Nondestructive Quality Evaluation Research Articles

Non-destructive three-dimensional characterization of micronized pulse seeds using X-ray microcomputed tomography

Non-destructive quality evaluation techniques are vital in 3D visualization and quantification of microstructural changes in micronized pulse kernels. The effects of micronization (infrared heating) on pulse microstructure are highly dependent on its treatment efficiency. This study employed X-ray micro-computed tomography (micro-CT) to characterize, visualize, and quantify the microstructural changes non-destructively. Four major pulses, chickpeas, fava beans, lentils, and yellow peas, were subjected to micronization at 180 °C of surface temperature for four different infrared (IR) exposure times (60, 80, 100, and 120 s). All samples were pre-conditioned at 20 % moisture content before the micronization process. Morphometric parameters (pore size, diameter, and pore distribution) were qualitatively measured using the reconstructed 2D and 3D images acquired by the X-ray micro-CT. Physico-chemical parameters, including loss of moisture (%), total porosity (%), volumetric expansion/shrinkage (%), and density (%) were quantified using Micro-CTAn software. The results showed that the porosity is significantly increased (p < 0.05) in all pulse types ranging from 0.60 ± 0.42 % to 12.1 ± 2.68 %, at prolonged IR exposure time. Significant volume expansion ranging from 3.82 %–33.56 % was shown by chickpeas and yellow peas, while fava beans and lentils showed volume shrinkage ranging from 4.26 %–24.31 % after micronization. Furthermore, density was decreased ranging from 0.79 ± 0.25% to 33.49 ± 0.42 % with increased IR time for all pulses except fava beans. The authors found that the microstructural changes in all tested pulses were optimum at 100 s–120 s of exposure time. Results emphasize the higher potential in modifying the microstructure of pulses using micronization processing. Therefore, future studies are suggested on developing predictive models to explore the relationships between microstructural changes and techno-functional properties.

Non-destructive determination of ginsenosides in ginseng by combined hyperspectral and X-ray techniques based on ensemble learning

The potential of hyperspectral imaging and X-ray techniques for the non-destructive determination of the ginsenosides Rg1 + Re and Rb1 in ginseng was investigated. The random forest (RF) models were established using spectral information extracted from hyperspectral data to predict ginsenosides content. The RF model was optimized by data pre-processing methods and feature screening methods. Multiple feature screening methods combined with partial least squares regression models were used to find hyperspectral image feature information (color information and texture information) related to ginsenosides. A significant positive correlation between density extracted from X-ray images and the ginsenosides content was found by building the univariate linear regression models. Finally, the prediction performance of the integrated learning model based on the three data blocks was better than the model constructed by single data blocks (Rg1 + Re: R2p = 0.8691, RMSEP = 0.0439%; Rb1: R2p = 0.8291, RMSEP = 0.0803%). The results indicate that the developed method is highly feasible for non-destructive evaluation of ginseng quality.

Non-Destructive Evaluation of Downy and Silver Birch Wood Quality and Stem Features from a Progeny Trial in Southern Sweden

This study investigated whether improved downy birch could perform as well as improved silver birch, and whether there was sufficient genetic variation and control for non-destructive testing (NDT) values to include them as selection traits in breeding programs. NDT tools were applied to a 15-year-old downy birch family trial intermixed with improved silver birch. Average diameters, fissured bark height, and grain angle were higher for silver than downy birch. The genetic analysis for downy birch provided estimates of narrow-sense heritability (h2) for acoustic velocity and Pilodyn penetration depth that were above 0.3 but had low genetic variation. Grain angle had relatively high genetic variability (18%) and an h2 of 0.20. A subsample of 49 trees had 4 mm cores x-rayed for wood density estimates, and 34 stems had 12 mm cores macerated for cell measurements. t-tests revealed that average wood density and cell measurements were not significantly different between species. For silver and downy birch, fiber length and vessel length increased between inner and outer measurement positions, and fiber length was reasonably correlated with acoustic velocity. Silver birch tended to have denser and stiffer wood, while downy birch had less rough bark and straighter grain, and these results are in agreement with existing knowledge. The h2 values were similar to those observed in other birch species and indicate there is potential to breed for improved wood density and grain angle in downy birch.

Classification and rapid non-destructive quality evaluation of different processed products of Cyperus rotundus based on near-infrared spectroscopy combined with deep learning

The quality of traditional Chinese medicine is very important for human health, but the traditional quality control method is very tedious, which leads to the substandard quality of many traditional Chinese medicine. In order to solve the problem of time-consuming and laborious traditional quality control methods, this study takes traditional Chinese medicine Cyperus rotundus as an example, a comprehensive strategy of near-infrared (NIR) spectroscopy combined with One-dimensional convolutional neural network (1D-CNN) and chaotic map dung beetle optimization (CDBO) algorithm combined with BP neural network (BPNN) is proposed. This strategy has the advantages of fast and non-destructive. It can not only qualitatively distinguish Cyperus rotundus and various processed products, but also quantitatively predict two bioactive components. In classification, 1D-CNN successfully distinguished four kinds of processed products of Cyperus rotundus with 100 % accuracy. Quantitatively, a CDBO algorithm is proposed to optimize the performance of the BPNN quantitative model of two terpenoids, and compared with the BP, whale optimization algorithm (WOA)-BP, sparrow optimization algorithm (SSA)-BP, grey wolf optimization (GWO)-BP and particle swarm optimization (PSO)-BP models. The results show that the CDBO-BPNN model has the smallest error and has a significant advantage in predicting the content of active components in different processed products. To sum up, it is feasible to use near infrared spectroscopy to quickly evaluate the effect of processing methods on the quality of Cyperus rotundus, which provides a meaningful reference for the quality control of traditional Chinese medicine with many other processing methods.

Non-destructive evaluation of melt-extruded part quality using in situ data

PurposeThis study aims to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing and use only the collected data to non-destructively identify printed parts that contain defects.Design/methodology/approachA set of sensors was created to collect real-time, in situ data from polymer ME 3D printing. A variance analysis was completed to identify an “acceptable” range for filament diameter on a popular desktop 3D printer. These data were used as the basis of a quality evaluation process to non-destructively identify spatial regions of printed parts in multi-part builds that contain defects.FindingsAnomalous parts were correctly identified non-destructively using only in situ collected data.Research limitations/implicationsThis methodology was developed by varying the filament diameter, one of the most common reasons for print failure in ME. Numerous other printing parameters are known to create faults in melt extruded parts, and this methodology can be extended to analyze other parameters.Originality/valueTo the best of the authors’ knowledge, this is the first report of a non-destructive evaluation of 3D-printed part quality using only in situ data in ME. The value is in improving part quality and reliability in ME, thereby reducing 3D printing part errors, plastic waste and the associated cost of time and material.

Research Progress on Non-Destructive Detection of Internal Quality of Fruits with Large Size and Thick Peel: A Review

Postharvest quality detection and grading are important factors to improve the commercial value of fruit. The natural biological characteristics bring challenges to the rapid and non-destructive evaluation of the internal quality of fruits with large size and thick peel. A lot of studies have tried to establish a feasible technique to achieve rapid, non-destructive, and accurate detection for this kind of fruit in commercial real-time grading. This article focuses on large-sized and thick-skinned fruits and comprehensively reviews the latest technical progress in the non-destructive detection of internal quality. It can provide a valuable reference for the development of postharvest processing technology for this kind of fruit.

Non-destructive compaction quality evaluation of runway construction based on GPR data

ABSTRACT Assessing the compaction quality of rockfill materials is an essential link in the runway construction. However, traditional on-site limited sampling detection is not only time-consuming and labour-intensive, but also destructive. Common ground penetrating radar (GPR) calculates the relative permittivity of a material by measuring its thickness. Nonetheless, assessing the thickness of runway construction materials poses a significant challenge. To address this challenge, this paper focuses on the Hilbert -Huang transform (HHT) analysis of GPR signal of different compacting materials, and then replaces the traditional method of calculating the relative permittivity by measuring the thickness of materials. At the same time, taking the runway as an example, this work verified a crest factor (CF) index effectiveness in predicting the relative compaction of rockfill material through HHT analysis of GPR signals collected in the field, with an average error rate of 4.03%. Finally, Kriging interpolation method is used to estimate the compaction quality of any point, and the corresponding heat map of compaction quality evaluation is generated to determine the area of insufficient compaction in the construction process. Highlights The GPR method is used for non-destructive testing of the compaction quality of runway rockfill materials; Compaction quality evaluation by multiple indicators of GPR data signal processing. The influence of number of roller passes, materials mix ratios and different rockfill materials on the compaction quality of runway are analysed. The applicability of the GPR method is verified by runway construction tests.

Non-destructive monitoring the freshness of sea bass fillets using Raman spectroscopy with orthogonal signal correction and multivariate analysis

TVB-N is a commonly used chemical indicator for assessing the freshness of aquatic products during storage. In this study, the combination of Raman spectroscopy with multivariate analysis for monitoring the freshness of sea bass (Lateolabrax japonicus) fillets was investigated. Orthogonal signal correction (OSC) method was applied to pre-process the Raman spectra of sea bass fillets with different freshness levels. Results suggested that PLSR models outperformed PCR and SVMR models in monitoring TVB-N values of sea bass fillets. In comparison, the OSC-PLSR model with full-band Raman spectra showed the best performance for predicting TVB-N contents, with R2C of 0.981, RMSEC of 1.162 mgN/100 g, R2CV of 0.804, RMSECV of 3.756 mgN/100 g, R2P of 0.898, RMSEP of 2.199 mgN/100 g, and RPD of 3.824. In addition, sea bass fillets with three different freshness grades (first-grade, second-grade, and spoilage) could be distinguished from the PCA score plot. The PLS-DA model showed an average classification accuracy of 91.11% for different freshness grades. The combination of Raman spectroscopy and chemometrics provided a new tool for the non-destructive evaluation of fish quality.

Non-destructive periodic poling quality evaluation of MgO:PPLN and Rb:PPKTP crystals based on crystal translation and parametric light generation

The work discloses a flexible, non-destructive, aberration-free, and automated periodic poling quality evaluation technique of the periodically poled crystals in the whole volume of the crystal with very high control and resolution. The method is based on a crystal translation in the directions perpendicular to the pump laser beam and the simultaneous recording of the parametric signal conversion efficiency in a simple single-pass parametric light generator setup. This technique allowed us to investigate and quantitatively describe the ferroelectric domain structure homogeneity of the periodically poled lithium niobate and potassium titanyl phosphate crystals with single, multi, and fan-out grating designs and offers a practicable overall efficiency assessment of the quasi-phase-matched device.

Pattern-recognizing-assisted detection of mildewed wheat by Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array

In order to timely discriminate wheat with different mildew rates, a Dyes/Dyes-Cu-MOF paper-based colorimetric sensor array was designed. Using array points to capture volatile gases of wheat with different mildew rates, and output RGB values. The correlation between ΔR/ΔG/ΔB values and odor components was established. The ΔG values of array points 2′ and 3′ showed the best correlation with mildew rate, with R2 of 0.9816 and 0.9642. The ΔR value of 3 and the ΔG value of 2 correlate well with the mildew rate, with R2 of 0.9625 and 0.9502, respectively. Then, the ΔRGB values are subjected to pattern recognition processing, and LDA achieves 100% correct discrimination for all samples, or divides high and low mildew areas. This method provides an odor-based monitoring tool for fast, visual and nondestructive evaluation of food safety and quality through visualization of odors produced by different mildew rates.

Determination of pitaya quality using portable NIR spectroscopy and innovative low-cost electronic nose

Pitaya (Hylocereus polyrhizus), also known as dragon fruit, is an exotic and highly valued fruit with a high amount of fiber and vitamins, and its quality is often related to attributes such as soluble solids, moisture content, and acidity. Traditional analytical techniques (e.g., gas chromatograph-mass spectrometry – GC–MS) for physicochemical quantification are costly and not environmentally friendly. This work proposes a quick and non-destructive evaluation of pitaya quality using low-cost near-infrared spectroscopy (NIRS) and electronic nose (e-nose) devices. Classification models for either NIR spectra or e-nose data as predictors presented accuracy higher than 90% when classifying samples according to their shelf-life index stage (SLI30, 50, 80, and 100). Total titratable acidity (TA) and pH could be predicted using partial least squares regression (PLSR) and NIR spectra as predictors with coefficients of determination (R2P) of 0.89 and 0.83, respectively, and root means square error (RMSEP) of 0.03 and 0.23, respectively. Similarly, PLSR models for the prediction of TA and pH using e-nose data achieved R2P of 0.85 and 0.86, and RMSEP of 0.04 and 0.22 respectively. RPD and RER values for NIRS show that all predictors can be used to at least distinguish between low and high values. The results demonstrate that inexpensive devices based on NIRS, and a novel low-cost e-nose could be used in combination for the prediction of TSS, pH, TA, moisture, and phenolics, as well as to classify pitaya according to their shelf-life stages.

Non-Destructive Quality Evaluation of Tropical Fruit (Mango and Mangosteen) Purée Using Near-Infrared Spectroscopy Combined with Partial Least Squares Regression

Mango and mangosteen are commercially important tropical fruits with a short shelf life. Fruit processing is one of the alternatives to extend the shelf life of these fruits. Purée is one of the processed products of fresh fruit. In this research, the quality of mango and mangosteen purée was analyzed. Titratable acidity (TA) and total soluble solids (TSS) were predicted using non-destructive near-infrared (NIR) spectroscopy. A partial least squares regression (PLSR) model was developed based on the NIR spectra with a wavelength ranging from 800 to 2500 nm. The PLSR model returned a coefficient of determination (r2) and a ratio of prediction to deviation (RPD) of 0.955 and 4.7 for TSS, and 0.784 and 2.2 for TA, in the mango purée. Similarly, the best model was selected for the TSS prediction in the mangosteen purée through PLSR, with an r2, a root mean square error of cross-validation (RMSECV), and RPD of 0.799, 0.3% malic acid, and 2.2, respectively. The results show the possible application of NIR spectroscopy in the product processing line, although a larger number of samples with wide variation in future studies are needed as an input to update the model, in order to obtain a more robust model.

Near-infrared spectroscopy as a green technology to monitor coffee roasting

Wet chemistry methods are traditionally used to evaluate the quality of a coffee beverage and its chemical characteristics. These old methods need to be replaced with more rapid, objective, and simple analytical methods for routine analysis. Near-infrared spectroscopy is an increasingly popular technique for nondestructive quality evaluation called a green technology.&#x0D; Our study aimed to apply near-infrared spectroscopy to evaluate the quality of coffee samples of different origin (Brazil, Guatemala, Peru, and Kongo). Particularly, we analyzed the roasting time and its effect on the quality of coffee. The colorimetric method determined a relation between the coffee color and the time of roasting. Partial least squares regression analysis assessed a possibility of predicting the roasting conditions from the near-infrared spectra.&#x0D; The regression results confirmed the possibility of applying near-infrared spectra to estimate the roasting conditions. The correlation between the spectra and the roasting time had R2 values of 0.96 and 0.95 for calibration and validation, respectively. The root mean square errors of prediction were low – 0.92 and 1.05 for calibration and validation, respectively. We also found a linear relation between the spectra and the roasting power. The quality of the models differed depending on the coffee origin and sub-region. All the coffee samples showed a good correlation between the spectra and the brightness (L* parameter), with R2 values of 0.96 and 0.95 for the calibration and validation curves, respectively.&#x0D; According to the results, near-infrared spectroscopy can be used together with the chemometric analysis as a green technology to assess the quality of coffee.

A novel low-cost capacitance-based approach for non-destructive evaluation of powder quality for food processing industries

In a high-volume food production line, several stainless-steel hoppers store, mix and dispense powdered ingredients to form food powders of the desired composition. Improper mixing at high speeds and caking (formation of lumps) during storage hamper powder homogeneity and quality. Currently, low throughput offline techniques such as spectroscopic analysis and powder flow tests are used to screen for powder mixing uniformity and caking. Herein, a flexible electrode system is proposed to detect capacitance changes based on the dielectric differences between different powders (beverage powders and their mixtures) and their subsequent variation from poor mixing/caking. The detection scheme successfully differentiated different powder mixtures and detected caking in powders non-invasively with good detection speed (50 ms) and low detection limits (15% for powder mixing and 1–43% for powder caking). Furthermore, the capacitive system integrated with an industrial food hopper successfully showcased an in-situ automated detection of powder caking.

Feature Normalization Reweighting Regression Network for Sugar Content Measurement of Grapes

The measurement of grape sugar content is an important index for classifying grapes based on their quality. Owing to the correlation between grape sugar content and appearance, non-destructive measurements are possible using computer vision and deep learning. This study investigates the quality classification of the Red Globe grape. The number of collected grapes in the range of the 15~16% measure is three times more than in the range of &lt;14% or in the range of the &gt;18% measure. This study presents a framework named feature normalization reweighting regression (FNRR) to address this imbalanced distribution of sugar content of the grape datasets. The experimental results show that the FNRR framework can measure the sugar content of a whole bunch of grapes with high accuracy using typical convolution neural networks and a visual transformer model. Specifically, the visual transformer model achieved the best accuracy with a balanced loss function, with the coefficient of determination R = 0.9599 and the root mean squared error RMSE = 0.3841%. The results show that the effect of the visual transformer model is better than that of the convolutional neural network. The research findings also indicate that the visual transformer model based on the proposed framework can accurately predict the sugar content of grapes, non-destructive evaluation of grape quality, and could provide reference values for grape harvesting.

X-ray-based machine vision technique for detection of internal defects of sterculia seeds.

An online machine learning system based on X-ray nondestructive quality evaluation technique was developed to detect internal defects of boat-fruited sterculia seed. The X-ray images of boat-fruited sterculia seed were first acquired by the detection system. Then, a boat-fruited sterculia seed net (BSSNet) was trained to identify the defective boat-fruited sterculia seeds based on the X-ray images. The BSSNet was evaluated with the accuracy, precision, specificity, and sensitivity as 94.64%, 93.51%, 92.37%, and 96.64%, respectively. Further, three classical CNN models including VGG16, Resnet, and Inception were trained on the same dataset with accuracy of 95.71%, 94.29%, and 94.64%, respectively. Compared with the classical CNN models, the BSSNet achieved similar or higher accuracy in X-ray images classification. Finally, an independent dataset containing 200 X-ray images was used to validate the performance of the BSSNet and obtained an accuracy of 96.5%. The results presented above demonstrated that this classification method has a great potential for industrial applications. PRACTICAL APPLICATION: An X-ray online detection system integrated with a machine vision model was used to evaluate the quality of boat-fruited sterculia seed. A low-power x-ray detection system can detect internal defects of the object and ensure safety in the production process. The developed machine vision can sort the boat-fruited sterculia seed with an accuracy of 96.5%. The proposed nondestructive detection system showed a good potential to be used in industrial applications.

Online Monitoring of Surface Quality for Diagnostic Features in 3D Printing

Investigation into non-destructive testing and evaluation of 3D printing quality is relevant due to the lack of reliable methods for non-destructive testing of 3D printing defects, including testing of the surface quality of 3D printed parts. The article shows how it is possible to increase the efficiency of online monitoring of the quality of the 3D printing technological process through the use of an optical contactless high-performance measuring instrument. A comparative study of contact (R130 roughness tester) and non-contact (LJ-8020 laser profiler) methods for determining the height of irregularities on the surface of a steel reference specimen was performed. It was found that, in the range of operation of the contact method (Ra 0.03–6.3 µm and Rz 0.2–18.5 µm), the errors of the contactless method in determining the standard surface roughness indicators Ra and Rz were 23.7% and 1.6%, respectively. Similar comparative studies of contact and non-contact methods were performed with three defect-free samples made of plastic polylactic acid (PLA), with surface irregularities within the specified range of operation of the contact method. The corresponding errors increased and amounted to 65.96% and 76.32%. Finally, investigations were carried out using only the non-contact method for samples with different types of 3D printing defects. It was found that the following power spectral density (PSD) estimates can be used as diagnostic features for determining 3D printing defects: Variance and Median. These generalized estimates are the most sensitive to 3D printing defects and can be used as diagnostic features in online monitoring of object surface quality in 3D printing.

Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches

A common fraud in the sweet almond industry is the presence of bitter almonds in commercial batches. The presence of bitter almonds not only causes unpleasant flavours but also problems in the commercialisation and toxicity for consumers. Hyperspectral Imaging (HSI) has been proved to be suitable for the rapid and non-destructive quality evaluation in foods as it integrates the spectral and spatial dimensions. Thus, we aimed to study the feasibility of using an HSI system to identify single bitter almond kernels in commercial sweet almond batches. For this purpose, sweet and bitter almond batches, as well as different mixtures, were analysed in bulk using an HSI system which works in the spectral range 946.6–1648.0 nm. Qualitative models were developed using Partial Least Squares-Discriminant Analysis (PLS-DA) to differentiate between sweet and bitter almonds, obtaining a classification success of over the 99%. Furthermore, data reduction, as a function of the most relevant wavelengths (VIP scores), was applied to evaluate its performance. Then, the pixel-by-pixel validation of the mixtures was carried out, identifying correctly between 61–85% of the adulterations, depending on the group of mixtures and the cultivar analysed. The results confirm that HSI, without VIP scores data reduction, can be considered a promising approach for classifying the bitterness of almonds analysed in bulk, enabling identifying individual bitter almonds inside sweet almond batches. However, a more complex mathematical analysis is necessary before its implementation in the processing lines.

Non-Destructive In-Process Quality Evaluation Of Plant-Sourced Food During Drying

Plant-sourced food has a crucial role in the human food supply as the source of calories and many valuable micronutrient compounds that enhance a balanced and healthy lifestyle. However, a portion of food intended for human consumption produced globally become wastage per year because of a lack of proper processing and preservation technologies. Drying is one of the broadly utilised preservation technologies in plant-sourced food. Most drying studies focused on post-process evaluation on product quality; meanwhile, monitoring and controlling amid the drying process (in-process) is essential because these activities can improve the drying condition and optimise the end product’s quality. Furthermore, the non-destructive inspection method is essential in developing smart food processing units and can be more efficient, rapid, reduced sample waste, and environmentally friendly than traditional inspection methods. The authors also discuss the overview of quality parameters investigated during plant-sourced food drying with in-process and non-destructive quality evaluation techniques. Finally, the authors also discuss the prospects and challenges of the real-time and non-destructive quality evaluation application.

Estimate residual strength of degraded bonded joints by combining analytical models with non-Destructive evaluations

ABSTRACT Durability issues associated with adhesive degradation have hindered widespread use of bonding in aircraft applications and contributed to several bond-related in-service accidents, fostering extensive related research. While the non-destructive evaluation (NDE) of bondline quality remains elusive, some authors suggested that NDE could establish bond strength thresholds, supporting the theoretical concept of a residual effective bond overlap (REBO). These authors related strength thresholds to NDE of specimens with artificial weak bonds only phenomenologically. This work proposes two physical-based approaches, combining NDE with analytical models for stress analysis, to estimate the residual strength of environmentally degraded metallic bonded joints considering an approximated REBO. These approches incorporate fundamental characteristics of two physically distinct NDE techniques and a water-related aging mechanism. Statistically treated experimental data for verification was obtained from aluminum-epoxy bonded joints typical of aeronautic application. Two sets of these joints were exposed in a hot/wet controlled environment – to induce representative degradations – for different periods, non-destructively inspected, and mechanically tested. Each set of specimens differed only in the surface preparation to prompt dissimilar failure modes – adhesion and cohesion failures. Estimated residual strengths for different levels of degradation and failure modes were in agreement with quasi-static loads in a conservative fashion.