Nondestructive Detection Method Research Articles

Self-Reporting Microsensors Inspired by Noctiluca Scintillans for Small-Defect Positioning and Electrical-Stress Visualization in Polymers.

Small defects induce concentrated electrical stress in dielectric polymers, leading to premature failure of materials. Existing sensing methods fail to effectively visualize these defects owing to the invisible-energy state of the electric field. Thus, it is necessary to establish a nondestructive method for the real-time detection of small defects in dielectric polymers. In this study, a self-reporting microsensor (SRM) inspired by Noctiluca scintillans is designed to endow materials with the ability of self-detection for defects and electrical stress. The SRM leverages the energy of a nearby electric field to emit measurable fluorescence, enabling defect localization and diagnosis as well as electrical-stress visualization. A controllable dielectric microsphere is constructed to achieve an adjustable electroluminescence threshold for the SRM, thereby increasing its detection accuracy while decreasing the electroluminescence threshold. The potential degradation in the polymer performance owing to SRM implantation is addressed by assembling long molecular chains on the SRM surface to spontaneously generate an interpenetrating network. Results of finite element analyses and experiments demonstrate that the SRM can effectively realize nondestructive visualization and positioning of small defects and concentrated electrical stress in polymers, positioning it as a promising sensing method for monitoring the electric field and charge distribution in materials.

A Non-Destructive Detection and Grading Method of the Internal Quality of Preserved Eggs Based on an Improved ConvNext.

As a traditional delicacy in China, preserved eggs inevitably experience instances of substandard quality during the production process. Chinese preserved egg production facilities can only rely on experienced workers to select the preserved eggs. However, the manual selection of preserved eggs presents challenges such as a low efficiency, subjective judgments, high costs, and hindered industrial production processes. In response to these challenges, this study procured the transmitted imagery of preserved eggs and refined the ConvNeXt network across four pivotal dimensions: the dimensionality reduction of model feature maps, the integration of multi-scale feature fusion (MSFF), the incorporation of a global attention mechanism (GAM) module, and the amalgamation of the cross-entropy loss function with focal loss. The resultant refined model, ConvNeXt_PEgg, attained proficiency in classifying and grading preserved eggs. Notably, the improved model achieved a classification accuracy of 92.6% across the five categories of preserved eggs, with a grading accuracy of 95.9% spanning three levels. Moreover, in contrast to its predecessor, the refined model witnessed a 24.5% reduction in the parameter volume, alongside a 3.2 percentage point augmentation in the classification accuracy and a 2.8 percentage point boost in the grading accuracy. Through meticulous comparative analysis, each enhancement exhibited varying degrees of performance elevation. Evidently, the refined model outshone a plethora of classical models, underscoring its efficacy in discerning the internal quality of preserved eggs. With its potential for real-world implementation, this technology portends to heighten the economic viability of manufacturing facilities.

Image and audio characterization of electromagnetic signals in cable defect detection

Power cables play a very important role in urban development. On the other hand, the integrity and the safe operation of power cables are crucial as even a minor potential mishap can result in serious loss of life and property. Hence, early diagnosis and warning of cable faults are imperative. Addressing the current lack of practical detection technologies, this study proposes a non-destructive testing method based on electromagnetic field principles. Through scanning the power-on power cables with probes, the electric field intensity around the cables can be measured, and the weak anomaly caused by structural and material defects can be detected using Superlets transformation. Additionally, to gain a better characterization of the faults, a digital post-processing approach consisting of imaging and sonification algorithms is developed to aid in pinpointing the location of the faults. Both numerical simulation and experimental test indicate that the proposed non-destructive detection method is feasible and can achieve good accuracy in locating cable faults. With image and audio characterization, the present method has great potential applications in ensuring the safety of power cables.

A Method for Non-destructive Detection of Moisture Content in Oilseed Rape Leaves Using Hyperspectral Imaging Technology

A Method for Non-destructive Detection of Moisture Content in Oilseed Rape Leaves Using Hyperspectral Imaging Technology

Peanut origin traceability: A hybrid neural network combining an electronic nose system and a hyperspectral system

Peanuts, sourced from various regions, exhibit noticeable differences in quality owing to the impact of their natural environments. This study proposes a fast and nondestructive detection method to identify peanut quality by combining an electronic nose system with a hyperspectral system. First, the electronic nose and hyperspectral systems are used to gather gas and spectral information from peanuts. Second, a module for extracting gas and spectral information is designed, combining the lightweight multi-head transposed attention mechanism (LMTA) and convolutional computation. The fusion of gas and spectral information is achieved through matrix combination and lightweight convolution. A hybrid neural network, named UnitFormer, is designed based on the information extraction and fusion processes. UnitFormer demonstrates an accuracy of 99.06 %, a precision of 99.12 %, and a recall of 99.05 %. In conclusion, UnitFormer effectively distinguishes quality differences among peanuts from various regions, offering an effective technological solution for quality supervision in the food market.

Quantitative detection of crude protein in brown rice by near-infrared spectroscopy based on hybrid feature selection

It is important for rice breeding and quality evaluation to predict the protein content of brown rice rapidly and easily. In this study, near-infrared spectroscopy (NIRS) was utilized to establish a model for detecting crude protein content in brown rice based on 349 samples prepared from three kinds of brown rice, and the performance of the model was evaluated. Improved interval partial least squares (iPLS) was used to divide and screen different feature intervals after spectral preprocessing in this research. On this basis, competitive adaptive reweighted sampling (CARS) optimized the selected feature intervals, and finally 14 effective spectral features concentrated in 1160 nm–1338 nm were selected from 1050 features. The above hybrid feature selection has more advantages than the single selection. Support vector regression (SVR) calibration model was established, and partial least squares regression (PLSR) model commonly used in similar studies was selected as a comparison. The optimal spectral preprocessing method was selected according to the model prediction effect. The coefficient of determination (R2), R2 of cross-validation set (Rcv2), root mean square error of prediction (RMSEP), and relative percent difference (RPD) evaluated for the prediction model reached 0.9185, 0.8876, 0.2040% and 3.5194, respectively. The results showed that the designed method can be used for the rapid determination of the crude protein content of brown rice, providing a convenient, efficient and non-destructive method for related detection.

Non-Destructive Assessment of Agarwood Resin Formation Status in Aquilaria crassna using Near-Infrared Spectroscopy

Agarwood is a unique and valuable resin synthesized within a few tree species of the tree family Thymelaeaceae primarily as a self-defence mechanism against stress causes such as fungal invasions. Instead, artificial inoculation leads to short-time harvesting practiced in intensive commercial-level cultivations. The extraction of this resin can be done only after the harvesting where accurate estimation of resin yield is crucial for avoiding immature or underdeveloped harvesting. However, the amount of resin accumulated in the trunk cannot be estimated by the outside inspection. As a result, trees are being cut down before they have reached their maximum harvest potential. Therefore, precise detection of fragrant resin formation status inside the tree has become one of the primary challenges in the agarwood production industry. Currently, the methods available are either destructive, inefficient, or inaccurate. As such, this study evaluates the possibility of the use of NIR spectroscopy as a non-destructive and quick detection method for agarwood formations status inside trees in the in-situ mode. Near-infrared (NIR) range (588–1,025 nm) spectra were acquired from resin-formed and non-formed areas from twenty tree trunks of agarwood with five different formation levels. The best agarwood formation areas of logs were assigned as class level 1 and the least was assigned as the class level 5. The spectra were acquired near the opening made for the artificial inoculation process of considered Aquilaria crassna tree trunks. The results of Soft Independent Modelling of Class Analogies showed consistent evidence of differences in the NIR spectral profiles of all levels of agarwood formations. The SIMCA discrimination between the resin developed versus not was achieved with 86% accuracy. The accuracy rates of the degree of the presence of agarwood were obtained as 80%, 100%, 85%, 90%, and 85% respectively, as assigned levels 1 to 5 in the SIMCA models. This research revealed the potential possibility of using NIR technology for the prediction of agarwood resin status inside the agarwood tree in A. crassna before harvesting the tree as a non-destructive and rapid method.
 
 Keywords: NIR spectroscopy, Agarwood, Aquilaria crassna, Non-destructive, Agarwood formation

Non-destructive detection of total acid of red globe grapes based on map fusion technique

Total acid is an important indicator of the internal quality of red globe grapes. This paper proposed a non-destructive method for the determination of total acid of red globe grapes based on hyperspectral fusion technique. A non-linear LSSVM prediction model based on spectral information, image information and the fusion of the two was built respectively for total acid. The results showed that the MSC-CARS-SPA-LSSVM model of samples about total acid built by fusing the spectra after feature wavelength extraction using the MSC-CARS-SPA algorithm and the image information after dimensionality reduction by the PCA algorithm using the graph fusion technique worked best. The correlation coefficients of the prediction sets of the optimal LSSVM model was 0.9907, which improved the accuracy over the unilateral models based on spectral or image information. A new method for nondestructive detection of total acid of red globe grapes by map fusion technique was discovered.

Label-Free and Real-Time Optical Detection of Affinity Binding of the Antibody on Adherent Live Cells.

Oblique-incidence reflectivity difference (OIRD) is a novel real-time, label-free, and nondestructive optical detection method and exhibits encouraging application in the detection of antibody/DNA microarrays. In this study, for the first time, an OIRD label-free immunoassay was achieved by using adherent live cells as the probe. The cells were cultured on glass cells, and the affinity binding of antibodies targeted on the HLA class I antigen of the cell surface was detected with an OIRD. The results show that an OIRD is able to detect the binding process of anti-human HLA-A, B, and C antibodies on MDA-MB-231 cells and HUVEC cells. Control experiments and complementary fluorescence analysis confirmed the high detection specificity and good quantitative virtue of the OIRD label-free immunoassay. Label-free OIRD imaging analysis of cell microarrays was further demonstrated successfully, and the underlying optical mechanism was revealed by combining the theoretical modeling. This work explores the use of live cells as probes for an OIRD immunoassay, thus expanding the potential applications of the OIRD in the field of pathological analysis, disease diagnosis, and drug screening, among others.

Application of lightweight YOLOv5 for walnut kernel grade classification and endogenous foreign body detection

The appearance quality grading of walnut kernels is extremely important in the deep processing of walnuts. Foreign body pollution in food is also a challenging problem that has been troubling food companies. Therefore, it is necessary to propose an efficient and non-destructive detection method for walnut kernels and their endogenous foreign bodies. To improve the quality and safety of walnut deep-processed products, this paper proposes an improved detection model based on the original YOLO V5. The improved model achieves a reduction in memory consumption by 38% and computational workload by 34%. Additionally, it increases the average precision of the model by 1.1% and improves CPU speed by 52%. When compared to other lightweight networks, our model demonstrates optimal overall performance. Moreover, our algorithm significantly enhances the precision of detecting small targets and target selection, as compared to the original model.By reducing the input image size of the model, it is able to meet the requirements of real-time detection of walnut kernels and their endogenous foreign bodies on the edge device Raspberry Pi. This provides a technical reference for the non-destructive detection of food quality and its endogenous foreign bodies.

Insights into soft short circuit-based degradation of lithium metal batteries.

The demand for electric vehicles with extended ranges has created a renaissance of interest in replacing the common metal-ion with higher energy-density metal-anode batteries. However, the potential battery safety issues associated with lithium metal must be addressed to enable lithium metal battery chemistries. A considerable performance gap between lithium (Li) symmetric cells and practical Li batteries motivated us to explore the correlation between the shape of voltage traces and degradation. We coupled impedance spectroscopy and operando NMR and used the new approach to show that transient (i.e., soft) shorts form in realistic conditions for battery applications; however, they are typically overlooked, as their electrochemical signatures are often not distinct. The typical rectangular-shaped voltage trace, widely considered ideal, was proven, under the conditions studied here, to be a result of soft shorts. Recoverable soft-shorted cells were demonstrated during a symmetric cell polarisation experiment, defining a new type of critical current density: the current density at which the soft shorts are not reversible. Moreover, we demonstrated that soft shorts, detected via electrochemical impedance spectroscopy (EIS) and validated via operando NMR, are predictive towards the formation of hard shorts, showing the potential use of EIS as a relatively low-cost and non-destructive method for early detection of catastrophic shorts and battery failure while demonstrating the strength of operando NMR as a research tool for metal plating in lithium batteries.

Research on non-destructive and rapid detection technology of foxtail millet moisture content based on capacitance method and Logistic-SSA-ELM modelling.

Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.

A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques

The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment.

Classification of Plenodomus lingam and Plenodomus biglobosus in Co-Occurring Samples Using Reflectance Spectroscopy

Under natural conditions, mixed infections are often observed when two or more species of plant pathogens are present on one host. Thus, the detection and characterization of co-occurring pest species is a challenge of great importance. In this study, we focused on the development of a spectral unmixing method for the discrimination of two fungi species, Plenodomus lingam and Plenodomus biglobosus, the pathogens of oilseed rape. Over 24 days, spectral reflectance measurements from Petri dishes inoculated with fungi were conducted. Four experimental combinations were used: the first two were pure fungal samples, while the other two were co-occurring fungal samples. The results of the study show the possibility of distinguishing, based on spectral characteristics, between P. lingam and P. biglobosus not only in pure but also in co-occurring samples. We observed the changes in the reflectance of electromagnetic radiation from the tested fungi over time and a strong correlation between the reflectance and changes in the areas of the mycelia on the Petri dishes. Moreover, the wavelengths most useful for spectral classification of the tested fungal mycelia were selected. Finally, a spectral unmixing model was proposed, which enables the estimation of the areas of two pathogens in co-occurring samples based on the spectral characteristics of the entire plate with an error smaller than 0.2. To our knowledge, the present study is the first report examining the use of reflectance spectroscopy methods for classifying pathogens on the same Petri dish. The study results indicate the feasibility of reflectance spectroscopy as a nondestructive sampling method for plant pathogen detection and classification.

Non-destructive detection of high-strength wind turbine bolt looseness using digital image correlation

Looseness of high-strength wind turbine bolts is one of the main types of mechanical failure that threaten the quality and safety of wind turbines, and how to non-destructively detect bolt loosening is essential to accurate assessment of operational reliability of wind turbine structures. Therefore, to address the issue of looseness detection of high-strength wind turbine bolts, this paper proposes a non-destructive detection method based on digital image correlation (DIC). Firstly, the mathematical relationships between the inplane displacement component of the bolt’s nut surface, the bolt’s preload force loss and the bolt loosening angle are both deduced theoretically. Then, experimental measurements are respectively conducted with DIC with different small bolt loosening angles. The results show that the bolt loosening angle detection method based on DIC has a detection accuracy of over 95%, and the bolt’s preload force loss evaluated by the deduced relationship has a good agreement with the empirical value. Therefore, the proposed DIC-based bolt loosening angle detection method can meet the requirements of engineering inspection, and can achieve quantitative assessment of preload forces loss of wind turbine bolt.

Photoacoustic speckle pattern interferometry for detecting cracks of different sizes.

A system and method for non-destructive detection of cracks of different width and depths based on digital speckle interferometry coupled with pulsed laser excitation is introduced and tested. Based on photoacoustic effect, acoustic waves are induced onto the rear of the samples by pumping a pulsed laser beam on it. The generated mechanical wave propagates from the rear surface of the sample to the front while front surface is monitored by speckle interferometry. In order to acquire information about surface deformation, the front surface is illuminated by continuous wave laser and interference are imaged onto the camera as speckle images. After processing the produced fringe patterns, it indicates the presence and location of the cracks in qualitative way. In this study, the system and method mentioned above are validated by detecting medium density fiberboard with simulated cracks. The fringe patterns from areas with or without defects are compared and discussed. Besides, the system and method to distinguish and predict cracks sizes is proposed and validated.

Operando Li metal plating diagnostics via MHz band electromagnetics

A nondestructive detection method for internal Li-metal plating in lithium-ion batteries is essential to improve their lifetime. Here, we demonstrate a direct Li-metal detection technology that focuses on electromagnetic behaviour. Through an interdisciplinary approach combining the ionic behaviour of electrochemical reactions at the negative electrode and the electromagnetic behaviour of electrons based on Maxwell’s equations, we find that internal Li-metal plating can be detected by the decrease in real part of the impedance at high-frequency. This finding enables simpler diagnostics when compared to data-driven analysis because we can correlate a direct response from the electronic behaviour to the metallic material property rather changes in the ionic behaviour. We test this response using commercial Li-ion batteries subject to extremely fast charging conditions to induce Li-metal plating. From this, we develop a battery sensor that detects and monitors the cycle-by-cycle growth of Li-metal plating. This work not only contributes to advancing future Li-ion battery development but may also serve as a tool for Li-metal plating monitoring in real-field applications to increase the useable lifetime of Li-ion batteries and to prevent detrimental Li-metal plating.

Evaluating quantum yield and ultraviolet–visible reflectance in baby pumpkins: Implications for oviposition behavior of pumpkin fruit flies

AbstractFrugivorous insects utilize both olfactory and visual cues to locate their host plants. Although volatiles have been extensively studied for detecting infested fruits, the role of visual cues in oviposition site selection remains mostly unknown among frugivorous insects. To investigate physiological changes in a host plant, we measured the quantum yield and reflectance of three wavelengths of light (350, 450 and 520 nm) after puncturing the surfaces of commercially grown pumpkins using insect pins during two different seasons outdoors. Quantum yields did not show significant differences between undamaged and simulated oviposition sites. Two wavelengths within the visual spectrum were similar between the two treatments. However, photon counts at 350 nm, in the ultraviolet range, were 1.76 fold higher in simulated oviposition sites than in undamaged sites, which was consistently observed across three field seasons. Considering that frugivorous insects and other phytophagous insects recognize the 350 nm wavelength for host identification and oviposition, we conclude that the disparities in ultraviolet reflectance can serve as a baseline for assessing the actual oviposition response of fruit flies. This finding contributes to the role of visual cues in the oviposition behavior of frugivorous insects and the development of a nondestructive detection method for pumpkin fruit flies.

Non-destructive, solvent-free quantification of wood preservatives in wood flour and wooden objects using GC-DTIMS

Qualitative and quantitative on-site detection methods for wood preservatives are of high value for the recycling industry and the occupational health and safety. Wood preservatives revealed as toxic to human and environment after decades of use. For the detection of contaminated wood and for processing of matured timber to particle boards a versatile detection method is needed. Especially historical wooden objects were treated with preservatives like pentachlorophenol, lindane or dichlorodiphenyltrichloroethane. This requires a non-destructive on-site detection method, that does not require specialized personnel. In this publication two methods are presented utilizing headspace sampling by solid-phase microextraction, subsequent separation using gas chromatography and detection by a drift tube ion mobility spectrometer (SPME–HS–GC-DTIMS). One method enables the quantitative detection of pentachlorophenol in wood flour and wood chips as they are used in wood processing industries. A limit of detection of 0.1 mg/kg was achieved using DIN 32645, which can be even more lowered. The second method enables non-destructive detection of pentachlorophenol, lindane, dichlorodiphenyltrichloroethane and other preservatives in wooden objects. Therefore, samples were prepared, which show a significantly lower concentration than typical treated objects, and used next to real samples for method validation. With the method contamination of the real samples and of the prepared samples of low concentration were proven.

K-mer Frequency Encoding Model for Cable Defect Identification: A Combination of Non-Destructive Testing Approach with Artificial Intelligence

This paper describes a non-destructive detection method for identifying cable defects using K-mer frequency encoding. The detection methodology combines magnetic leakage detection equipment with artificial intelligence for precise identification. The cable defect identification process includes cable signal acquisition, K-mer frequency encoding, and artificial intelligence-based identification. A magnetic leakage detection device detects signals via sensors and records their corresponding positions to obtain cable signals. The K-mer frequency encoding method consists of several steps, including cable signal normalization, the establishment of K-mer frequency encoding, repeated sampling of cable signals, and conversion for comparison to derive the K-mer frequency. The K-mer frequency coding method has advantages in data processing and repeated sampling. In the identification step of the artificial intelligence identification model, an autoencoder model is used as the algorithm, and the K-mer frequency coding method is used to introduce artificial parameters. Proper adjustments of these parameters are required for optimal cable defect identification performance in various applications and usage scenarios. Experiment results show that the proposed K-mer frequency encoding method is effective, with a cable identification accuracy rate of 91% achieved through repeated sampling.