contaminants, especially in global conservation priority areas. This study examined the provenance, partitioning, and enrichment of metals in the floodplain of the mining-affected Santa Cruz catchment, The Philippines. Composite geochemical fingerprinting of fine sediment samples (n = 36) was performed using a stepwise statistical screening procedure (range test, Kruskal-Wallis H test, discriminant function analysis) to derive the optimum set of tracers. Using a standard unmixing model, flood deposits downstream of the mining areas were shown to be predominantly mining-induced (71.9 ± 7.7 %), followed by natural erosion from gullies and stream banks (15.1 ± 11.0 %) and agricultural sediment (13.0 ± 5.1 %). Element partitioning data (Log Kd = 1.3–6.6) during a high flow event indicated that metals are dominantly associated and transported via suspended particulate matter. Background concentrations of Ni and Cr were found to be orders of magnitude higher than the threshold values set by international sediment quality guidelines (SQGs), emphasizing the need for site-specific SQGs in mineralised areas. Enrichment factor values indicated low to significant contamination of flood deposits relative to natural and agricultural sediment (EF 1.0–5.5). Furthermore, it was demonstrated that using different conservative elements could considerably influence the enrichment factor calculation. Based on tracer screening tests, Th was shown to be the most suitable reference element. The findings provide new insights on the application of geochemical tracers in a mining setting and integrating fingerprinting approaches with traditional assessment techniques to improve the reliability of contamination risk assessment in other areas facing similar sediment pollution problems.

A statistical review on the optimal fingerprinting approach in climate change studies

A statistical review on the optimal fingerprinting approach in climate change studies

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics.

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm-1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm-1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.

Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features.

Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components -such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids- are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.

A Survey on Fingerprinting Technologies for Smartphones Based on Embedded Transducers

Smartphones are a vital technology, they improve our social interactions, provide us a great deal of information and bring forth the means to control various emerging technologies, like the numerous IoT devices that are controlled via smartphone apps. In this context, smartphone fingerprinting from sensor characteristics is a topic of high interest not only due to privacy implications or potential use in forensics investigations, but also because of various applications in device authentication. In this work we review existing approaches for smartphone fingerprinting based on internal components, focusing mostly on camera sensors, microphones, loudspeakers and accelerometers. Other sensors, i.e., gyroscopes and magnetometers, are also accounted, but they correspond to a smaller body of works. The output of these transducers, which convert one type of energy into another, e.g., mechanical into electrical, leaks through various channels such as mobile apps and cloud services, while there is little user awareness on the privacy risks. Needless to say, miniature physical imperfections from the manufacturing process make each such transducer unique. One of the main intentions of our study is to rank these sensors according to the accuracy they provide in identifying smartphones and to give a clear overview on the amount of research that each of these components triggered so far. We review the features which can be extracted from each type of data and the classification algorithms that have been used. Last but not least, we also point out publicly available datasets which can serve for future investigations.

MRI Advancements in Musculoskeletal Clinical and Research Practice.

Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.

The spatio-temporal dynamics of suspended sediment sources based on a novel indexing approach combining Bayesian geochemical fingerprinting with physically-based modelling

Applications of sediment source fingerprinting continue to increase globally as the need for information to support improved management of the sediment problem persists. In our novel research, a Bayesian fingerprinting approach using MixSIAR was used with geochemical signatures, both without and with informative priors based on particle size and slope. The source estimates were compared with a newly proposed Source Sensitivity Index (SSI) and outputs from the INVEST-SDR model. MixSIAR results with informative priors indicated that agricultural and barren lands are the principal sediment sources (contributing ∼5–85% and ∼5–80% respectively during two sampling periods i.e. 2018-2019 and 2021–2022) with forests being less important. The SSI spatial maps (using % clay and slope as informative priors) showed >78% agreement with the spatial map derived using the INVEST-SDR model in terms of sub-catchment prioritization for spatial sediment source contributions. This study demonstrates the benefits of combining geochemical sediment source fingerprinting with SSI indices in larger catchments where the spatial prioritization of soil and water conservation is both challenging but warranted.

Fast in vivo assay of creatine kinase activity in the human brain by 31 P magnetic resonance fingerprinting.

A new and efficient magnetisation transfer 31 P magnetic resonance fingerprinting (MT-31 P-MRF) approach is introduced to measure the creatine kinase metabolic rate between phosphocreatine (PCr) and adenosine triphosphate (ATP) in human brain. The MRF framework is extended to overcome challenges in conventional 31 P measurement methods in the human brain, enabling reduced acquisition time and specific absorption rate (SAR). To address the challenge of creating and matching large multiparametric dictionaries in an MRF scheme, a nested iteration interpolation method (NIIM) is introduced. As the number of parameters to estimate increases, the size of the dictionary grows exponentially. NIIM can reduce the computational load by breaking dictionary matching into subsolutions of linear computational order. MT-31 P-MRF combined with NIIM provides , and estimates in good agreement with those obtained by the exchange kinetics by band inversion transfer (EBIT) method and literature values. Furthermore, the test-retest reproducibility results showed that MT-31 P-MRF achieves a similar or better coefficient of variation (<12%) for and measurements in 4 min 15 s, than EBIT with 17 min 4 s scan time, enabling a fourfold reduction in scan time. We conclude that MT-31 P-MRF in combination with NIIM is a fast, accurate, and reproducible approach for in vivo assays in the human brain, which enables the potential to investigate energy metabolism in a clinical setting.

Identifying the source of movie piracy: cryptographic fingerprinting for tracking movie content

Background: The unauthorized distribution of movies through piracy has become a widespread issue affecting both movie studios and OTT streaming partners. This research introduces a novel method utilizing cryptographic fingerprinting to track movie content, with the goal of identifying the precise source of piracy. Methods: The developed specialized software that utilizes the cryptographic fingerprinting algorithm to analyze pirated movie content obtained from various online piracy platforms. The software employs computer vision techniques to identify the source of piracy, including the specific theatre, showtime, and recording location within the theatre. Results: Our experiments demonstrated the effectiveness of the proposed cryptographic fingerprinting approach. The software successfully detected and analyzed pirated movie content from a wide range of online piracy platforms. Conclusions: The cryptographic fingerprinting technique allows for the identification of the source of piracy, including the specific theatre, showtime, and recording location within the theatre. By swiftly reporting these details, our software aims to aid in the prevention of future piracy incidents, ultimately eliminating the source of piracy.

Effects of solvent-based adhesive removal on the subsequent dual analysis of fingerprint and DNA

The combined approach of classical fingerprinting and DNA profiling is a powerful tool in forensic investigations of latent “touch” traces. However, little attention has been paid to the organic solvents frequently used in dactyloscopic laboratories to facilitate the separation of adhesive evidence prior to fingerprint development and downstream effects on subsequent DNA profiling. In the present study, we tested a selection of adhesive removers (n = 9) and assessed their potential impact on DNA recovery and amplification by PCR. Thereby, we identified and characterized novel PCR inhibitors. All investigated chemicals contain volatile organic compounds that evaporate under normal indoor atmospheric conditions. Exposure to certain solvents resulted in increased DNA degradation, but only if evaporation was prevented. A series of adhesive-removal experiments were conducted with prepared mock evidence (self-adhesive postage stamps affixed to paper envelope) to investigate the impact of treatment time and the location of applied traces on DNA recovery and dactyloscopy, respectively. Due to the early onset of print decomposition, we found that only a short treatment time was compatible with the development of fingerprints on the adhesive side of a stamp. Solvents also removed DNA from the adhesive surface, thus resulting in a marked shift in the substrate distribution of recovered DNA from the stamp to the envelope, but not in the reverse direction. Furthermore, we observed that treatment with conventional fingerprint reagents lead to a significant reduction in the amounts of DNA recovered from stamps, while the additional use of adhesive removers did not significantly enhance this effect.

Quality Assessment and Classification of Codonopsis Radix Based on Fingerprints and Chemometrics.

In China, Codonopsis Radix (CR) is frequently consumed both as food and medicine. Here, a comprehensive strategy based on fingerprinting and chemometric approaches was created to explore the influence of origins, storage time and kneading processing on the quality of CR. Firstly, high-performance liquid chromatography with diode array detection was used to obtain the fingerprints of 35 batches of CR from six different origins and 33 batches of CR from varying storage times or kneading procedures. Secondly, chemometric methods including similarity analysis (SA), principal component analysis (PCA), hierarchical clustering analysis (HCA), and two-way orthogonal partial least square with discriminant analysis (O2PLS-DA) were used to evaluate the differences of chemical components in CR so as to identify its source and reflect its quality. Moreover, 13 and 16 major compounds were identified as marker compounds for the discrimination of CR from different origins, storage time and kneading processing, respectively. Furthermore, the relative content of the marker components and the exact content of Lobetyolin were measured, indicating that the contents of these components vary significantly between various CR samples. Meanwhile, the chemical components of CR were identified using Mass spectrometry. According to the findings of our investigation, the quality of CR from Gansu was the best, followed by Shanxi and then Sichuan. The quality of CR from Chongqing and Guizhou was poor. At the same time, the quality of CR was the best when it was kneaded and stored for 0 years, indicating that the traditional kneading process of CR is of great significance. Conclusively, HPLC fingerprint in conjunction with chemical pattern recognition and component content determination can be employed to differentiate the raw materials of different CR samples. Additionally, it is also a reliable, comprehensive and prospective method for quality control and evaluation of CR.

Cocoa quality: Chemical relationship of cocoa beans and liquors in origin identitation

In this study, HS-SPME-GC–MS was applied in combination with machine learning tools to the identitation of a set of cocoa samples of different origins. Untargeted fingerprinting and profiling approaches were tested for their informative, discriminative and classification ability provided by the volatilome of the raw beans and liquors inbound at the factory in search of robust tools exploitable for long-time studies. The ability to distinguish the country of origin on both beans and liquors is not so obvious due to processing steps accompanying the transformation of the beans, but this capacity is of particular interest to the chocolate industry as both beans and liquors can enter indifferently into the processing of chocolate. Both fingerprinting (untargeted) and profiling (targeted) strategies enable to decipher of the information contained in the complex dataset and the cross-validation of the results, affording to discriminate between the origins with effective classification models.

BINO: Automatic recognition of inline binary functions from template classes

In this paper, we propose BINO, a static analysis approach that relieves reverse engineers from the challenging task of recognizing library functions that have been inlined. BINO recognizes inline calls of methods of C++ template classes (even with unknown data types). We do this through a binary fingerprinting and matching approach. Our fingerprint model captures syntactic and semantic features of an assembly function, along with its Control-Flow Graph structure. Using these fingerprints and subgraph isomorphism, it recognizes inline method calls in a target binary. BINO automates the fingerprints generation phase by parsing the source code of the template classes and automatically building appropriate binaries with representative inline calls of said methods. We evaluate BINO by performing experiments on a dataset of 555 GitHub C++ projects containing 10,600 inline functions, exploring several optimization levels that allow the compiler to inline function calls. We show that our approach can recognize inline function calls to the most used methods of well-known template classes with an F1-Score up to 63% with the -O2, -O3, and -Ofast optimizations levels.

Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity

Deep learning-enabled device fingerprinting has proven efficient in enabling automated identification and authentication of transmitting devices. It does so by leveraging the transmitters' unique features that are inherent to hardware impairments caused during manufacturing to extract device-specific signatures that can be exploited to uniquely distinguish and separate between (identical) devices. Though shown to achieve promising performances, hardware fingerprinting approaches are known to suffer greatly when the training data and the testing data are generated under different channels conditions that often change when time and/or location changes. To the best of our knowledge, this work is the first to use MIMO diversity to mitigate the impact of channel variability and provide a channel-resilient device identification over flat fading channels. Specifically, we show that MIMO can increase the device classification accuracy by up to about 50 percent when model training and testing are done over the same channel and by up to about 70 percent when training and testing are done over different fading channels.

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era.

The Internet-of-Things (IoT) has been used with greater frequency to track peoples' daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

Insights into the FTIR Spectral Fingerprint of Saffron (Crocus sativus L.) Stigmas After Gentle Drying Treatments

Drying of the delicate, red stigmas of the Crocus sativus L. flower is necessary to produce saffron, the most expensive spice in the world. So far, laborious and sample destructive methods were applied to get vital insight into this process following key physicochemical changes. Vibrational spectroscopy tools that allow molecular fingerprinting of plant tissues via multivariate data analysis are still not exploited. This study aimed at gaining new insights into the Fourier-Transform Infrared (FTIR) spectra of saffron on different gentle drying treatments in vacuum or by short-time heating with varying sample loading, energy input, duration etc. Diagnostic spectral bands that were exposed using Principal Component Analysis were assigned to C=O stretching in vinyl or cyclic esters, amides or other inter-molecular interactions of importance for functionality. Above all, the peak at 1160 cm−1 (typical of C-O-C glycosidic bridges) proved a distinguishing feature of short-time heated vs vacuum-dried saffron. Other critical quality attributes of the dried stigmas (physical structure, color, chemical composition), assessed with Scanning Electron Microscopy (SEM), colorimetry, UV-Vis spectrometry and High-Performance Liquid Chromatography (HPLC), indicated both positive and negative effects per drying method. Our work highlights the novelty to combine non-destructive FTIR spectroscopy with conventional techniques for a more insightful evaluation of desired or undesired changes after saffron dehydration. Moreover, the spectral fingerprinting approach offers a cost-effective, eco-friendly solution for rapid, non-invasive control of the raw material that is of high interest for food and nutraceutical applications.

Predicting Indonesian coffee origins using untargeted SPME − GCMS - based volatile compounds fingerprinting and machine learning approaches

Predicting Indonesian coffee origins using untargeted SPME − GCMS - based volatile compounds fingerprinting and machine learning approaches

Ultra Wideband (UWB) Localization Using Active CIR-Based Fingerprinting

Indoor positioning systems using Ultra Wideband (UWB) achieve high positioning accuracy (<30 cm). However, traditional localization approaches require many packet exchanges (e.g. two-way ranging) or challenging clock synchronization (e.g. time difference of arrival). To remedy this, we propose active fingerprinting using the channel impulse response (CIR) from a single UWB packet received at each UWB anchor. The proposed neural network anchor-subset selection method with Savitzky-Golay filter achieves a low mean absolute error (20.9-87.0 cm), in contrast to signal strength based fingerprinting approaches that realize accuracies of 2-3 m. Finally, with CIR interpolation the data collection overhead is reduced.

Pyrolysis GC-MS Characterization of Plastic Debris from the Northern Gulf of Alaska Shorelines

Plastics and their breakdown components are accumulating at alarming rates in global ecosystems, including the Gulf of Alaska. Obtaining knowledge of the chemical composition of plastics is important because different types of plastics are manufactured using different types of polymers and copolymers for specialized applications. We employed pyrolysis GC-MS for the chemical characterization of 115 plastic debris samples of different physical and optical properties nonrandomly collected from shorelines in the northern Gulf of Alaska. A two-tier approach of peak fingerprinting and mass spectral data of marker peaks provided higher confidence in the data. The analyses did not appear to be compromised by the physical properties, optical properties, or weathering conditions of the debris. Polyethylene and polypropylene, the low-density floating plastics possibly of oceanic origins, were some of the most frequently detected polymers in the Katmai National Park and Preserve and Kenai Fjords National Park. Interestingly, a more diverse composition of polyethylene, polypropylene, polystyrene, poly(ethylene terephthalate), and poly(vinyl chloride) was detected in Western Prince William Sound shorelines, possibly of oceanic as well as terrestrial origins. Additional benefits of the pyrolysis GC-MS method included the detection of copolymers and plastic additives in the same analytical runs.