Non-destructive Technology Research Articles

Advances in Emerging Non-Destructive Technologies for Detecting Raw Egg Freshness: A Comprehensive Review

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals.

Dendroclimatological study of ancient trees integrating non-destructive techniques.

Based on the need to protect previous ancient trees and the development of dendroclimatology, the use of non-destructive technologies in tree-ring research has gained increasing attention. This study focuses on the ancient Pinus tabulaeformis in Yu Xiang Forest Farm in Henan Province. Firstly, samples were collected using the traditional Increment borers and the Resistograph, a non-destructive method. Subsequently, the peak-valley analysis was used to filter the data obtained by the Resistograph to extract the tree ring width sequence, and the data's accuracy was verified by correlation analysis with tree ring width sequence by the Increment borers. Then, the optimal filtering method and an appropriate comprehensive threshold were determined, and tree ring width and density sequences were successfully extracted. Following that, the growth trend and residual resistance in the measurement process were corrected using linear fitting and Ensemble Empirical Mode Decomposition (EEMD) technology, thereby establishing the tree-ring width and density index series, which were further validated through correlation analysis and t-tests. Finally, analysis of the correlation with climatic factors, identified the main limiting factors for tree growth, and the accuracy of the tree-ring information extracted by the Resistograph was further verified. The results showed that spite of certain differences between the tree-ring width indices extracted by the Resistograph and the Increment borer, they were generally reliable. The radial growth of the ancient P.tabulaeformis in Yu Xiang Forest Farm is primarily influenced by temperature, with the maximum density of the tree rings responding more significantly to the mean maximum temperature, while the minimum density of the tree rings responded more significantly to the mean minimum temperature. These results not only provide a scientific and accurate age for the protection of ancient trees and verify the reliability of the data obtained by the Resistograph, but also facilitate the use of non-destructive technology for in-depth study of ancient trees, therefore enhancing our understanding of how climate change affects tree growth and provide valuable insights for the future protection and management of these ancient trees.

Application of nondestructive techniques for peach (Prunus persica) quality inspection: A review.

Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the fruit for firmness) are both subjective and destructive, which tend to diminish the integrity of fruit samples, consequently undermining their market value. Compared to traditional detection methods, nondestructive technology offers efficient and noninvasive solutions for rapidly and accurately assessing internal external quality of peaches. This can significantly enhance product classification and quality assurance while reducing the need for extensive human resources and minimizing potential physical damage to peaches. This review provided a comprehensive overview of nondestructive techniques for peach quality evaluation, including visible/near-infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric and optical properties, fluorescence spectroscopy, electronic nose/tongue, and acoustic vibration methods. It also evaluates the effectiveness of each technique in assessing internal quality, maturity, and disease detection of peaches. The advantages and limitations of each method were also summarized. This study focuses specifically on peaches and encompasses all existing nondestructive testing methods, providing valuable insights and references for future studies in the field of peach quality analysis using nondestructive testing methods.

Nondestructive evaluation of microstructure and mechanical property of post-processing annealed selective laser melted 316L stainless steel by laser ultrasonics

By using the laser ultrasonics nondestructive technology, combined with the microstructure and mechanical property of post-processing annealed selective laser melted 316L stainless steel characterized by scanning electron microscopy (SEM), electron backscattering diffraction (EBSD), and mechanical property testing, selective laser melted 316L stainless steel after post-processing annealing was evaluated. The results show that the frequency domain attenuation coefficient of ultrasonic waves is positively correlated with the average grain size; the time domain attenuation coefficient of ultrasonic waves is negatively correlated with the low angle boundary content. The tensile strength has a good correlation with the time domain attenuation coefficient and wave speed, the correlation coefficients R2 are 0.95 and 0.89 respectively; the yield strength correlates with the time domain attenuation coefficient, the correlation coefficient R2 is 0.76; the elongation has a good correlation with the frequency domain attenuation coefficient and wave speed, the correlation coefficient R2 is 0.90 and 0.86 respectively.

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

International Cooperation between Korea and Cambodia to Conserve and Manage Angkor Stone Heritage using Non-destructive Technology

ABSTRACT The stone team of the National Research Institute of Cultural Heritage (NRICH) in Korea implemented a technology transfer project 2016 − 2020 to manage heritage monuments during the Angkor period in Cambodia. This saw staff from both countries’ conservation organisations participating in training, education, and joint research, and making joint academic presentations on technology transfer. The research subjects included statues recovered from the Don Tei and Phnom Bei Temple located near Angkor Thom; the Nokorbachey Temple in Kampong Cham Province, the southern stronghold; and the Banteay Chhmar in Banteay Meanchey Province, a central transportation point in the northwest. Non-destructive technology from Korea was used to investigate the condition of the Angkor ruins, evaluating the suitability of the approach and modifying it as appropriate, and training was conducted to improve the participants’ problem-solving and technology application abilities. This exchange project and the data it generated demonstrate the benefits of continuing this approach to develop the conservation skills and problem-solving abilities of managers of Angkor ruins, and ultimately contribute to the preservation of Asian cultural heritage.

Predicting freshness and shelf-life of tomatoes by destructive and non-destructive technologies

Predicting freshness and shelf-life of tomatoes by destructive and non-destructive technologies

Delayed Luminescence in Monitoring the Postharvest Ripening of Tomato Fruit and Classifying According to Their Maturity Stage at Harvest

Ripening is an irreversible phenomenon involving several physiological, biochemical and organoleptic changes that affect the nutritional value, colour, texture and taste of fruit. Amongst the physical non-destructive technologies, Delayed Luminescence (DL) has been reported able to provide valid information on the functional status of living biological systems, reflecting changes in quality parameters following the occurrence of certain metabolic processes. In this study, we followed up and analysed the variations in the colour and DL signals emitted by tomato fruit as a function of their ripening degree. Four lots of tomato fruit, similar to each other for cultivar and geographical origin, were considered. Tomatoes were harvested at various maturity stages and stored in the dark at room temperature. The changes in DL emission trends were monitored at different times after harvesting for a period of about 20 days and correlated with the a*/b* colour ratio, which is normally used as an indicator of fruit ripeness degree. Results highlighted that the emission intensities, as well as the parameters by fitting DL decay curves, were strongly dependent on harvest maturity, varying significantly even as fruit ripeness progressed after their harvesting. Significant differences were also found between DL signals emitted by field-ripened tomatoes at the beginning of storage and those emitted by postharvest-ripened tomatoes at the end of the storage period. Delayed Luminescence may be used as a fast, reliable and sensitive tool for evaluating, more generally, the sensorial quality of fruit during postharvest storage.

‘Eggology’ and mathematics of a quail egg: An innovative non-destructive technology for evaluating egg parameters in Japanese quail

Quail eggs, the smallest ones among poultry species, require special methodological aspects for their non-destructive examination and quality analysis. Using eggs from a cross between the Japanese and Texas breeds, we devised a methodology for defining the main geometric parameters of quail eggs. Calculation formulae were proposed to estimate indirectly egg volume and surface area. Our findings on the weights of structural egg components enabled to obtain mathematical equations for computing the weights of shell, yolk and albumen, depending on the complex of measured parameters including the egg weight, its volume and surface area. When taken as a whole, the results of our study can be regarded as the most thorough methodological approach to date for the execution of comprehensive investigations of quail egg quality. They will be applicable and instrumental in areas of food research and emerging technologies, including the aspects of storage, packing, and processing of quail eggs.

Reverse Engineering Based on Digital Data Capture In Situ as a Methodology for the Study of Space Labor Risk in Construction Works and Its Applicability in BIM

In the intelligent manufacturing process and digitization, new research spaces have been created, such as reverse engineering and its use for the detection of risks in construction. For this reason, in this research, an analysis methodology is proposed that, through terrestrial laser scanner records in different phases of the work, allows for an analysis and evaluation of the identification of security risks. In addition, by capturing different processes, it can be determined not only interferences from different operators that can generate risk but also establish quality standards in construction, such as determining the thicknesses of the coating of the concrete manufactured “in situ”. Therefore, this research responds to quality analysis in the material execution of the activity and construction of formwork systems and their safety, as well as defining the location of the static and dynamic elements of the model. This value of the morphometric parameter of the building and the scene to be represented characterizes the morphometrics of the work execution activities and finally evaluates the semantic information model of the BIM construction, in which the deformations between the ideal model and what was executed. With the results of this study, it can be concluded that the latest 3D model reconstruction techniques (BIM), together with comparison algorithms, can determine millimeter precision, and with that, it can establish a better method for inspection, evaluation, and control through non-destructive technology.

Non-destructive methods for mango ripening prediction: Visible and near-infrared spectroscopy (visNIRS) and laser Doppler vibrometry (LDV)

With up to 19% of mango fruit being lost during ripening, the need for non-destructive technologies to predict internal physiochemical traits is paramount. This study compared two non-destructive technologies, visible and near-infrared spectroscopy (visNIRS) and laser Doppler vibrometry (LDV), for predicting the ripeness of mango fruit in two cultivars, ‘Kent’ and ‘Keitt’. An internal quality index (IQI) in ‘Kent’ was predicted using visNIRS (RP2 = 0.729, RMSEP = 0.532) using partial least squares regression, which gave a single measure for ripeness incorporating firmness, sweetness, and pulp colour. This model was improved by using the sum of the individual sugar contents (glucose, sucrose, and fructose) over the conventional total soluble solids (TSS) measure. LDV provided poor predictions of firmness (R2 < 0.5) in both ‘Kent’ and ‘Keitt’ using least squares regression line. The resonant frequency, as measured by LDV, decreased linearly with time, while firmness quantified destructively (quasi-static) showed an exponential decrease, suggesting the vibrational and destructive firmness measure distinct characteristics, which would contribute to poor model performance. These results showed that LDV is not suitable for assessing mango ripening. While visNIRS models have been successful at predicting quality traits, our results suggested that using individual sugar content in place of TSS can improve the prediction of ripening. This understanding of the strengths and limitations of both visNIRS and LDV, and how they relate to destructive quality measurements, can be used to improve postharvest management practices whilst reducing commercial losses in the mango industry.

Comparative Study of Ground Penetrating Radar Systems with Time Delay Methods for Accurate Pavement Thickness and Dielectric Constant Estimation

The applicability of using ground penetrating radar (GPR) as a non-destructive technology for estimating the thickness and dielectric constant of pavement layers has been extensively documented in the literature. Time delay methods (TDMs) have been the most commonly used by researchers. However, various studies have reported different results with respect to the suitability of different GPR systems and TDMs for dielectric constant and thickness estimation. Thus, this study aims to compare the use of ground-coupled GPR and air-coupled GPR (AC-GPR) for estimating the dielectric constant of pavement materials. In the context of TDMs, the study examines three methodologies for assessing the traveled distance in the investigated layer. The study involves laboratory experiments using asphalt and dry sand. Also, it comprises a sensitivity analysis to examine the effect of different experimentation setups. The study employed two GSSI antennas: 51600S (GC-51600S) and GSSI 42000S (AC-42000S). Using the manufacturer’s recommended operation setups, the results generally indicated the possibility of simplifying the TDM calculation by considering the 42000S and 51600S antennas as monostatic for pavement materials with different dielectric constants (≥3) with an error rate within 5%. Moreover, the study indicates that using AC-GPR may be inappropriate for common midpoint analysis using the default mounting setups. These findings provide valuable insights into the suitability and limitations of different GPR systems and calculation methods for accurately estimating pavement’s dielectric constant and thickness.

MnN4 embedded zeolite-templated carbon for methylamine and trimethylamine sensing: Insights from DFT study

From the perspective of raw material losses and human health, monitoring fish-based food quality has become more prominent. As a nondestructive technology, gas sensors have received extensive attention for evaluating the freshness of fish. In this study, the sensing and electronic properties of methylamine (MA) and trimethylamine (TMA) molecule adsorption on MnN4 embedded zeolite templated carbon (MnN4-C) were studied by density functional theory (DFT) method. The results show that the MnN4-C exhibits good structural stability, and displays high sensitivity toward MA and TMA gases by analysis of the interaction distance, adsorption energy, charge transfer and sensing response. It is worth noting that the ambient humidity has demonstrated no effect on the gas-sensing properties of MnN4-C for the studied gases. Meanwhile, applying a positive electric field can increase the adsorption energy and charge transfer, which enhances the MA and TMA gas sensitivity of MnN4-C. The recovery time reveals that MnN4-C can be an excellent reusability sensor for MA and TMA gases. This study provides theoretical direction for exploring the sensing application of MnN4-C in evaluating freshness of fish.

Evaluation of non-destructive techniques for inspection of post- tensioned concrete bridges

Reinforced concrete bridges with post-tensioned (PT) cables are particularly critical structures, as the degradation of the tendons is not fully detectable through conventional investigation methods and/or through visual inspections, due to the intrinsic nature of the structural typology. In fact, PT cables are encased in plastic or metal ducts which aim at protecting them from corrosion, but at the same time hide them making it impossible to evaluate their condition by visual inspection alone. Deterioration conditions of PT systems are broadly categorized into strand defects and grout defects. The deteriorated conditions of PT systems usually lead to a loss of prestressing force, with impacts on the bridge management in the form of reduced load capacity and structural safety, need for expensive rehabilitation interventions, and traffic disruption.Modern inspection guidelines have identified non-destructive (ND) methods suitable for evaluating the conditions of the tendons and the grout. Moreover, ND technologies used in other industries may have potential for PT bridge assessments. The present contribution aims at presenting a simple procedure to rank the non-destructive (ND) methods, that are currently used for investigating the deterioration of tendons and grout, through a series of metrics formulated to evaluate the various technologies under four different aspects, namely accuracy of measurement, ease of use, cost, and impact on the operation of the bridge. The goal is to provide the bridge owners with a decision tool that can assist them in the selection of the optimal ND technology for the evaluation of a particular strand or grout defect.

ESTUDIO COMPARATIVO TERMOGRÁFICO MEDIANTE SMARTPHONE Y CÁMARA EN EDIFICIOS

The cost of thermographic cameras has significantly decreased in recent years, making it an accessible and cost-effective solution for building and infrastructure inspection. With advancements in technology, low-cost thermographic devices have now become readily available in the market. These cost-effective devices offer a more convenient and accessible option for individuals and businesses to perform onsite temperature measurements. In this paper, we examine the capabilities and limitations of a thermal-equipped smartphone and the potential impact on the thermography industry by comparison with a dedicated thermographic camera. The advantages of thermography still hold true for recent low-cost devices: non-invasive inspections, quick and efficient measurements, and hidden defects easily detected. The low-cost thermal-equipped smartphone proved to be a highly maneuverable system, easy to use and with option of overlapping with real color image, what facilitates the interpretation. The low-cost system is also characterized by a larger field of view but also by a lower resolution, what makes it impracticable for measuring small objects. In any case, integration of non-destructive technologies in smartphones opens up a whole new world of possibilities for spreading the inspection of as-built buildings and infrastructures and democratizing the technology. Keywords: Low-Cost; inspection; thermal bridges; energy sustainability; energy efficiency; digital twins.

An Innovative Non-destructive Technology for Controlling the Storage Period of Chicken Eggs Using Egg Parameters

We proposed here a non-destructive technology for pre-sorting eggs into subclasses characterized by a specifical acceptable shelf life and quality requirements. Egg characteristics were identified suiting a predictive model for controlling storage periods. Accordingly, the relationships of egg parameters were assessed, with weight loss (ΔW) during storage being the best indicator of changes in egg contents variables. Using changes in ΔW, we established three indicators with the maximum effect on fast egg drying and shrinkage. These included egg weight (W), egg volume-to-surface area ratio, and air cell diameter. The relationship formulae were derived to estimate the ΔW value accurately. This approach enables to judge regarding the potential of each particular egg subclass for its acceptable weight shrinkage and assign an optimal storage period for it. The proposed non-invasive analytical method can be implemented in industrial conditions for both table and hatching eggs with the available set of automated technological equipment.

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review.

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease.

Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people's daily diet. China has always been a big apple consumer, and with the improvement of people's life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease.

Dam concrete quality evaluation and prediction model based on fractal characteristics of elastic-wave computed tomography

The evaluation and prediction of dam concrete quality have important theoretical significance for preventing catastrophes in concrete dams and predicting their long-term safety characteristics. To realize a comprehensive index for the evaluation and prediction of dam concrete characteristics, the fractal characteristics of dam concrete were analyzed using elastic-wave computed tomography (EWCT). The dam concrete quality evaluation model was established by combining the defined EWCT fractal dimensions with the strength and maturity of the dam concrete. Furthermore, a dam concrete artificial neural network (ANN) prediction model with a multi-index input layer was developed. The results indicated that the EWCT images exhibited the early evolution and distribution characteristics of dam concrete specimens, and there was a strong positive correlation between the EWCT fractal dimension and the age of the dam concrete. The grey relational analysis coefficient between the EWCT fractal dimension and fully graded concrete strength was similar to the coefficient between the wet-screened and fully graded concretes. Among the five dam concrete detection indices used in the ANN, the weight contribution rate of the EWCT fractal dimension to the dam concrete ANN prediction model was 23.15 %. In conclusion, the fractal dimension may be used as a critical index in dam concrete detection to pave the way for developing intelligent dam concrete detection and monitoring based on non-destructive technology.

Observation and Measurement of Ice Morphology in Foods: A Review.

Freezing is an effective technology with which to maintain food quality. However, the formation of ice crystals during this process can cause damage to the cellular structure, leading to food deterioration. A good understanding of the relationship between food microstructure and ice morphology, as well as the ability to effectively measure and control ice crystals, is very useful to achieve high-quality frozen foods. Hence, a brief discussion is presented on the fundamentals/principles of optical microscopic techniques (light microscopy), electronic microscopic techniques (transmission electron microscopy (TEM) and scanning electron microscopy (SEM)), as well as other non-invasive techniques (X-rays, spectroscopy, and magnetic resonance) and their application to measuring ice formation rates and characterizing ice crystals, providing insight into the freezing mechanisms as well as direct monitoring of the entire process. And, in addition, this review compares (the negative and positive aspects of) the use of simple and cheap but destructive technologies (optical microscopy) with detailed microscopic technologies at the micro/nanometer scale but with pretreatments that alter the original sample (SEM and TEM), and non-destructive technologies that do not require sample preparation but which have high acquisition and operational costs. Also included are images and examples which demonstrate how useful an analysis using these techniques can be.