Intrinsic Fingerprint Research Articles

Nondestructive material characterization and component identification in sheet metal processing with electromagnetic methods

Electromagnetic methods for non-destructive evaluation (NDE) are presented, with which sheet metal components can be identified and their material properties can be characterized. The latter is possible with 3MA, the Micromagnetic Multiparametric Microstructure and stress Analyser. This is a combination of several micromagnetic NDE methods that make it possible to analyse the microstructure in a ferromagnetic material and to determine quantitative values of the mechanical material properties or the stress state. In the case of cold forming, the 3MA application for pre-process testing of sheet metal is discussed. Based on the 3MA information, the formability of the sheets can be predicted. To apply 3MA in-line, the influence of the relative speed and the relative distance between the 3MA probe head and the sheet was investigated. In a second study, a spatially resolved eddy current (EC) method was used to create an image of the intrinsic material microstructure of a component for its identification and traceability. It turned out, that these intrinsic fingerprint images can still be recognized even after subsequent plastic deformation or coating of the surface. This enabled the development of a marker-free traceability method for sheet metal processing. It is based on a low-cost array sensor and a specimen identification using robust and partly redundant features of the fingerprint images processed by machine learning (ML).

Synergistic enhancement of ultrahigh SERS activity via Cu2O@Ag Core-Shell structure for accurate label-free identification of breast tumor subtypes

Conventional methodology for accurate identification of breast tumor subtypes is complex and time-consuming. Label-free SERS (surface-enhanced Raman scattering) enables rapid detection of biomolecules and provides intrinsic fingerprint information of analytes. Exploring reliable SERS bioprobes with high sensitivity and repeatability is essential for efficient typing of cancer cells. In this study, a novel core-shell noble metal-semiconductor Cu2O@Ag heterostructure with uniform morphology and outstanding SERS activity for label-free detection has been proposed. The Cu2O@Ag SERS substrates exhibit exceptional sensitivity to detect 4 nitrophenthiol (4NTP) at extreme low concentration (10−15 M), and the SERS spectra demonstrate excellent selectivity and repeatability (RSD ≈ 8.84%). The enhancement mechanism is attributed to the synergistic enhancement of photoinduced charge transfer and boosted surface plasmon resonance effect, confirmed by reduced fluorescence lifetime of methylene blue on SERS substrates and enhanced electromagnetic field intensity via finite-difference time-domain computational simulation. The ultrahigh sensitivity and good signal stability endow Cu2O@Ag SERS bioprobe potential for accurate detection of four breast cancer cells subtypes. The rich fingerprint information from SERS spectra is processed using linear differentiation analysis, revealing a high accuracy of 93.3% for subtypes classification. These results demonstrate an excellent SERS bioprobe with high sensitivity and repeatability for precision diagnosis has been developed.

Dynamic Anti-Counterfeiting Labels with Enhanced Multi-Level Information Encryption

Information encryption is an important means to improve the security of anti-counterfeiting labels. At present, it is still challenging to realize an anti-counterfeiting label with multi-function, high security factor, low production cost, and easy detection and identification. Herein, using inkjet and screen printing technology, we construct a multi-dimensional and multi-level dynamic optical anti-counterfeiting label based on instantaneously luminescent quantum dots and long afterglow phosphor, whose color and luminous intensity varied in response to time. Self-assembled quantum dot patterns with intrinsic fingerprint information endow the label with physical unclonable functions (PUFs), and the information encryption level of the label is significantly improved in view of the information variation in the temporal dimension. Furthermore, the convolutional residual neural networks are used to decode the massive information of PUFs, enabling fast and accurate identification of the anti-counterfeit labels.

Vancomycin functionalization of gold nanostars for sensitive detection of foodborne pathogens through surface‐enhanced Raman scattering

AbstractIn this paper, we report a sensitive surface‐enhanced Raman scattering (SERS) biosensor for the detection of food‐borne pathogens on the basis of recognition of bacterial cells through the specific targeting agent vancomycin. Vancomycin‐functionalized gold nanostars (GNSs–4–MBA–van) were synthesized as SERS tags for specific and sensitive quantification of food pathogens. The Raman reporter molecule 4–mercaptobenzoic acid (4–MBA) was also a linker molecule to attach vancomycin to gold nanostars. The proposed vancomycin‐based SERS technique achieved a detection limit of 5.7 and 8.2 CFU/ml for Staphylococcus aureus and Escherichia coli, respectively. The logarithm of the bacterial concentration ranged from 101 to 107 CFU/ml and demonstrated a linear relationship with the SERS intensity of the characteristic 4–MBA Raman peak at 1,081 cm−1. Principle component analysis was used to discriminate the two pathogens simultaneously by using reproducible and specific intrinsic fingerprints and intracellular components obtained through SERS. Moreover, the spiking tests revealed that GNSs–4–MBA–van is effective as a potential biosensor for detecting pathogens in the fresh meat and food‐processing industries.

Splicing forgery localization via noise fingerprint incorporated with CFA configuration

Noise is the inherent intrinsic fingerprint in digital images and is often used for forgery localization. Most noise-based methods assume that the noise is similar over the whole image and can be considered as white Gaussian noise. However, the noise is different in various regions, which degrade the performance of these noise-based methods. To reduce the impact of impractical assumptions, in this paper, we propose an effective noise fingerprint incorporated with CFA configuration for splicing forgery localization. The noise of interpolated pixels is expected to be suppressed after interpolation, and the relationship between the noise levels of adjacent acquired and interpolated pixels is only related to the interpolation algorithm, which is constant in the original image. We utilize a dual tree wavelet based denoising algorithm to extract the noise from the green channel and compute the standard deviation of the noise for acquired and interpolated pixels, respectively. The noise level of acquired and interpolated pixels are then obtained by the geometric mean of the noise standard deviations. Finally, the ratio of noise levels between acquired and interpolated pixels can be a fingerprint to locate tampered regions. Experiments conducted on publicly available databases demonstrate that the proposed approach outperforms previous methods for detecting splice tampering. Moreover, the proposed method is robust to Gaussian filtering and JPEG compression attacks.

A pan-tumor-siRNA aptamer chimera to block nonsense-mediated mRNA decay inflames and suppresses tumor progression

Immune-checkpoint blockade (ICB) therapy has changed the clinical outcome of many types of aggressive tumors, but there still remain many cancer patients that do not respond to these treatments. There is an unmet need to develop a feasible clinical therapeutic platform to increase the rate of response to ICB. Here we use a previously described clinically tested aptamer (AS1411) conjugated with SMG1 RNAi (AS1411-SMG1 aptamer-linked siRNA chimeras [AsiCs]) to inhibit the nonsense-mediated RNA decay pathway inducing tumor inflammation and improving response to ICB. The aptamer AS1411 shows binding to numerous mouse and human tumor cell lines tested. AS1411 induces tumor cytotoxicity in long incubation times, which allows for the use of the aptamer as a carrier to target the RNAi inhibition to the tumor. The AS1411-SMG1 AsiCs induce a strong antitumor response in local and systemic treatment in different types of tumors. Finally, AS1411-SMG1 AsiCs are well tolerated with no detected side effects.

Deep Learning-Based Sensor Array: 3D Fluorescence Spectra of Gold Nanoclusters for Qualitative and Quantitative Analysis of Vitamin B6 Derivatives.

Vitamin B6 derivatives (VB6Ds) are of great importance for all living organisms to complete their physiological processes. However, their excess in the body can cause serious problems. What is more, the qualitative and quantitative analysis of different VB6Ds may present significant challenges due to the high similarity of their chemical structures. Also, the transfer of deep learning model from one task to a similar task needs to be present more in the fluorescence-based biosensor. Therefore, to address these problems, two deep learning models based on the intrinsic fingerprint of 3D fluorescence spectra have been developed to identify five VB6Ds. The accuracy ranges of a deep neural network (DNN) and a convolutional neural network (CNN) were 94.44-97.77% and 97.77-100%, respectively. After that, the developed models were transferred for quantitative analysis of the selected VB6Ds at a broad concentration range (1-100 μM). The determination coefficient (R2) values of the test set for DNN and CNN were 93.28 and 97.01%, respectively, which also represents the outperformance of CNN over DNN. Therefore, our approach opens new avenues for qualitative and quantitative sensing of small molecules, which will enrich fields related to deep learning, analytical chemistry, and especially sensor array chemistry.

The Euler characteristic and topological phase transitions in complex systems

In this work, we use methods and concepts of applied algebraic topology to comprehensively explore the recent idea of topological phase transitions (TPTs) in complex systems. TPTs are characterized by the emergence of nontrivial homology groups as a function of a threshold parameter. Under certain conditions, one can identify TPTs via the zeros of the Euler characteristic or by singularities of the Euler entropy. Recent works provide strong evidence that TPTs can be interpreted as the intrinsic fingerprint of a complex network. This work illustrates this possibility by investigating various networks from a topological perspective. We first review the concept of TPTs in brain networks and discuss it in the context of high-order interactions in complex systems. We then investigate TPTs in protein–protein interaction networks using methods of topological data analysis for two variants of the duplication–divergence model. We compare our theoretical and computational results to experimental data freely available for gene co-expression networks of S. cerevisiae, also known as baker’s yeast, as well as of the nematode C. elegans. Supporting our theoretical expectations, we can detect TPTs in both networks obtained according to different similarity measures. We then perform numerical simulations of TPTs in four classical network models: the Erdős–Rényi, the Watts–Strogatz, the random geometric, and the Barabasi–Albert models. Finally, we discuss the relevance of these insights for network science. Given the universality and wide use of those network models across disciplines, our work indicates that TPTs permeate a wide range of theoretical and empirical networks, offering promising avenues for further research.

Mass Spectrometry Imaging Reveals In Situ Behaviors of Multiple Components in Aerosol Particles.

The inhalation of atmospheric particles is deleterious to human health. However, as a complex mixture, tracing the behaviors of multiple components from real aerosol particles is crucial but unachievable by the existing methods. Here, taking advantage of the intrinsic fingerprints of elemental carbon (EC) and organic carbon (OC) in carbonaceous aerosol (CA) upon laser irradiation, we proposed a label-free mass spectrometry imaging method to visualize and quantify the deposition, translocation and component variation of CA in organs. With this method, the heterogeneous deposition, clearance and release behavior of CA in lung, that more OC was released in parenchyma and OC was cleared faster than EC, was observed. The translocation of CA to extrapulmonary organs including kidney, liver, spleen and even brain was also verified and quantified. By comparing the ratio of OC to EC, an organ-specific release behavior of OC from CA during circulation was revealed. In orthotopic lung and liver tumor, OC was found to penetrate more into tumor foci than EC. This technique provides deeper information for understanding the systemic health effects of aerosol particles.

Mass Spectrometry Imaging Reveals In Situ Behaviors of Multiple Components in Aerosol Particles

AbstractThe inhalation of atmospheric particles is deleterious to human health. However, as a complex mixture, tracing the behaviors of multiple components from real aerosol particles is crucial but unachievable by the existing methods. Here, taking advantage of the intrinsic fingerprints of elemental carbon (EC) and organic carbon (OC) in carbonaceous aerosol (CA) upon laser irradiation, we proposed a label‐free mass spectrometry imaging method to visualize and quantify the deposition, translocation and component variation of CA in organs. With this method, the heterogeneous deposition, clearance and release behavior of CA in lung, that more OC was released in parenchyma and OC was cleared faster than EC, was observed. The translocation of CA to extrapulmonary organs including kidney, liver, spleen and even brain was also verified and quantified. By comparing the ratio of OC to EC, an organ‐specific release behavior of OC from CA during circulation was revealed. In orthotopic lung and liver tumor, OC was found to penetrate more into tumor foci than EC. This technique provides deeper information for understanding the systemic health effects of aerosol particles.

Inside Back Cover: Mass Spectrometry Imaging Reveals In Situ Behaviors of Multiple Components in Aerosol Particles (Angew. Chem. Int. Ed. 43/2021)

A novel mass spectrometry imaging strategy to trace and quantify carbonaceous particles in situ is reported by Jie Sun, Caiqiao Xiong, Zongxiu Nie et al. in their Research Article on page 23225. The method provides not only the spatial biodistribution of carbonaceous aerosol at sub-organ level, but also chemical information on multiple components from these particles based on their intrinsic fingerprints upon laser ablation. This work provides a new opportunity to understand how ambient particles translocate and release their surface organic matter in vivo.

Scanning the Voice of Your Fingerprint With Everyday Surfaces

Due to the premise of uniqueness and acceptance, fingerprint has been the most adopted biometric technologies in high-impact applications (e.g., smartphone security, monetary transactions and international-border verification). Although there are an array of commercial fingerprint scanners across different sensing modalities including optical, capacitive, thermal and ultrasonic, existing fingerprint technologies are vulnerable to spoofing attacks via fake-finger in Kang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2003. In this paper, we investigate a new dimension of fingerprint sensing based on the friction-excited sonic wave (in simpler words, ”voice of fingerprint”) from a user swiping his fingertip on everyday surfaces. Specifically, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SonicPrint</i> to leverage the intrinsic fingerprint ridge information in sonic wave for user identification. First, the complex ambient noise is isolated from the sonic wave using background isolation and adaptive segmentation models. Afterward, a series of multi-level friction descriptors that highlight the target fingerprint information is extracted. These descriptors are fed to a specially designed ensemble classifier for user identification. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SonicPrint</i> is practical as it leverages in-built microphones in smart devices, requiring no hardware modifications. As the first exploratory study, our experimental results with 31 participants over three different swipe actions on 12 different types of materials show up to a 98 percent identification accuracy.

Sensitive and label-free detection of protein secondary structure by amide III spectral signals using surface-enhanced Raman spectroscopy

Proteins and peptides perform a vital role in living systems, however it remains a challenge for accurate description of proteins at the molecular level. Despite that surface-enhanced Raman spectroscopy (SERS) can provide the intrinsic fingerprint information of samples with ultrahigh sensitivity, it suffers from the poor reproducibility and reliability. Herein, we demonstrate that the silver nanorod array fabricated by an oblique angle deposition method is a powerful substrate for SERS to probe the protein secondary structures without exogenous labels. With this method, the SERS signals of two typical proteins (lysozyme and cytochrome c) are successfully obtained. Additionally, by analyzing the spectral signals of the amide III of protein backbone, the influence of concentration on the folding status of proteins has been elucidated. With the concentration increasing, the components of α-helix and β-sheet structures of lysozyme increase while the secondary structures of cytochrome c almost keep constant. The SERS method in this work offers an effective optical marker to characterize the structures of proteins.

Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage

Taking advantage of surface-enhanced Raman scattering (SERS) methodology with its unique ability to collect abundant intrinsic fingerprint information and noninvasive data acquisition we set up a SERS-based approach for recognition of physically induced DNA damage with further incorporation of artificial neural network (ANN). As a proof-of-concept application, we used the DNA molecules, where the one oligonucleotide (OND) was grafted to the plasmonic surface while complimentary OND was exposed to UV illumination with various exposure doses and further hybridized with the grafted counterpart. All SERS spectra of entrapped DNA were collected by several operators using the portable spectrometer, without any optimization of measurements procedure (e.g., optimization of acquisition time, laser intensity, finding of optimal place on substrate, manual baseline correction, etc.) which usually takes a significant amount of operator's time. The SERS spectra were employed as input data for ANN training, and the performance of the system was verified by predicting the class labels for SERS validation data, using a spectra dataset, which has not been involved in the training process. During that phase, accuracy higher than 98% was achieved with a level of confidence exceeding 95%. It should be noted that utilization of the proposed functional-SERS/ANN approach allows identifying even the minor DNA damage, almost invisible by control measurements, performed with common analytical procedures. Moreover, we introduce the advanced ANN design, which allows not only classifying the samples but also providing the ANN analysis feedback, which associates the spectral changes and chemical transformations of DNA structure.

Source smartphone identification by exploiting encoding characteristics of recorded speech

Source device identification has become a hot topic in multimedia forensics recently. In this paper, a novel method is proposed for source smartphone identification by using encoding characteristics as the intrinsic fingerprint of recording devices. The encoding characteristics for the smartphones of 24 popular models derived from 7 mainstream brands are investigated and statistical features of some important parameters are extracted as the discriminative features for the smartphone identification. To keep a balance between reasonable feature dimension and high classification rate, a two-step feature selection strategy consisting of Variance Threshold and SVM-RFE is designed to choose the optimal features. Experimental results show that the proposed method can achieve high identification rates of 97.89% and 98.04% for the live recorded database (CKC-SD) and the TIMIT recaptured database (TIMIT-RSD), respectively, and furthermore our scheme performs better when compared with two typical source identification approaches using recorded speeches. In addition, robustness of the proposed features is evaluated while confronting double compression attack.

Recent Progress on Liquid Biopsy Analysis using Surface-Enhanced Raman Spectroscopy.

Traditional tissue biopsy is limited in understanding heterogeneity and dynamic evolution of tumors. Instead, analyzing circulating cancer markers in various body fluids, commonly referred to as “liquid biopsy”, has recently attracted remarkable interest for their great potential to be applied in non-invasive early cancer screening, tumor progression monitoring and therapy response assessment. Among the various approaches developed for liquid biopsy analysis, surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most powerful techniques based on its high sensitivity, specificity, tremendous spectral multiplexing capacity for simultaneous target detection, as well as its unique capability for obtaining intrinsic fingerprint spectra of biomolecules. In this review, we will first briefly explain the mechanism of SERS, and then introduce recently reported SERS-based techniques for detection of circulating cancer markers including circulating tumor cells, exosomes, circulating tumor DNAs, microRNAs and cancer-related proteins. Cancer diagnosis based on SERS analysis of bulk body fluids will also be included. In the end, we will summarize the “state of the art” technologies of SERS-based platforms and discuss the challenges of translating them into clinical settings.

Barkhausen noise as an intrinsic fingerprint for ferromagnetic components

In this work, the suitability of magnetic Barkhausen noise as an intrinsic fingerprint of ferromagnetic components is determined. This technique has potential for this application because it is based inherently on the near surface microstructural features of ferromagnetic materials. All materials typically display some variation in their microstructure simply as a result of the manufacturing process (rolling, heat treating, welding, grinding), thus providing the potential that Barkhausen noise measurements between nominally similar components will be unique. In this work, we demonstrate that Barkhausen noise measurements are both repeatable in time for a single sample made from a ferromagnetic material, and unique across a number of instances of nominally similar samples. The results of this work have profound implications in a variety of applications in forensics and physical security. For example, consider the fabrication of the next generation of tamper-evident containers and devices using conventional processes for steel. In addition, since Barkhausen noise is sensitive to changes in microstructures due to grinding and welding, these measurements can be used to detect clandestine repairs and tampering. Finally, because the method inherently results in a time series measurement at each point on a sample, it is expected to have far higher dimensionality than physically similar eddy current measurement. However, the higher dimensionality of Barkhausen measurements suggest that they should be harder to counterfeit than eddy current measurements.

Setting Up a Surface-Enhanced Raman Scattering Database for Artificial-Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes.

The quality of input data in deep learning is tightly associated with the ultimate performance of the machine learner. Taking advantage of the unique merits of surface-enhanced Raman scattering (SERS) methodology in the collection and construction of a database (e.g., abundant intrinsic fingerprint information, noninvasive data acquisition process, strong anti-interfering ability, etc.), herein we set up a SERS-based database of deoxyribonucleic acid (DNA), suitable for artificial intelligence (AI)-based sensing applications. The database is collected and analyzed by silver nanoparticles (Ag NPs)-decorated silicon wafer (Ag NPs@Si) SERS chip, followed by training with a deep neural network (DNN). As proof-of-concept applications, three kinds of representative tumor suppressor genes, i.e., p16, p21, and p53 fragments, are readily discriminated in a label-free manner. Prominent and reproducible SERS spectra of these DNA molecules are collected and employed as input data for DNN learning and training, which enables selective discrimination of DNA target(s). The accuracy rate for the recognition of specific DNA target reached 90.28%.

Source camera identification via low dimensional PRNU features

Identifying the source of digital images is the key task in the community of image forensics. Sensor pattern noise dominantly serves as an intrinsic fingerprint or feature for dealing with the problem of source camera identification. However, how to decrease the dimensionality of the pattern noise while guaranteeing the detection power remains a hot topic. The goal of this paper is to investigate the problem of source camera identification for natural images in JPEG format. By considering the image texture, we propose to design a new classifier with adopting a weight function, leading to the remarkable reduction of the feature dimensionality. In the extensive experiments, it is verified that our proposed algorithm performs comparably with the prior art. Besides, the robustness of the proposed classifier is also evaluated when the query images are attacked by post-processing techniques such as JPEG compression, noise adding, noise removing and image cropping.

Headspace fingerprint as a potential multivariate intrinsic indicator to monitor temperature variation of thermal in-pack processes: A case-study on broccoli puree

Abstract The aim of the presented study was to evaluate the potential of a headspace fingerprint as a multivariate intrinsic indicator for monitoring temperature history variation that can occur, for example, during in-pack food processing at the product level. Using solid-phase micro-extraction gas chromatography mass spectrometry (SPME-GC–MS), we monitored the extracted volatile fraction of a series of 8 well-defined thermally processed broccoli purees, differing only in the maximal process temperature reached. Our results showed that the relative composition of the extracted volatile fraction clearly depended on the processing intensity (as a measure for thermal variation) applied. In addition, this headspace fingerprinting approach, including multivariate data analytical approaches, allowed a swift selection of specific intrinsic fingerprint markers. The evaluation of the concentration of each of these markers allows to discriminate between the different processing intensities. However, we suggest to perform a linear combination of the information from relevant identified intrinsic fingerprint markers given the increased reliability of a multiple response indicator. The presented approaches are a promising proof-of-principle that has potential to be exploited for monitoring other processing non-uniformities as well. Industrial relevance Thermal treatment is by far still the most commonly used preservation method for food with a high water content. In-pack retort treatment commonly results in some temperature non-uniformity occurring during the treatment at both the reactor and/or the product level. While the use of thermocouples for acquiring time-temperature history is often an excellent solution, the use of intrinsic food components as indicators can be of great advantage for process monitoring. So far usually pre-selected single component intrinsic indicators were suggested. In this work, we are presenting the utilization of the headspace fingerprint as a multivariate intrinsic indicator instead of using a single compound. From the industrial point of view, we present an innovative concept of monitoring product specific extracted volatile fraction, followed by selection of specific intrinsic fingerprint markers and linearly combining the information from those markers. While the advanced data analysis for selection of the fingerprint markers will have to be done per specific product, only the linear combination of the specific data from the markers can be used for routine analysis. Such concept can provide increased reliability due to the utilization of a non-pre-selected multiple response indicator with a relatively feasible monitoring of the markers using gas chromatography mass spectrometry (GC–MS).