Nondestructive Testing Of Quality Research Articles

Frequency selection method with support vector machine to minimize the impact of individual differences on the electrical impedance spectra of fruits

Individual differences in electrical impedance spectroscopy (EIS) parameters is a technical bottleneck in fruit quality detection. To achieve non-destructive fruit quality testing and minimize the impact of individual differences, this paper proposes a frequency selection + support vector machine strategy based on the frequency response characteristics of biological tissues and the path of current flow. This study found obvious differences in the EIS parameters of different samples. The measurement stability was affected by the electrode type and frequency. The destructive needle electrode was much less affected by individual differences than the non-destructive patch electrode. The effect of low frequencies was greater than that of high frequencies. The heterogeneity of the fruit peel and pulp and the contact area between the electrode and peel were the main reasons for individual differences. When detecting banana maturity, the highest precision of the models was 98.9% by proposed methods; however, the generalizability of the model and its application for other fruit quality tests still require further verification and optimization. The results promote the practical application of EIS in the field of fruit quality assessment and provide new insights for the quality evaluation of other agricultural products.

NIR spectroscopy for quality assessment and shelf-life prediction of kiwifruit

In this study, a non-destructive quality testing method along with shelf-life prediction of Xu Xiang ready-to-eat kiwifruit were developed using near-infrared spectroscopy (NIR) techniques. Several traditional quality indicators (hardness, soluble solids content, and dry matter) were evaluated. Partial least squares regression (PLS) was used to predict the intrinsic quality attributes of the samples. Competitive adaptive reweighted sampling algorithm (CARS) and uninformative variable elimination (UVE) algorithm were used to select the characteristic wavelengths. Prediction models for hardness, soluble solids content and dry matter were developed. The results showed that the prediction ability of the models could be improved by screening the characteristic wavelengths of CARS and UVE. Among them, the CARS-SNV-PLS model based on soluble solids had the best prediction ability (RMSEP of 0.430 and Rp2 of 0.958). Then, an NIR-based residual shelf-life prediction model was obtained by linking the measured quality indicators to the residual shelf-life, which was well validated with an RMSEP of 1.64 and an Rp2 of 0.939. Therefore, this study demonstrated the potential of combining CARS, SNV, and PLS for the non-destructive testing of ready-to-eat kiwifruit to provide technical support and solution.

Determination of soluble solids content in tomatoes with different nitrogen levels based on hyperspectral imaging technique.

Tomato is sweet and sour with high nutritional value, and soluble solids content (SSC) is an important indicator of tomato flavor. Due to the different mechanisms of nitrogen uptake and assimilation in plants, exogenous supply of different forms of nitrogen will have different effects on the growth, development, and physiological metabolic processes of tomato, thus affecting the tomato flavor. In this paper, hyperspectral imaging (HSI) technique combined with neural network prediction model was used to predict SSC of tomato under different nitrogen treatments. Competitive adaptive reweighed sampling (CARS) and iterative retained information variable (IRIV) were used to extract the feature wavelengths. Based on the characteristic wavelength, the prediction models of tomato SSC are established by custom convolutional neural network (CNN) model that was constructed and optimized. The results showed that the SSC of tomato was negatively correlated with nitrogen fertilizer concentration. For tomatoes treated with different nitrogen concentrations, the residual predictive deviation (RPD) of CARS-CNN and IRIV-parallel convolutional neural networks (PCNN) reached 1.64 and 1.66, both more than 1.6, indicating good model prediction. This study provides technical support for future online nondestructive testing of tomato quality. PRACTICAL APPLICATION: The CARS-CNN and IRIV-PCNN were the best data processing model. Four customized convolutional neural networks were used for predictive modeling. The CNN model provides more accurate results than conventional methods.

Towards Non-Destructive Quality Testing of Complex Biomedical Devices—A Generalized Closed-Loop System Approach Utilizing Real-Time In-Line Process Analytical Technology

Towards Non-Destructive Quality Testing of Complex Biomedical Devices—A Generalized Closed-Loop System Approach Utilizing Real-Time In-Line Process Analytical Technology

Non-destructive determination of internal soluble solid content in pomelo using visible/near infrared full-transmission spectroscopy

This study presented a method employing visible/near-infrared (VIS/NIR) full-transmission spectroscopy for the accurate, non-destructive determination of total soluble solid content (SSC) in pomelos. Parameters for acquiring spectroscopic signals from pomelos were optimized and a non-destructive testing model for SSC was developed. The variability of SSC across different parts of the fruit was investigated to identify the optimal calibration position. Additionally, the accuracy of static and dynamic spectral acquisition parameters was compared, and an optimization plan was proposed. Experimental results demonstrated that the proposed method facilitated rapid and precise SSC testing in pomelo fruit, which could advance the pomelo industry and serve as a reference for non-destructive internal quality testing of other large-sized fruit.

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms.

Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network.

Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Influence of full penetration welding on the multispectral signal of the process radiation during laser beam welding of austenitic stainless steel

A 100% visual inspection of the welds carried out is not always possible for economic reasons. Different methods of process monitoring are therefore used for non-destructive testing of process and weld seam quality. In the following, the recorded multispectral signals of the reflective process radiation are evaluated using descriptive statistics and characteristics of a full penetration weld during laser beam welding of austenitic stainless steel are worked out. The aim is to detect and localize unintentional full penetration welding at an early stage and thus avoid rejects. For this purpose, adapted sample geometries with varying material thicknesses are prepared for the test series. As a result, the need to vary the welding process parameters to achieve the different welding states (full and partial penetration weld) is avoided. By evaluating the measured values of the wavelength-selective signals, a significant influence on the signal intensity and the signal frequency could be confirmed.

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology.

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Bibliometrics and Visual Analysis of Non-Destructive Testing Technology for Fruit Quality

This study examined the development and trends in non-destructive testing technology for fruit quality. The status of the research field and the application hotspots were investigated to provide a reference for future research in this field. Relevant studies on the non-destructive testing of fruit quality published between 1993 and 2022 were identified in the core database Web of Science. The temporal distribution, spatial distribution, literature features, research progress, and leading research hotspots were quantified and visualised using bibliometrics. The findings revealed that there continues to be active research and publications on non-destructive testing technology for fruit quality, with a good development trend. China and the USA are the major contributors to research on non-destructive testing technology for fruit quality. The major research institutions include Zhejiang University and the United States Department of Agriculture. The major papers are published in Postharvest Biology and Technology and Acta Horticulturae, among others. These studies mainly focus on agriculture, food, and gardening, among other topics. The detection indices mainly concern internal quality, such as sugar degree and soluble solids, and apparent quality, such as hardness. The detection technologies mainly include electronic nose (E-nose) technology, machine vision technology, and spectral detection technology. In the future, technological developments in artificial intelligence and deep learning will further promote the maturation and application of non-destructive testing technologies for fruit quality.

Ginsenoside Rg2 content prediction in Panax ginseng based on the fusion of hyperspectral wavelengths combined with chemometric analysis

The content of ginsenosides plays a crucial role in determining the quality of Panax ginseng C. A. Meyer. In order to clarify the potential applicability of hyperspectral imaging (HSI) for fast detecting the quality of ginseng, this study investigated the use of HSI combined with chemometric models to predict the ginsenoside Rg2 content. The results indicated that the prediction efficiency can be improved through complete wavelength fusion of different shots and the effective wavelength fusion obtained by different selection methods. Notably, the integrated utilization of the partial least squares regression (PLSR) algorithm and the fusion of effective wavelengths exhibited the best results in Rg2 content prediction, with the maximum coefficient of determination (R2) and relative percent deviation (RPD) values of 0.939 and 3.35, respectively, as well as the lowest root mean squared error (RMSE) value of 24.2 μg/g, indicating excellent model performance. This study provides valuable insights for the development and application of portable HSI equipment for rapid and non-destructive testing of Panax ginseng quality.

Quasi Non-Destructive Quality Assessment of Thermally Sprayed AISI 316L Coatings Using Polarization Measurements in 3.5% NaCl Gel Electrolyte

There is currently a lack of suitable methods of non-destructive quality assessment of thermally sprayed coatings. Therefore, this study investigates the suitability of polarization measurements that are adapted to the special needs of thermally sprayed coatings for non-destructive quality testing. For this purpose, a gel electrolyte containing 3.5% NaCl and a measuring cell based on the three-electrode arrangement were developed to prevent the corrosion medium from infiltrating the typical microstructure of thermally sprayed coatings (pores and microcracks). The newly developed method was evaluated on AISI 316L coatings deposited by high velocity air fuel (HVAF) and atmospheric plasma spraying (APS). The polarization curves showed significant differences as a result of spraying process-related changes in the coating microstructure. Even slight differences in oxide content within the AISI 316L coating produced by APS can be detected by the new method. In order to verify the new findings, the coatings were analyzed regarding their microstructure by optical microscope, scanning electron microscope and energy dispersive X-ray spectroscopy. The measuring cell and gel electrolyte developed offer a promising opportunity to evaluate the quality of thermally sprayed coatings in a largely non-destructive manner using polarization curves.

AIseed: An automated image analysis software for high-throughput phenotyping and quality non-destructive testing of individual plant seeds

Seed quality is the key to seedling development, which affects field yield. Researchers, breeders, and processors may need to investigate seed quality on a large scale, which requires high-throughput methods, which are currently lacking. To address this bottleneck, we have developed a novel software named AIseed that automatically captures and analyzes great traits, such as the shape, color, or texture features of individual seeds, which are rarely effortlessly and stably determined by conventional measurements. The AIseed software is based on machine vision technology and enables high-throughput handling and phenotyping of various-sized plant seeds. After phenotyping for a total of 54 features, AIseed numbers each seed to facilitate further seed quality assessment or grain detection. In addition, AIseed includes modules for seed quality detection and prediction, such as seed clarity, purity, vigor, and viability testing, which are based on machine learning or deep learning models developed by analyzing the associations between seed quality indicators and the features obtained by the software. Through a series of experiments, this paper has confirmed that AIseed has a high performance in extracting phenotypic features and testing seed quality from images for seeds of several plants of different sizes, with high speed and high precision in separating seeds from the background and then analyzing them. In summary, it is a non-destructive and highly effective software for accurate seed phenotyping and seed quality assessment, which is essential for both breeding and yield improvement.

The nondestructive testing of Hayward kiwifruit quality treated with CPPU based on the electrical characteristics

The nondestructive testing of Hayward kiwifruit quality treated with CPPU based on the electrical characteristics

Lining Technology of Saturated Sand Layer Underground Diversion Tunnel and the Measurement of Concrete Lining Thickness

In order to improve the construction technology quality of the diversion tunnel lining in saturated sand stratum, the lining technology of underground diversion tunnel in saturated sand stratum is studied in detail in this paper. Steel trolley is used to complete binding production, and HBT-20 concrete pump is used to transport concrete. Concrete construction starts from bottom plate and side arch. After the construction, the quality of the diversion tunnel lining is tested nondestructively by the impact elastic wave, and the thickness and defects of the lining are analyzed by the reflection characteristics and spectral relationship of the impact elastic wave. The strength of lining concrete is measured by the propagation characteristics of R wave and the relationship between wave velocity and strength. The results show that the maximum additional bending strain of tunnel wall is about 300 με. At measuring point B7, the thickness and strength of concrete lining can be accurately measured by nondestructive quality testing technology.

Determination of Tibetan tea quality by hyperspectral imaging technology and multivariate analysis

Tibetan tea is a dark tea native to Ya'an, and its taste and quality are closely related to the contents of tea polyphenols (TPs) and free amino acids (FAAs). In this study, TPs and FAAs were determined by chemometrics and the grades of Tibetan tea were distinguished. Then, hyperspectral data were collected, a variety of preprocessing methods were used to preprocess the spectral data, and principal component analysis (PCA) was used for feature dimensionality reduction. Results showed that the combination of the preprocessing method and machine learning (ML) could achieve a higher prediction effect. Savitzky Golay (SG)-Standard Normal Variable (SNV)-PCA-Extratree had the best predictive ability for TPs (Rp2=0.9248, RMSEP=0.4842, and RPD=3.646). For detecting FAAs, SG-Multiplicative Scatter Correction (MSC)-PCA-Extratree had the best predictive ability (Rp2=0.8736, RMSEP=0.159, and RPD=2.813). In addition, tea grade can be determined by SG-MSC-PCA-Support Vector Machine (SVM) with 100% accuracy, recall, and precision. In sum, hyperspectral imaging technology (HSI) can be used as an alternative method for rapid, non-destructive testing of tea quality.

Study on Near-infrared Spectroscopy Non-destructive Testing of Strawberry Quality

Non-destructive testing is a technology that has been developed and applied rapidly in recent years. It is widely used in the quality testing of vegetables and fruits, which solves some problems faced in traditional fruit and vegetable testing. As far as strawberry is concerned, this kind of fruit has high market value, but it is not easy to preserve. It is also necessary to conduct non-destructive testing on strawberry quality. It is a detection technology to improve the testing effect and avoid strawberry damage, which plays a positive role in the development of strawberry planting industry. In the non-destructive testing technology of fruits and vegetables, near-infrared spectroscopy is a relatively ideal testing technology. This paper mainly explores the application of near-infrared spectroscopy in the non-destructive testing of strawberry quality, providing ideas for better understanding the application of this technology in strawberry quality testing.

Application Status and Prospect of Impedance Spectroscopy in Agricultural Product Quality Detection

The nondestructive testing of agricultural products has always been a key technology for the modernization of agriculture and food. By applying a sinusoidal voltage (current) excitation signal of variable frequency, the relationship between the amplitude, frequency and phase of the response signal is obtained, and the measured response function in a certain frequency range is obtained, constructing the correlation between impedance spectroscopy and matter properties. Electrical impedance spectroscopy (EIS) is a widely used method for the nondestructive characterization of agricultural products, and its applications in the agricultural field has attracted increasing attention. This paper summarizes the research of electrical impedance spectroscopy (EIS) in the detection of grain quality, fruit and vegetable quality, meat quality and food quality from 2005 to 2022. The potential and development direction of electrical impedance spectroscopy in the nondestructive testing of agricultural product quality are prospected, which provides a reference for scientific researchers who applied electrical impedance spectroscopy in agricultural product quality detection.

Nondestructive testing of the quality of different grades of creamy strawberries based on Laida algorithm

Rapid nondestructive testing of fruit quality is one of the hotspots in food industry research. This paper studied the quality detection of different grades of cream strawberries. Preprocessing methods such as detrending, moving average smoothing (MA), and standard normal variables (SNV) were used to eliminate spectral data errors. The competitive adaptive reweighted sampling algorithm (CARS), successive projection algorithm (SPA), the combination of the above two algorithms (CARS+SPA), and the self-programming algorithm based on the Laida criterion (collectively referred to as the Laida algorithm) were used to reduce the dimension of data, and a partial least squares regression prediction model was established. The results show that when the Laida algorithm predicted the soluble solid content (SSC), total acid (TA), and vitamin C (VC) content of strawberries, the prediction set correlation coefficients were 0.919, 0.931, and 0.907, respectively, the values of RPD were 3.15, 4.00, and 3.44, respectively, the relative errors of the predicted values and the measured values were 3.93%, 5.74%, and 3.69%, respectively. Practical applications This shows that it is feasible to use the Laida algorithm to extract the characteristic wavelengths and establish a prediction model for the SSC, TA, and VC content in cream strawberries. This study can provide a theoretical basis for the development of a rapid detection instrument for the quality of creamy strawberries.

Design and test of portable comprehensive quality non-destructive detector for grape bunches based on spectrum

Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in growing period. The detector includes spectrum acquisition probe, spectrometer, lithium battery, halogen lamp light source, ARM board and peripheral circuit. Based on MFC development tool, the real-time analysis and processing software of the detector is written by C++ language. The optimal partial least squares regression (PLSR) detection model of multi-quality parameters was implanted into the hardware device. This paper selects the red globe grape bunches in the growth period as the research samples, collects the visible / near-infrared diffuse reflectance spectrum information, and then uses the established PLSR model to detect the Soluble solid content(SSC), Total acid (TA) and pH of the samples to generate comprehensive quality parameters. So as to realize the nondestructive testing of multiple quality and comprehensive quality parameter of red globe grape bunches in the growth period. In conclusion, the detector can realize real-time and non-destructive detecting of red globe grape bunches in growth period aiming at the multiple quality and comprehensive quality. Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in the growth period. The detector included spectrum acquisition probe, spectrometer, lithium battery, halogen lamp light source, advanced RISC machines (ARM) board and peripheral circuit. Based on microsoft foundation classes (MFC) development tool, the real-time analysis and processing software of the detector was written by C++ language. The optimal partial least squares regression (PLSR) detection model of multi-quality parameters was implanted into the hardware device. This paper selected the red globe grapes bunches in the growth period as the research samples, collected the visible/near infrared diffuse reflectance spectrum information, and then used the established PLSR model to detect the soluble solid content (SSC), total acid (TA) and pH of the samples to generate comprehensive quality parameter. So as to realize the nondestructive detecting of comprehensive quality of red globe grapes bunches in the growth period. In conclusion, the detector could realize real-time and non-destructive detecting of red globe grapes bunches in growth period aiming at the comprehensive quality.