Nondestructive Detection Technique Research Articles

Residual opening of fatigue cracks due to the wake of plasticity

The concept of a breathing crack and the clapping mechanism, when a crack periodically opens and closes subject to a cyclic or harmonic excitation, are widely utilised in studies concerning contact acoustic nonlinearity (CAN) and nonlinear phenomena. The investigation of these phenomena is particularly important for the development of reliable non-destructive fatigue crack detection techniques. The aim of the current contribution is to demonstrate the characteristic behaviour of a fatigue crack under excitation loading using a simple analytical model that accounts for the wake of plasticity behind the crack tip, which leads to the plasticity-induced fatigue crack closure phenomenon. It is believed that incorporation of these features and more realistic behaviour of fatigue cracks can help to improve the methods for the detection of nonlinear phenomena associated with CAN. The results of the present work indicate that ultrasonic inspections based on the CAN using a bilinear stiffness model may underestimate the length of the detected crack(s) due to the plasticity-induced crack closure phenomenon. Despite many modelling assumptions, the present work can serve as a starting point for the development of accurate models for different crack geometries and load histories. The latter may help to develop effective nondestructive techniques for detection and identification of fatigue cracks and prevention of fatigue failures.

Development of surface‐enhanced Raman spectroscopy evidence swabs using a silver nanoparticle biosynthesis for the detection of animal blood

AbstractIdentifying the type of body fluid recovered from crime scenes before DNA typing may be critical to qualify its relevancy to the case. Presumptive tests are often used first to detect blood traces; however, they require confirmation, are destructive, and are susceptible to false positives. This study aims to develop a method of collection and identification for nonhuman blood using biosynthesized SERS swabs as a more accurate, specific, and nondestructive detection technique. Silver nanoparticles were synthesized directly on nylon swabs using a biosynthesis reaction and curcumin as the reducing agent to form the nanoparticles. The detection of bovine blood was investigated by analyzing decreasing volumes of blood swabbed with the surface‐enhanced Raman spectroscopy (SERS) swabs. Although swabbing 30 and 20 μl of blood resulted in the most resolved spectra, swabbing 10 μl of blood still produced identifiable peaks. Wetted swabs were used to analyze dried bovine bloodstains on cotton fabrics and glass slides: The spectra collected when swabbing the bloodstain on the glass sides had a higher signal‐to‐noise ratio than on fabrics. The SERS swabs were subsequently used to collect and analyze horse and sheep blood. Although Raman bands characteristic of blood were identifiable for each specie, showing that the SERS swabs allowed the successful detection of the three animal blood species, the spectra from the three species could not be differentiated. This study demonstrates the efficiency of the biosynthesis to grow nanoparticles on the swabs and shows their viability to be used as SERS substrates to collect and identify animal blood.

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review.

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.

Antimony speciation on a dithiothreitol-functionalized two-dimensional Au@Ag array by surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS), a fast and non-destructive detection technique, can be potentially used for the on-site analysis of antimony species. However, SERS quantification of antimony species has been rarely performed. This study fabricated a two-dimensional (2D) Au@Ag–dithiothreitol (DTT) array for antimony speciation analysis. This array was able to detect antimony at concentrations as low as Sb(III) = 1.05 µg·L−1 and Sb(V) = 3.81 µg·L−1. Moreover, it exhibited good uniformity with a relative standard deviation of 16.2% and recoveries of 88–93%. The results of three-dimensional finite-difference time-domain simulations revealed that the 2D Au@Ag array with 25 nm-Au cores, 6 nm-Ag shells, and 2 nm-interparticle gaps created a strong localized surface plasmon resonance. The DTT functionalization of the 2D Au@Ag array facilitated selective adsorption and SERS detection of antimony ions. By combining the electromagnetic and chemical mechanisms, antimony speciation analysis was successfully performed using the 2D Au@Ag–DTT array, suggesting a new on-site method for monitoring small molecules.

Seedlessness detection in ‘White Malaga’ table grapes using near-infrared spectroscopy

‘White Malaga’ table grapes are seeded and widely grown in Thailand. They are converted by induction into seedless grapes to increase their value. It is difficult to identify seedlessness in table grapes without destroying the grape berry. The present work thus described a quick and non-destructive method for detecting and predicting seedlessness in ‘White Malaga’ table grapes by using near-infrared (NIR) spectroscopy together with chemometric analysis. The NIR spectra of 280 grape samples were recorded after harvest. Firmness, total soluble solids (TSS), pH, titratable acidity (TA), tartaric acid, number of seeds, and relevant physical properties were analysed. The width and weight of plant growth regulator (PGR) treatments were significantly lower than those in the untreated grapes, while the length, firmness, TA, and tartaric acid were not significantly different. Partial least square (PLS) regression was used to investigate the prediction. Classification models, namely principal component analysis (PCA) and quadratic discriminant analysis (QDA), were used to identify seedlessness. It was found that, QDA, as a representative of linear classification, resulted in the best classification of seeded and seedless performance, where the percentages of predictive ability (%PA), the percentages of model stability (%MS), and the percentages of correctly classified (%CC) were 97.27, 98.57, and 96.23%, respectively, for the training set with no pre-processing. Therefore, the NIR spectroscopy technique can be a non-destructive technique for seedlessness detection in ‘White Malaga’ table grapes.

Relationship between Concrete Hole Shape and Meso-Crack Evolution Based on Stereology Theory and CT Scan under Compression.

To achieve more accurate prediction of the potential failure location and to conduct a deeper analysis of the failure mechanism of concrete constructions, it is critical to probe the evolution process of internal meso-cracks that bear various intensities of load. While a computer Tomography (CT) test provides a non-destructive detection technique for obtaining the internal meso-damage state of concrete, traditional image processing and Digital Image Correlation (DIC) are ineffective in extracting meso-damage information from concrete CT images. On the other hand, by observing the shape change law of concrete’s internal holes under load, it is proposed to use the hole roundness and area fraction formula, developed based on the stereology principle and morphology, to characterize and predict the potential failure location. Four features particularly addressed include the CT image as a whole, image equal partitioning, crack and non-crack areas, and representative holes. The approach is to explore the variation law of critical hole shape parameters, especially the hole roundness under different loading stages, and analyze the relationship between the change in hole shapes and the final macro-crack positions. It is found that compared with the average area fraction, the average hole roundness value of cross section images is more sensitive to the change in stress. In both uniform partitioning and non-uniform partitioning, the average hole roundness value near the final macro-crack location exhibits an increase trend with the stress, while the smoothing effect caused by the hole roundness averaging always exists. Near the final macro-crack location, the roundness of each individual hole is positively associated with the stress, while away from the final macro-crack location such a relation may not be observed. This trend expounds the evolution process of meso-damage in concrete, and the finding can be used to predict the accurate locations of macro-cracks.

Development of automatic detection model for stem-end rots of ‘Hass’ avocado fruit using X-ray imaging and image processing

Postharvest qualitative avocado losses attributable to stem-end rot are serious problems in various main production areas. Postharvest inspection of avocadoes is still commonly performed by hand; this process depends upon only slight differences in fruit appearance or firmness, and it can lead to misclassification. The development of a non-destructive stem-end rot detection technique would help improve objective inspection and reduce misclassification. Therefore, this study proposes an X-ray machine vision approach to automatically detect stem-end rot in avocados. Four-hundred-and-twenty-two X-ray images of fully ripened ‘Hass’ avocadoes were obtained to develop and validate an automatic classification model. The stem-end rot features visible in the X-ray images were extracted using two image processing operations: image segmentation and fruit-outline evaluation. Then, an extremely randomized trees model classified rot development into two classes (< 20% and ≥ 20%) using two features extracted from the X-ray image. The overall classification accuracy was 0.89 with an area under the curve of 0.93 and a recall of 0.89, which suggests the possibility of performing stem-end rot inspections via X-ray imaging and image processing.

Nondestructive identification of wood species by terahertz spectrum

AbstractTerahertz time‐domain spectroscopy (THz‐TDS) has become an emerging nondestructive detection technique due to the unique properties of THz waves; it offers safety, penetrability, and fingerprint identification, especially for commonly used non‐polar materials. Here, we measure the differences in THz spectra of four wood blocks (manglietia, amur linden, black walnut, and ebony) in the frequency range of 0.1–0.9 THz. In addition, we demonstrate the principal components analysis (PCA) applied in THz absorption spectra; the principal components extracted from original data by PCA can replace the original absorption coefficient data and can clearly distinguish the wood types. Results show that THz‐TDS combined with PCA can be used as an effective nondestructive identification method in the field of public security, food detection, and customs anti‐smuggling initiatives.

Rapid discrimination of authenticity in wheat flour and pasta samples using LIBS

In this study the possibility of using laser-induced breakdown spectroscopy (LIBS) to discriminate durum and common wheat samples both in flour and pasta and determine the adulteration ratio was explored. For this purpose, 120 common and 119 durum wheat samples including different genotypes were collected from various regions of Anatolia. LIBS spectra were evaluated with partial least squares-discriminant analysis (PLS-DA) and PLS to identify the wheat type and adulteration ratios. In PLS-DA study, sensitivity and specificity of calibration and cross-validation were found as 1.000 and 0.990. The coefficients of determination and limits of detection for durum flour adulteration were found to be 0.999 and 0.52%, respectively. The study demonstrated that LIBS is a promising tool and has potential to become a rapid, reliable, environmental friendly and nondestructive analytical technique for identification and detection of wheat flour adulteration in pasta.

The perspectives of broadband metasurfaces and photo-electric tweezer applications

Abstract With strong demands of real-time monitoring of biomolecules or environmental pollutants, overcoming technical hurdles on control and detection of freely diffusive nanoscale objects become a question of issue to solve in a variety of research fields. Most existing optical techniques inevitably require labeling to the target material, which sometimes denature the measuring biomaterials. For highly efficient real-time monitoring without complicated pretreatment or labeling, many successes in development of label-free or non-destructive detection techniques via increased sensitivity were accomplished by the additional structures. Metasurface-based two-dimensional photonic/electric devices have recently represented extraordinary performances in both manipulation and sensing for various small particles and biochemical species, repeatedly overcoming the limit of detection achieved right before. In parallel, various metasurface-based devices were also introduced promoting transportation of targets into optical hotspot sites, overcoming diffusion limits. We noted this point, therefore, reviewed two major research fields such as metasurface-assisted material sensing and transportation technologies that have contributed to present prospective sensing technologies, then showed perspective views on how great synergy can be created when two technologies are cleverly integrated. Recently, a trend of conceptual merging of optical detection and transporting schemes beyond both diffraction limit and diffusion limit leads to a creation of exceptional performance in molecular detections. In this review, the trends of the latest technologies accomplishing this purpose by hybridization of various composite materials and functional metasurfaces will be introduced.

An accurate and effective single-seed moisture selection method based on nuclear magnetic resonance (NMR) in maize

Kernel moisture content (KMC) is important for maize grain development, harvest, and storage. Breeders always consider KMC as a necessary indicator when conducting breeding programs. The fact that a large number of samples are required for the typical breeding process necessitates the development of accurate, high-throughput, and non-destructive detection techniques for KMC. Here, we proposed an effective method based on nuclear magnetic resonance (NMR) spectroscopy to track moisture variation in maize kernels and select offspring with low KMC at harvest. Both moisture mass and content could be accurately measured via regression analysis (R2 = 0.9924 and 0.9139, respectively). Magnetic resonance imaging technology was also applied to monitor the spatial distribution of moisture in individual kernels and ears. On the other hand, with two generations of KMC selection experiments, we demonstrated the effectiveness of the proposed method. KMC was measured for self-crossing kernels of two elite hybrids, namely Zhengdan958 and Xianyu335, and partial kernels were classified into a high-kernel-moisture group (HMG) or low-kernel-moisture group (LMG). The progenies were then selected for kernels having a high KMC in HMG and low KMC in LMG. After selection, KMCs in LMG were significantly lower than those in the HMG in both hybrids at generations F2 and F3. Finally, phenotypic response surveys revealed that KMC selection significantly affected the flowering time. Our results demonstrate the effectiveness of the NMR-based platform for optimizing KMC trait, with potential applications in maize breeding programs.

Hyperspectral Image Recovery via Reliability-Weighted Compressed Sensing for Hardware Trojan Detection

Hyperspectral imaging is a popular style of image acquisition which captures a multitude of two-dimensional images at different frequencies to yield more information from a scene. For imaging in certain frequency ranges, point-scanning images pixel-by-pixel is the only option for imaging and consequently can require prohibitively long measurement times. This paper introduces a non-uniform compressed sensing strategy for recovering whole hyperspectral images with much fewer samples than normally required. Our methods use the known variability of measurements across training subjects to weight random sampling toward unreliable hyperspectral points. This strategy is used to heavily reduce remote hyperspectral image acquisition time while maintaining or improving recovery performance. To test our methods, we focus on the problem of recovering hyperspectral images for non-destructively detecting malicious dormant hardware Trojans hidden in integrated circuits. These non-destructive detection techniques require remote hyperspectral measurements of the backscattering electromagnetic side channel; which can only be performed by point-scanning with existing technologies. We detect covert hardware Trojan circuit modifications with state-of-the-art performance while requiring up to ten times fewer measurements than prior methods. We compare performance of a uniform-random and our weighted-random compressed sensing strategy for the 2-dimensional discrete cosine transform bases as well as with learned dictionary bases.

Rapid Screening of β-Agonists in Animal Hair by Thermal Desorption Electrospray Ionization Mass Spectrometry

Even though the use of β-agonists in livestock has been banned in many countries, β-agonists abuse is still out of control owing to time-consuming and complicated determination techniques. This study applied ambient thermal desorption electrospray ionization mass spectrometry (TD-ESI/MS) as a new strategy for rapidly screening seven β-agonists. As it does not require pretreatment, this method allows on-site and real-time detection. Samples were collected directly using a simple metal probe. Analytes on the probe were desorbed and ionized by TD-ESI, after which analyte ions were detected using a triple quadrupole mass analyser. The results showed that a thermal desorption temperature of 320°C and nebulizer pressure of 3 psi demonstrated the highest sensitivity. Excellent linear relationships were obtained in concentrations ranging from 10 to 400 μg/kg. The limit of detection of the method was 1-10 μg/kg with RSDs of 7.2-14.6%. A positive sample was verified using HPLC coupled with triple quadrupole tandem mass spectrometry, consistent with the results from TD-ESI/MS. This method was the first to apply TD-ESI/MS to rapid analyse β-agonists in livestock hair and was proven to be accurate, fast, and environmentally friendly. Because qualitative screening for each analysis was completed within approximately 1 min, the method could contribute to preventing non-compliance in the flow of animal-derived food to the market. This method was a promising method for the rapid screening of β-agonists in livestock hair at any time of breeding without imposing any harm to animal which proven to be a rapid, in situ, and non-destructive detection technique without any pretreatment.

Development of an intelligent laser biospeckle system for early detection and classification of soybean seeds infected with seed-borne fungal pathogen (Colletotrichum truncatum)

There is a need for developing rapid and non-destructive techniques for the early detection of seed-borne fungal pathogen because they can be an essential step towards adopting effective disease control measures. Existing techniques for detecting seed-borne diseases have poor sensitivity towards early stages of pathogen development (i.e., when seeds are asymptomatic) and they are also expensive, time-consuming, complex, require mycological skills and destructive testing operations. Aiming at overcoming the above limitations of the existing techniques, a novel laser biospeckle based method is proposed for early detection of seed-borne fungal infection in conjunction with machine learning. Soybean seeds infected by low concentrations (102-106 spores ml−1) of Colletotrichum truncatum were analysed by using full field biospeckle analysis to establish the possible relationship between biological activity in early stages of pathogen infection, with and without the use of frequency filtering. The results demonstrate that the biospeckle activity (BA), for both, raw and frequency filtered data was significantly high (p < 0.05) for the diseased seeds even for low inoculum concentrations. Moreover, the amplitude values of mid frequency spectral components for diseased seeds were higher than those of lower and higher spectral components which correspond to the BA of fungal infected seeds. Several classical machine learning algorithms were trained to model the response of healthy and diseased samples after parameter optimisation. Obtained results showed that k-nearest neighbour (k-NN), decision tree (DT), and artificial neural network (ANN) based predictive models presented strong robustness and high performance with overall accuracy reaching up to 96.94% for classifying diseased seeds.

Fraud Detection in Meat Using Hyperspectral Imaging

Fraud detection in meat is a challenging task for researchers, consumers, industries, and regulatory agencies. Traditional approaches for fraud detection are time-consuming, complicated, laborious, and expensive; they require technical skills. Therefore, much effort has been devoted in academia and industry to developing rapid and nondestructive optical techniques for fraud detection in meat. Among them, hyperspectral imaging has gained enormous attention and curiosity throughout the world. Hyperspectral imaging is an emerging analytical technique that combines spectroscopy and imaging in one system to acquire spectra and spatial information from an object simultaneously. Hyperspectral imaging is the only analytical technology that answers commonly asked analytical questions such as what chemical species are in the samples, how much, and most importantly, where they are located. Therefore, the technology will undoubtedly play indispensable roles in research and industry for fraud detection in the coming days.

Characterizing the Layer Structures of the Lacquerware From the Palace Museum by Terahertz Imaging in Reflection Geometry

Chinese lacquerware is an important invention of arts and crafts in China. In this study, Chinese lacquerware is characterized using terahertz reflectometric imaging. The lacquerware studied herein comprises an ornamental wood panel covered by multiple layers of lacquers to portray motifs. For characterizing lacquerware, a terahertz time-domain spectroscopic reflectometric imaging system is proposed. The role of the proposed terahertz imaging system in highlighting the interface between layers during stratigraphic buildup in reflection geometry is proved. The proposed system provides a universal method for assessing the structural information of lacquered objects in a contactless and non-invasive manner; moreover, it provides two-dimensional images, subsurface three-dimensional images, and stratigraphic images (b-scans) in a contactless and non-invasive manner. Using the proposed system, we examine the buried layers of the lacquerware, including faults in the wooden layer and damages in the lacquerware. Research shows the promising prospects of terahertz time-domain spectroscopic reflectometric imaging as a non-destructive detection technique suited to lacquerware.

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts.

Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.

Micro-focus computed tomography for turbine blade based on all-optical bremsstrahlung source

Computed tomography is a major technique for nondestructive detection of internal defects of dense materials and large-size devices. It is widely used in material science, railway, aerospace, national defense, military industry and other industries. At present, conventional high-energy industrial computed tomography system uses the X-ray source based on traditional thermionic RF electron gun, which can only provide millimeter source size, thus limiting its imaging spatial resolution. A high-energy micro-focus X-ray source is the key means to realize high-resolution high-energy industry computed tomography. As an emerging accelerator technology, the laser wakefield accelerator is a promising candidate for the micro-focus high-energy industrial computed tomography. This article reports experimental results of a micro-focus X-ray source based on laser wakefield acceleration and a computed tomography for a turbine blade. Using a 20 TW Ti: sapphire laser system, an electron beam with a charge of (140±44) pC is generated through ionization-induced injection, and then an all-optical bremsstrahlung X-ray source with an accumulated source size of 25 μm is obtained by using a 1.5 mm tungsten target. Using this source, a preliminary compressed-sensing-based computed tomography for a turbine blade is performed.

Freezing resistance evaluation of rose stems during frost dehardening using electrical impedance tomography

BackgroundElectrical impedance tomography (EIT) has rarely been applied in plant science, particularly to study plant resistance to abiotic and biotic stresses. In this study, we evaluated the freezing resistance of floribunda roses (Rosa Floribunda) during frost dehardening using the EIT technique to identify a new method for rapid and non-destructive measurement of plant freezing resistance.ResultsThe current was the excitation source, the boundary voltage value was measured, and then the boundary voltage reconstructed value was formed. Using an imaging algorithm, the two-dimensional (2D) distribution of impedance or impedance variation was reconstructed. The EIT reconstructed values decreased obviously with the decline in freezing temperatures. The EIT reconstructed values of stems had the best fit to the logistic equation, and subsequently, the semi-lethal temperatures were calculated. The freezing resistance results evaluated using EIT reconstructed values were linearly correlated with the results of the traditional electrolyte leakage (EL) method (r = 0.93, P < 0.01).ConclusionsIn conclusion, after freezing tests, the reconstructed values of EIT images could be used to quantitatively evaluate the freezing resistance of floribunda rose stems. The present study provides a reference for the further application of the EIT technique for non-destructive and rapid detection of plant freezing resistance.

A New Approach for Quantitative Surface-Enhanced Raman Spectroscopy through the Kinetics of Chemisorption.

Surface enhanced Raman spectroscopy (SERS) is a non-destructive, highly sensitive, and rapid analytical tool, which has been widely used in different fields, especially for trace quantities of analyte. However, using SERS for reliable quantitative sample analysis is still a great challenge. Herein, a new approach to quantitative SERS analysis at nanostructured substrates that does not require an internal standard or well-ordered nanostructured SERS substrates is developed. This method is based on the kinetics of chemisorption, that is, on a homogeneous surface, the time taken for adsorption of an adsorbate (adenine or melamine) to reach equilibrium negatively correlates with the concentration of the adsorbate. Quantitative analysis is achieved by using in situ SERS to acquire the adsorption profile of the adsorbate and enabling the adsorption equilibrium time to be calculated. There is excellent correlation between the adenine and melamine SERS response over adsorption equilibrium time with concentration, and the correlation coefficients are 0.9906 and 0.9682, respectively. Moreover, milk sample spiked with the melamine is also studied, and the standard recovery rate is 106%. This work demonstrates a novel, non-destructive, and cost-effective quantitative SERS detection technique, which can broaden applications across multiple fields.